{ "api_version": "1.0", "description": "Every published project.", "source": "https://fair-forward.github.io/datasets/", "documentation": "https://fair-forward.github.io/datasets/api/v1/index.json", "license": { "spdx": "CC0-1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/", "note": "Covers the catalogue metadata in this file: you may republish these records freely, with or without attribution. It does NOT cover the linked assets. Each record's `license` field describes the terms of the asset itself, which Fair Forward does not own; a null value means no license has been recorded, not that the asset is unlicensed or free to reuse." }, "version": "465e9286b50af245", "count": 89, "vocabularies": { "sdgs": [ "SDG 2", "SDG 4", "SDG 5", "SDG 7", "SDG 8", "SDG 9", "SDG 10", "SDG 11", "SDG 12", "SDG 13", "SDG 14", "SDG 15", "SDG 16" ], "data_types": [ "Drone Imagery", "Geospatial/Remote Sensing", "Images", "Meterological", "Other", "Tabular", "Text", "Voice" ], "countries": [ { "name": "Angola", "iso2": "AO" }, { "name": "Benin", "iso2": "BJ" }, { "name": "Brazil", "iso2": "BR" }, { "name": "Cameroon", "iso2": "CM" }, { "name": "Colombia", "iso2": "CO" }, { "name": "Cote d'Ivoire", "iso2": "CI" }, { "name": "Democratic Republic of Congo", "iso2": "CD" }, { "name": "Ecuador", "iso2": "EC" }, { "name": "Ethiopia", "iso2": "ET" }, { "name": "Ghana", "iso2": "GH" }, { "name": "India", "iso2": "IN" }, { "name": "Indonesia", "iso2": "ID" }, { "name": "Kenya", "iso2": "KE" }, { "name": "Madagascar", "iso2": "MG" }, { "name": "Mozambique", "iso2": "MZ" }, { "name": "Niger", "iso2": "NE" }, { "name": "Nigeria", "iso2": "NG" }, { "name": "Pakistan", "iso2": "PK" }, { "name": "Rwanda", "iso2": "RW" }, { "name": "Senegal", "iso2": "SN" }, { "name": "South Africa", "iso2": "ZA" }, { "name": "Tanzania", "iso2": "TZ" }, { "name": "Togo", "iso2": "TG" }, { "name": "Uganda", "iso2": "UG" }, { "name": "Zambia", "iso2": "ZM" } ], "regions": [ "East Africa", "Global", "West Africa" ], "maturity_stages": [ "dataset", "model", "pilot", "usecase", "business" ] }, "projects": [ { "id": "ui_0", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_0-african_trees_for_climate_resilience_a/", "aliases": [ "african_trees_for_climate_resilience", "african_trees_for_climate_resilience_a" ], "title": "African Trees for Climate Resilience: A Comprehensive Database", "description": { "text": "Extensive bioinformatics resource that leverages tree species’ distribution, medicinal, food provision, and other trait data, together with southern African trees’ climate relationships and growth characteristics for climate adaptation and mitigation planning. The data can serve to promote the use of indigenous trees for reforestation, regenerative agriculture, ecological restoration, human health and livelihood support, and urban afforestation programs to adapt to and mitigate the impacts of climate change.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Angola", "iso2": "AO" }, { "name": "Democratic Republic of Congo", "iso2": "CD" }, { "name": "Kenya", "iso2": "KE" }, { "name": "Mozambique", "iso2": "MZ" }, { "name": "Nigeria", "iso2": "NG" }, { "name": "South Africa", "iso2": "ZA" }, { "name": "Tanzania", "iso2": "TZ" }, { "name": "Zambia", "iso2": "ZM" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Images", "Tabular", "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Stellenbosch University", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Prof. Guy F. Midgley University of Stellenbosch (gfmidgley@sun.ac.za)", "access_note": null, "links": { "dataset": [ { "label": "gbif.org", "url": "https://www.gbif.org/" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "An extensive relational floristic and plant functional database which, together with matching biogeoclimatic data sets and implementation of the distribution model, may describe the biogeoclimatic relationships and projects the growth and ecological success of all sufficiently recorded Southern African trees under current and future climatic conditions", "provenance": "curated" }, "model_characteristics": { "text": "Automation of a species distribution model that leverages a novel mechanistically based algorithm for 1) quantification of currently suitable planting-range conditions and 2) projection of climate risk for future planting-range suitability. This primary screening effort can be cross-referenced for adaptation and mitigation use-value sources to aid in tree species selection.", "provenance": "curated" }, "how_to_use": { "text": "The primary application of this work will include identifying indigenous species that can enhance ecological resilience by mapping adaptation and mitigation opportunities and assessing climate risks to African trees. Existing cutting-edge functional niche modeling will allow for the identification of areas for optimal use of African trees based on the results of tree growth performance. This will promote the use of indigenous trees for reforestation, regenerative agriculture, ecological restoration, human health and livelihood support, and urban afforestation programs to adapt to and mitigate the impacts of climate change.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_0/images/placeholder_image.jpeg" }, { "id": "ui_1", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_1-benmangroves2425_multidimensional_open_datasets_fo/", "aliases": [ "benmangroves2425_multidimensional_open_datasets_fo" ], "title": "BenMangroves2425: Multidimensional open datasets for developing AI-based models on mangroves health and carbon stock", "description": { "text": "BenMangroves2425 integrates multi-source environmental, ecological, and socio-economic data for assessing mangrove health, degradation drivers, and restoration potential. The dataset includes:\n• High-resolution drone imagery (multispectral and hyperspectral) for mangrove vegetation structure, canopy cover, and biomass estimation for 600 monitoring plots.\n• In situ mangrove forest inventory measurements from 600 monitoring plots spanning the entire mangroves in Benin (which encompasses two RAMSAR sites). This covers tree diameter, height, species composition, regenerations, and health status.\n• Soil quality analyses, including soil organic carbon, Total Nitrogen, granulometry, CEC, phosphorus, and potassium.\n• Water quality analyses, including soil nutrients (Nitrates, Nitrites, Sulfate, Ammonium, Orthophosphate) and pollution (Iron, Cadmium, Lead, and Zinc).\n• Socio-economic survey data on community livelihoods, mangrove forests’ resources use, and climate change adaptation strategies.\nThis dataset is unique in providing a multi-season, and multi-dimensional view of mangrove ecosystems in West Africa, enabling advanced AI/ML-based modelling for degradation prediction, restoration prioritization, carbon stock modelling, and climate resilience assessment.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Benin", "iso2": "BJ" } ], "regions": [ "West Africa" ], "sdgs": [ "SDG 13" ], "data_types": [ "Drone Imagery", "Tabular", "Other" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Université d’Abomey-Calavi - Laboratoire de Biomathématiques et d'Estimations Forestières (Benin)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Glèlè Kakai Romain (glele.romain@gmail.com) & Salako Valere (salakovalere@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "figshare: items", "url": "https://plus.figshare.com/account/mycontent/items" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The dataset provides georeferenced, annotated drone imagery with clear landuse and landcover classes, groundtruth data, and standardized protocols. The datatset also provide field Carbon inventoried data paired with the drone imagery. As such, users can train AI models for carboj estimation is harsh mangroves ecosystems. Its high resolution, temporal coverage, and open accessibility enable accurate, scalable environmental monitoring and carbon estimation. Furthermore, the dataset include (1) Soil quality analyses, including soil organic carbon, Total Nitrogen, granulometry, CEC, phosphorus, and potassium; (2) Water quality analyses, including soil nutrients (Nitrates, Nitrites, Sulfate, Ammonium, Orthophosphate) and pollution (Iron, Cadmium, Lead, and Zinc), and (3) Socio-economic survey data on community livelihoods, mangrove forests’ resources use, and climate change adaptation strategies; allowing to develop models aiming at understanding how local soil, water, and socio-économic profile affect carbon stock.", "provenance": "curated" }, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_1/images/placeholder_image.jpeg" }, { "id": "ui_2", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_2-powering_rural_futures_in_west_africa/", "aliases": [ "powering_rural_futures_in_west_africa" ], "title": "Powering Rural Futures in West Africa: AI-Driven Demand Data for Smarter Electrification", "description": { "text": "The project provides two openly accessible datasets that were developed through a complete, reproducible data pipeline combining machine learning with stochastic energy-system simulation. The first dataset contains predicted appliance ownership and household counts for all 1,209 administrative level 2 regions (adm2) across Nigeria, Ghana, Togo, Benin, and Niger, derived from satellite-based features and socio-economic indicators using models trained on more than 3,500 household surveys. The second dataset consists of high-resolution synthetic electricity demand profiles generated with the RAMP tool, offering minute-by-minute load curves for an entire year for each adm2 region. Together, these datasets provide a unique, representative, and scalable foundation for understanding residential electricity demand in regions where measured data is scarce or entirely unavailable.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Benin", "iso2": "BJ" }, { "name": "Ghana", "iso2": "GH" }, { "name": "Niger", "iso2": "NE" }, { "name": "Togo", "iso2": "TG" }, { "name": "Nigeria", "iso2": "NG" } ], "regions": [], "sdgs": [ "SDG 7" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Reiner Lemoine Institut gGmbH (RLI)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Reiner Lemoine Institut, Catherina Cader (catherina.cader@rl-institut.de)", "access_note": null, "links": { "dataset": [ { "label": "dataverse.harvard.edu: dataset.xhtml", "url": "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3S7KPQ" }, { "label": "dataverse.harvard.edu: dataset.xhtml", "url": "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9WT7FJ" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The two datasets provide complementary, high-resolution information on household electricity demand across 1,209 administrative level 2 regions in West Africa. The ML dataset contains per-region estimates of household numbers, appliance ownership across 17 categories, and cluster identifiers reflecting typical appliance-use behaviour. The demand dataset includes both full-year, minute-resolution load profiles (527,040 time steps per region) and aggregated daily curves, along with summary statistics such as minimum, maximum, mean, and total annual demand. Files are structured as standardized CSVs, organized by country, and kept in manageable sizes. Users can easily import the data into analytical workflows for energy planning, electrification modelling, scenario design, or spatial analysis. Because the pipeline is fully open source, users may also retrain models, adjust appliance usage parameters, or generate new simulations tailored to local contexts. Together, the datasets offer granular, scalable, and customizable inputs for researchers, utilities, developers, and policymakers working on electricity access and energy-system planning.", "provenance": "curated" }, "model_characteristics": null, "how_to_use": { "text": "The datasets can be used directly for energy planning, electrification modelling, mini-grid prefeasibility assessments, academic research, or scenario analysis. Users may download the ML dataset to analyse expected appliance adoption patterns or to integrate the predicted household numbers into broader socio-economic models. The synthetic demand profiles can be imported into any energy modelling environment (e.g., Python, R, Excel, PowerFactory, PyPSA, OSeMOSYS) to simulate grid expansion, evaluate supply adequacy, or study temporal consumption behaviour. Because the full codebase is open source, users can also adapt individual steps of the pipeline—such as updating input features, retraining the ML model with local survey data, or running customized RAMP simulations—to generate new or localized demand profiles. Access to both datasets is free under a CC-BY 4.0 license, and additional resources such as documentation, example scripts, and workshop materials are available via GitHub and Harvard Dataverse. This ensures that researchers, planners, and practitioners can build on the existing workflow at no cost and with minimal technical barriers.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_2/images/placeholder_image.jpeg" }, { "id": "ui_3", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_3-forest_carbon_sequestration_in_the_congo/", "aliases": [ "forest_carbon_sequestration_in_the_congo" ], "title": "Forest carbon sequestration in the Congo Basin: combining In Situ Data and Artificial Intelligence to unlock climate finance", "description": null, "kind": [ "dataset" ], "countries": [ { "name": "Cameroon", "iso2": "CM" }, { "name": "Democratic Republic of Congo", "iso2": "CD" } ], "regions": [], "sdgs": [], "data_types": [], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "World Resources Institute (WRI)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "World Resources Institute, Kendie Kenmoe (Kendie.Kenmoe@wri.org)", "access_note": null, "links": { "dataset": [ { "label": "data.cmr.forest-atlas.org", "url": "https://data.cmr.forest-atlas.org/" }, { "label": "data.cod.forest-atlas.org", "url": "https://data.cod.forest-atlas.org/" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_3/images/placeholder_image.jpeg" }, { "id": "ui_4", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_4-datasets_for_transportation_impact_evaluation_in/", "aliases": [ "classification_data_and_model_for_transportation", "datasets_for_transportation_impact_evaluation_in" ], "title": "Datasets for transportation impact evaluation in urban settings in Colombia", "description": { "text": "The team developed a labeled training dataset, derived from 50cm or better satellite imagery, based on a novel, pre-defined road space classification taxonomy appropriate for training and deployment of large-scale deep-learning models", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Colombia", "iso2": "CO" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model", "tags": [ "dataset", "model" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Fundación Despacio", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Fundación Despacio (mafe@despacio.org), World Resources Institute", "access_note": null, "links": { "dataset": [ { "label": "GitHub: UrbanInfraDL", "url": "https://github.com/yangshao2/UrbanInfraDL" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Dataset and model available in Github. Dataset of roadspace from 15 areas of Bogota each 1Km. \nBogotá’s orthophoto and GIS layers. \nData Dictionary", "provenance": "curated" }, "model_characteristics": { "text": "The model that was tested for the entire city of Bogotá to clasify urban roadspace with 98% reliability.", "provenance": "curated" }, "how_to_use": { "text": "This resource is useful for anyone working on urban mobility analysis, road infrastructure planning, or land-use evaluation in cities of the Global South. The UrbanInfraDL repository provides a deep learning pipeline for segmenting road infrastructure -- roads, sidewalks, and bicycle lanes -- from satellite imagery, with a focus on Bogota, Colombia.\n\nYou can use the provided patch extraction tool and training scripts for three segmentation architectures (DeepLabV3+, SegFormer, U-Net) to train models that classify urban road space from your own satellite imagery. This makes it possible to evaluate how road space is allocated across different transport modes and to support evidence-based advocacy for more equitable infrastructure distribution.\n\nResearchers and developers can extend this work by applying the pipeline to other cities with similar urban structures, or by incorporating additional annotation classes (e.g., bus lanes, green spaces) to broaden the analysis. The modular design -- separate patch extraction and model training steps -- makes it straightforward to experiment with different architectures or hyperparameters.\n\nKnown limitations: No pre-trained model weights or sample datasets are included in the repository; you will need your own high-resolution TIFF satellite imagery and corresponding label files. The repository does not document its Python dependencies, so some setup effort is required. The codebase is a research prototype (8 commits) rather than a production-ready tool.\n\nSource: https://github.com/yangshao2/UrbanInfraDL", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_4/images/placeholder_image.jpeg" }, { "id": "ui_5", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_5-quantifying_colombian_mangroves_aboveground_biomas/", "aliases": [ "mapping_blue_carbon_quantifying_mangrove_carbon", "quantifying_colombian_mangroves_aboveground_biomas" ], "title": "Quantifying Colombian mangroves aboveground biomass and carbon content", "description": { "text": "This open-access dataset supports machine learning (ML) applications for mangrove forest monitoring, addressing the need for more openly available and well-annotated datasets to calibrate and validate ML models. It focuses on improving the estimation of above-ground biomass (AGB) and above-ground carbon (AGC) in Colombian Caribbean mangroves. The pilot area, Via Parque Isla de Salamanca National Natural Park (VIPIS) in the Magdalena department, is a Ramsar and UNESCO Biosphere Reserve. Existing global AGB and AGC models often lack the precision and cost-effectiveness needed for regional monitoring.\nThe dataset includes both plot-level and tree-level field measurements from 20 newly surveyed plots, providing detailed attributes such as DBH, height, species, AGB (from allometric equations), and AGC. Using these field data, high-resolution satellite imagery, and machine learning models, satellite-based AGB and AGC maps covering ~6,000 ha of mangroves at 10 m spatial resolution have also been generated.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Colombia", "iso2": "CO" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Marine and Coastal Research Institute \"José Benito Vives de Andréis\" (INVEMAR) Colombia, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "CTTC María Cuevas (mcuevas@cttc.es), INVEMAR Cristian Montes (cristian.montes@invemar.org.co)", "access_note": null, "links": { "dataset": [ { "label": "acceso-datos-ambientales-invemar.hub.arcgis.com: about", "url": "https://acceso-datos-ambientales-invemar.hub.arcgis.com/maps/a0cab53befd44804923800a194c703d6/about" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "This geodatabase contains geospatial information collected and processed under the LACUNA project, aimed at quantifying above-ground biomass (AGB) and above-ground carbon (AGC) in Colombian mangroves, specifically in the VIPIS area (Vía Parque Isla de Salamanca). The data are organized into two main datasets: Ground_truth and Satellite AGB/AGC.\nGround_truth Dataset – Field and reference data:\n• MangroveTrees (point layer): Structural details of individual trees, including DBH, height, species, AGB, and AGC, for trees in 20 sampled plots.\n• MangrovePlot (point layer): Centroids of the 20 plots, with aggregated AGB and AGC per plot.\n• TreesCanopy (polygon layer): Tree canopy projections with attributes linked to MangroveTrees.\n• MangrovePlots (polygon layer): Plot boundaries (10 × 10 m) with estimated AGB and AGC values.\nSatellite AGB/AGC Dataset – Remote-sensing derived data:\n• Provides 10 m resolution maps of AGB and AGC for 2025, generated using Sentinel-1 and Sentinel-2 data combined with in-situ measurements and a Random Forest machine learning model.", "provenance": "curated" }, "model_characteristics": { "text": "Geospatial dataset for quantifying above-ground biomass (AGB) and above-ground carbon (AGC) in Colombian mangroves, focused on Vía Parque Isla de Salamanca (VIPIS) National Natural Park. Created by INVEMAR (Instituto de Investigaciones Marinas y Costeras). Data organized into \"Ground_truth\" (field measurements) and \"Adapted_AOI\" (reference data layers). Available formats: Shapefile, WFS, geodatabase, JPG, PNG, and PDF. Researchers: MSc. Venus Lorena Rocha G. and Esp. Claudia Correa (LabSIS, INVEMAR). Access is free; users must acknowledge INVEMAR as the source.\n\nSource: https://acceso-datos-ambientales-invemar.hub.arcgis.com/maps/a0cab53befd44804923800a194c703d6/about", "provenance": "auto-enriched" }, "how_to_use": { "text": "The geodatabase provides detailed plot-level and tree-level field data from Colombian Caribbean mangroves, including measurements such as DBH, height, species, above-ground biomass (AGB), and above-ground carbon (AGC). Users can directly integrate the Ground_truth dataset with other similar field datasets to train and validate machine learning models for accurate estimation of mangrove biomass and carbon stocks.\nThe satellite dataset supports spatially explicit analysis of forest structure, carbon dynamics, and environmental factors influencing mangrove ecosystems. Combined with additional datasets, it can help improve model robustness, enable cross-site comparisons, and enhance predictive accuracy.\nBeyond modeling, the dataset facilitates carbon stock quantification, conservation planning, forest management, and climate policy development.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_5/images/mangroves.png" }, { "id": "ui_6", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_6-detecting_forest_degradation_by_predicting_biomass/", "aliases": [ "african_biomass_challenge_with_open_cocoa", "detecting_forest_degradation_by_predicting_biomass" ], "title": "Detecting forest degradation by predicting biomass in cocoa plantations in Cote d'Ivoire", "description": { "text": "The AI model based on this dataset enables efficient and cost-effective remote monitoring of biomass changes. This is crucial for assessing reforestation success and detecting forest degradation due to cocoa farming. Also it reduces the need for extensive and expensive on-the-ground surveys. The dataset and the models developed from it aim to predict biomass levels in shaded regions of Côte d'Ivoire using a combination of GEDI, Sentinel-2 satellite imagery, and ground truth biomass measurements.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Cote d'Ivoire", "iso2": "CI" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 15" ], "data_types": [ "Images" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Data354, Zindi", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Data354 (gabriel.fonlladosa@data354.co)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: Africa_Biomass_dataset", "url": "https://huggingface.co/datasets/data354/Africa_Biomass_dataset" } ], "usecase": [], "additional": [ { "label": "Africa Biomass Challenge (zindi.africa)", "url": "https://zindi.africa/competitions/africa-biomass-challenge" }, { "label": "google.com", "url": "https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjn657i3p-VAxXTRfEDHUweFDcQFnoECAoQAQ&url=https%3A%2F%2Fstorage.googleapis.com%2Fdownload%2Fstorage%2Fv1%2Fb%2Findaba-public%2Fo%2FIssouf_TOURE.pdf%3Fgeneration%3D1724089094997741%26alt%3Dmedia&usg=AOvVaw35fJfv4r2g0OJp3rZLpYIt&opi=89978449" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Forest aboveground biomass (AGB) dataset for Cote d'Ivoire. 263 field plots measured between 2021 and 2023. Data Type: Tabular (CSV, also available as Parquet). Columns: identifiant (plot ID), dates (measurement date), Latitude (4.04 to 9.99), Longitude (-8.09 to -2.74), biomass_mg_ha (0.91 to 391 t/ha). Biomass calculated using allometric equations from field measurements of tree height, diameter at chest height, density, and species. Dataset size: 17.8 kB. License: CC BY 4.0.\n\nSource: https://huggingface.co/datasets/data354/Africa_Biomass_dataset", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Dataset intended for building AGB estimation models using satellite imagery (GEDI, Sentinel-2) combined with ground truth measurements from 263 plots in Cote d'Ivoire. Input: satellite imagery paired with field data (tree height, diameter, density, species). Output: biomass prediction in tonnes per hectare (t/ha). Addresses the scarcity of locally-developed AGB estimation models for African tropical forests. License: CC BY 4.0.\n\nSource: https://huggingface.co/datasets/data354/Africa_Biomass_dataset", "provenance": "auto-enriched" }, "how_to_use": { "text": "The Africa Biomass Dataset enables immediate applications in biomass estimation, land-use monitoring, and carbon-stock analysis using existing remote sensing and machine-learning tools, making it useful for climate modelling, nature-based solutions, and sustainable land-management planning. Researchers can extend this work by integrating higher-resolution satellite imagery, adding ground-truth data, or fine-tuning models for country-specific ecosystems, though care must be taken to account for regional imbalances, sparse labels, and ecological variability, an ethical AI assessment is recommended before replication. The dataset opens opportunities for collaboration across climate scientists, AI researchers, and environmental agencies, and documentation on Hugging Face provides guidance for developers.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_6/images/cocoa_biomass.png" }, { "id": "ui_7", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_7-ai_for_mangrove_carbon_credits_turning/", "aliases": [ "ai_for_mangrove_carbon_credits_turning", "watchmytree" ], "title": "AI for Mangrove Carbon Credits: Turning Forest Data into Climate Action in Côte d’Ivoire", "description": { "text": "This dataset contains biomass and carbon stock records from mangroves in Côte d’Ivoire (sites of Sassandra and Fresco). It includes measurements of aboveground and belowground biomass and carbon stock, soil carbon stock, as well as bands derived from Sentinel-2 and Sentinel-1 satellite imagery.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Cote d'Ivoire", "iso2": "CI" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Data354", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Data354 (gabriel.fonlladosa@data354.co)", "access_note": null, "links": { "dataset": [ { "label": "figshare: 30258772", "url": "https://figshare.com/articles/dataset/The_First_Open_Dataset_of_Mangrove_Above-_and_Belowground_Biomass_and_Soil_Carbon_Stocks_in_C_te_d_Ivoire_Insights_from_Fresco_and_Sassandra_/30258772" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_7/images/placeholder_image.jpeg" }, { "id": "ui_8", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_8-using_locallanguage_ai_advise_women_in/", "aliases": [ "lifting_up_women_through_land_ownership", "using_locallanguage_ai_advise_women_in" ], "title": "Using local-language AI advise women in DRC on land ownership - Haki des femmes", "description": { "text": "Haki will leverage voice technology to provide access to legal information and support for women in Katanga and Lualaba provinces of the Democratic Republic of Congo to ensure they have the right to access, use, inherit, control, and own land. Majority of women in DRC often lose their access to land after the passing of a loved one or husband due to lack of knowledge of land rights. This solution will help women to access information and legal support in securing their land rights in Kiswahili. This was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "Democratic Republic of Congo", "iso2": "CD" } ], "regions": [], "sdgs": [ "SDG 5", "SDG 2", "SDG 10" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Core23Lab", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "Core23Lab (engage@core23lab.org)", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "play.google.com: details", "url": "https://play.google.com/store/apps/details?id=org.core23lab.hdf&pcampaignid=web_share&pli=1" } ], "additional": [ { "label": "Lifting Up Women Through Land Ownership (mozillafoundation.org)", "url": "https://www.mozillafoundation.org/de/blog/lifting-up-women-through-land-ownership/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Haki des Femmes is an Android app by Core23lab providing legal information on land ownership rights for women in the Katanga and Lualaba provinces of DRC. Content is in Kiswahili. Covers rights to access, use, inherit, control, and own land. The app uses voice technology for interaction. Data safety: does not share data with third parties; personal data encrypted in transit; users can request data deletion. Available for all ages on Google Play.\n\nSource: https://play.google.com/store/apps/details?id=org.core23lab.hdf", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Haki des Femmes is a voice-enabled Android application by Core23lab. Users speak queries in Kiswahili about land ownership rights, and the app returns relevant legal information and guidance. Designed for women in the Katanga and Lualaba provinces of DRC who risk losing land access after the death of a family member. Developer contact: devs.core23lab@gmail.com.\n\nSource: https://play.google.com/store/apps/details?id=org.core23lab.hdf", "provenance": "auto-enriched" }, "how_to_use": { "text": "Haki des Femmes is a ready-to-use voice-enabled chatbot app that provides women in the Democratic Republic of Congo with accessible legal information about land ownership rights. It operates in Congolese Swahili (Kiswahili) and can be downloaded directly from the Google Play Store by searching for \"Haki des femmes.\"\n\nThe app is designed for community organizations, legal aid providers, and development practitioners working on women's land rights in the DRC's Katanga and Lualaba provinces. Many women in these communities are unaware of existing laws that allow them to own land, face barriers to proper documentation, or lack legal marriages that would confer inheritance rights. The chatbot simplifies this legal information through a voice interface, helping women understand the concrete steps needed to secure land ownership -- including the process of legalizing marriages as a prerequisite for land rights.\n\nDevelopment practitioners can use Haki des Femmes as a model for building similar legal information tools in other contexts. The approach of combining voice technology with local-language legal guidance could be adapted for other jurisdictions or legal domains where access to legal literacy is a barrier.\n\nHaki des Femmes was developed by Core23Lab as part of Mozilla's 2023-24 Common Voice Kiswahili program, which funds projects using Kiswahili voice technology to support marginalized groups in Kenya, Tanzania, and the DRC. The team conducted surveys of women in Katanga and Lualaba provinces to identify specific knowledge gaps about land ownership before designing the chatbot, an approach worth replicating in similar projects. More background on the project rationale is available on the Mozilla Foundation blog.\n\nKnown limitations: The app is specific to DRC land law and Congolese Swahili and is not directly applicable to other countries or legal systems. Voice-based interaction requires a smartphone with a microphone and internet access.\n\nSources:\n- https://play.google.com/store/apps/details?id=org.core23lab.hdf\n- https://www.mozillafoundation.org/en/blog/lifting-up-women-through-land-ownership/", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_8/images/placeholder_image.jpeg" }, { "id": "ui_9", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_9-ecuadorian_dataset_on_access_demand_/", "aliases": [ "ecuador_electricity_access__supply_data", "ecuadorian_dataset_on_access_demand_" ], "title": "Ecuadorian Dataset on Access, Demand, & Availability of Electricity Supply", "description": { "text": "This project has created a web platform that centralises and visualises energy consumption and production data in Ecuador. It integrates historical and real-time information from official sources such as CENACE, CELEC and INAMHI. The platform presents this information in the form of graphs and heat maps. Additionally, a second application uses satellite images to predict incidents in the electrical system associated with meteorological variables. The project aimed to address a lack of data by making energy information more accessible and representative, enabling proactive management in the face of climatic events. Alongside the platform, the project involved measuring device consumption to improve energy efficiency, and the resulting dataset is publicly available to researchers and students.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ecuador", "iso2": "EC" } ], "regions": [], "sdgs": [ "SDG 7", "SDG 11" ], "data_types": [ "Meterological", "Text" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "ESPOL University", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "ESPOL University (jecordov@espol.edu.ec)", "access_note": null, "links": { "dataset": [ { "label": "DOI: 6491", "url": "https://doi.org/10.57967/hf/6491" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Currently, a dataset containing observations incorporating energy consumption and production data (MW/h), as well as meteorological information including temperature, precipitation and wind speed variables, is hosted on Hugging Face. This information is obtained from official sources and measuring devices. As the processed information is public, there are no related ethical issues; however, it is limited to what is shared by official sources.", "provenance": "curated" }, "model_characteristics": { "text": "The model developed as part of this project is an incident prediction system designed to operate as an early warning system for Ecuador's electrical infrastructure. Specifically, it uses integrated satellite images and meteorological information from INAMHI, such as climatic variables like storms or strong winds, to anticipate possible failures or incidents in the electrical infrastructure associated with these weather conditions. This prediction system is a powerful tool for critical infrastructure planning and risk reduction, and is intended for future integration into the risk management systems of electricity sector companies. However, its main limitation is the availability of data from official sources, as it requires satellite images and meteorological information from INAMHI to operate.", "provenance": "curated" }, "how_to_use": { "text": "The project model focuses on predicting incidents within Ecuador's electrical system by integrating consumption and production data with meteorological information (INAMHI) and satellite images to create an AI-driven early warning system. Key use cases include anticipating failures associated with extreme weather, providing in-depth analysis of energy consumption and production through visualisations such as heat maps, and managing energy efficiency at the device level. The main limitations identified were the ongoing challenge of integrating data from multiple official sources, and the need for additional funding to ensure the project's long-term sustainability. Plans to improve and scale up the project focus on integrating the predictive model into the risk management systems of electric utilities and expanding institutional collaboration with CENACE, CELEC and INAMHI to ensure the platform and public dataset are continuously updated.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_9/images/placeholder_image.jpeg" }, { "id": "ui_10", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_10-monitoring_the_impact_of_palm_oil/", "aliases": [ "machine_learning_training_data_for_continental", "monitoring_the_impact_of_palm_oil" ], "title": "Monitoring the impact of palm oil monoculture, shrimp aquaculture & mining in continental Ecuador and the Galapagos using AI", "description": { "text": "The dataset can help to build systems, that can monitor the impact of palm oil monoculture, shrimp aquaculture, mining and other land transformations in continental Ecuador and Galapagos. The project created a 20.000 points land use/cover classification training dataset from existing data, with labels that can be used to train multi-spectral Earth observation (EO) data machine learning (ML) models covering continental Ecuador and the Galapagos islands.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ecuador", "iso2": "EC" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Ecociencia", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Fundacion Ecociencia (carmenjosse@ecociencia.org)", "access_note": null, "links": { "dataset": [ { "label": "Kaggle: data", "url": "https://www.kaggle.com/datasets/mapbiomasecuador/lulc-training-data-for-ecuador-ml/data" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Two datasets available: \n- BaseDatosValidacionFinal30052: This is the raw dataset containing 20,000 georeferenced points along with their respective land cover classifications.\n- LULC Training Data for Ecuador ML: This dataset builds upon the first by incorporating additional information on the conservation status of each point. This includes whether the location falls within protected areas, indigenous territories, areas under government forest incentive programs, and other conservation-related designations.", "provenance": "curated" }, "model_characteristics": { "text": "Land use and land cover (LULC) training dataset for Ecuador by MapBiomas Ecuador. Contains 20,000 georeferenced points with land cover classifications derived from visual interpretation of LANDSAT satellite imagery covering 1985 to 2023. An enhanced version adds conservation status information: protected area designations, indigenous territory boundaries, government forest incentive programs, and other conservation-related designations. Format: ZIP. Size: ~1.7 MB. License: CC BY 4.0.\n\nSource: https://www.kaggle.com/datasets/mapbiomasecuador/lulc-training-data-for-ecuador-ml/data", "provenance": "auto-enriched" }, "how_to_use": { "text": "This dataset will allow for a better understanding of land transformation dynamics taking place, such as forest conversion to palm oil monoculture, mangrove transformation to shrimp aquaculture, water bodies and estuarine vegetation impacted by mining, natural grasslands encroached upon by expanding forest plantation, and more. It also has the potential to identify recovery cases. For example, the Galapagos data might provide the ability to estimate if invasive species control programs have had a positive impact in vegetation regeneration or if governmental forest incentives are promoting deforestation reduction in the Ecuadorian Amazon. The land’s conservation status has the potential to predict risk of future transformation.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_10/images/placeholder_image.jpeg" }, { "id": "ui_11", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_11-indigenous_knowledge_meets_ai_ethical_monitoring/", "aliases": [ "indigenous_knowledge_meets_ai_ethical_monitoring" ], "title": "Indigenous Knowledge Meets AI: Ethical monitoring of climate stress and biodiversity: sounds of elephants and Katip (Ltome-Katip) in Kenya and the Ecuadorian Amazon", "description": { "text": "The Ltome-Katip datasets are the first Indigenous-labelled bioacoustic datasets designed specifically to support the development of ethical AI for biodiversity monitoring. Co-created by Indigenous data stewards from the Samburu tribe in northern Kenya and the Shuar Nation in the Ecuadorian Amazon, the recordings focus on two sentinel species: Ltome (elephant) and Katip (rodent), both of which signal ecological shifts under climate stress. All data were collected, annotated, and governed by the Indigenous communities, following locally defined protocols rooted in Indigenous data sovereignty. These datasets are not only scientifically valuable — they establish a precedent for how Indigenous communities can lead in setting standards for responsible, consent-based AI development.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Ecuador", "iso2": "EC" }, { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing", "Other" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Space4Innovation, Namunyak Conservancy, GEO Indigenous Alliance, MUSAP & Rochester Institute for Technology", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Space4Innovation, Diana Mastracci (diana@space4innovation.com)", "access_note": null, "links": { "dataset": [ { "label": "space4innovation.github.io: index", "url": "https://space4innovation.github.io/ltomekatip/index.html" } ], "usecase": [ { "label": "arbimon.org: namunyak-conservancy-reteti-eleph…", "url": "https://arbimon.org/p/namunyak-conservancy-reteti-elephant-sanctuary" }, { "label": "arbimon.org: insights", "url": "https://arbimon.org/p/shakiam-ecuadorian-amazon/insights" } ], "additional": [ { "label": "When Future Already Here Now Its Being Filmed Diana Mastracci Sanchez Y5Rue (linkedin.com)", "url": "https://www.linkedin.com/pulse/when-future-already-here-now-its-being-filmed-diana-mastracci-sanchez-y5rue" }, { "label": "Embracing Uncertainty Hidden Strength Science Diana Mastracci Sanchez Qdj5E (linkedin.com)", "url": "https://www.linkedin.com/pulse/embracing-uncertainty-hidden-strength-science-diana-mastracci-sanchez-qdj5e" }, { "label": "Bridging Worlds Indigenous Led Innovation Remote Mastracci Sanchez Yjqfe (linkedin.com)", "url": "https://www.linkedin.com/pulse/bridging-worlds-indigenous-led-innovation-remote-mastracci-sanchez-yjqfe" }, { "label": "Ltome Katip Indigenous Led Labelling Inclusive Ai Addressing Human Wildlife Conflict And (rit.edu)", "url": "https://www.rit.edu/dirs/research/ltome-katip-indigenous-led-labelling-inclusive-ai-addressing-human-wildlife-conflict-and" }, { "label": "Professor Helps Bring Machine Learning Indigenous Communities (rit.edu)", "url": "https://www.rit.edu/news/professor-helps-bring-machine-learning-indigenous-communities" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Ltome-Katip Indigenous Bioacoustic Dataset\nRegions: Samburu (Kenya) · Shuar (Ecuadorian Amazon)\nCustodians: Chief Titus Letaapo (Samburu tribe) (Namunyak Conservancy), Chief Mario Vargas Shakaim (Shuar Nation) (MUSAP Biological Station), and Space4Innovation\nThis dataset contains Indigenous-labelled bioacoustic recordings from two ecosystems—semi-arid savannah and tropical rainforest—collected through AudioMoth bioacustic sensors. Data include species-specific sounds (e.g., elephants, rodents), environmental background, and associated metadata following the CARE Principles for Indigenous Data Governance.\nUse cases: biodiversity monitoring, species classification, human–wildlife conflict alerts, and AI model training for conservation.\nLimitations: class imbalance (key species overrepresented), environmental noise, and spatial clustering; users should apply noise filtering and ethical review before reuse. These audio data are collected 24/7 when deployed in time periods ranging from hours to several weeks. The data are acquired from multiple microphones spread across the study site. Each microphone has a unique serial number and the geographic locations are provided using GPS. The data are time stamped, however there are data gaps in time and space due to logistics, equipment failure, or power loss. The original data are stored as 16-bit WAV files and are available. To make the data more widely available, they have been uploaded to the Arbimon.org platform. The Arbimon cloud platform is built for bioacoustics analysis using various ML .", "provenance": "curated" }, "model_characteristics": { "text": "The Ltome-Katip system uses Indigenous-labelled bioacoustic data to train AI models that detect and classify species like elephants in Samburu (Kenya) and rodents in the Ecuadorian Amazon. These models are already being used to monitor biodiversity, understand ecological stress, and support early warning systems rooted in Indigenous governance. What sets Ltome-Katip apart is that both the dataset and its governance model were co-designed by Indigenous communities. All development follows the CARE Principles for Indigenous Data Sovereignty, and any replication must go through an Ethical AI Assessment to ensure consent, transparency, and benefit-sharing. This project sets a new global benchmark for community-led, responsible AI in biodiversity and conservation.\n\nThe data is processed using the Arbimon platform, where recordings are visualized as spectrograms and labelled through bounding boxes by trained Indigenous data stewards. The outputs — including geospatial and temporal metadata — can be downloaded as CSV files and used for further machine learning or integration with other ecological datasets. These tools are already generating insights into ecosystem change and human–wildlife conflict. The core team is now actively designing Ltome-Katip 2, a next-phase expansion that will deepen Indigenous-led data infrastructure, extend sensor coverage, and explore AI integration with the Namunyak app. While plans are in development, we are currently seeking aligned funding to support this work, which will remain entirely Indigenous-led and ethically governed at every stage.", "provenance": "curated" }, "how_to_use": { "text": "The Ltome-Katip datasets can already be used to detect and classify species such as elephants and rodents, enabling real-time biodiversity monitoring and alerts for human–wildlife conflict. They also support ecosystem health assessments by capturing patterns in species richness, activity cycles, and climate-driven changes. Indigenous-led early warning systems are already being built using these datasets and dashboards, allowing communities to visualize and act on local ecological shifts. Researchers can extend this work by adding new species, integrating satellite data, applying transfer learning, or developing explainable AI tools to improve accuracy and cross-ecosystem usability. All reuse must respect Indigenous data sovereignty, undergo an ethical AI review, and credit the original communities. The Ltome-Katip core team is actively seeking funding for the next phase — Ltome-Katip 2 — which will expand the sensor network, strengthen community data infrastructure, and integrate AI capabilities into the Namunyak Indigenous app.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_11/images/placeholder_image.jpeg" }, { "id": "ui_12", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_12-aipowered_detection_of_diseases_for_cashew/", "aliases": [ "aipowered_detection_of_diseases_for_cashew", "cadi_ai_project_ml4cashew", "drone_images_of_disease_manifestations_in" ], "title": "AI-powered detection of diseases for Cashew farmers in Ghana", "description": { "text": "Imagine, that you are a small-holder farmer in Ghana fearing crop disease in your Cashew farm. You also know that early intervention could increase yields by up to 30% - The Cashew Disease Identification (CADI AI) dataset and application is there to make early detection of diseases in cashew plantations in Ghana through AI possible. This helps securing livelihoods, boosting food security, and fueling further economic growth. You will be able to use an openly accessible data set (4,736 UAV images), a machine learning model, and a desktop app to replicate this approach.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13" ], "data_types": [ "Drone Imagery" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "AGPL 3.0", "spdx": "AGPL-3.0", "url": null }, "organizations": { "provided_by": { "text": "Kara Agro", "links": [ { "name": "Kara Agro", "url": "https://karaagro.com/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "KaraAgro (darlington@gudra-studio.com)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: CADI-AI", "url": "https://huggingface.co/datasets/KaraAgroAI/CADI-AI" } ], "usecase": [ { "label": "Hugging Face: CADI-AI", "url": "https://huggingface.co/KaraAgroAI/CADI-AI" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "A responsible AI Assessment was undertaken for this dataset / use case to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/ \n\nLicense: https://www.gnu.org/licenses/agpl-3.0.html", "provenance": "curated" }, "model_characteristics": { "text": "CADI-AI (Cashew Disease Identification with AI) by KaraAgro AI Foundation, funded by GIZ through MOVE and FAIR Forward initiatives on behalf of BMZ. Model: YOLOv5x object detection, trained on 3,788 drone-captured images at 640x640 input resolution. Detects 3 classes: insect damage, disease (microbial), and abiotic stress. Performance (mAP@50): 0.648 overall, 0.815 insect, 0.588 disease, 0.542 abiotic. Dataset: 4,736 images total (train/val/test) with 22,610 annotated bounding boxes in YOLO format. Dataset license: CC BY-SA 4.0. Model license: AGPL-3.0. Demo available on Hugging Face Spaces; desktop app on GitHub (karaagro/cadi-ai).\n\nSource: https://huggingface.co/datasets/KaraAgroAI/CADI-AI, https://huggingface.co/KaraAgroAI/CADI-AI", "provenance": "auto-enriched" }, "how_to_use": { "text": "The CADI-AI project is useful for anyone working on cashew crop health monitoring, agricultural extension, or precision agriculture in West Africa. It provides both a labeled image dataset and a ready-to-use pre-trained model for detecting three types of cashew tree health issues -- abiotic stress, disease damage, and insect damage -- from drone-captured imagery.\n\nIf you want to try the model immediately, a live demo is available on HuggingFace Spaces where you can upload your own cashew tree images and see detection results without any setup. For deployment in the field, a desktop application is also available on GitHub. These tools allow agricultural extension officers and agronomists to identify health issues across cashew plantations quickly, enabling targeted interventions rather than blanket treatments.\n\nThe dataset itself contains 4,736 high-resolution drone images (1600x1300 pixels) with over 22,000 annotated instances across the three health-issue classes, licensed under CC-BY-SA 4.0. Researchers and developers can use this data to train improved detection models or to extend the approach to other tree crops. The annotations are in YOLO format, and the pre-trained YOLOv5x model achieves a mean average precision (mAP@50) of 0.65, with strongest performance on insect damage detection (0.82 mAP@50) due to its distinct visual features. Disease and abiotic stress classes are harder to distinguish because their symptoms can overlap in field conditions -- an area where further research could improve accuracy.\n\nA detailed datasheet documenting the data collection methodology is available via the HuggingFace dataset card. The dataset (approximately 3.78 GB) and model (approximately 173 MB) can be downloaded from HuggingFace after acknowledging the license terms.\n\nCost and resources: The dataset and model are freely available. Deploying the model requires only standard compute resources. The CADI-AI project was created by the KaraAgro AI Foundation, funded by GIZ and BMZ through the FAIR Forward and MOVE programs.\n\nSources:\n- https://huggingface.co/datasets/KaraAgroAI/CADI-AI\n- https://huggingface.co/KaraAgroAI/CADI-AI", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_12/images/Screenshot 2025-05-13 at 11.58.32.png" }, { "id": "ui_13", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_13-supporting_food_security_and_climate_change/", "aliases": [ "ghana_biomass_challenge_ghana_crop_disease", "supporting_food_security_and_climate_change" ], "title": "Supporting food security and climate change adaptation: AI-powered crop disease identification for maize, tomatoes, pepper in Ghana", "description": { "text": "This dataset helps to build and improve crop disease detection systems for smallholder farming in Ghana and similar West African contexts. It covers three crops -- tomatoes, pepper and maize -- with 22 disease and health classes in total, making it one of the more comprehensive Afrocentric crop disease image collections available. The data was collected in 10 districts of the Ashanti Region of Ghana by the RAIL-KNUST team and the Plant Protection and Research Services Directorate (PPRSD) of the Ministry of Food and Agriculture. A 3-month long data challenge was hosted on Zindi based on this data set and the 3 winning models are also available as open source resources to serve as your base models in your research into crop diseases in Ghana.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Images" ], "maturity": { "stage": "Dataset > Model", "tags": [ "dataset", "model" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Kwame Nkrumah University of Science and Technology (KNUST), Responsible AI Lab, Plant Protection and Regulatory Services Directorate (Ghana)", "links": [ { "name": "Responsible AI Lab", "url": "https://rail.knust.edu.gh/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ, Digital Transformation Centre Ghana, GIZ", "links": [ { "name": "GIZ", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Responsible AI Lab (RAIL) at Kwame Nkrumah University of Science and Technology (rail@knust.edu.gh)", "access_note": null, "links": { "dataset": [ { "label": "Kaggle: crop-disease-ghana", "url": "https://www.kaggle.com/datasets/responsibleailab/crop-disease-ghana" } ], "usecase": [ { "label": "Kaggle: code", "url": "https://www.kaggle.com/datasets/responsibleailab/crop-disease-ghana/code" } ], "additional": [ { "label": "Ghana Crop Disease Detection Challenge (zindi.africa)", "url": "https://zindi.africa/competitions/ghana-crop-disease-detection-challenge" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Afrocentric crop disease dataset by Responsible AI Lab. Contains annotated leaf images showing healthy specimens and disease-affected leaves at various crop development phases. Data Type: Image. Size: ~20 GB. License: CC BY 4.0. Version 16 (last modified March 2025). Created with a focus on African agricultural diversity.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/crop-disease-ghana\n\nAlso see here for the Zindi challenge: https://zindi.africa/competitions/ghana-crop-disease-detection-challenge", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Crop disease identification application using computer vision and deep learning on annotated leaf images from African crops. Input: leaf images. Output: disease detection and classification. Dataset created by Responsible AI Lab in collaboration with the Plant Protection and Research Services Directorate (PPRSD) of Ghana's Ministry of Food and Agriculture. Dataset openly available under CC BY 4.0.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/crop-disease-ghana", "provenance": "auto-enriched" }, "how_to_use": { "text": "This dataset is intended for building and improving crop disease detection systems for smallholder farming in Ghana and similar West African contexts. It covers four crops --tomatoes, pepper and maize -- with 22 disease and health classes in total, making it one of the more comprehensive Afrocentric crop disease image collections available.\n\nYou can use this dataset to train image classification models that identify specific diseases from leaf photos. With nearly 25,000 raw images captured from local farms in Ghana (October-December 2022), plus over 100,000 augmented images with a ready-made train/test split, the dataset is structured for direct use in standard image classification workflows. The raw images are also available separately if you prefer to apply your own augmentation or splitting strategy.\n\nThe dataset is particularly valuable because it captures disease symptoms as they actually appear on farms in Ghana -- subtle, at various stages, and under real field conditions. This makes models trained on this data more likely to perform well in practical agricultural advisory tools than models trained on laboratory images. Agricultural technology developers, extension services, and research institutions can use it to build mobile apps or decision-support tools that help farmers identify and respond to crop diseases early.\n\nResearchers can extend this work by combining it with other crop disease datasets to improve cross-regional generalization, or by adding whole-plant and field-level imagery to complement the current leaf-level focus. The dataset is licensed under CC BY 4.0 and is available on Kaggle (approximately 20 GB). A free Kaggle account is required for download.\n\nKnown limitations: The images are from specific farming regions in Ghana, so models trained exclusively on this data may not generalize well to crops grown under different conditions elsewhere. The dataset focuses on leaf-level symptoms and does not include whole-plant or field-level imagery.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/crop-disease-ghana", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_13/images/placeholder_image.jpeg" }, { "id": "ui_14", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_14-facilitating_access_to_financial_applications_in/", "aliases": [ "facilitating_access_to_financial_applications_in", "financial_inclusion_speech_dataset_for_some" ], "title": "Facilitating access to financial applications in informal settings in four Ghanaian dialects: Akuapem Twi, Ashante Twi, Fante and Ga.", "description": { "text": "This speech dataset for the Ghanian languages Akan (Akuapem Twi, Asante Twi, Fante) and Ga includes 104,000 utterances (speech) across the four dialects/languages with approximately 200 speakers per dialect/language. This amounts to about 148 hours of speech in total. The dataset was developed to support the development of financial applications in native Ghanaian languages to allow illiterate and semi-literate people to fully benefit from digital financial services. Secondly, it aims to answer research questions related to domain-specific vs. general-purpose dataset development, dialects, as well as NLP system development in low resource settings. Overall, a total of 83,829 audios were recorded from which the datasets were published and made publicly accessible.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 8" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Ashesi University, Nokwary Technologies", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dennis Asamoah Owusu (dowusu@ashesi.edu.gh)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: Financial-Inclusion-Speech-Dataset", "url": "https://github.com/Ashesi-Org/Financial-Inclusion-Speech-Dataset" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The data is freely available for use based on the provided open source license and courtesy the funding from Lacuna Fund. We performed a stratified random sampling (5%) of the data for each language and reviewed it to get the following quality assessments.\n\n0.1% of the Ga audios were of low quality\n1.3% of the Fanti audios were of low qaulity.\n1.6% of the Asanti Twi audios were of low quality.\n2.8% of the Akuapem Twi audios were of low quality.\nLow quality means that what the user recorded did not match the given prompt either because there was a truncation or the recording was totally different from the prompt.", "provenance": "curated" }, "model_characteristics": null, "how_to_use": { "text": "The dataset might be used to devise more inclusive banking platforms that better understand users in in four Ghanaian dialects: Akuapem Twi, Ashante Twi, Fante and Ga. It can thereby help to achive more financial inclusion.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_14/images/pexels-kwei-kofi-8493777.jpg" }, { "id": "ui_15", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_15-discover_ghanaian_voices_a_dataset_for/", "aliases": [ "discover_ghanaian_voices_a_dataset_for", "explore_how_to_make_ai_systems" ], "title": "Discover Ghanaian Voices: A Dataset for AI & Linguistic Research in Ghanaian accented English.", "description": { "text": "The Accent Classification Dataset (Ghana) is a collection of audio recordings from native and non-native English speakers across Ghana's diverse regions. Participants read the same three scripts, capturing distinct regional accents and speech patterns. This consistent dataset is valuable for linguistic analysis, accent classification, and speech recognition research focused on Ghanaian English.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "ODbL 1.0", "spdx": "ODbL-1.0", "url": null }, "organizations": { "provided_by": { "text": "RAIL - KNUST", "links": [ { "name": "RAIL - KNUST", "url": "https://rail.knust.edu.gh/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "RAIL - KNUST (rail@knust.edu.gh)", "access_note": null, "links": { "dataset": [ { "label": "Kaggle: accent-classification-dataset-gha…", "url": "https://www.kaggle.com/datasets/responsibleailab/accent-classification-dataset-ghana" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Audio recordings of native and non-native English speakers from various regions of Ghana. Each participant reads the same 3 predefined scripts. License: Open Database License (ODbL). Data Type: Audio (ZIP archive). Metadata includes age, ethnicity, and region of each speaker. Total size: ~177 MB. Maintained by the Responsible AI Lab.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/accent-classification-dataset-ghana", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Use the audio recordings and metadata (age, ethnicity, region) to train accent classification or speech recognition models for Ghanaian English. Input: audio recordings of speakers reading 3 scripts. Output: regional accent classification or speech transcription. The dataset supports linguistic diversity analysis across Ghanaian regions.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/accent-classification-dataset-ghana", "provenance": "auto-enriched" }, "how_to_use": { "text": "This dataset is useful for anyone working on speech recognition, natural language processing, or voice technology that needs to handle Ghanaian English accents. It contains audio recordings from native and non-native English speakers across various regions of Ghana, with each participant reading the same three predefined scripts to ensure consistency.\n\nYou can use this data to train or fine-tune accent classification models, improve automatic speech recognition systems for Ghanaian English speakers, or conduct research on regional dialect variation within Ghana. Each audio file is paired with demographic metadata -- age, ethnicity, and region -- allowing you to segment and filter recordings by speaker background. With three recordings per participant, you can also study within-speaker consistency and across-region variation.\n\nThe dataset is particularly relevant for developers building voice-enabled applications intended for Ghanaian users, where standard English speech models often underperform due to accent variation. By training on this data, you can build systems that are more inclusive and accurate for this population.\n\nData was collected via Telegram using custom bots and scripts, with identity verification and audio quality validation steps. Participants were instructed to record in quiet environments. The dataset is approximately 185 MB, licensed under the Open Database License (ODbL), and is available on Kaggle with a free account.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/accent-classification-dataset-ghana", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_15/images/placeholder_image.jpeg" }, { "id": "ui_16", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_16-mapping_cocoa_landscapes_in_ghana_reference/", "aliases": [ "mapping_cocoa_landscapes_in_ghana_reference" ], "title": "Mapping Cocoa Landscapes in Ghana: Reference Data for Tracking Land Use Change", "description": { "text": "This dataset was produced by the Centre for Remote Sensing and Geographic Information Services (CERSGIS) as part of the project Reference Data Collection for Improving Land Use Change Mapping in Ghana. The primary objective was to develop high-quality reference data to enhance the accuracy of remote sensing-based land use and land cover (LULC) change mapping using machine learning methods in Ghana’s cocoa production landscapes.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "World Resources Institute (WRI), Centre for Remote Sensing and Geographic Information Services (CERSGIS), NASA SERVIR Global Collaborative, Earth System Science Center", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Center for Remote Sensing and Geographic Information Services CERSGIS (fkmawusi@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Zenodo: 15778396", "url": "https://zenodo.org/records/15778396" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Content:\n• 21,031 geocoded cocoa farm polygons (including agroforestry and shadeless cocoa)\n• 14,192 homogeneous (shadeless) cocoa polygons digitized from farm plots\n• 20,035 additional points/polygons for other land uses (informal gold mining, degraded forest, oil palm, rubber)\n• 485 anonymised household clusters (from 4,444 individual surveys) providing socioeconomic context\n\nCollection methods:\n• OpenForis Ground (field-based polygon collection)\n• Collect Earth Online (land use mapping)\n• KoboToolbox (household survey data)\n\nPurpose: Reference dataset for remote sensing, land cover classification, and land use change mapping in cocoa production landscapes.\n\nLimitations:\n• Polygons represent portions of farms, not legal or property boundaries.\n• Farm sizes do not reflect entire holdings.\n• Not suitable for certification or compliance purposes.", "provenance": "curated" }, "model_characteristics": { "text": "Reference dataset for training remote sensing and machine learning models for land use/land cover classification in Ghana's cocoa landscapes. Contains: 21,031 geocoded cocoa farm polygons (including 14,192 homogeneous shadeless cocoa plots), 20,035 points and polygons for other land uses (informal gold mining, degraded forest, oil palm, rubber), and 485 anonymized household survey records (derived from 4,444 individual surveys). Collected September 2024 to March 2025 using OpenForis Ground, Collect Earth Online, and KoboToolbox. License: CC BY 4.0. Format: ZIP (~30.6 MB). Created by CERSGIS (University of Ghana), with WRI and NASA SERVIR. Note: cocoa farm polygons do not represent property or farm boundaries and should not be used for legal or compliance purposes.\n\nSource: https://zenodo.org/records/15778396", "provenance": "auto-enriched" }, "how_to_use": { "text": "What can be done immediately:\n• Train and validate machine learning models for cocoa farm detection, deforestation monitoring, and land use classification.\n• Use as a benchmark dataset to evaluate remote sensing products in heterogeneous tropical landscapes.\n• Support policy analysis on sustainable cocoa, land degradation, and restoration planning in Ghana.\nHow to extend or improve:\n• Add new field reference data from other cocoa-producing regions (e.g., Côte d’Ivoire, Nigeria) to increase transferability.\n• Integrate household-level socioeconomic data to study drivers of land use change and cocoa–forest dynamics.\n• Combine with climate and soil datasets to model sustainability scenarios.\nLimitations / ethical use:\n• Must not be used for farm-level regulation or compliance; polygons are reference only.\n• Potential imbalances between cocoa vs. non-cocoa land use classes should be addressed in model training.\n• Users are encouraged to conduct an ethical AI assessment before deploying derived models.\nCost considerations:\n• Dataset itself is open access (no cost).\n• Small-scale applications (e.g., testing models in Google Earth Engine or QGIS) incur negligible costs.\n• Larger-scale ML training and national-scale mapping may require cloud compute budgets\n• Train and validate machine learning models for cocoa farm detection, deforestation monitoring, and land use classification.\n• Use as a benchmark dataset to evaluate remote sensing products in heterogeneous tropical landscapes.\n\nHow to extend or improve: \n• Add new field reference data from other cocoa-producing regions (e.g., Côte d’Ivoire, Nigeria) to increase transferability.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_16/images/placeholder_image.jpeg" }, { "id": "ui_17", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_17-explore_the_agrivoltaic_dataset_dive_into/", "aliases": [ "discover_the_effectiveness_of_the_energy", "explore_the_agrivoltaic_dataset_dive_into" ], "title": "Explore the Agrivoltaic Dataset: Dive into real data comparing harvests under solar panels and open-sun farming.", "description": { "text": "The Agrivoltaic system offers a transformative solution for farming communities by providing a means to generate electricity without sacrificing agricultural productivity. The dataset showcases the effectivenesss of Solar PVs and also crop yield under solar PVs and in the open-sun on the same farm.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Kwame Nkrumah University of Science and Technology (KNUST), Responsible AI Lab", "links": [ { "name": "Responsible AI Lab", "url": "https://rail.knust.edu.gh/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "RAIL - KNUST (rail@knust.edu.gh)", "access_note": null, "links": { "dataset": [ { "label": "Kaggle: agrivoltaic-dataset-ghana", "url": "https://www.kaggle.com/datasets/responsibleailab/agrivoltaic-dataset-ghana" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Pilot agrivoltaic system data from Ghana comparing crop performance under solar PV panels versus open-sun farming. License: CC BY 4.0. Data Type: Tabular. 3 experimental plots: Plot 1 (control, no PV panels), Plot 2 (agrivoltaic with raised PV panels), Plot 3 (traditional ground-mounted PV on bare land). Plots 1 and 2 divided into 9 subplots each. Crops: tomatoes, chilli pepper, eggplant (3 replicates each). Includes PV panel energy generation data and crop performance data. Total size: ~4.3 MB. Maintained by the Responsible AI Lab.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/agrivoltaic-dataset-ghana", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Compare crop yields (tomatoes, chilli pepper, eggplant) under agrivoltaic panels versus open-sun control plots. The dataset provides side-by-side energy generation and harvest data from 3 plots with 9 subplots each, enabling analysis of whether raised solar PV panels affect crop productivity. Input: plot-level crop and energy measurements. Output: comparative yield and energy performance across agrivoltaic and control conditions.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/agrivoltaic-dataset-ghana", "provenance": "auto-enriched" }, "how_to_use": { "text": "This dataset is valuable for anyone evaluating the feasibility of agrivoltaic systems -- combining solar energy generation with crop production on the same land -- in tropical climates. It contains measurements from a pilot installation in Ghana comparing three setups: a traditional open-sun control field, an agrivoltaic system with raised solar panels over crops, and a conventional ground-mounted solar installation on bare land.\n\nYou can use this data to directly compare crop yields (tomatoes, chili pepper, and eggplant) under solar panels against open-sun farming, and to assess energy output from different panel configurations. The experimental design includes three replicates per crop across two growing plots (control and agrivoltaic), allowing for statistical analysis of yield differences. This makes the dataset suitable for informing feasibility assessments and investment decisions around dual-use land strategies in similar climatic zones.\n\nDevelopment practitioners and policymakers can draw on these results to evaluate whether agrivoltaic systems offer a practical path to addressing both food security and clean energy access simultaneously. Researchers can extend this work by replicating the experimental design with different crop varieties, panel heights, or spacing configurations, or by combining the data with economic models to assess the financial viability of agrivoltaic installations at scale.\n\nCost and resources: The dataset itself is small (approximately 4.3 MB) and freely available on Kaggle under a CC BY 4.0 license. A free Kaggle account is required for download.\n\nSource: https://www.kaggle.com/datasets/responsibleailab/agrivoltaic-dataset-ghana", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_17/images/agrivoltaic.png" }, { "id": "ui_18", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_18-phenological_dataset_for_ecological_forecasting_ph/", "aliases": [ "forecasting_availaibiltiy_of_tropical_forest_resou", "phenological_dataset_for_ecological_forecasting_ph" ], "title": "Phenological Dataset for Ecological Forecasting (PheDEF Project)", "description": { "text": "The health of tropical forest ecosystems faces pressures from climate change, threatening the sustainable supply of leaves, flowers and fruits which provide important resources for wildlife, domestic animals and human settlements. Monitoring the timing of plant life cycle events (phenology) is one effective way to track the availability of plant resources and the impact of climate change and weather variability on their sustainable supply. This dataset is on 48 weeks of liana and tree phenology from ground observations, traditonal ecological knowedge and camera traps in the canopy in two tropical forest ecosystems (a moist semi-deciduous and a dry semi-deciduous forest). The dataset also includes land surface phenology from satellite images and in situ weather data. Phenology data from multiple sources and climate data could be combined via a machine learning model that can be used to predict phenology at community and landscape scales. This data will enhance the representation of tropical African forests in phenology research and contribute meaningful data from tropical African forests for machine learning applications in climate, forests and biodiversity conservation. The images and phenology labels could also be used to train an automation of identifying phenology events in forest canopy images.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Tabular", "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "University of Energy and Natural Resources (Ghana), University of Twente", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Bismark Ofosu-Bamfo (bismark.ofosu-bamfo@uenr.edu.gh), Daniel Yawson (daniel.yawson@uenr.edu.gh), Raul Zurita-Milla (r.zurita-milla@utwente.nl), Rosa Aguilar (r.aguilar@utwente.nl)", "access_note": null, "links": { "dataset": [ { "label": "DOI: zenodo.15704554", "url": "https://doi.org/10.5281/zenodo.15704554" }, { "label": "DOI: d97e338b-dc94-4e3d-a473-6dd3d4b48…", "url": "https://doi.org/10.4121/d97e338b-dc94-4e3d-a473-6dd3d4b48898.v1" }, { "label": "DOI: 9e6b4bca-f3d3-40f3-a8f5-4f71f7790…", "url": "https://doi.org/10.4121/9e6b4bca-f3d3-40f3-a8f5-4f71f7790c2f.v1" }, { "label": "DOI: 7e6d7ca3-060d-4ca5-bd83-d779b598c…", "url": "https://doi.org/10.4121/7e6d7ca3-060d-4ca5-bd83-d779b598c11d.v1" } ], "usecase": [], "additional": [ { "label": "Realistic Phenology Data Key To Predicting Crop Cycles Dr Ofosu Bamfo 2 (gna.org.gh)", "url": "https://gna.org.gh/2025/07/realistic-phenology-data-key-to-predicting-crop-cycles-dr-ofosu-bamfo-2/" }, { "label": "5Byh6F7 (g.co)", "url": "https://g.co/kgs/5byh6f7" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Description of clean folder (raw folder also available)\nThe folder contains files of clean datasets employed for various datasets. \n i. climate_dataset.csv\nii. daily_climate_gr_data.csv\niii. ground_phenology_dataset.csv\niv. pheno_pulse_dataset.csv\nv. rbg_chromatic_coordinates.csv\nvi. tek_phenology_dataset.csv\nvii. Satellite images derived phenology (provided at https://data.4tu.nl)\n\nLicense\nCreative Commons Attribution 4.0 International", "provenance": "curated" }, "model_characteristics": { "text": "Five CSV datasets for ecological forecasting of plant phenology in Ghana's tropical forests, collected over 48 weeks (July 2024 to June 2025) at Bobiri Forest Reserve and Boabeng Fiema Monkey Sanctuary. Ground phenology dataset (28 variables): tree and liana observations including flowering phases, fruiting stages, and leaf development. Traditional Ecological Knowledge dataset (10 variables): community-reported phenology from 10 villages. Phenocam dataset (22 variables): RGB indices and vegetation indices (GRVI, exG) from camera monitoring. Citizen science classification dataset: leafing, flowering, and fruiting event classifications. Climate dataset (15 variables): wind, precipitation, temperature, humidity, and seasonal data for both sites. License: CC BY 4.0. Created by University of Energy and Natural Resources (Ghana) and University of Twente.\n\nSource: https://doi.org/10.5281/zenodo.15704554", "provenance": "auto-enriched" }, "how_to_use": { "text": "The PheDEF dataset offers a rich, multi-source foundation for ecological forecasting and phenological research in West African tropical forests. It covers 48 weeks of observations (July 2024 -- June 2025) from two sites in Ghana -- Bobiri Forest Reserve and Boabeng Fiema Monkey Sanctuary -- and brings together ground phenology, satellite imagery, climate records, traditional ecological knowledge, phenocam indices, and citizen science classifications, all linked by common date and site identifiers.\n\nYou can use this resource to investigate how weather patterns drive flowering and fruiting timing by cross-referencing the ground phenology observations with co-located climate data (temperature, precipitation, humidity, wind, dew point). Researchers working on remote sensing validation can compare the satellite-derived vegetation indices (NDVI, EVI, GNDVI, and seven others from Sentinel-2, Landsat, and MODIS imagery) against field-observed phenological stages to assess how well space-based monitoring captures on-the-ground seasonal changes. The citizen science classifications -- over 100 MB of volunteer labels for leafing, flowering, and fruiting events -- can be benchmarked against the expert ground-truth observations to study the reliability of community-contributed data.\n\nA distinctive feature of PheDEF is its traditional ecological knowledge component: community interviews from 10 villages documenting local phenological calendars, including respondent demographics. This opens the door to research that integrates Indigenous and scientific knowledge systems for forest management and conservation planning.\n\nThe ground observation data is available as CSV files from Zenodo (https://zenodo.org/records/15704554), while the satellite imagery and vegetation indices (~30 GB for Sentinel-2, ~1.5 GB for Landsat, plus MODIS GeoTIFFs) are hosted on 4TU.ResearchData. All data is openly accessible and free to download under a CC BY 4.0 license. Detailed documentation on data formats and variable definitions is provided at each repository.\n\nSources: https://zenodo.org/records/15704554, https://doi.org/10.4121/d97e338b-dc94-4e3d-a473-6dd3d4b48898.v1, https://doi.org/10.4121/9e6b4bca-f3d3-40f3-a8f5-4f71f7790c2f.v1, https://doi.org/10.4121/7e6d7ca3-060d-4ca5-bd83-d779b598c11d.v1", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_18/images/placeholder_image.jpeg" }, { "id": "ui_19", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_19-enable_cashew_cocoa_and_coffee_farmers/", "aliases": [ "dronebased_agricultural_dataset_for_crop_yield", "enable_cashew_cocoa_and_coffee_farmers" ], "title": "Enable Cashew, Cocoa and Coffee farmers to make good business decisions - Drone-based Agricultural Dataset for Crop Yield Estimation in Ghana and Uganda", "description": { "text": "This dataset supports yield estimation, crop type detection and classification, fruit detection and counting, and fruit maturity stage detection (unripe, ripe, and spoiled) for three products that are important sources of livelihood for millions of households in Sub-Saharan Africa.\n \n It contains 14,870 drone images with bounding box annotations of cashew, cocoa, and coffee trees collected across multiple farms in Ghana and Uganda. Conventional methods of yield estimation are expensive, require a lot of labor and time, and are prone to error due to incomplete ground observations. This results in poor crop yield estimations and hinders farmers’ ability to appropriately plan and manage their fields and production pipelines. This dataset will help transform African agriculture into agribusiness by allowing for the development of yield estimation solutions that enable farmers to make good business decisions. Having key details about agricultural production readily accessible enables a timely harvest, helping farmers ensure healthy, fresh produce and, in addition, better sales.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Ghana", "iso2": "GH" }, { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13" ], "data_types": [ "Images" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Kara Agro, Makerere University (AI Lab, Marconi Lab), National Coffee Research Institute, National Crops Resources Research Institute", "links": [ { "name": "Kara Agro", "url": "https://karaagro.com/" } ] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Darlington Akogo (darlington@gudra-studio.com), KaraAgro (https://www.karaagro.com/index.html)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: Drone-based-Agricultural-Dataset-…", "url": "https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "14,870 drone images with YOLO-format annotations for crop yield estimation. License: CC BY 4.0. Data Type: Image + Text annotations. Ghana subset: 8,784 images (16,000 x 13,000 px) covering cashew (4,715 images) and cocoa (4,069 images). Uganda subset: 6,086 images (4,000 x 3,000 px) covering cashew (3,086 images) and coffee (3,000 images). Cashew labels: cashew_tree, flower, immature, mature, ripe, spoilt. Cocoa labels: cocoa-tree, cocoa-pod-immature, cocoa-pod-mature-unripe, cocoa-pod-riped, cocoa-pod-spoilt. Coffee labels: coffee, unripe, ripening, ripe, spoilt. DOI: 10.57967/hf/0959. Created by KaraAgro AI Foundation, funded by Lacuna Fund.\n\nSource: https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Train object detection models (YOLO format) for crop yield estimation, crop type detection, fruit counting, and maturity stage classification. Input: high-resolution drone images of cashew, cocoa, and coffee trees. Output: bounding box predictions with class labels for tree type and fruit maturity (immature, mature/unripe, ripe, spoilt, flower). Ghana instance counts include: cashew_tree (1,107), flower (16,757), immature (11,766), mature (4,244), ripe (11,721), spoilt (518), cocoa-pod-mature-unripe (10,786), cocoa-tree (2,831), cocoa-pod-immature (2,401), cocoa-pod-riped (4,193), cocoa-pod-spoilt (2,018).\n\nSource: https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation", "provenance": "auto-enriched" }, "how_to_use": { "text": "This drone-based agricultural dataset is designed for anyone working on crop yield estimation, crop health monitoring, or object detection in smallholder farming contexts. It contains 14,870 high-resolution drone images of cashew, cocoa, and coffee crops from Ghana and Uganda, each paired with bounding box annotations that label individual fruits by maturity stage -- immature, mature, ripe, and spoilt.\n\nYou can use these images to train models that count and classify fruits from aerial imagery, enabling plot-level yield estimation without manual field counts. The maturity-stage labels also support crop health monitoring, since spoilt fruit detection can flag disease or post-harvest loss issues early. Because the dataset covers three different cash crops across two countries, it lends itself to cross-crop and cross-region transfer learning experiments -- for example, testing whether a model trained on Ghanaian cashew generalises to Ugandan cashew, or adapting a cocoa detector for coffee.\n\nResearchers and developers should note that the Ghana images (16,000 x 13,000 px, collected by KaraAgro AI) are significantly higher resolution than the Uganda images (4,000 x 3,000 px, collected by Makerere AI Lab, Uganda Marconi Lab, and NCRRI). This difference may require separate preprocessing pipelines or resolution-aware training strategies if combining both sources.\n\nThe annotations use the YOLO object detection format, so the data can be loaded directly into standard YOLO-based training pipelines. The dataset repository also includes PDF documentation covering collection methodology and variable definitions.\n\nThe full dataset (~45.6 GB) is openly available on HuggingFace under a CC BY 4.0 license: https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation\n\nSource: https://huggingface.co/datasets/KaraAgroAI/Drone-based-Agricultural-Dataset-for-Crop-Yield-Estimation", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_19/images/Screenshot 2025-05-13 at 11.49.19.png" }, { "id": "ui_20", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_20-our_language_our_data_cocreating_equitable/", "aliases": [ "mozilla_community_licence_project", "our_language_our_data_cocreating_equitable" ], "title": "Our language, our data: Co-creating equitable governance models with African language communities - language dataset created: Dholuo Speech", "description": { "text": "The “DhoNam: Dholuo Speech dataset” is a speech corpus designed to supercharge Automatic Speech Recognition (ASR) and other speech technologies for Dholuo, one of Kenya’s major indigenous languages. This dataset contains native-speaker audio recordings collected through a platform where users read aloud a displayed sentence. The dataset includes the audio recordings and the corresponding prompt/sentence that was read.\n\nThe dataset was part of a programme of Mozilla Foundation, that piloted alternative approaches to governance of AI training data, that balance opening up innovation, with centering community choice. Mozilla and its partners, Maseno Centre for Applied Artificial Intelligence, Maseno University, worked with the Dholuo language community to pilot a novel, community-centered license on the Common Voice platform, as a proof of concept that can then be replicated and adapted. Mozilla envisions a world in which Common Voice advances open technology through a range of governance approaches, guided by the communities themselves. \n\nThe Dholuo speech dataset was created by researchers from Maseno Centre for Applied Artificial Intelligence, Maseno University and members from the Dholuo language community from Kenya. The pilot was conducted by Dr. Lilian Wanzare, Language Community Associate for Common Voice, from Nairobi, Kenya.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [ "Global" ], "sdgs": [ "SDG 10" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "Nwulite Obodo Open Data Licence 1.0 (NOODL-1.0)", "spdx": null, "url": "https://licensingafricandatasets.com/nwulite-obodo-license" }, "organizations": { "provided_by": { "text": "Maseno University (Maseno Centre for Applied Artificial Intelligence), Members from the Dholuo language community", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation, FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Dr. Lilian Wanzare , Dept. of Computer Science, School of Computing and Informatics, Maseno University", "access_note": null, "links": { "dataset": [ { "label": "datacollective.mozillafoundation.org: cmjepxo6t08nmmk07iauvua6v", "url": "https://datacollective.mozillafoundation.org/datasets/cmjepxo6t08nmmk07iauvua6v" } ], "usecase": [], "additional": [ { "label": "In Country Programmes (mozillafoundation.org)", "url": "https://www.mozillafoundation.org/en/common-voice/in-country-programmes/" }, { "label": "Common Voice Piloting Alternative Language Data Licenses Workshop Kenya Maseno University (maseno.ac.ke)", "url": "https://www.maseno.ac.ke/common-voice-piloting-alternative-language-data-licenses-workshop-kenya-maseno-university" }, { "label": "igf2025.sched.com", "url": "https://igf2025.sched.com/event/24FLE/ws-#323-new-data-governance-models-for-african-nlp-ecosystems" }, { "label": "Watch (youtube.com)", "url": "https://www.youtube.com/watch?v=TYnn-mOyS1A" } ], "documents": [] }, "content": { "data_characteristics": { "text": "This dataset contains native-speaker audio recordings collected through a platform where users read aloud a displayed sentence. \n\nCollection Timeframe:\nCollected in 2025, between October and November 2025, as part of the Dholuo Voice Data Collection Project.\n\nRecording Conditions: Indoor environments with no background noise, Recorded via smartphones through a web interface\n\nDomains Represented:\nGeneral\nAgriculture\nTechnology and robotics\nHealthcare\nNews and current affairs\nThese domains reflect real spoken Dholuo usage.\nTotal duration: 184,838.28 sec (3080.64 min, 51.34 hr)\r\n\r\nNumber of Speakers: 59\r\n\r\nNumber of Reviewers: 7\r\n\r\nTotal audio files: 26,091\r\n\r\nAverage clip length: 7.08 sec\r\n\r\nMinimum clip length: 1.72 sec\r\n\r\nMaximum clip length: 62.64 sec", "provenance": "curated" }, "model_characteristics": null, "how_to_use": { "text": "DhoNam: Dholuo Speech dataset is a speech corpus designed to supercharge Automatic Speech Recognition (ASR) and other speech technologies for Dholuo, one of Kenya’s major indigenous languages.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_20/images/placeholder_image.jpeg" }, { "id": "ui_21", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_21-imarika__translating_weather_information_into/", "aliases": [ "imarika__translating_weather_information_into", "innovate_africa_challenge_ai_for_climate" ], "title": "Imarika - Translating weather information into actionable advisory for farmers through AI in Kenya", "description": { "text": "IMARIKA by Strathmore University’s iLabAfrica is building low-cost automatic weather station networks to provide access to accurate, local weather information in rural Kenya. This granular data allows advisory services for small-holder farmers to shift from using generic information to location-specific guidance, delivered through farmer organizations and digital platforms. IMARIKA also works on translating weather data into actionable recommendations, addressing farmers’ difficulty in interpreting weather information.\n\nIMARIKA by Strathmore University’s iLabAfrica is also the winner of the \"Innovate Africa Challenge on Climate Action\",  - chosen for its potential to advance climate adaptation for small-holder farmer communities. Together with Smart Africa, FAIR Forward had supported seven startups through intensive technical and business support to develop their AI businesses focused on mitigating and adapting to climate change in four countries through the Innovate Africa Challenge format in its first iteration focusing on AI for Climate Action.\n\nIMARIKA was also part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10", "SDG 5" ], "data_types": [ "Meterological", "Text", "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": null, "organizations": { "provided_by": { "text": "Strathmore University’s iLabAfrica", "links": [] }, "catalyzed_by": { "text": "Smart Africa, FAIR Forward - AI for All, GIZ , Intellecap, intel.liftoff, Climate Change AI; Mozilla Foundation", "links": [ { "name": "Smart Africa", "url": "https://smartafrica.org/" }, { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ, Gates Foundation", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Betsy Muriithi , Strathmore University’s iLabAfrica", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "GitHub: imarika-weather-pipeline", "url": "https://github.com/iLab-DSU/imarika-weather-pipeline" }, { "label": "GitHub: Agricultural-Recommendations-Chat", "url": "https://github.com/iLab-DSU/Agricultural-Recommendations-Chat" } ], "additional": [ { "label": "Chatbots Local Weather Reports And A Boon For Kenyas Smallholder Farmers (foundation.mozilla.org)", "url": "https://foundation.mozilla.org/blog/chatbots-local-weather-reports-and-a-boon-for-kenyas-smallholder-farmers/" }, { "label": "Awards (mozillafoundation.org)", "url": "https://www.mozillafoundation.org/en/what-we-fund/programs/common-voice-kiswahili-awards/awards/" }, { "label": "Innovate Africa Challenge (smartafrica.org)", "url": "https://smartafrica.org/innovate-africa-challenge/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Weather data pipeline for rural Kenya. Fetches readings from the Wireless Planet API (api.wirelessplanet.co.ke) every 3 hours. License: MIT. Data Type: Time-series weather readings. Processing: Apache Spark 3.3.0 structured streaming with data cleaning, mean-based imputation, and Z-score anomaly detection (threshold: 10.0). Storage: PostgreSQL 14 (raw table: weather_raw; processed table: weather_clean with device_id, date, temperature, wind, precipitation, anomaly flags). Throughput: ~40-50 records/second, <30 seconds end-to-end latency. Daily aggregation compresses ~1,000 raw readings into ~35 daily summaries. Requires Docker, 8GB+ RAM, and a weather API account.\n\nSource: https://github.com/iLab-DSU/imarika-weather-pipeline", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Two components: (1) Weather Pipeline -- Apache Spark streaming processor ingests weather API data via Kafka, cleans it, detects anomalies, and stores raw + processed data in PostgreSQL. Produces daily weather summaries per device. Stack: Docker Compose, Kafka, Spark 3.3.0, PostgreSQL 14, Python. (2) Agricultural AI Assistant -- LangGraph-based advisory chatbot for 6 East African crops (beans, cassava, finger millet, maize, sorghum, sweet potatoes). Uses QLoRA-fine-tuned Llama 3.2 3B Instruct model, a knowledge graph (30+ nodes, 95+ edges), Chroma vector database, and OpenWeather API integration. Supports English and Swahili. Runs locally via Ollama. Requires 4GB+ GPU VRAM for inference or CPU (slower).\n\nSource: https://github.com/iLab-DSU/imarika-weather-pipeline, https://github.com/iLab-DSU/Agricultural-Recommendations-Chat", "provenance": "auto-enriched" }, "how_to_use": { "text": "For anyone interested in translating weather information into actionable advisory for farming practices, Imarika's Agricultural AI Assistant which is an intelligent agricultural advisory system powered by LangGraph, Knowledge Graph, and Ollama models specialized in 6 East African crops with multilingual support (English/Swahili) is worth looking into. \nAnyone running several weather stations with a need to process the data may be interested in Imarika's real-time weather data processing pipeline built with Apache Spark, Kafka, and PostgreSQL. \n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_21/images/placeholder_image.jpeg" }, { "id": "ui_22", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_22-empowering_women_across_india_with_audio/", "aliases": [ "digital_audio_content_creation_for_womens", "empowering_women_across_india_with_audio" ], "title": "Empowering Women across India with audio messages in their native languages on Health, Sustainable Agriculture and Education", "description": { "text": "Empowering Women Across India with Voice-based Knowledge in Their Native Languages: By using openly accessible text-to-speech models from the Indian Institute of Science (IISc) and MeitY's Digital India Bhashini Division, Audiopedia created health-related 400+ audio messages that can be shared with civil society organizations for awareness raising and capacity building", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10", "SDG 5" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC-BY-SA 4.0", "spdx": "CC-BY-SA-4.0", "url": null }, "organizations": { "provided_by": { "text": "Audiopedia", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Audiopedia Foundation (https://www.audiopedia.foundation/), Marcel Heyne (contact@audiopedia.org)", "access_note": null, "links": { "dataset": [ { "label": "audiopedia.app.box.com: 4wtqy4idpnilf3b3abisuuu2vdi0oi1q", "url": "https://audiopedia.app.box.com/s/4wtqy4idpnilf3b3abisuuu2vdi0oi1q" } ], "usecase": [ { "label": "audiopedia.foundation: bharat", "url": "https://www.audiopedia.foundation/bharat" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Audiopedia has created useful content on various topics including health, education, financial literacy. This data is available in text and audio format in multiple languages incl. low-resource languages.", "provenance": "curated" }, "model_characteristics": { "text": "For the generation of audio messages, Audiopedia used openly accessible text-to-speech models from the Indian Institute of Science (IISc) and MeitY's Digital India Bhashini Division.", "provenance": "curated" }, "how_to_use": { "text": "Civil society organizations can use the existing voice content to generate tailored, AI-powered messages in various Indian languages across different topics including health, education, agriculture and other social topics. Civil society organizations can deploy these messages in rural and semi-urban areas using easy-to-use digital tools incl. WhatsApp messaging or feature phones.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_22/images/placeholder_image.jpeg" }, { "id": "ui_23", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_23-combatting_air_pollution_and_ghg_emissions/", "aliases": [ "combatting_air_pollution_and_ghg_emissions", "hyperlocal_mapping_of_air_pollution_and", "open_air_pollution_data_for_patna" ], "title": "Combatting Air Pollution and GHG Emissions in India through hyperlocal AI-powered mapping", "description": { "text": "Under this initiative, a novel approach is employed by leveraging citizen scientists and IoT-based low-cost sensors to collect hyperlocal air quality data. This data is used to identify pollution sources and risk zones, facilitating targeted actions by regulatory authorities.To showcase data outreach, the project features the VAYU Android-based application and the VAYU citizen portal digital stack, which support targeted interventions and customized solutions backed by AI/ML algorithms. These tools potentially develop new approaches in air pollution management while reducing public investment costs.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "UNDP India", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "UNDP India (registry.in@undp.org)", "access_note": null, "links": { "dataset": [ { "label": "vayu.undp.org.in", "url": "https://vayu.undp.org.in/" } ], "usecase": [ { "label": "GitHub: VAYU_OpenAir", "url": "https://github.com/undpindia/VAYU_OpenAir" }, { "label": "GitHub: vayu-gnn", "url": "https://github.com/EconAIorg/vayu-gnn/tree/main" }, { "label": "GitHub: VayuAssist", "url": "https://github.com/Alphawarrior21/VayuAssist" }, { "label": "GitHub: ClearSky", "url": "https://github.com/akbp24/ClearSky" }, { "label": "GitHub: vayu_airnode", "url": "https://github.com/sherwaldeepesh/vayu_airnode" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The data for this project is collected by static and dynamic sensor. Static sensors are placed at known hotspots for air pollution while dynamic sensors are moved by citizen scientist continously. \n• 2 Cities\n• 100+ Senors\n• 150+ Volunteers\n• 1000+ Records Collected\n• ~10Million Data Points", "provenance": "curated" }, "model_characteristics": { "text": "Sensors data is continously shared with specific pollutoin boards, and us ecase dvelopeed under project combined AI and ML to detect pollution sources and provide early alerts, enabling rapid response and policy action.", "provenance": "curated" }, "how_to_use": { "text": "VAYU OpenAir provides an end-to-end open-source platform for hyperlocal air pollution mapping, built through a collaboration between UNDP, GIZ, the Government of India, the University of Nottingham, Development Alternatives, D-Coop, and citizen scientists across India. The platform includes open data, open algorithms, and open software from hyperlocal mapping campaigns in Patna and Gurgaon.\n\nThe core platform consists of three components: a mobile app for field-level data collection and viewing air quality readings, a web portal dashboard for visualising pollution data, and a backend API serving data to both interfaces. All source code is available at github.com/undpindia/VAYU_OpenAir under an MIT license.\n\nPractitioners can use VAYU OpenAir in several ways. If you are working on urban air quality in Indian cities or comparable contexts, you can deploy the existing platform to run your own hyperlocal mapping campaigns -- the mobile app supports citizen-science data collection, while the web portal provides ready-made visualisation tools for stakeholders and policymakers. If you already have air quality data and want to build predictive or analytical tools on top of it, several community-built open-source projects demonstrate what is possible:\n\n- **vayu-gnn** -- A Graph Neural Network that predicts hyperlocal pollutant levels up to 8 hours ahead by combining VAYU sensor readings with weather data, elevation, river distance, and urban density features. Available under GPL-3.0 at github.com/EconAIorg/vayu-gnn.\n- **VayuAssist** -- A RAG-based chatbot designed for government policymakers, providing air quality insights, AQI trend analysis, and mitigation strategies. Built with Streamlit and OpenAI GPT-3.5 Turbo. Available under MIT at github.com/Alphawarrior21/VayuAssist.\n- **ClearSky** -- Jupyter Notebooks for predictive pollution analysis with 3D visualisation. Available at github.com/akbp24/ClearSky.\n- **vayu_airnode** -- ARIMA time-series analysis for individual pollutants (CH4, CO, CO2, NO2, PM10, PM2.5) plus temperature and humidity, with a Streamlit interface. Available under Apache-2.0 at github.com/sherwaldeepesh/vayu_airnode.\n\nThese community tools illustrate concrete extension points: from short-term pollution forecasting to policy-support chatbots to time-series analysis of specific pollutants. Developers looking to extend the ecosystem can build on any of these as starting points.\n\nCost and resources: The platform and all community tools are open-source, so the primary costs are compute infrastructure for hosting the backend and any ML model training. Setup documentation is provided in each repository.\n\nSources:\n- https://vayu.undp.org.in/\n- https://github.com/undpindia/VAYU_OpenAir\n- https://github.com/EconAIorg/vayu-gnn/tree/main\n- https://github.com/Alphawarrior21/VayuAssist\n- https://github.com/akbp24/ClearSky\n- https://github.com/sherwaldeepesh/vayu_airnode", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_23/images/airpollution.png" }, { "id": "ui_24", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_24-making_ai_speak_9_indian_languages/", "aliases": [ "iisc__open_voice_data_in", "making_ai_speak_9_indian_languages" ], "title": "Making AI speak 9 Indian languages: Hindi, Bengali, Marathi, Telugu, Bhojpuri, Kannada, Magadhi, Chhattisgarhi, Maithili - Open-source text-to-speech models", "description": { "text": "This dataset is part of the initiative SYSPIN (SYnthesizing SPeech in INdian languages), that develops large open-source Text-to-Speech (TTS) corpora, i.e., speech training datasets and AI models in 9 Indian languages, namely – Bengali, Bhojpuri, Chhattisgarhi, Hindi, Kannada, Magahi, Maithili, Marathi, and Telugu. This data is useful for those wishing to build voice-enabled, user-friendly applications in Indian languages. Through voice-based solutions, these AI models can be used to empower communities that do not have access to digital services in their own native language. They can be used by private companies, researchers, NGOs or government departments to develop digital applications. \n \n The datasets and TTS models are available to the AI community and anyone wishing to build voice-enabled applications in Indian languages. Given the distribution of speakers. some of the languages are especially useful for programmes wishing to engage with rural communities and promote sustainable agriculture in rural settings.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Indian Institute of Science (IISc)", "links": [] }, "catalyzed_by": { "text": "Bhashini, FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Dr. Prasanta Ghosh (prasantag@iisc.ac.in)", "access_note": null, "links": { "dataset": [ { "label": "syspin.iisc.ac.in", "url": "https://syspin.iisc.ac.in/datasets" } ], "usecase": [ { "label": "Hugging Face: SYSPIN", "url": "https://huggingface.co/SYSPIN" }, { "label": "bhashini.gov.in: explore-models", "url": "https://bhashini.gov.in/ulca/model/explore-models" } ], "additional": [ { "label": "Loud And Clear Open Source For Indias Linguistic Diversity (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/news/loud-and-clear-open-source-for-indias-linguistic-diversity/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The Data is fully open-sourced and can be accessed at:\n • IISc website: \n TTS Dataset:\n • License: CC-BY 4.0\n • Data Type: Audio recording\n • Sentence creation: Variety of domains covered in the sentences/text and phonetically rich sentence selection\n • Accounts for dialect variability\n • Voice artist selection and balanced duration per voice artist \n • Voice recording: 40 hours of recording by 1 male voice artist and 1 female voice artist for each of the 9 languages\n • Studio-quality audio with 48kHz, 24 bits per sample from every voice artist\n • Data was collected and created over 3 years from 2021-2024", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data:\n The validated sentences were recording in a recording room (size 10'3\" x 5'9\") by voice artists using a Neumann TLM-103 studio microphone and Audio Interface UAD Apollo Twin X. \n Application:\n IISc has organised the LIMMITS challenges (2023, 2025) as part of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) to build TTS voices, share web API for evaluation, and allow researchers to contribute towards the development of streaming and neural codec-based TTS systems. \n Audio files of Audiopedia Foundation’s content created in the low-resource language Chhattisgarhi using IISc TTS Chhattisgarhi model can be found here.", "provenance": "curated" }, "how_to_use": { "text": "The TTS models can be used to develop innovative solutions and voice-based services for Indians in their native language. This would be especially beneficial for those who cannot read/write or have speech and visual disabilities but can access digital services through audio/voice-based mediums. The data and models are a crucial stepping stone for developing such assistive technologies. Including low-resource languages, some of which do not even have enough print/digital literature, would benefit vulnerable communities for whom language barriers make access to technological solutions even more difficult. Given the distribution of speakers. some of the languages are especially useful for programmes wishing to engage with rural communities and promote sustainable agriculture in rural settings. The 720 hours of open-source TTS data also present immense opportunities for academic and industrial research.\n\nYou can directly access and use the models on Bashini here via web and API: https://anuvaad.bhashini.gov.in/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_24/images/pexels-photo-18636912.jpg" }, { "id": "ui_25", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_25-open_source_ai_pest_control_for/", "aliases": [ "open_source_ai_pest_control_for", "wadhwani_ai__open_ai_for" ], "title": "Open source AI Pest Control for smallholder Cotton Farmers in India", "description": { "text": "Wadhwani AI has developed a mobile app to support cotton farmers combat pest infestations, a major threat to cotton productivity. For this, they have collaborated with FAIR Forward – AI for All, a project of German Development Cooperation. The app allows farmers to take photos of pheromone traps, which capture male bollworms, and uses an AI model to count the pests in the images. Based on the AI-generated counts, the app provides recommendations to farmers on how to respond. This technology empowers small-scale farmers to make informed decisions, reduce uncertainty in yield, and improve their livelihoods.\n \n As the codebase, dataset and research are openly available it can be used to build on, test and research similar applications in other contexts. This would esp. apply to problems of identification and counting of agricultural pests.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Images" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "Apache 2.0", "spdx": "Apache-2.0", "url": null }, "organizations": { "provided_by": { "text": "Wadhwani AI", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Wadhwani AI (agri-ai@wadhwaniai.org), Github (https://github.com/WadhwaniAI/pest-monitoring)", "access_note": null, "links": { "dataset": [ { "label": "source.coop: description", "url": "https://source.coop/repositories/wadhwani-ai/wiai-pm-open-data/description" }, { "label": "GitHub: pest-management-opendata", "url": "https://github.com/WadhwaniAI/pest-management-opendata?tab=readme-ov-file" }, { "label": "Hugging Face: pest-management-opendata", "url": "https://huggingface.co/datasets/wadhwani-ai/pest-management-opendata" } ], "usecase": [ { "label": "GitHub: pest-monitoring", "url": "https://github.com/WadhwaniAI/pest-monitoring" } ], "additional": [ { "label": "Pest Management Ai Solution (wadhwaniai.org)", "url": "https://www.wadhwaniai.org/programs/pest-management/pest-management-ai-solution/" }, { "label": "3394486.3403363 (dl.acm.org)", "url": "https://dl.acm.org/doi/10.1145/3394486.3403363" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Wadhwani AI with support from German Development Cooperation provided training data consisting of ca. 13,000 images:\n \n (a) These images were captured by farmers and farm extension workers since 2018\n (b) The dataset contains two types of images: those with pests and bounding boxes labelling either pink bollworm (PBW) or American bollworm (ABW), and those without pests, representing real-world user behavior.\n (c) License: Apache 2.0", "provenance": "curated" }, "model_characteristics": { "text": "Wadhwani AI with support from German Development cooperation provided the codebase of the pest identification model:\n \n (a) At its core, this repository packages an object detection implementation. \n (b) While there are several object detection implementations, and even implementation aggregations, there are none that completely solve for the challenges faced here (e.g., highly diverse image quality, model size restrictions)\n (c) License: Apache 2.0", "provenance": "curated" }, "how_to_use": { "text": "The resources can be used to build on, test and research similar applications in other contexts, esp. applying to problems of identification and counting of agricultural pests.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_25/images/placeholder_image.jpeg" }, { "id": "ui_26", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_26-providing_better_information_on_sexual_and/", "aliases": [ "gramvaani_automatic_speech_recognition_asr", "providing_better_information_on_sexual_and" ], "title": "Providing better information on sexual and reproductive health and rights of young people through the Kahi Ankahi Baatein infoline (Hindi LLM finetuning)", "description": { "text": "Improve the Kahi Ankahi Baatein (KAB) platform by fine-tuning Hindi LLMs for better user experience.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": null, "organizations": { "provided_by": { "text": "GramVaani", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Gram Vaani (contact@gramvaani.org)", "access_note": null, "links": { "dataset": [ { "label": "GitLab: SRHR automated QnA-KAB.xlsx", "url": "https://gitlab.com/gramvaani/giz_kab_bert_sourcecode/-/blob/main/SRHR%20automated%20QnA-KAB.xlsx?ref_type=heads" } ], "usecase": [ { "label": "GitLab: giz_kab_bert_sourcecode", "url": "https://gitlab.com/gramvaani/giz_kab_bert_sourcecode" } ], "additional": [ { "label": "Answering Questions On Sexuality Through Kahi Ankahi Baatein 2 (gramvaani.org)", "url": "https://gramvaani.org/answering-questions-on-sexuality-through-kahi-ankahi-baatein-2/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The dataset provides a small sample of manually transcribed voice data from the KAB helpline (https://gramvaani.org/indias-new-sexual-health-info-line/). The voice recordings are in Hindi as well as the transcriptions. The dataset consists of transcriptions of Questions that people have asked as well as the corresponding answers from the database. The dataset is further tagged with other useful labels such as domain or relevance of answer provided.", "provenance": "curated" }, "model_characteristics": { "text": "A Transformer-based architecture, specifically BERT (bert-base-multilingual-cased), is employed using TensorFlow. The model is trained on question pairs for a similarity classification task. Positive samples consist of similar question pairs, while negative samples involve random question pairs. The pretrained multilingual BERT model from HuggingFace is utilized to obtain embeddings for each input.Eventually, as a result the model generates similarity scores for question pairs in the test set.", "provenance": "curated" }, "how_to_use": { "text": "There are two resources to build on and use:\n1) The dataset can be used as a resource to fine-tune Q&A capabilities of LLMs specifically for the Sexual and Reproductive Health domain. The dataset can be further used to quality check and/or improve Speech to Text models. \n2) the fine-tuned model can be used for down-stream applications that need Hindi LLM capabilities in the Sexual and Reproductive Health domain.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_26/images/placeholder_image.jpeg" }, { "id": "ui_27", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_27-making_ai_speak_mundari__opensource/", "aliases": [ "100_hours_of_text_to_speech", "making_ai_speak_mundari__opensource" ], "title": "Making AI speak Mundari - Open-source text-to-speech data and model for Mundari, India", "description": { "text": "100 hours of Text to Speech Dataset for Mundari Language", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model", "tags": [ "dataset", "model" ] }, "license": { "name": "BY-NC-SA-FS", "spdx": null, "url": null }, "organizations": { "provided_by": { "text": "Karya Inc.", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Karya (operations@karya.in)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: dataset-hindi-mundari-translation", "url": "https://github.com/karya-inc/dataset-hindi-mundari-translation" }, { "label": "GitHub: dataset-mundari-tts", "url": "https://github.com/karya-inc/dataset-mundari-tts" } ], "usecase": [ { "label": "GitHub: MunTTS-A-Text-to-Speech-System-Fo…", "url": "https://github.com/microsoft/MunTTS-A-Text-to-Speech-System-For-Mundari" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Translation: This corpus contains a collection of sentence pairs that have been translated from Hindi to Mundari. The dataset was created as part of a collaboration between Microsoft Research India, Indian Institute of Technology Kharagpur, and Karya. This dataset is realeased under the non-commercial version of the Karya Public License. Please read the \"License\" section below for a high-level summary of what you are allowed and not allowed to do under this license. The dataset contains 17,826 sentence pairs of Hindi to Mundari translations. The translations are stored in a single TSV file translation-hi-unr.tsv with UTF-8 encoding. Each line in the TSV file contains two columns, separated by a tab character. The first column contains the Hindi sentence, and the second column contains the corresponding Mundari translation.\n\nText-to-speech:The Mundari TTS dataset corpus contains a total of 26,870 audio files, each containing a single utterance spoken by one of the two speakers. The audio is recorded in 32-bit PCM format with a sampling rate of 44.1 kHz. The dataset includes transcripts for each audio file in Mundari script. The recordings were collected in a sound-treated room using a high-quality microphone and preamp.", "provenance": "curated" }, "model_characteristics": { "text": "MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in linguistic technology for underrepresented languages by collecting and processing data to build a speech synthesis system. We begin our study by gathering a substantial dataset of Mundari text and speech and train end-to-end speech models. We also delve into the methods used for training our models, ensuring they are efficient and effective despite the data constraints. We evaluate our system with native speakers and objective metrics, demonstrating its potential as a tool for preserving and promoting the Mundari language in the digital age.", "provenance": "curated" }, "how_to_use": { "text": "The Mundari TTS dataset corpus includes recordings from two different speakers: a female speaker and a male speaker. The female speaker contributed 19,868 recordings, while the male speaker contributed 7,002 recordings.\r\n\r\nThe total size of the corpus is approximately 17 GB (7 GB compressed). Therefore, the full dataset is hosted in cloud storage (instead of this github repository). This repository contains a sample of 100 recordings from each speaker. Please email data@karya.in for a link to download the full dataset.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_27/images/placeholder_image.jpeg" }, { "id": "ui_28", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_28-farmerchat_delivering_personalized_farm_advice_to/", "aliases": [ "farmerchat_delivering_personalized_farm_advice_to", "farmerchat_open_aipowered_agricultural_advisory_fo" ], "title": "FarmerChat: Delivering Personalized Farm Advice to 1.6 Million Farmers Across Five Countries", "description": { "text": "FarmerChat is an AI assistant built to help smallholder farmers make better field-level decisions by delivering timely, localized advice to help them grow more, earn more, and adapt to changing weather. It is designed for low-connectivity areas and available in over 15 languages including Swahili, Portuguese, Spanish, Hindi, Telugu, Amharic, Hausa, and English.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" }, { "name": "Kenya", "iso2": "KE" }, { "name": "Nigeria", "iso2": "NG" }, { "name": "Ethiopia", "iso2": "ET" }, { "name": "Brazil", "iso2": "BR" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Other" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Digital Green", "links": [] }, "catalyzed_by": { "text": "Digital Green, in parts by FAIR Forward - AI for All, GIZ", "links": [ { "name": "in parts by FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "Multiple Funders, among them: The Rockefeller Foundation, Gates Foundation, BMZ and Sequoia Climate Fund, CISCO Foundation", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Digital Green (contact@digitalgreen.org)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: DG_Open", "url": "https://github.com/digitalgreenorg/DG_Open" }, { "label": "Hugging Face: farmerchat-queries-large", "url": "https://huggingface.co/datasets/DigiGreen/farmerchat-queries-large" } ], "usecase": [ { "label": "play.google.com: details", "url": "https://play.google.com/store/apps/details?id=org.digitalgreen.farmer.chat&hl=en_US" } ], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/11jmgluDCSqyMzP9s_VJCMgZVGkdmXePH/view?usp=sharing" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The FarmerChat system draws from an agronomic content corpus that powers FarmerChat's advisory responses — curated from ICAR, national extension services, and field-validated crop guidance across India, Kenya, Ethiopia, Nigeria, and Brazil. This content is organized by crop, geography, and query type, enabling the system to return locally grounded answers rather than generic advice.\n\nKey characteristics: multilingual (15 languages), multi-country (5 geographies), low-bandwidth optimized, and continuously updated through fine-tuning and RLHF loops informed by farmer feedback and agronomist review. Query volume has reached 9 million across 1.6 million farmers, providing a rare at-scale signal on what smallholder farmers actually ask and what advice they act on.\n\nResponsible AI: FarmerChat's content pipeline includes human agronomist review before deployment in any new crop or geography. Outputs are grounded in verified extension material to reduce hallucination risk. Digital Green has invested in RLHF infrastructure to surface low-quality responses for expert correction.", "provenance": "curated" }, "model_characteristics": { "text": "Multiple models are available based on the shared data. Please refer to the GitHub repository.", "provenance": "curated" }, "how_to_use": { "text": "FarmerChat can be engaged at four levels depending on your organization's technical capacity, timeline, and deployment goals.\n\nOpen Build Partner: Technically equipped organizations can build independently on Digital Green's public infrastructure. The full codebase is available at github.com/digitalgreenorg/DG_Open (Apache 2.0) and datasets are on GitHub and Hugging Face. Digital Green publishes and maintains the open source foundation; the partner owns everything from development through deployment.\n\nCommunity Partner: For organizations ready to launch quickly without requiring Digital Green staff time. Digital Green provides and maintains the FarmerChat platform at no cost, along with an onboarding toolkit, co-brandable campaign templates, and remote training support. The partner leads all in-country training and farmer outreach.\n\nCustom Partnership: For organizations that want Digital Green staff actively involved in their deployment. Depending on scope, this can range from research-grade validation — where Digital Green contributes expert agronomic content review, M&E frameworks, and pre-built analytics dashboards — through to full co-implementation, where Digital Green dedicates product and engineering staff to customize across various features of localization and the tech stack.\n\nTo get started, organizations should reach out to Digital Green to find the partnership tier that best suits their needs.\n\nBasic eligibility: farmers should be reachable in a language FarmerChat supports (including English, Hindi, Telugu, Amharic, Kiswahili, Hausa, Portuguese, Spanish, and others), have access to an Android smartphone directly or via a shared-device model, and the project should have a credible distribution path through an extension network, cooperative, government department, or telecom partnership.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_28/images/digital_green.png" }, { "id": "ui_29", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_29-predicting_crop_health_using_opensource_geospatial/", "aliases": [ "ml4eo_telangana_crop_identification", "open_dataset_of_farm_boundaries_and", "predicting_crop_health_using_opensource_geospatial" ], "title": "Predicting Crop Health using open-source geospatial data and ground truth data collected in Telangana State", "description": { "text": "This AI application and replication-kit is about an AI-based crop type map for Telangana. This should be of use to anyone wishing to support sustainable farming practices and evidence-based decision-making for agricultural. Also, this can be used to empower you, as a local business, and NGOs, or a government agency to develop digital agricultural applications. The application focuses on creating localized ground-truth data on several different crop related variables to train AI models tailored to Telangana's diverse agricultural landscape. This includes an open-access ground-truth dataset, a data challenge with openly available models for yield prediction, a crop classification model using satellite imagery applied to map the whole state of Telangana, and a roadmap for AI implementation in agriculture. Datasets, AI models, and crop maps are all openly available as an open-source replication kit and digital public good.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "WRMS - Weather Risk Management Services, ZINDI, Vertify Earth", "links": [ { "name": "WRMS - Weather Risk Management Services", "url": "https://wrmsglobal.com/" }, { "name": "ZINDI", "url": "https://zindi.africa/" }, { "name": "Vertify Earth", "url": "https://vertify.earth/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "WRMS (connect@wrmsglobal.com)", "access_note": null, "links": { "dataset": [ { "label": "dataexplorer.ts.adex.org.in: 583e8f01-160e-4f51-bde5-31dc7f2a5…", "url": "https://dataexplorer.ts.adex.org.in/dataset/583e8f01-160e-4f51-bde5-31dc7f2a5887" } ], "usecase": [ { "label": "GitHub: zindi_crop_health", "url": "https://github.com/pranavmyname/zindi_crop_health" }, { "label": "zindi.africa: telangana-crop-health-challenge", "url": "https://zindi.africa/competitions/telangana-crop-health-challenge" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The Data is fully open-sourced and can be accessed at the Telangana Data Exchange Platform: ADEX\n • License: CC BY 4.0\n • Data Type: Tabular\n • Key Variables: Crop type (6), Crop Health, Crop Yield, Irrigation Methods.\n • Appr. No of observations: > 12000 \n • Appr. No of dimensions: 45 \n • Covering 6 Mandals in Telangana\n • Format: dbf, csv\n \n The data was collected over the span of 1.5 years covering different crop cycles between 2022 and 2024. The Data Collection was conducted by WRMS via on-the-ground visits of the fields and by surveying farmers in Telangana State.\n\nA responsible AI Assessment was undertaken for this dataset / use case to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data:\n The ground truth data was combined with open-source Sentinel-2 data to create a geospatial dataset with crop type as labels.\n The processed dataset can be accessed here: Processed Data\n Code for Crop Classification Model:\n Code to create the training data and for training the models for crop mapping can be accessed here: Code\n Application:\n The resulting shapefiles that predict crop type for the state of Telangana are deployed on DICRA – a digital public good ran by the Indian Development Bank NABARD and UNDP: DICRA\n Based on the collected data, GIZ organised a data challenged together with Zindi to build state-of-the-art crop mapping models. The outcomes of the challenge can be accessed here: Zindi", "provenance": "curated" }, "how_to_use": { "text": "You can make use of the existing models on yield prediction and crop type mapping to infer other similar datapoints – for example neighbouring states of Telangana. Additionally, you can try to combine the dataset with other existing open-source datasets from the ADEX platform and conduct research/commercial scoping on these. \n The present dataset is quite unique in the fact that it includes observations such as irrigation methods, crop health and expected yield. You may combine these with other data sources, i.e. satellite/remote sensing data (open-source or commercial) to build high-quality crop monitoring/recommendation systems to support various agricultural entities, farmers and decision makers in India. This could also inform commercial products. \n As crop mapping is a prominent use-case, the data can be used for benchmarking different available crop datasets and models or inform foundational geospatial models as well as constitute to a region-wide crop mapping. Potential partners on the ground for collaboration, commercial involvement or funding are the Telangana Government, specifically the IT and Agricultural department and Nabard Dicra.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_29/images/placeholder_image.jpeg" }, { "id": "ui_30", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_30-microfinance_industry_network_india_use_of/", "aliases": [ "developing_a_concept_with_mfin_on", "microfinance_industry_network_india_use_of" ], "title": "MicroFinance Industry Network India use of voice technology (Gramvaani)", "description": { "text": "Contract name: Automation of components of the MFIN-CGRM CRM solution \n Follow-up project from former MFIN engagement to move from prototype to production and implement the remaining use-cases.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "GramVaani", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "GramVaani (contact@gramvaani.org)", "access_note": { "kind": "documents", "markdown": "This project has been concluded as an internal review of MFINS workflows. There has been technical integration and scoping of AI based solutions for improved costumer satisfaction and productivity but no novel dataset has been created and/or model trained.\nSee technical report for an overview and learnings from this activity." }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [ { "label": "Project report", "url": "https://fair-forward.github.io/datasets/projects/ui_30/documents/Project report.pdf" } ] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_30/images/placeholder_image.jpeg" }, { "id": "ui_31", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_31-forest_forward_ii_using_ai_to/", "aliases": [ "ai_for_forest_conservation", "forest_forward", "forest_forward_ii_using_ai_to" ], "title": "Forest Forward II: Using AI to map carbon in forests using High Carbon Stock Approach and assess fire vulnerability of forests to Combat Climate Change and Protect Livelihoods in India", "description": { "text": "Mapping carbon content in forests: This application leverages advanced geospatial technologies, such as remote sensing and AI, to support forest conservation efforts in India. The application will allow stakeholders to better monitor forest health, estimate biomass, and contribute to climate mitigation efforts.\n Fire Vulnerability Mapping: This application would help develop a fire vulnerability map which could help to identify areas that are prone to fires based on several forest degradation indicators.", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "MIT", "spdx": "MIT", "url": null }, "organizations": { "provided_by": { "text": "Vertify Earth", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Michael Anthony ", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "GitHub: biomass-dl-model-training", "url": "https://github.com/vertify-earth/biomass-dl-model-training" }, { "label": "GitHub: ForestFireGoa", "url": "https://github.com/vertify-earth/ForestFireGoa" }, { "label": "Hugging Face: biomass-model", "url": "https://huggingface.co/vertify/biomass-model" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The datasets used for this projects are all publicly available or accessible via common platforms such as Planet.com \nMore details to follow.", "provenance": "curated" }, "model_characteristics": { "text": "This model is trained to predict above-ground biomass (AGB) in tropical and subtropical forests using multi-source satellite imagery at the pixel level. Specifically:\r\n\r\nPrediction Unit: Estimates biomass at individual pixel level (typically 10-40m resolution depending on input data)\r\nOutput: Biomass density in Mg/ha (megagrams per hectare)\r\nInput Data: Processes multi-sensor data including Sentinel-1, Sentinel-2, Landsat-8, PALSAR, and DEM\r\nApplication Scope: Best suited for tropical and subtropical forest ecosystems in South/Southeast Asia\r\nBiomass Range: Validated for forests with biomass between ~40-460 Mg/ha", "provenance": "curated" }, "how_to_use": { "text": "The pipeline is designed to handle multi-source satellite imagery and corresponding biomass data with pixel-level precision. Key aspects include:\r\n\r\nEnd-to-End Workflow: From raw data ingestion to model training, evaluation, and deployment\r\nAdvanced Feature Engineering: Comprehensive spectral indices, texture features, spatial gradients, and PCA components\r\nStable Neural Architecture: Utilizes a custom StableResNet with residual connections and layer normalization for robust biomass regression\r\nMulti-Site Data Processing: Capable of processing and integrating data from multiple geographically distinct study sites\r\nFlexible Deployment: Includes HuggingFace deployment capabilities with Gradio interface\r\nMemory Efficient Processing: Chunk-based processing for handling large satellite images", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_31/images/goa-forest.jpg" }, { "id": "ui_32", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_32-open_soil_data_to_impove_soil/", "aliases": [ "geoai_for_soil_conservation_innovations_for", "improving_soil_health_and_supporting_climateresili", "open_soil_data_to_impove_soil" ], "title": "Open Soil Data to impove soil health and support climate-resilient, regenerative agriculture practices in Telangana - GeoAI for Soil Conservation", "description": { "text": "Machine Learning System for Predicting Soil Parameters from Sentinel-2 Satellite Data. Cooperation with the Government of Telangana (India).", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Bonn Consulting, Fraunhofer Institute", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Bonn Consulting (jan.redlich@bonnconsulting.group)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: geo-ai", "url": "https://github.com/NaLamKI/geo-ai" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_32/images/placeholder_image.jpeg" }, { "id": "ui_33", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_33-voice_tech_for_all_building_inclusive/", "aliases": [ "making_the_indian_voice_datasets_more", "voice_tech_for_all_building_inclusive" ], "title": "Voice Tech for All: Building Inclusive Speech AI for India with different accents and speaking styles", "description": { "text": "This hackathon challenged teams to build an AI system that can turn written text into natural-sounding speech across multiple Indian languages, including less-represented ones. Participants created voices that can reflect different accents and speaking styles, making the technology more personal and inclusive. Using open data, they made voice technology more accessible for diverse communities. The solutions will be integrated into a voice assistant designed to support pregnant women in low-income communities.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Indian Institute of Science (IISc), Artpark", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr. Prasanta Ghosh (prasantag@iisc.ac.in)", "access_note": null, "links": { "dataset": [ { "label": "spiredatasets.ee.iisc.ac.in: syspincorpus", "url": "https://spiredatasets.ee.iisc.ac.in/syspincorpus" }, { "label": "spiredatasets.ee.iisc.ac.in: englishttscorpus", "url": "https://spiredatasets.ee.iisc.ac.in/englishttscorpus" }, { "label": "spiredatasets.ee.iisc.ac.in: gujaratittscorpus", "url": "https://spiredatasets.ee.iisc.ac.in/gujaratittscorpus" } ], "usecase": [ { "label": "Hugging Face: Spark_somya_TTS", "url": "https://huggingface.co/somyalab/Spark_somya_TTS" }, { "label": "Hugging Face: Voice-Tech-CDAC-Submission", "url": "https://huggingface.co/roymukund/Voice-Tech-CDAC-Submission/tree/main" }, { "label": "Hugging Face: voice-tech-for-all-v2", "url": "https://huggingface.co/vinaybabu/voice-tech-for-all-v2" }, { "label": "Hugging Face: voice-tech-for-all-challenge-v2", "url": "https://huggingface.co/immverse-ai/voice-tech-for-all-challenge-v2" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "\"The Data is fully open-sourced and can be accessed at:\n • IISc website: \n TTS Dataset:\n • License: CC-BY 4.0\n • Data Type: Audio recording\n • Sentence creation: Variety of domains covered in the sentences/text and phonetically rich sentence selection\n • Accounts for dialect variability\n • Voice artist selection and balanced duration per voice artist \n • Voice recording: 40 hours of recording by 1 male voice artist and 1 female voice artist for each of the 9 languages\n • Studio-quality audio with 48kHz, 24 bits per sample from every voice artist\n • Data was collected and created over 3 years from 2021-2024\"", "provenance": "curated" }, "model_characteristics": { "text": "The winning models can be accessed on their respective repositories here: \nSomya: https://huggingface.co/somyalab/Spark_somya_TTS\n\nCDAC_SVNIT: https://huggingface.co/roymukund/Voice-Tech-CDAC-Submission/tree/main\n\nLTRC-SPL: https://huggingface.co/vinaybabu/voice-tech-for-all-v2\n\nimmverse_ai: https://huggingface.co/immverse-ai/voice-tech-for-all-challenge-v2", "provenance": "curated" }, "how_to_use": { "text": "The TTS models can be used to develop innovative solutions and voice-based services for Indians in their native language. This would be especially beneficial for those who cannot read/write or have speech and visual disabilities but can access digital services through audio/voice-based mediums. The data and models are a crucial stepping stone for developing such assistive technologies. Including low-resource languages, some of which do not even have enough print/digital literature, would benefit vulnerable communities for whom language barriers make access to technological solutions even more difficult. Given the distribution of speakers. some of the languages are especially useful for programmes wishing to engage with rural communities and promote sustainable agriculture in rural settings.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_33/images/placeholder_image.jpeg" }, { "id": "ui_34", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_34-aipowered_monitoring_of_forest_degradation_and/", "aliases": [ "aipowered_monitoring_of_forest_degradation_and" ], "title": "AI-powered monitoring of forest degradation and impact of restoration programs in India's Eastern Himalayas", "description": { "text": "An AI-Driven Dataset for Nature-Positive Livelihoods and Forest Restoration in Eastern Himalayas.This open-access dataset and digital MRV (Monitoring, Reporting, and Verification) toolkit was developed to help communities, NGOs and policymakers in the Eastern Himalayas measure and manage the links between forests, livelihoods and climate resilience. The project was created in response to a persistent data gap in the Himalayan region where limited open data and difficult terrain have hindered effective monitoring of forest degradation and the impact of restoration programs. Using locally calibrated AI models trained on Indian biomass data, the system combines satellite observations with ground level socio-economic surveys to measure forest dependency, identify degradation hotspots and track restoration outcomes. All datasets ranging from community level livelihood surveys in Sikkim and Mizoram to forest biomass and land use layers in Assam are openly available. Together they form a replicable, open-source “digital public good” that can be used to train local AI models for forest monitoring, biodiversity accounting or impact assessment. By providing a scalable, context-aware dataset, this initiative enables indigenous start-ups, environmental NGOs and impact investors to quantify nature positive outcomes, align with IRIS+ indicators, and guide conservation investments.\nThe goal is to make environmental monitoring inclusive and data driven, empowering local actors to track changes in forest carbon, identify erosion risks and measure the socio-economic benefits of restoration. Ultimately, this approach helps governments, researchers and entrepreneurs build locally adapted AI systems that support both ecosystem regeneration and sustainable livelihoods in fragile mountain regions.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 15", "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Vertify Earth, Earth Analytics India Pvt Ltd, Balipara Foundation, Alsisar Impact", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Vertify.earth - Michael Anthony (michael@vertify.earth), \nAlsisar Impact (anuj@alsisarimpact.com )", "access_note": null, "links": { "dataset": [ { "label": "GitHub: biomass-prediction-NorthEastIndia", "url": "https://github.com/vertify-earth/biomass-prediction-NorthEastIndia" }, { "label": "Zenodo: 16536024", "url": "https://zenodo.org/records/16536024" } ], "usecase": [ { "label": "felt.com: Nature-Audit-on-Agapi-Sourcing-Si…", "url": "https://felt.com/embed/map/Nature-Audit-on-Agapi-Sourcing-Sites-JBeP3LpnTCmabCOFb9AVrAC?loc=27.317897%2C88.595673%2C15.25z" } ], "additional": [ { "label": "Edit (docs.google.com)", "url": "https://docs.google.com/spreadsheets/d/1WaunaB-HU-j9UDs-X5Fn1vJ15H4ENMe-lcEgWM9l3Eg/edit?usp=sharing" } ], "documents": [] }, "content": { "data_characteristics": { "text": "This dataset integrates geospatial and socio-economic information to monitor forest restoration and livelihoods in the Eastern Himalayas. It combines satellite data from Sentinel-1, Sentinel-2, Landsat-8, and PALSAR with 500+ ground truth points and nearly 300 household surveys collected in Assam, Sikkim, and Mizoram. The data support AI applications for biomass estimation, deforestation detection and nature positive impact assessment.\nA locally trained biomass model improves accuracy for South Asian forest types, addressing calibration bias in global datasets. All personal identifiers have been removed, and sensitive coordinates anonymized to protect community privacy. The dataset is published under a CC-BY 4.0 license and maintained by Vertify.earth GmbH and Earth Analytics India Pvt Ltd, ensuring its ongoing usability as an open and replicable foundation for environmental AI and digital MRV systems.", "provenance": "curated" }, "model_characteristics": { "text": "The AI model estimates forest biomass and vegetation health in the Eastern Himalayas using multi-source satellite data combined with local ground measurements. It outputs Above-Ground Biomass (AGB) maps, enabling accurate tracking of forest carbon, degradation, and restoration progress.\nThe model accepts pre-processed satellite imagery as input and produces spatial biomass layers compatible with MRV dashboards. It runs on standard Python environments and can be adapted for other tropical regions with local calibration.\n\nCalibrated for South Asian forests; retraining is needed for other regions. Seasonal cloud cover can affect results. All training data are anonymized, and community consent was obtained. Users should validate outputs locally before application. Released under CC-BY 4.0 for open reuse and replication. Please credit Vertify.earth and Earth Analytics India and share improvements through the Vertify.earth GitHub", "provenance": "curated" }, "how_to_use": { "text": "This dataset and AI model can be used immediately for forest monitoring, restoration planning and impact measurement in mountain and forested regions. Users can map biomass, detect forest loss or assess community-level livelihood impacts by combining the open dataset with freely available satellite imagery. NGOs, research institutions, investors can directly apply the dataset to design or evaluate nature positive projects, while developers can integrate the AI model into their own MRV dashboards. Researchers and AI developers can extend this work by retraining the biomass model with local field data from other regions or adding new layers such as soil moisture or biodiversity indicators. Ethical replication should include community consent and data validation steps to ensure fair and context aware use.\n\nData access: Free (CC-BY 4.0) via Vertify.earth GitHub\r\n\r\nModel use: Free, standard cloud or local compute", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_34/images/placeholder_image.jpeg" }, { "id": "ui_35", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_35-combatting_climate_disinformation_in_indonesian_la/", "aliases": [ "combatting_climate_disinformation_in_indonesian_la", "use_case_development_indonesia" ], "title": "Combatting Climate Disinformation in Indonesian languages with AI", "description": { "text": "This AI application is about developing an AI-based system to tackle climate misinformation in Indonesia, focusing on creating accurate and accessible information tools. The initiative aims to build a multilingual disinformation detection system, covering Bahasa Indonesia and over 10,6 million words and 21 million words translation from Minangkabau, Balinese, and Buginese to classify and verify climate-related content. The application supports stakeholders, including government agencies, NGOs, and communities, in countering false narratives, promoting awareness, and enabling evidence-based decision-making. The AI outcomes include open-access datasets, disinformation, and topic classification models, a user-friendly website, and a chatbot. These tools empower users to identify climate misinformation, enhance public literacy, and foster collaboration for sustainable climate actions. The project emphasizes inclusivity, accessibility, and long-term impact, providing resources for researchers, local businesses, and policy-makers to advance AI applications in combating misinformation. Prosa has developed an AI-powered chatbot to address climate misinformation. The platform's website is available at faktaiklim.prosa.ai, and the chatbot can be accessed via Telegram (Fakta Iklim Bot), with open-source code available on GitHub. User guides are provided in four languages: Indonesian, Minangkabau, Balinese, and Bugis.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Indonesia", "iso2": "ID" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Prosa.ai", "links": [] }, "catalyzed_by": { "text": "Bappenas - Ministry of State Development Planning (Indonesia), FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Prosa AI (business@prosa.ai)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: prosa-text", "url": "https://huggingface.co/prosa-text" } ], "usecase": [ { "label": "faktaiklim.prosa.ai", "url": "https://faktaiklim.prosa.ai/" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The data for this project is fully open-sourced and can be accessed through Hugging Face.\n ● License: CC BY-SA 4.0\n ● Data Type: Tabular\n ● Key Variables: Climate disinformation classification, topic categorization, language distribution (Bahasa Indonesia and regional languages: Minangkabau, Balinese, Buginese)\n ● Format: CSV\n ● Data Collection Period: Over 3 months, covering diverse misinformation cases in climate-related domains\n \n Data collection involved web crawling and inputs from local communities and experts, using sources like government websites, news articles, and scientific literature. Annotations by trained linguists and marginalized community members followed strict guidelines for consistency. The datasets are formatted for easy integration with AI tools.", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data and Pretrained Models:\n Use the multilingual datasets and pre-trained models (IndoBERT, IndoBERTNusa) to detect climate misinformation. The processed dataset can be accessed here: Processed Data\n \n Code for AI Models:\n Access the GitHub repository containing scripts for preprocessing datasets, fine-tuning language models, and training disinformation classifiers. These can be customized for various applications.\n \n Application Deployment:\n The resulting models are integrated into platforms like Kominfo's Anti-Hoax portal and social media crawling APIs. Additionally, a chatbot leveraging these AI models is available to verify climate-related information in real-time. The chatbot and models are publicly accessible via APIs to support further innovation and integration by developers.", "provenance": "curated" }, "how_to_use": { "text": "You can leverage AI models and datasets to develop or enhance applications to address climate misinformation and advance public education. These resources can be adapted to new languages, regions, or even other misinformation topics, such as health or political issues. Combining the datasets with additional sources, like satellite imagery or weather data, could lead to innovative AI applications, including real-time fact-checking tools or misinformation monitoring dashboards. Researchers and developers can also use the models to benchmark against other datasets or refine methodologies, unlocking opportunities for both academic and commercial advancements.\n \n These tools can be integrated into platforms like government portals or social media moderation systems to maximize impact. Interactive educational applications, such as gamified platforms or public learning modules, could further engage communities. Collaboration with stakeholders—including government agencies, NGOs, and private companies—can help scale the tools, secure funding, and drive wider adoption. Through partnerships and expanded deployment, this project can contribute to fostering critical awareness and combating misinformation effectively.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_35/images/placeholder_image.jpeg" }, { "id": "ui_36", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_36-high_carbon_stock_approach_mapping_forests/", "aliases": [ "hcsa_high_carbon_stock_mapping_in", "high_carbon_stock_approach_mapping_forests" ], "title": "High Carbon Stock Approach: Mapping Forests to Combat Climate Change and Protect Livelihoods in Indonesia", "description": { "text": "As the world's largest tropical rainforest, Indonesia’s forests are disappearing faster than decision-makers can respond - largely due to the lack of accessible and trusted data. Our project delivers validated, open-source High Carbon Stock forest maps, using AI and satellite imagery to identify high-carbon, high-biodiversity forests, regenerating forests, and low-carbon land suitable for sustainable development.\n\nThe data is public, transparent, and scalable, empowering NGOs, indigenous communities, and governments to prevent deforestation and reduce emissions into actionable classes:\n\nHigh, Medium, and Low-Density Forests (Conservation Priority):\nForests with closed or patchy canopies, high biodiversity value, and significant carbon storage that are essential for climate mitigation and ecosystem protection.\n\nYoung Regenerating Forests:\nTransitional forests with growing carbon sequestration potential, requiring forward-looking protection strategies to prevent future deforestation.\n\nScrub and Open Land:\nLow-carbon areas suitable for sustainable development, enabling economic growth without compromising forest integrity.\n\nImpact & Use Cases\n\nThe resulting maps empower:\n\n- Policymakers: to strengthen spatial planning and climate-aligned land-use policies\n\n- NGOs: to prioritize conservation efforts and monitor forest loss\n\n- Businesses: to meet “no deforestation” commitments, reduce supply-chain risk, and cut greenhouse gas emissions\n\nGlobal Recognition & Scalability\nThe approach aligns with the globally recognized HCS methodology and has been nominated for the UN SDG Game Changer award – Category 2: Planet – Innovation for our climate and environment “Artificial Intelligence Meets Jungle/ Forest Forward—a strong signal of technical credibility and market relevance. From Indonesia, the project adaptation scale on demand by Goa government, India and Senegal, West Africa.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Indonesia", "iso2": "ID" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY-SA 4.0", "spdx": "CC-BY-SA-4.0", "url": null }, "organizations": { "provided_by": { "text": "High Carbon Stock Approach (HCSA) Foundation, Jaringan Kerja Pemetaan Partisipatif (Seknas JKPP)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All - GIZ, Bappenas - Ministry of State Development Planning (Indonesia)", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "JKPP (seknas@jkpp.org), HCSA (info@highcarbonstock.org), EcoVision Lab (jandirk.wegner@uzh.ch)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: HCSA_Indonesia_Forest_Plot_Data_2…", "url": "https://huggingface.co/datasets/HCSA/HCSA_Indonesia_Forest_Plot_Data_2023" } ], "usecase": [ { "label": "highcarbonstock.org: mapping-portal", "url": "https://highcarbonstock.org/mapping-portal/" } ], "additional": [ { "label": "Ai Meets Jungle How Artificial Intelligence Is Saving Indonesias Forests (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/news/ai-meets-jungle-how-artificial-intelligence-is-saving-indonesias-forests/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The finalized HCS dataset and maps are available on Hugging Face HCSA/Forest_Plot_Data_2023, and Global Forest Watch.\n \n • License: Open-source (CC BY 4.0) \n • Data Type: Geospatial and tabular \n • Key Variables: Vegetation classification, carbon stock levels, validation points, and FPIC (Free, Prior and Informed Consent) compliance\n • Appr. No of observations: Covers ~1.5 million hectares of tropical forests \n • Appr. No of dimensions: 60+ attributes, covering vegetation classifications, carbon stock levels, biomass measurements, land-use types, validation points, and FPIC (Free, Prior and Informed Consent) compliance data\n • Covering 150 field plot high-carbon stock areas across Sumatra, Kalimantan, and West Papua\n • Format: Geo TIFF, shapefile, CSV\n The data adheres to FAIR (Findable, Accessible, Interoperable, Reusable) principles and includes anonymization measures to protect sensitive information.\n\nAlso, for this dataset / use case, a responsible AI Assessment was undertaken to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data:\n Field measurements (e.g., biomass and tree species) were combined with Sentinel-2 imagery and NASA GEDI lidar to enhance map accuracy. The resulting geospatial data includes classifications of High Carbon Stock forests and other land-use categories.\n Model Development:\n AI models, trained using Convolutional Neural Networks (CNNs) and calibrated with aerial LiDAR and ground-truth data, are hosted on the HCSA GitHub repository: HCS Mapping Code.\n Application:\n Validated HCS maps are deployed on Google Earth Engine and Global Forest Watch for conservation planning and policy support, with an ODK-based mobile app under development for offline data collection. The project integrates FPIC (Free, Prior, and Informed Consent) principles throughout, ensuring community involvement and respect for land rights in data collection and mapping.", "provenance": "curated" }, "how_to_use": { "text": "Investment Opportunity\nInvestor support accelerates platform scaling, regional expansion, and integration with ESG reporting, carbon accounting, and supply-chain tools—unlocking long-term commercial and climate value.\n\nWhy Donor Support Matters\nFunding ensures continued data validation, open access, capacity building, and long-term sustainability—maximizing climate, biodiversity, and social returns. \n\nPath Forward\nGovernment collaboration enables institutionalization, national scaling, and integration into official planning and monitoring systems—supporting long-term, climate-aligned development.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_36/images/placeholder_image.jpg" }, { "id": "ui_37", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_37-building_inclusive_voice_technologies_in_the/", "aliases": [ "building_inclusive_voice_technologies_in_the", "text_data_collection_indonesia" ], "title": "Building inclusive voice technologies in the 3 Indonesian Languages Balinese, Bugis and Minangkabau through open language datasets", "description": { "text": "FAIR Forward and Prosa.ai collected AI training data and trained models for three digitally underrepresented languages of Indonesia: Balinese, Bugis and Minangkabau. Existing works on underrepresented languages mostly focus on machine translation and simple language understanding tasks, such as sentiment analysis and topic classification. More complex tasks such as open-domain dialogue and dialogue summarization, are still left behind for these underrepresented languages which leads to a poor evaluation suite for assessing the capability of large language models (LMs) in these underrepresented languages. Mitigating this limitation, we introduce NusaDialogue, the first underrepresented languages datasets consisting of manually annotated dialogue along with its summary.\n\nNusaDialogue covers 3 underrepresented languages under the Malayo-Polynesian languages group, i.e., Minangkabau (min), Balinese (ban), and Buginese (bug), each consisting of 10,000 dialogue and summary pairs. The dialogues in NusaDialogue encompass numerous topics including culture, occupation, politics, science, history, news, sports, religion, etc", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Indonesia", "iso2": "ID" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model", "tags": [ "dataset", "model" ] }, "license": { "name": "CC-BY-SA 4.0", "spdx": "CC-BY-SA-4.0", "url": null }, "organizations": { "provided_by": { "text": "Prosa.ai", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Prosa AI (business@prosa.ai)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: nusa-dialogue", "url": "https://huggingface.co/datasets/prosa-text/nusa-dialogue" }, { "label": "Hugging Face: nusa-translation", "url": "https://huggingface.co/datasets/prosa-text/nusa-translation" } ], "usecase": [ { "label": "Hugging Face: indobert-nusa", "url": "https://huggingface.co/prosa-text/indobert-nusa" } ], "additional": [ { "label": "Entdeckung Der Sprachlichen Vielfalt Indonesiens Fair Forward Und Prosa Ai Auf Dem Weg Zu Inklusiver Ki Sprachtechnologie (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/entdeckung-der-sprachlichen-vielfalt-indonesiens-fair-forward-und-prosa-ai-auf-dem-weg-zu-inklusiver-ki-sprachtechnologie/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "NusaDialogue covers 3 underrepresented languages under the Malayo-Polynesian languages group, i.e., Minangkabau (min), Balinese (ban), and Buginese (bug), each consisting of 10,000 dialogue and summary pairs. The dialogues in NusaDialogue encompass numerous topics including culture, occupation, politics, science, history, news, sports, religion, etc. \n\nThe non-translationese annotation nature makes NusaDialogue suitable for representing the actual day-to-day use of these underrepresented languages. In addition, we ensure that the dataset is annotated by a balanced number of male and female annotators to make the dataset represent a more balanced demography.\n\nAlso, a responsible AI Assessment was undertaken for this dataset / use case to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "Pretrained Model: IndoBERT-Nusa, a BERT language model fine-tuned from indobert-large-p2 on Balinese, Buginese, and Minangkabau data. Framework: PyTorch / HuggingFace Transformers. License: CC-BY-SA 4.0. Tasks: fill-mask, topic classification, language identification. Topic classification F1 scores: Balinese 84.23, Buginese 82.03, Minangkabau 86.30. Training Data: NusaTranslation dataset (~140,000 sentence pairs per language, Indonesian to target language). Dialogue Data: NusaDialogue provides ~10,000 dialogue-summary pairs per language (3 million+ words total) across 16 topic categories, annotated by 100 qualified native speakers. Code for AI Models: load via HuggingFace (`prosa-text/indobert-nusa`).\n\nSource: https://huggingface.co/datasets/prosa-text/nusa-dialogue, https://huggingface.co/datasets/prosa-text/nusa-translation, https://huggingface.co/prosa-text/indobert-nusa", "provenance": "auto-enriched" }, "how_to_use": { "text": "Supporting Minority Languages in the Digital Space:\n\nThe digital divide is often more pronounced for speakers of minority and underrepresented languages. Including these languages in summarization datasets helps bridge this gap, ensuring that digital platforms and technologies are accessible and beneficial to a broader range of linguistic communities.\n\nEncouraging Linguistic Research\n\nThe creation of NusaDialogue can stimulate interest and research in linguistics, language preservation, and computational linguistics. This can lead to a better understanding of the unique linguistic features of these languages and the development of more tailored language technologies.\n\nAddressing Bias and Fairness in NLP\n\nThrough NusaDialogue , there is an opportunity to address biases present in NLP systems that are often trained on data from dominant languages. This can contribute to the development of more fair and unbiased language technologies.\n\nIn conclusion, the social impact of NusaDialogue extends beyond technology to cultural preservation, education, inclusivity, and cross-cultural understanding. It has the potential to empower communities, promote linguistic diversity, and contribute to a more equitable and interconnected global society.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_37/images/placeholder_image.jpeg" }, { "id": "ui_38", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_38-empowering_coastal_inhabitants_in_indonesia_levera/", "aliases": [ "blue_economy_for_climate_mitigation_and", "empowering_coastal_inhabitants_in_indonesia_levera" ], "title": "Empowering coastal inhabitants in Indonesia: leveraging AI, community-based approaches and local Weather measurements for enhanced climate adaptation and a thriving “blue economy”", "description": { "text": "This dataset helps researcher observe daily weather changes, analyze local climate patterns, and support research, planning, or environmental modeling through machine learning and data science in Balai Silvofishery Maros Regency, South Sulawes. Weather Data is a collection of environmental condition records gathered from a monitoring point located in Balai Silvofishery Maros Regency. The dataset includes key weather parameters such as temperature, humidity, rainfall, light intensity, wind speed, and other atmospheric indicators. This data can enable accurate weather predictions which are critical for small-scale fishing, ecotourism, and coastal protection. By empowering fishermen and women with real-time insights, the dataset can serve to build systems that enhance safety, optimise livelihoods, and support sustainable local, blue economies.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Indonesia", "iso2": "ID" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 14" ], "data_types": [ "Meterological" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Common Room Networks Foundation", "links": [ { "name": "Common Room Networks Foundation", "url": "https://commonroom.info/" } ] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Common Room Networks Foundation (email.commonroom@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: WeatherData", "url": "https://github.com/insaninfonesia/WeatherData" } ], "usecase": [ { "label": "colabs.commonroom.info: login", "url": "https://colabs.commonroom.info/login/?next=%2F" } ], "additional": [ { "label": "Ai Climate Resilience Meets Local Know How (giz.de)", "url": "https://www.giz.de/en/newsroom/stories/ai-climate-resilience-meets-local-know-how" } ], "documents": [] }, "content": { "data_characteristics": { "text": "License: CC-BY-SA 4.0. Data Type: Environmental monitoring records. Locations: Balai Silvofishery, Maros Regency, South Sulawesi Province and Pulo Aceh, Aceh Besar Regency, Aceh Province, Indonesia. Key Variables: temperature, humidity, rainfall, light intensity, wind speed. Collection period: since August 2025 (~7 months of data). Format: compressed archives (WS_MAROS_April-November2025.zip, WS_PULO_ACEHwoNew.txz). Known data quality issues: pressure and wind speed values entirely absent; temperature constant at 27.7C; humidity around 80%; irregular data intervals; Pulo Aceh data incomplete due to power outages and limited internet. Contact: infonesiainsan@gmail.com.\n\nSource: https://github.com/insaninfonesia/WeatherData", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Model: https://colabs.commonroom.info/login/?next=%2F with username: guest_viewer and the password: guest_viewer", "provenance": "curated" }, "how_to_use": { "text": "This data and AI models built on the data can enable systems that provide accurate weather predictions which are critical for small-scale fishing, ecotourism, and coastal protection. By empowering fishermen and women with real-time insights, such systems can enhance safety, optimise livelihoods, and support sustainable local, blue economies.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_38/images/placeholder_image.jpeg" }, { "id": "ui_39", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_39-mitigating_the_impacts_of_oil_palm/", "aliases": [ "advancing_oil_palm_mapping_with_social", "mitigating_the_impacts_of_oil_palm" ], "title": "Mitigating the impacts of oil palm cultivation on forests and climate change in Indonesia through AI and social forestry", "description": { "text": "This dataset contributes to improved understanding and mitigation of the impacts of oil palm cultivation on forests and climate change. It can also serve to support sustainable forest management practices and empower local communities in forest resource management. \n\nThe project created open and accessible training and evaluation datasets for machine learning applications focused on forest cover and change in Indonesia. It contains collections of polygons of uniform land cover, with labels indicating land cover type and, possibly, information on land cover change over time. To label land cover patches, the team has reviewed high-resolution satellite imagery facilitated by Collect Earth Online (CEO) and augmented it with field visits to difficult-to-classify areas. It has prioritized areas with significant landcover diversity with a preference for areas with significant smallholder production of oil palm, coconut, and other commodities - given the well-known difficulties of distinguishing oil palm \nfrom coconut palm with lower resolution imagery.\n\nPossible users of these outputs are local communities engaged in community forestry in Indonesia, as well as researchers, policymakers, and civil society organizations. The dataset also contributes to social forestry, which recognizes that people who depend on local forests are best placed to look after them, and that allowing communities to manage and use forest resources can have positive social, environmental and economic impacts. Social forestry is a broad term and has different names in different places. Some examples are community forestry, village forestry, participatory forestry, community-based forest management and people-centred forestry.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Indonesia", "iso2": "ID" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 15" ], "data_types": [ "Drone Imagery", "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "RECOFTC", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "RECOFTC, Peter Cutter (peter.cutter@recoftc.org)", "access_note": null, "links": { "dataset": [ { "label": "Zenodo: 15618532", "url": "https://zenodo.org/records/15618532" } ], "usecase": [], "additional": [ { "label": "Advancing Oil Palm Mapping Social Forestry And Machine Learning (recoftc.org)", "url": "https://www.recoftc.org/projects/advancing-oil-palm-mapping-social-forestry-and-machine-learning" } ], "documents": [] }, "content": { "data_characteristics": { "text": "This dataset focuses on understanding and mitigating the impacts of oil palm cultivation on forests and climate change in Indonesia. It includes high-quality spatial reference data sets that are designed for machine learning applications. The data is organized into 4x4 km plots and contains information on land cover, metadata, and corresponding satellite imagery.\n\nThe dataset aims to fill gaps in forest cover mapping data, particularly concerning oil palm plantations. It is intended to support sustainable forest management practices and empower local communities in managing forest resources.\n\nWhile the dataset is comprehensive, users should be aware of potential limitations, such as biases in the data collection process or imbalances in land cover representation. These factors could affect the accuracy of analyses conducted using the dataset.\n\nTo ensure responsible and ethical use, the project engages university faculty, students, and local partners in the data collection process, promoting transparency and community involvement. \n\nThe dataset is maintained and updated by the project team, which includes academic and local stakeholders. Users can access the data under an open license, allowing for reuse and adaptation, provided that proper attribution is given. This makes it a valuable resource for development practitioners, NGOs, and government agencies looking to address environmental challenges in Indonesia.", "provenance": "auto-enriched" }, "model_characteristics": { "text": "The project focuses on using artificial intelligence (AI) to improve the mapping of forest cover in Indonesia, particularly in areas affected by oil palm cultivation. In simple terms, the AI application helps identify and analyze changes in forest areas by processing satellite images and other spatial data.\n\nThe model expects input in the form of satellite imagery and spatial data, which includes information about land cover and metadata. The output produced by the model is detailed maps that show the extent of forest cover and changes over time, specifically highlighting areas impacted by oil palm plantations.\n\nWhile the project aims to provide valuable insights, there are known limitations. The accuracy of the AI model depends on the quality of the input data. If the satellite images or spatial data are outdated or incomplete, the results may not fully reflect the current situation. Additionally, there may be biases in the data collection process, which could affect the model's predictions.\n\nTo ensure responsible AI use, the project involves ethical assessments and engages local communities in the data collection process. This approach helps to empower local stakeholders and ensures that the data reflects their needs and perspectives.\n\nThere are no specific critical software or hardware requirements mentioned, but the project utilizes convolutional neural network models, which typically require access to computing resources capable of processing large datasets.\n\nUsers interested in building new products based on this dataset should credit the source work from RECOFTC, as it contributes to understanding and mitigating the impacts of oil palm cultivation on forests and climate change.", "provenance": "auto-enriched" }, "how_to_use": { "text": "This dataset can help you to get an improved understanding of the impacts of oil palm cultivation on forests and climate change. It can also serve to support sustainable forest management practices and empower local communities in forest resource management.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_39/images/placeholder_image.jpeg" }, { "id": "ui_40", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_40-early_warning_system_advisory_services_on/", "aliases": [ "early_warning_system_advisory_services_on", "early_warning_system_ldri" ], "title": "Early Warning System: Advisory services on climate-smart farming for small-holder farmers", "description": { "text": "The Early Warning System (EWS) is an AI-powered platform that monitors farming activities and supports climate-smart precision agriculture for smallholder farms. It uses geo-referenced data collected from farms through mobile phones and high-resolution satellite imagery. The system provides crop monitoring, yield prediction, pest/disease detection, and localized agricultural advisory services in multiple languages. The platform is currently accessed through both WhatsApp by farmers and a self-serve platform/API for institutional users.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": null, "organizations": { "provided_by": { "text": "Local Development Research Institute (LDRI)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "LDRI (thinking@developlocal.org)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 19ZMqreP_lWXE13gbTGcdgYFlNSXXB9AN", "url": "https://drive.google.com/drive/u/0/folders/19ZMqreP_lWXE13gbTGcdgYFlNSXXB9AN" } ], "usecase": [ { "label": "vuli.ai", "url": "https://www.vuli.ai/" } ], "additional": [ { "label": "Smart Sowing Ai For More Harvest In Kenya And Uganda (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/smart-sowing-ai-for-more-harvest-in-kenya-and-uganda/" }, { "label": "Food Nutrition Early Warning Mechanism (developlocal.org)", "url": "https://www.developlocal.org/food-nutrition-early-warning-mechanism/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Ground truth geo-tagged data from 1,521 farms across 5 regions in Kenya\n Types of data: Crop images, farm boundaries, crop conditions, weekly activity logs\n Coverage: Multiple crop types including maize, beans, sweet potatoes, cassava, kales, Irish potatoes, cowpeas, soya beans, and indigenous vegetables\n Language data: ~150 hours of voice data and transcriptions in KiEmbu, Kikuyu, Kiswahili and Luhya\n Timeline: Data collected over multiple growing seasons from 2022-2024\n Collection conducted by LDRI in partnership with champion farmers and agronomists.", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data and Models:\n 1. Uses geo-tagged images collected from farms and annotated for crop types, health status, and specific afflictions (pests/diseases/nutrition deficiency) \n 2. Derived vegetation indices, moisture indices, and time-series data from satellite imagery.\n 3. A corpus of voice and text data is being developed for local language processing.\n 4. Crop classification models using CNN architectures : Identifies \n 5. Crop Yield prediction models \n 6. Language models for agricultural advisory in multiple local languages (in development)\n Application:\n The system can be used for:\n • Early warning of potential crop failure\n • Yield prediction and crop health monitoring\n • Localized agricultural advisory in multiple languages\n • Farm productivity tracking and management\n • Regional agricultural monitoring and planning", "provenance": "curated" }, "how_to_use": { "text": "The Eearly Warning System provides a comprehensive platform that combines AI-powered crop monitoring with localized advisory services. Organizations can access the data and models through :A self-serve platform and API for accessing predictions and analytics; Mobile applications for farmers to receive personalized crop management recommendations in local language; Integration possibilities with existing agricultural extension services and Analytics dashboards for value chain stakeholders and policy institutions. The system can be scaled to new regions and crops through transfer learning and the modular architecture allowing for continuous improvement of individual components. The platform supports both individual farmer-level insights and aggregated regional analytics to optimize crop yields and manage stressors effectively, making it valuable for various stakeholders across the agricultural sector.\nThis use case is also linked to the development of a business model / funding model for open source AI as a stepping stone towards financially viable operations. It was part of Villgro Africa's and \"FAIR Forward — AI for All\"'s six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_40/images/placeholder_image.jpeg" }, { "id": "ui_41", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_41-citizen_chatbot_of_the_kenyan_office/", "aliases": [ "citizen_chatbot_of_the_kenyan_office", "office_of_data_protection_commission_odpc" ], "title": "Citizen chatbot of the Kenyan Office of the Data Protection Commissioner", "description": { "text": "The citizen chatbot enables the Kenyan public to access information about Kenya's data protection laws and regulation in an easily accessible conversation on the ODPC's website. The aim is to make legal information more understandable and facilitate access. The content database of the chatbot was developed jointly with expert from the ODPC and tested with citizens.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 16" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": null, "organizations": { "provided_by": { "text": "Tech Innovators Network Kenya (THiNK)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "THiNK (support@think.ke)", "access_note": { "kind": "info", "markdown": "GitHub - think-ke/sheng-dataset: A collection of various datasets for the Sheng Language · GitHub\n\nChatbot launched here: https://www.odpc.go.ke/" }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "For the content database of the chatbot, Q&A pairs created and tested together with ODPC and citizens. In a second iteration, the chatbot is being transitioned from RASA to a RAG infrastructure (LLM-based). Status: November 2025\n\nA responsible AI Assessment was undertaken for this dataset / use case to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "The first version of the chatbot used a RASA architecture with NLU for accessing the Q&A pairs corresponding to user inquiries. As of November 2025, the chatbot is being transitioned to an LLM-based, RAG infrastructure.", "provenance": "curated" }, "how_to_use": { "text": "The initial RASA-based chatbot was built with a dataset created with ODPC officials. The new, RAG-based version of the chatbot will be linked to a document database to minimise the risk of hallucinations. It will be integrated with the Office of the Data Protection Officer, hence it is not directly re-usable - though entirely built on open-source tools.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_41/images/placeholder_image.jpeg" }, { "id": "ui_42", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_42-paza_sauti__chatbot_and_ivr/", "aliases": [ "paza_sauti__chatbot_and_ivr", "support_mozilla_swahili_grantee__part" ], "title": "Paza Sauti - chatbot and IVR service in Swahili to raise awareness about the use of collateral (security) to access credit for women in Kenya", "description": { "text": "The project is developing a chatbot and an interactive voice response service that will provide voice-enabled services in the domain of business registration and raise awareness about the use of collateral (security) to access credit in Kenya. The main objective is to increase financial literacy around moveable properties as collateral, particularly for women in business, and in particular agriculture, for purposes of accessing credit. Although there has been an increase in the ease of getting credit, most members of the population are still unaware of their capability to access further credit as a result of using moveable properties as collateral. This was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 5", "SDG 2" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Tech Innovators Network Kenya (THiNK)", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "THiNK (support@think.ke)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: sheng-dataset", "url": "https://github.com/think-ke/sheng-dataset/" } ], "usecase": [ { "label": "GitHub: mpsr", "url": "https://github.com/think-ke/mpsr" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_42/images/placeholder_image.jpeg" }, { "id": "ui_43", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_43-hello_government__better_citizen_services/", "aliases": [ "ai_conversational_chatbot_with_govstack", "hello_government__better_citizen_services" ], "title": "Hello, government - better citizen services in Kenya through \"AI Chatbots\" as a replicable open-source building block", "description": { "text": "There is a significant opportunity to make digitized government services in Kenya more easily discoverable and, by extension, enhance their accessibility and use by citizens through chatbot technology. Based on NLP technology, chatbots can help citizens access information on Kenya’s eGovernment services through text and voice inputs in Kiswahili and English. This project foresees the development of an AI Conversational Chatbot using the GovStack Building Block Approach - so as to create (a) a chatbot for easier service discoverability; and (b) a chatbot framework that can be reused as a digital public good.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Tech Innovators Network Kenya (THiNK)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ & Digital Transformation Centre Kenya", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "THiNK (support@think.ke)", "access_note": { "kind": "info", "markdown": "Dataset/Use-Case has been developed as an MVP that is being integrated with eCitizen. Current publishing status unclear. Please contact the responsible authors for an update and access to the data: support@think.ke" }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_43/images/placeholder_image.jpeg" }, { "id": "ui_44", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_44-enabling_machine_translation_from_kiswahili_into/", "aliases": [ "building_parallel_corpora_for_kenyas_indigenous", "enabling_machine_translation_from_kiswahili_into" ], "title": "Enabling machine translation from Kiswahili into the indigenous East African languages Kidaw'ida, Kalenjin, and Dholuo, preserving these languages & supporting crowd-sourced voice recognition via Mozilla Common Voice for these languages", "description": { "text": "The dataset was created to enable translation from Kiswahili, which is the national language in Kenya, into three indigenous languages, namely, Kidaw'ida, Kalenjin, and Dholuo. All three languages are low resource, especially Kidaw'ida, which has only around 400,000 speakers and is at immediate risk of loss. By collecting the text and speech data, the project has taken a step towards preservation of these languages . The dataset consists of three parallel corpora: Kidaw'ida-Kiswahili; Kalenjin-Kiswahili; Dholuo-Kiswahili. On average, each corpus has thirty thousand sentence pairs. \n\nIn addition, the dataset has helped to preserve, revitalise and elevate the languages Kidaw'ida, Kalenjin, and Dholuo by making them work on Mozillas Common Voice Platform. The text and speech datasets are thereby supporting crowd-sourced voice recognition, promoting linguistic diversity and empoweromg local communities by enabling Natural Language Processing applications tailored to their needs. Namely, the resulting voice recognition datasets can be used to build AI voice applications such as advisory systems in local languages for farmers, citizens etc that \"understand\" speech input (instead of written input). As of August 2025, 120 hours of speech data have been recorded on Common Voice in Dholuo (with 14692 sentences available), 92 hours have been recorded for Kalendjin (With 29900 sentences available) and 56 hours have been recorded for Kidaw'ida (with 11773 sentences available). To download the crowd-sourced speech datasets in Kidaw'ida, Kalenjin, and Dholuo, visit https://datacollective.mozillafoundation.org/datasets?q=common+voice", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [ "East Africa" ], "sdgs": [ "SDG 10", "SDG 5", "SDG 2" ], "data_types": [ "Voice", "Text" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "Apache 2.0", "spdx": "Apache-2.0", "url": null }, "organizations": { "provided_by": { "text": "United States International University Africa (USIU) - Kenya, Kabarak University, Maseno University (Kenya)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Audrey Mbogho (ambogho@usiu.ac.ke)", "access_note": null, "links": { "dataset": [ { "label": "Zenodo: 13355021", "url": "https://zenodo.org/records/13355021" } ], "usecase": [], "additional": [ { "label": "2501.11003 (arxiv.org)", "url": "https://arxiv.org/pdf/2501.11003" }, { "label": "Low Resource Language Data (github.com)", "url": "https://github.com/waleghwa/low-resource-language-data" }, { "label": "Datasets (datacollective.mozillafoundation.org)", "url": "https://datacollective.mozillafoundation.org/datasets?q=common+voice" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The dataset consists of three parallel corpora: Kidaw'ida-Kiswahili; Kalenjin-Kiswahili; Dholuo-Kiswahili. On averate, each corpus has thirty thousand sentence pairs. This dataset is available on Zenodo and on GitHub where it will continue to be grown and its quality improved. The project had a total of 105 speakers who came from different parts of each region where the three languages are spoken. Thus, different regional accents were included in the speech data, making the dataset more representative. Furthermore, both male and female speakers were contributing to the voice data.", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data: Three parallel corpora for machine translation -- Kidaw'ida-Kiswahili, Kalenjin-Kiswahili, and Dholuo-Kiswahili. Approx. 30,000 sentence pairs per language pair. License: CC-BY-4.0. Format: ZIP archive (3.3 MB). DOI: 10.5281/zenodo.13355021. Published: August 21, 2024. Application: Train machine translation models to translate from Kiswahili into three Kenyan indigenous languages. Data also available on GitHub with ongoing updates planned. PI: Audrey Mbogho (USIU-Africa). Funded by: Lacuna Fund.\n\nSource: https://zenodo.org/records/13355021", "provenance": "auto-enriched" }, "how_to_use": { "text": "> The datasets can help to build translation services from Kiswahili into three indigenous languages, namely, Kidaw'ida, Kalenjin, and Dholuo. \n> The datasets can help to preserve these three low resource languages, promote linguistic diversity and empower local communities by enabling Natural Language Processing applications tailored to their needs.\n> the resulting voice recognition datasets can be used to build AI voice applications such as advisory systems in local languages for farmers, citizens etc that \"understand\" speech input (instead of written input).", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_44/images/kiswahili.png" }, { "id": "ui_45", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_45-miti360_a_comprehensive_dataset_for_aipowered/", "aliases": [ "miti360_a_comprehensive_dataset_for_aipowered", "miti360_a_machine_learning_ready_dataset" ], "title": "Miti360: A Comprehensive Dataset for AI-Powered Forest Monitoring", "description": { "text": "Miti360 is an integrated, machine-learning ready dataset for individual-tree and stand-level reforestation monitoring that fuses high-resolution drone orthophotos, terrestrial stereo and single images, precise ground measurements (tree height, crown diameter, basal diameter and GPS locations), species labels, and multi-year weather time series for nearby stations. The collection is designed to support detection, segmentation, species classification, and biophysical parameter estimation (e.g., crown diameter, height, biomass proxies), and to enable linking short-term growth dynamics to weather. Data are provided in standard GIS and ML formats (GeoTIFF, JPEG, JSON, SHP, time-series API) for immediate integration into research and operational pipelines.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing", "Images" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Dedan Kimathi University of Technology (Kenya)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Prof. Ciira Maina (ciira.maina@dkut.ac.ke), Centre for Data Science and Artificial Intelligence, Dedan Kimathi University of Technology", "access_note": null, "links": { "dataset": [ { "label": "GitHub: miti360", "url": "https://github.com/DeKUT-DSAIL/miti360" } ], "usecase": [], "additional": [ { "label": "Video 69618432 (dw.com)", "url": "https://www.dw.com/en/artificial-intelligence-boosts-kenyas-forestry-conservation/video-69618432" }, { "label": "Treedetection.Html (dekut-dsail.github.io)", "url": "https://dekut-dsail.github.io/projects/treedetection.html" }, { "label": "1728502 (agu.confex.com)", "url": "https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1728502" }, { "label": "11060524 (ieeexplore.ieee.org)", "url": "https://ieeexplore.ieee.org/document/11060524" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Miti360 contains the following core components and formats (quantities as provided):\n- Orthophotos (GeoTIFF): high-resolution stitched orthomosaics (2 GeoTIFFs) and 844 orthophoto tiles exported for annotation. \n- Tree crown annotations (orthophoto): ~24,000 annotated crowns (bounding boxes) exported in JSON; 1,208 crowns have species labels and are also provided as a 1208-feature shapefile (SHP). \n- Ground measurements (numeric): 1,208 measurement records (600 individual trees sampled twice across two campaigns) in JSON — fields include tree height, crown diameter, basal diameter, and GPS coordinates. \n- Terrestrial single images: 1,208 JPEG images paired with semantic segmentation masks (mask files exported alongside images). \n- Terrestrial stereo images: 2,416 JPEG stereoscopic image pairs (left/right) with corresponding masks for 3D/photogrammetric workflows. \n- Weather time series: daily data spanning 8 years from 40 stations, accessible via an API endpoint for linking meteorological drivers to growth. \nAll annotations and metadata use common interchange formats (JSON, SHP, GeoTIFF, JPEG) to enable GIS analyses and ML training without format conversion", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data and Pretrained Models: ML-ready dataset for forest monitoring from a 770-hectare reforested area in Kieni Forest, Kenya. Aerial imagery: 2 orthophotos + 844 tiles (TIF). Tree annotations: 57,058 crown bounding boxes (JSON) and 1,208 species records (CSV). Ground measurements: 1,208 trees with height, crown diameter, basal diameter, GPS coordinates (collected July 2024 - February 2025). Terrestrial images: 1,208 smartphone photos + 2,416 stereo image pairs (JPEG). Weather data: 8 years of daily precipitation and temperature from 40 stations (API). CRS: EPSG:21037. License: CC-BY 4.0. Code for AI Models: Jupyter Notebooks available in the GitHub repository. Data hosted on Google Cloud Storage.\n\nSource: https://github.com/DeKUT-DSAIL/miti360", "provenance": "auto-enriched" }, "how_to_use": { "text": "The Miti360 dataset is intended for training and assessing machine learning models in various forestry applications.\n- Use the orthophoto tiles and bounding box annotations to train models for individual tree detection, counting, and classification from aerial imagery.\n- Use the terrestrial stereoscopic images to develop and benchmark 3D computer vision models for automating forest inventory, such as estimating biophysical parameters.\n- Combine the ground-truth measurements (TH, CD, BD) and weather data to train models that determine quantitative relationships between tree growth and local weather conditions.\n- Leverage the multi-year data (2024-2025) to analyse changes in biophysical parameters and determine which tree species are growing well in the reforested area.\r\n\nThe dataset is designed to be integrated. By augmenting this dataset with others, one can\n- Combine Miti360 with other global forestry datasets mentioned in the report (like NEON Crowns or Auto Arborist) to train models with greater geographic diversity, addressing a key gap this dataset was built to fill.\n- Augment the dataset with socio-economic data on local farming to explore and validate the contribution of farmers to reforestation, an application suggested in the report.\r\n\nCost & resources one can use:\n- Cost: Only associated compute costs\n- Resources: The source code used for data analysis as well as preliminary models trained on the dataset will be made available.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_45/images/placeholder_image.jpeg" }, { "id": "ui_46", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_46-dataenabled_climate_shock_absorbance_through_agrof/", "aliases": [ "climate_resilience_through_agroforestry", "dataenabled_climate_shock_absorbance_through_agrof" ], "title": "Data-enabled climate shock absorbance through agroforestry (Agrof4resilience)", "description": { "text": "The Agrof4Resilience geospatial datasets are open-access utilized by artificial intelligence (AI) and machine learning (ML) algorithms that are aimed at creating more robust, productive, resilient and diverse agro-ecological systems to achieve productive agro-ecosystems that have potential to improve resilience over time. This is crucial especially in dryland which are plagued with land degredation and other effects of climate change. The data demonstrates that agroforestry in Kenya is fairly low while models developed from the data indicate high agroforestry potential in Kenya. This is affected by socioeconomic factors such as education levels, occupation, age, landsize, income, gender, marital status, cultural beliefs, and family size. These datasets could be utilized to demostrate regions and practices that are environementally and socio-economically sound. This could provide insights on practices that could enhance livelihoods and promote environmental resilience in target communities across Kenya. The data was collected from 35 out of 47 counties in Kenya, spread across four predetermined transects that cover four main agro-ecological zones, and stored in a freely accessible database within icipe’s servers.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "creative commons non-commercial (any) data-ena", "spdx": null, "url": null }, "organizations": { "provided_by": { "text": "International Center of Insect Physiology and Ecology (ICIPE)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "International Center of Insect Physiology and Ecology ICIPE (dg@icipe.org)", "access_note": null, "links": { "dataset": [ { "label": "dmmg.icipe.org: data-enabled-climate-shock-absorb…", "url": "https://dmmg.icipe.org/dataportal/dataset/data-enabled-climate-shock-absorbance-and-ecosystem-resilience" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "This geospatial datasets comprises both land use/landcover (LULC) data, plot sampling and socio-economic data from 35 out of 47 counties in Kenya spread across four main agroclimatic zones in Kenya. It contains machine learning-ready data of 2,391 LULC points and polygones of the collected LULC classes, that show the different land uses across Kenya. It also contains a total of a total of 8,194 plant location (gps) points of individual tagged plants collected from a total of 224 1-ha plots from 35 counties in Kenya. Information on the species identification, diameter at breast height (DBH), height, and canopy size is provided. In addition, socio-economic data from 46 focused group discussions with over 44% women and 18% youth conducted within the 35 counties is provided and clearly labeled, detailing agroforestry systems (AFSs) preferences, gender roles, and youth participation in agroforestry systems (AFSs).", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data: Open-access geospatial datasets covering 35 of 47 Kenyan counties, sampled across 200+ hectares. Over 20,000 land use points and over 15,000 individual plant samples. Key Variables: plant species identification, height, diameter at breast height (DBH), photographs, GPS locations. Dataset layers: land use points, land use polygons, plot sample points, plot sample polygons, study area, water lines. Format: Shapefile (.shp, .shx, .dbf, .prj, .cpg, .qmd). License: CC BY-NC 2.0. PI: Elfatih Abdel-Rahman. Funded by: Lacuna Fund through MERIDIAN Institute. Project duration: May 2024 - July 2025.\n\nSource: https://dmmg.icipe.org/dataportal/dataset/data-enabled-climate-shock-absorbance-and-ecosystem-resilience", "provenance": "auto-enriched" }, "how_to_use": { "text": "The database provides information on tree/plant-level, plot-level and landscape scale across Kenya. Measurements such as DBH, tree height, canopy size and tree identification have been provided. In addition, socio-economic data such as agroforestry systems (AFSs) preferences, gender roles, and youth participation in AFSs are also provided. The datasets are readily and freely available to users who can integrate it to AI and ML algorithms and models as ground-truth data to train and/or validate the models associated and/or related to agroforestry and carbon sequestration across Kenya. The models developed using the Agrof4Resilience data could utilize and take advantage of satellite data from various sources that are both freely available or with commercial licences.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_46/images/placeholder_image.jpeg" }, { "id": "ui_47", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_47-aipowered_livestock_health_system_enabling_local/", "aliases": [ "aipowered_livestock_health_system_enabling_local" ], "title": "AI-powered livestock health system enabling local communities easy access to disease information on demand and in Kiswahili.", "description": { "text": "LivHealth Kiswahili Corpus aims to empower local communities to correctly identify livestock syndromes and get timely interventions from qualified livestock practitioners. Using Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), the project has built Kiswahili text-to-speech models for disseminating disease information to marginalized communities. Working closely with their partner, One Health Center in Africa (OHRECA) based at ILRI, they have enhanced the functionality of the LivHealth system to enable local communities easy access to disease information on demand and in Kiswahili. This was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [ "East Africa" ], "sdgs": [ "SDG 10", "SDG 2" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Badili Innovations", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "Badili Innovations (info@badili.co.ke)", "access_note": null, "links": { "dataset": [ { "label": "livhealth-kc.badili.co.ke", "url": "https://livhealth-kc.badili.co.ke/" } ], "usecase": [ { "label": "livhealth-kc.badili.co.ke", "url": "https://livhealth-kc.badili.co.ke/" } ], "additional": [ { "label": "New App And Local Language Translations Improve Livestock Health In Kenya And Tanzania (foundation.mozilla.org)", "url": "https://foundation.mozilla.org/blog/new-app-and-local-language-translations-improve-livestock-health-in-kenya-and-tanzania/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Data Type: Text, Voice. Language: Kiswahili. Country: Kenya. The corpus contains livestock disease information translated from English to Kiswahili, including disease descriptions, symptoms, and treatment protocols. Data is collected via a smartphone app used by Community Disease Reporters (CDRs) and pastoralists. Maintained by Badili Innovations in partnership with One Health Center in Africa (OHRECA) at ILRI.\n\nSource: https://livhealth-kc.badili.co.ke/", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Application: LivHealth uses NLP and ML to provide Kiswahili text-to-speech output of livestock disease information, enabling communities with literacy challenges to access disease identification and treatment guidance. Users input livestock symptoms and receive spoken Kiswahili responses. Requires an internet-connected device with audio playback. Built by Badili Innovations in partnership with OHRECA at ILRI. Part of a Mozilla Common Voice innovation challenge supporting open-source voice technology in East Africa.\n\nSource: https://livhealth-kc.badili.co.ke/", "provenance": "auto-enriched" }, "how_to_use": { "text": "LivHealth is a web-based knowledge platform that makes livestock health information accessible in Kiswahili. Developed by Badili Innovations in partnership with the International Livestock Research Institute (ILRI), it is designed for livestock practitioners, extension workers, and development organisations operating in East African Swahili-speaking communities.\n\nYou can access the platform directly at livhealth-kc.badili.co.ke to browse structured livestock health knowledge in Kiswahili. This makes it a practical resource for field programmes that need locally relevant, language-appropriate veterinary and animal health information -- whether for training community animal health workers, supporting extension services, or informing livestock health interventions.\n\nThe platform spans three functional areas: information systems for structured livestock health knowledge, data management covering collection, synthesis, analytics and visualisation, and monitoring, evaluation and learning (MEL) to support tracking of livestock health interventions.\n\nFor questions about integrating LivHealth into livestock health programmes or extending the platform, contact the development team through Badili Innovations at badili.co.ke. ILRI provides the domain expertise on livestock health content.\n\nSource: https://livhealth-kc.badili.co.ke/, https://badili.co.ke", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_47/images/placeholder_image.jpeg" }, { "id": "ui_48", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_48-wezesha_na_kabambe__offline_swahili/", "aliases": [ "wezesha_na_kabambe__offline_swahili" ], "title": "Wezesha na Kabambe - offline Swahili audio chatbot for women farmers in Kenya", "description": { "text": "This Swahili audio chatbot provides agricultural information for women farmers and does not need internet connectivity . It is developed in collaboration with rural small-holder women farmers in Kenya. Using the Mozilla Swahili data sets, the mobile-enabled chatbot can be used on both feature phones (kabambes) and smartphones by rural smallholder farmers. The interactive Swahili chatbot is powered by a database of frequently asked questions from smallholder women farmers, a marginalized and digitally excluded group. It is inspired by existing familiarity, adoption, and acceptance of mobile technologies in rural areas in Kenya.\n\nThis was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2", "SDG 5" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Moi University, Technical University of Kenya", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "info@mu.ac.ke", "access_note": { "kind": "info", "markdown": "Current publishing status unclear. Please contact the responsible authors for an update and access to the data: info@mu.ac.ke" }, "links": { "dataset": [], "usecase": [], "additional": [ { "label": "Wezesha Na Kabambe Building Tech With Smallholder Farmers In Western Kenya (foundation.mozilla.org)", "url": "https://foundation.mozilla.org/blog/wezesha-na-kabambe-building-tech-with-smallholder-farmers-in-western-kenya/" } ], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_48/images/placeholder_image.jpeg" }, { "id": "ui_49", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_49-chamachat__powering_chama_loan_groups/", "aliases": [ "chamachat__powering_chama_loan_groups" ], "title": "ChamaChat - powering Chama loan groups through voice AI and a chatbot", "description": { "text": "A Chama management system with a chatbot that interacts with members and gives voice replies in Kiswahili via SMS and Whatsapp. It connects to the group Payment API, ie M-Pesa API. Members can interact with the Chama admin bot on a variety of functions, including instance check balance, loan requests and receiving transaction statements. \n\nThis was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Ujuzi Craft LTD", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "UjuziCraft (info@ujuzicraft.com)", "access_note": { "kind": "info", "markdown": "Current publishing status unclear. Please contact the responsible authors for an update and access to the data: https://ujuzicraft.com/" }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_49/images/placeholder_image.jpeg" }, { "id": "ui_50", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_50-providing_farmers_in_kenya_and_bihar/", "aliases": [ "aiep_mvp_development", "providing_farmers_in_kenya_and_bihar" ], "title": "Providing farmers in Kenya and Bihar, India with high-quality, personalized information through AI and developing blueprints for AI-powered Agriculture Information Exchange Platforms", "description": { "text": "Four prototypes of an AI-powered Agriculture Information Exchange Platforms were developed through four initiatives: \n1. DynAG: \nFocused on rice, wheat, and maize, DynAG's platform uses a RAG pipeline powered by LLMs like the GPT suite to provide farmers with advisory services. Accessible via chatbots, Android and Jio-based mobile apps, IVR, and SMS, it ensures wide reach. With machine translation and speech recognition, the platform supports communication in English and Hindi while utilizing CGIAR's data backbone for accurate, data-driven insights. \n \n 2. Digital Green: \nDigital Green's platform supports agricultural value chains like dairy, coffee, and wheat in Bihar and Kenya. It integrates a RAG pipeline, using LLMs under evaluation, and offers services through an app, Telegram, and IVR. Features like machine translation, speech technology, image recognition, and weather data enable localized insights. The platform supports multiple languages, including Hindi, Swahili, Gikuyu, and Marathi, ensuring flexibility across regions. The project has contributed to Digital Greens current FarmerChat.\n \n 3. Viamo and Partners: \n This platform targets farmers in Kenya and Bihar, focusing on beans and wheat, respectively. Using IVR, SMS, and WhatsApp, the platform is built on a RAG pipeline powered by LLMs, with potential for intent and entity recognition. Machine translation and speech-enabled IVR support English, Hindi, and Swahili, making it accessible across regions. The initiative is part of a broader plan to enhance voice technology for agricultural advisory. \n \n 4. Tech for Her: \n Tech for Her empowers women farmers in Bihar and Kenya, focusing on tomatoes and cattle. Accessible via IVR, WhatsApp, and a mobile app, the platform uses LLM-powered intent and entity recognition for personalized chatbot interactions and speech-enabled IVR for voice support. AI-based video generation further enhances engagement. Available in English, Hindi, and Swahili, it caters to the specific needs of women in agriculture.\n\n5. Opportunity International/Digifarm:\nWhatsApp based bot in Kenya that emerged out of a custom-built solution tested in Malawi.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" }, { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Digital Green, Viamo, Tech for Her, DynAg, Digifarm, Opportunity International", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation", "links": [] } }, "contact": "Digital Green (info@digitalgreentrust.org), Viamo and Partners (https://viamo.io/contact/), Tech for Her (contact@agrevolution.in), DynAg, Digifarm, Opportunity International (info@oid.org)", "access_note": null, "links": { "dataset": [ { "label": "dev.platform.farmer.chat: login", "url": "https://dev.platform.farmer.chat/login" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The platform allows for uploading and downloading AI model training data for sharing with the community. The data owner can set up access rights for the shared datasets", "provenance": "curated" }, "model_characteristics": { "text": "Four AI-powered Agriculture Information Exchange Platform prototypes providing personalized advisory services to farmers in Kenya and Bihar, India. DynAG covers rice, wheat, and maize using a RAG pipeline powered by LLMs, accessible via chatbots, Android/Jio apps, IVR, and SMS in English and Hindi. Digital Green supports dairy, coffee, and wheat value chains via app, Telegram, and IVR with machine translation, speech technology, and image recognition in Hindi, Swahili, Gikuyu, and Marathi. Viamo targets beans and wheat farmers via IVR, SMS, and WhatsApp with LLM-based RAG in English, Hindi, and Swahili. Tech for Her focuses on women farmers growing tomatoes and raising cattle via IVR, WhatsApp, and mobile app with AI-based video generation in English, Hindi, and Swahili. The platform (FarmStack by Digital Green) allows uploading and sharing AI model training data with configurable access rights.\n\nSource: https://dev.platform.farmer.chat/login", "provenance": "auto-enriched" }, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_50/images/AIEP.png" }, { "id": "ui_51", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_51-making_ai_understand_3_east_african/", "aliases": [ "making_ai_understand_3_east_african" ], "title": "Making AI understand 3 East African languages: Kiswahili, Kinyarwanda and Luganda - Open-source speech-to-text datasets - Mozilla Common Voice", "description": { "text": "By collecting more than 1064 hours of AI recorded speech in Kiswahili (by 03/2026), this effort created the largest open-source voice dataset of diverse Swahili speakers for speech recognition (speech-to-text). You can use this resource to build AI systems understanding spoken Swahili e.g. in cellphones. In addition, voice datasets were collected in Kinyarwanda and Luganda (560 hours of recorded speech). This facilitates access to information in mother tongues, including by illiterate persons and persons in rural settings.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Kenya", "iso2": "KE" }, { "name": "Rwanda", "iso2": "RW" }, { "name": "Uganda", "iso2": "UG" } ], "regions": [ "East Africa" ], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC0 1.0", "spdx": "CC0-1.0", "url": null }, "organizations": { "provided_by": { "text": "Mozilla Common Voice", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ, Gates Foundation, FCDO", "links": [] } }, "contact": "Mozilla Common Voice (support@mozilladatacollective.com)", "access_note": null, "links": { "dataset": [ { "label": "datacollective.mozillafoundation.org", "url": "https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=sw" }, { "label": "datacollective.mozillafoundation.org", "url": "https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=lg" }, { "label": "datacollective.mozillafoundation.org", "url": "https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw" } ], "usecase": [], "additional": [ { "label": "Creating Community Driven Datasets Report 032023 Giz Mozilla.Pdf (bmz-digital.global)", "url": "https://www.bmz-digital.global/wp-content/uploads/2023/03/Creating-Community-Driven-Datasets-Report-032023-GIZ-Mozilla.pdf" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The datasets for the three languages consist of text data and the corresponding voice data. They are hosted on Mozilla's Common Voice Platform. for more information on the Kinyarwanda dataset, also see https://fair-forward.github.io/datasets?search=kinya&project=ui_59-a_large_scale_collection_of_voice \n\nA responsible AI Assessment was undertaken for the Kiswahili dataset case to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. The assessment included a Gender Action Plan — a comprehensive strategy to ensure women and gender diverse communities are equitably represented.For more information, see: https://www.mozillafoundation.org/en/blog/a-gender-action-plan-to-make-voice-technology-more-inclusive/", "provenance": "curated" }, "model_characteristics": { "text": "Open-source speech-to-text datasets for three East African languages hosted on Mozilla's Common Voice platform. Kiswahili: Common Voice Scripted Speech 25.0, 20.87 GB, MP3 format. Kinyarwanda: Common Voice Scripted Speech 25.0, 57.18 GB, MP3 format. Luganda: Common Voice Scripted Speech 25.0, 11.06 GB, MP3 format. All three datasets are designed for automatic speech recognition (ASR) tasks. License: CC0-1.0.\n\nSource: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=sw", "provenance": "auto-enriched" }, "how_to_use": { "text": "Mozilla Common Voice provides large, open speech datasets for three East African languages -- Kiswahili (20.87 GB), Kinyarwanda (57.18 GB), and Luganda (11.06 GB) -- intended for building Automatic Speech Recognition (ASR) systems. These are collections of read speech recordings in MP3 format, paired with TSV metadata containing transcriptions and speaker information.\n\nThis resource is valuable for anyone developing voice-based applications, virtual assistants, transcription tools, or accessibility services for East African language communities. You can use the recordings and transcriptions to train and evaluate ASR models, or to fine-tune existing multilingual speech models for better performance on these specific languages. The datasets are also suitable for linguistic research on phonetics, prosody, or speaker variation across the three languages.\n\nResearchers and developers can extend this work by combining Common Voice data with other speech corpora to improve model robustness, or by using the trained models as a foundation for downstream tasks like keyword spotting, voice-controlled interfaces, or spoken-language understanding. Because the datasets are released under a CC0-1.0 license (public domain), they can be freely used, modified, and redistributed for any purpose without attribution requirements -- making them particularly suitable for commercial applications and integration into products.\n\nThe datasets are hosted on the Mozilla Data Collective platform. To download, create a free account at datacollective.mozillafoundation.org, navigate to the dataset page for the language you need, accept the user agreement, and download the compressed archive. Programmatic access is also available through the platform's REST API for automated pipelines.\n\n- Kiswahili: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=sw\n- Kinyarwanda: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw\n- Luganda: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=lg\n\nSources:\n- https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=sw\n- https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=lg\n- https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw\n- https://datacollective.mozillafoundation.org/", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_51/images/phoneswahili.jpg" }, { "id": "ui_52", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_52-helping_to_measure_solar_energy_adoption/", "aliases": [ "helping_to_measure_solar_energy_adoption", "labelled_open_solar_panel_data_for" ], "title": "Helping to measure solar energy adoption across Madagascar via AI - Labelled Open solar panel data for Madagascar", "description": { "text": "This dataset will help data scientists, government and users to measure solar energy adoption across Madagascar. It laid the groundwork needed to develop a solar panel detection algorithm working in Madagaskar. Notably, this project represented all regions of the country; instead of focusing only on big cities, it also covered average and small villages as well as coasts and mountains.\n\nThe team annotated 2,125 Google Earth satellite images and 9,202 drone images, forming a combination of low and high-definition solar panel views in Madagascar. The Madagascar Initiatives for Digital Innovation (MAIDI) team performed field checks for up to 25% of satellite images and, in total, annotated 22,488 polygons.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Madagascar", "iso2": "MG" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Association Maidi", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Fabienne Rafidiharinirina (f.rafidiharinirina@association-maidi.mg), Association Maidi (assomaidi@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "openstat-madagascar.com: 131-donnees-sur-l-energie-solaire…", "url": "https://openstat-madagascar.com/bdd/energie-et-environnement/131-donnees-sur-l-energie-solaire-et-labellisation-d-images-de-panneaux-photovoltaiques-a-madagascar" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_52/images/solar_bmz.jpg" }, { "id": "ui_53", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_53-detecting_sentiments_and_combatting_hate_speech/", "aliases": [ "a_nigerian_twitter_sentiment_corpus_for", "detecting_sentiments_and_combatting_hate_speech" ], "title": "Detecting sentiments and combatting hate speech in Hausa, Igbo, Nigerian-Pidgin and Yorùbá - NaijaSenti: a Nigerian Corpus for Multilingual Sentiment Analysis", "description": { "text": "The NaijaSenti dataset is the first large-scale human-annotated Twitter sentiment dataset for Hausa, Igbo, Nigerian-Pidgin, and Yorùbá, the four most widely spoken languages in Nigeria. It consists of around 30,000 annotated tweets per language (except for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. These datasets are useful not only for sentiment analysis but also for hate speech detection. The open-source package consists of datasets, trained models, sentiment lexicons, and code. With more than 200 million people and 522 native languages, Nigeria is the most populous and linguistically diverse country in Africa, as well as the third most multilingual country in the world. The majority of the population speaks either Hausa, Igbo, Yorùbá, or Nigerian-Pidgin.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Nigeria", "iso2": "NG" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Bayero University (Kano, Nigeria), Ahmadu Bello University (Zaria, Nigeria), Masakhane NLP, HausaNLP, Kaduna State University", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "NaijaSenti / HausaNLP (hausanlp@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: NaijaSenti", "url": "https://github.com/hausanlp/NaijaSenti" } ], "usecase": [ { "label": "Hugging Face: naija-twitter-sentiment-afriberta…", "url": "https://huggingface.co/Davlan/naija-twitter-sentiment-afriberta-large" } ], "additional": [ { "label": "2201.08277 (arxiv.org)", "url": "https://arxiv.org/pdf/2201.08277" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The project developed and made the following resources available: 1. Manually Annotated Twitter Sentiment Dataset 2. Manually Annotated Sentiment Lexicon 3. Semi-automatically Translated emotion lexicon 4. Semi-automatically Translated sentiment lexicon 5. Large Scale Unlabeled Twitter Sentiment Corpus 6. Stop-words for Hausa, Igbo, Pidgin and Yoruba 7. text collection, filtering, processing and labeling methods to create datasets for these low-resource languages. 8. evaluation og a range of pre-trained models and transfer strategies on the dataset. 9. release of datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages. Authors: Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Said Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alipio Jeorge, and Pavel Brazdil", "provenance": "curated" }, "model_characteristics": null, "how_to_use": { "text": "NaijaSenti can be used to advance sentiment analysis and other downstream NLP tasks in the languages involved and to work on hate speech detection. For such follow-up work on hate speech, see also: https://arxiv.org/abs/2501.08284", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_53/images/sentiment.pn.jpg" }, { "id": "ui_54", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_54-promoting_energy_conservation_and_market_analysis/", "aliases": [ "promoting_energy_conservation_and_market_analysis", "residential_energy_and_weather_dataset_rewd" ], "title": "Promoting energy conservation and market analysis in Pakistan through Residential Energy and Weather Data (REWD)", "description": { "text": "This dataset helps to understand energy consumption patterns in relation to weather conditions in Pakistan. This can guide policymaking on energy and energy conservation, sustainabe energy initiatives and private sector use (market assessment and strategic planning as new entrants in the restructured energy market). \nThe project produced an energy dataset of residential consumption data from buildings across six climatic zones. The energy dataset is recorded on a 1-minute granularity for entire household usage as well as for individual appliances. With over a year of detailed energy consumption data and 18 months of weather data collected from a diverse array of households across six distinct climatic zones, it is one of the most comprehensive datasets of its kind. This meteorological dataset has been accumulated over a period of 18 months for the six urban centers. This weather data comprises of up to 10 essential variables such as temperature, atmospheric pressure, humidity and Precipitation, thus providing an all-around perspective of the environmental elements impacting energy consumption.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Pakistan", "iso2": "PK" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 7" ], "data_types": [ "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Lahore University (LUMS)", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Lahore University of Management Sciences LUMS (naveedarshad@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "lei.lums.edu.pk: residential-energy-and-weather-da…", "url": "https://lei.lums.edu.pk/datasets/residential-energy-and-weather-data-pakistan.html" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "This Dataset is available in the webpage of the Lahore University. The dataset consist oth three parallel corpora: \n1. Electricity consumption datacollected over year through advanced smart meters. The dataset consists of 60 households belonging to six major urban centers in varying climatic zones across the country. It is recorded on a 1-minute granularity for entire household usage as well as for individual appliances. \n2. Metadata encompassing building attributes and demographic information for each household. The households in the dataset represent a wide and mixed demographic, social structure, and financial background.\n3. Meteorological dataset created with infromation of 18 months for six urban centers. It comprises of up to 10 essential variables: temperature, atmospheric pressure, humidity, dew, precipitation, wind direction, wind speed, solar radiation, solar energy and UV index", "provenance": "curated" }, "model_characteristics": { "text": "Residential Energy and Weather Data Pakistan (REWD-P), provided by Lahore University of Management Sciences (LUMS). Contains electricity consumption data from 60 households across six urban centers (Lahore, Multan, Peshawar, Karachi, Islamabad, Skardu) recorded at 1-minute granularity for whole-household usage and individual appliances. Energy data covers 1 year; weather data covers 18 months with up to 10 variables (temperature, atmospheric pressure, humidity, dew, precipitation, wind direction, wind speed, solar radiation, solar energy, UV index). Includes building attributes and demographic metadata. Format: CSV. Open-source access.\n\nSource: https://lei.lums.edu.pk/datasets/residential-energy-and-weather-data-pakistan.html", "provenance": "auto-enriched" }, "how_to_use": { "text": "The resources can support policymaking and inform energy sustainability initiatives. They can namely be utilized by key stakeholders such as the Ministry of Climate Change, Ministry of Energy (Power Division), Punjab Energy Efficiency & Conservation Authority, and National Energy Efficiency & Conservation Authority (NEECA) Moreover, private sector companies can use the data for market assessment purposes and strategic planning as new entrants in the restructured energy market. The resources can also support further academic research and innovation.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_54/images/placeholder_image.jpeg" }, { "id": "ui_55", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_55-landslide_and_flood_disaster_hotspot_monitoring/", "aliases": [ "landslide_and_flood_disaster_hotspot_monitoring", "landslide_monitoring_use_case", "onsite_image_and_weather_data_for" ], "title": "Landslide and flood disaster hotspot monitoring using computer vision in Rwanda", "description": { "text": "The iMaster-DocuCam Landslide Monitoring System by Hesotech GmbH provides long-term, continuous visual documentation of landslide-prone areas in Rwanda. A pan-tilt-zoom (PTZ) camera captures high-resolution images (up to 1 cm per cell at 500 m distance) at user-defined intervals, while supplementary sensors record temperature, rainfall, humidity, and GPS data. All data is stored in a single database and accessible via web browser from any location. The system is designed for low energy consumption and can be powered by solar panels or generators, making it suitable for remote areas. A demo version and online user manual are available at https://manual.docucam.hesotech.eu/en.\n\nSource: https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/", "provenance": "auto-enriched" }, "kind": [ "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 11" ], "data_types": [ "Images", "Meterological" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Hesotech GmbH", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Hesotech (info@hesotech.de)", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "hesotech.de: imaster-docucam-landslide-monitor…", "url": "https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/" } ], "additional": [ { "label": "Papers.Cfm (papers.ssrn.com)", "url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5087469" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The system captures high-resolution images (up to 1 cm per cell at 500 m distance) at predefined intervals alongside environmental data (temperature, rainfall, humidity, GPS). Data is stored in a single database and accessible via web browser. The hardware consists of a PTZ camera and electronics cabinet with mini-PC; optional expansions include a local weather station, LTE/3G/4G connection, antenna, generator, solar panel, and custom sensor interfaces. Connectivity via local WiFi or LTE/3G/4G. No physical markers required in the monitored area; virtual markers can be created on-screen. Supports time-lapse generation, built-in ruler measurements, and image annotation (text, shapes, arrows, pixelation of sensitive areas).\n\nSource: https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Performs long-term, continuous visual monitoring to detect small and large, slow and fast surface changes for landslide and earth movement analysis. Automated image capture at user-defined intervals with simultaneous collection of weather and GPS data enables cause-effect correlation. Does not require physical markers; users create virtual markers on-screen. Generates time-lapse recordings to visualize changes over time. Web-browser-based access from any location. Low energy consumption allows operation via solar panel or generator in off-grid areas. Developed by Hesotech GmbH.\n\nSource: https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/", "provenance": "auto-enriched" }, "how_to_use": { "text": "## How to Use This Resource\n\nThe iMaster-DocuCam system by Hesotech enables long-term, high-resolution visual monitoring of landslides, earthfalls, rockfalls, and earth movements across large areas. It is designed for deployment in environments like Rwanda where continuous observation of hazard-prone slopes is critical for disaster risk reduction.\n\nPractitioners working in disaster risk management, climate adaptation, or infrastructure protection can use this system to establish automated visual monitoring stations that capture images at predefined intervals (for example, every two hours), achieving a resolution of up to 1 cm per cell at 500 m distance. All captured data can be viewed remotely through a standard web browser (Chrome or Firefox) from any location -- no proprietary software is required. This makes the system practical for distributed teams and remote field sites.\n\nThe built-in analysis tools allow you to place virtual measurement points on images without physical markers in the field, measure surface changes over time, generate time-lapse videos to visualise movement patterns, and correlate camera data with complementary sensor readings for cause-effect analysis. The system can be extended with a local weather station, GPS data collection, custom sensor interfaces, and solar panel power supply for off-grid deployment, making it suitable for rural and infrastructure-limited contexts.\n\nA comprehensive online manual with animated tutorials is available at [manual.docucam.hesotech.eu/en](https://manual.docucam.hesotech.eu/en), along with a demo version that allows you to explore the interface before committing to a deployment. Full product specifications and contact details can be found on the [Hesotech product page](https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/).\n\nSource: https://www.hesotech.de/en/products-services/imaster-docucam-landslide-monitoring-system/", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_55/images/placeholder_image.jpeg" }, { "id": "ui_56", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_56-mbaza_chatbot_rwanda_for_health_related/", "aliases": [ "develop_mbaza_ai_chatbot_for_covid", "mbaza_chatbot_rwanda_for_health_related" ], "title": "Mbaza Chatbot Rwanda for health related inquiries by citizens", "description": { "text": "The Mbaza AI Chatbot was awarded as one of the winning projects of the #SmartDevelopmentHack, an international hackathon organized by the German Federal Ministry for Economic Cooperation and Development (BMZ). The hackathon called for innovative digital solutions to tackle the challenges caused by the coronavirus outbreak in low- and middle-income countries. The chatbot was developed iteratively, the USSD channel was the first to go production, while the web and IVR versions reached only staging phase. Important reported metrics for the Mbaza covid-19 ussd chatbot: 4,165,037 users in total\n37,812,089 sessions in total\n7,254,441 sessions in January 2022\n1,337,614 users in January 2022\n4,347,713 session in December 2021\n914,530 users in December 2021 \n10,074 users in September 2024\n69,992 sessions in September 2024\n170,857 users in 2024\n1,073,451 sessions in 2024", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 10" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "CC0 1.0", "spdx": "CC0-1.0", "url": null }, "organizations": { "provided_by": { "text": "Digital Umuganda", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ, Digital Transformation Centre Rwanda", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Digital Umuganda, Samuel Rutunda (samuel@digitalumuganda.com), Audace Niyonkuru", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: common-voice-kinyarwanda-text-dat…", "url": "https://huggingface.co/datasets/DigitalUmuganda/common-voice-kinyarwanda-text-dataset" }, { "label": "Hugging Face: common-voice-kinyarwanda-english-…", "url": "https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset" }, { "label": "datacollective.mozillafoundation.org", "url": "https://datacollective.mozillafoundation.org/" } ], "usecase": [ { "label": "GitHub: Mbaza-chatbot", "url": "https://github.com/Digital-Umuganda/Mbaza-chatbot" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The tabular dataset for the RASA model can be requested from the startup Digital Umuganda or the implementing partner RBC (as this was specific to daily statistics, guidelines, and announcements for pandemic prevention published by the authority). The Common Voice datasets can be found under the dataset links:", "provenance": "curated" }, "model_characteristics": { "text": "Conversational chatbot built on the Rasa open-source framework, providing COVID-19 information to Rwandan citizens in English and Kinyarwanda. Covers outbreak statistics, symptoms, prevention tips, fines/penalties, testing center locations, testing procedures, costs, lockdown areas, and vaccination information. Developed by Digital Umuganda. The USSD channel reached production; web and IVR versions reached staging phase. Reported usage: 4,165,037 total users and 37,812,089 total sessions. Source code available on GitHub.\n\nSource: https://github.com/Digital-Umuganda/Mbaza-chatbot", "provenance": "auto-enriched" }, "how_to_use": { "text": "This work can be used by anyone interested to build a conversational chatbot in English and Kinyarwanda that needs to provide key information during disease outbreaks such as checking outbreak statistics, showing some prevention tips, checking testing centers, vaccination information etc.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_56/images/placeholder_image.jpeg" }, { "id": "ui_57", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_57-tunga_agricultural_voicebot__agricultural_advise/", "aliases": [ "tunga_agricultural_chatbot", "tunga_agricultural_voicebot__agricultural_advise" ], "title": "Tunga Agricultural Voicebot - Agricultural Advise for Farmers in Kinyarwanda", "description": { "text": "In Rwanda, many farmers struggle to access timely, personalized agricultural information. Traditional channels—like radio, TV, and online sources—offer limited reach and interactivity, while extension services and a national call center, staffed by only two agents for over two million farmers, face capacity constraints. To address these gaps, the Centre for the Fourth Industrial Revolution Rwanda (C4IR), with support from GIZ Fair Forward (catalyzer) and Kigali Natural Language Processing (KiNLP) (technology partner), developed a 24/7 AI-enabled Interactive Voice Response (IVR) tool for the Ministry of Agriculture and Animal Resources. Accessible via a Kinyarwanda-speaking hotline, this tool will provide critical support such as pest and disease diagnosis, agro-climatic advisories, and updates on programmes of the Ministry of Agriculture and Animal Resources (MINAGRI), including crop insurance and climate-resilient practices. By utilizing AI and IVR technology, this project will make agricultural advisories more accessible, timely, and responsive to farmers’ needs.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Ministry of Agriculture and Animal Resources (MINAGRI), Centre for the Fourth Industrial Revolution Rwanda (C4IR), Kigali Natural Language Processing (KiNLP)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Center for the Fourth Industrial Revolution Rwanda (info@c4ir.rw)", "access_note": null, "links": { "dataset": [ { "label": "fair-forward.github.io", "url": "https://fair-forward.github.io/datasets?search=tunga&project=ui_87-tunga_agrichatbot_suite" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": { "text": "Currently, the voice bot is not publicly accessible.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_57/images/agribot.png" }, { "id": "ui_59", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_59-a_large_scale_collection_of_voice/", "aliases": [ "a_large_scale_collection_of_voice" ], "title": "A large scale collection of voice data in Kinyarwanda to allow the building of inclusive language technology", "description": { "text": "This effort created the largest open-source voice dataset of diverse Kinyarwanda speakers for speech recognition (speech-to-text). It collected more than 2380 hours of AI voice data in Kinyarwanda (by 03/2026). You can use this resource to build AI systems understanding spoken Kinyarwanda e.g. in cellphones . This facilitates access to information in mother tongue, including by illiterate persons and in rural settings.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC0 1.0", "spdx": "CC0-1.0", "url": null }, "organizations": { "provided_by": { "text": "Digital Umuganda, Mozilla Common Voice", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Digital Umuganda (samuel@digitalumuganda.com)", "access_note": null, "links": { "dataset": [ { "label": "datacollective.mozillafoundation.org", "url": "https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw" }, { "label": "Hugging Face: common-voice-kinyarwanda-english-…", "url": "https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset" } ], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Common Voice Scripted Speech 25.0 - Kinyarwanda: a collection of read speech recordings in Kinyarwanda hosted on Mozilla Data Collective. Size: 57.18 GB. Format: MP3. Designed for automatic speech recognition (ASR). License: CC0-1.0. Additionally, the common-voice-kinyarwanda-english-dataset by Mbaza NLP on Hugging Face provides a Kinyarwanda-English bilingual dataset with 721,395 rows (619,000 train / 61,600 validation / 41,200 test) for training multilingual ASR systems. Size: 166 MB (CSV). License: CC-BY-4.0.\n\nSource: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw, https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Provides speech-to-text training data for Kinyarwanda. The Common Voice dataset (57.18 GB, MP3, CC0-1.0) enables building ASR systems that transcribe spoken Kinyarwanda into text. The complementary Kinyarwanda-English dataset by Mbaza NLP (721,395 rows, CC-BY-4.0) supports multilingual ASR development. Both datasets can be used to build voice-enabled applications such as speech recognition on mobile devices.\n\nSource: https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw, https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset", "provenance": "auto-enriched" }, "how_to_use": { "text": "## How to Use This Resource\n\nThis resource provides Kinyarwanda voice and text data through two complementary channels, making it valuable for anyone working on speech technology, language preservation, or multilingual applications for Rwanda and the broader East African region.\n\nThe Mozilla Data Collective hosts the \"Common Voice Scripted Speech 25.0 - Kinyarwanda\" dataset, which contains 57.18 GB of MP3 audio recordings released under a CC0-1.0 license, meaning it can be used without any restrictions. This is the primary source for building or improving Automatic Speech Recognition (ASR) systems that handle Kinyarwanda. The dataset can be accessed directly through the [Mozilla Data Collective platform](https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw).\n\nA complementary bilingual compilation is available on [Hugging Face as mbazaNLP/common-voice-kinyarwanda-english-dataset](https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset), providing 721,395 text transcription entries spanning both Kinyarwanda and English, with pre-defined train, validation, and test splits (approximately 619,000 / 61,600 / 41,200 entries respectively). This dataset is licensed under CC-BY-4.0. Note that this Hugging Face version currently contains text transcriptions only; audio files are planned for a future release.\n\nResearchers and developers can use these resources to train multilingual ASR systems, build voice-enabled applications for Kinyarwanda speakers, or conduct linguistic research on this under-resourced language. The bilingual structure of the Hugging Face dataset also opens possibilities for cross-lingual transfer learning and translation tasks. Both datasets are accessible through standard data science tools and the Hugging Face datasets library.\n\nSources:\n- https://datacollective.mozillafoundation.org/datasets?q=common+voice&locale=rw\n- https://huggingface.co/datasets/mbazaNLP/common-voice-kinyarwanda-english-dataset", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_59/images/voice_ai.png" }, { "id": "ui_60", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_60-solutions_from_space_rwandas_smart_harvest/", "aliases": [ "ml4eo_crop_mapping_solution_rwanda", "solutions_from_space_rwandas_smart_harvest" ], "title": "Solutions from space: Rwanda’s smart harvest planning - mapping crop type with AI for rice, maize, Irish potatoes and beans", "description": { "text": "When weather and crop yields become unpredictable, reliable information is crucial. In Rwanda, digital maps are showing for the first time exactly where which crops grow, helping to better adapt agriculture to climate change. Around one third of all food worldwide is lost between the field and the plate, for example due to pest infestation, incorrect storage or long transport routes. Climate change is exacerbating these losses: irregular rainfall, dry seasons and extreme weather events are jeopardising harvests and making planning increasingly difficult for farmers. Rwanda’s agricultural sector also faces these challenges. \n\nThis automated crop type mapping system for Rwanda can be used in various applications e.g. improving agricultural policy and business decisions, integrating with yield estimation frameworks for spatially detailed predictions of seasonal production, crop health monitoring, crop insurance design, value chain assessments, supply and demand forecasting, and more.\n\nIt leverages multi-sensor remote sensing data (Sentinel 1 & 2, dynamic world, SRTM). The crop mapping pipeline can be used to map the four most important staple foods in Rwanda (rice, maize, beans, Irish potatoes) in four Rwandan districts, and produce timely, 10-meter resolution crop type maps. \nReference data from different sources including project field work, global research projects, and Rwandan authorities were used to fine-tune the geospatial foundational model Galileo for the downstream task of mapping what type of crop grows where. The reference data collected in the field was supported through the creation of polygon labels with PlanetScope imagery in collaboration with Rwandan academia. The crop mapping pipeline, the fine-tuned model, and the project's reference data are openly available for further usage.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "MIT", "spdx": "MIT", "url": null }, "organizations": { "provided_by": { "text": "Rwanda Space Agency (RSA), the Rwandan Ministry of Agriculture, Alliance of Bioversity International and CIAT", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All (GIZ), Digital Transformation Centre Rwanda", "links": [] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Benson Kenduiyvo (b.kenduiywo@cgiar.org)", "access_note": null, "links": { "dataset": [ { "label": "dataverse.harvard.edu: dataset.xhtml", "url": "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/O7IDTD" } ], "usecase": [ { "label": "GitHub: crop-type-mapping", "url": "https://github.com/crop-type-mapping" } ], "additional": [ { "label": "Digital Maps Agriculture Rwanda (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/digital-maps-agriculture-rwanda/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The overall good accuracy yielded by the approach under conditions with small field sizes, varying elevation and high cloud cover are promising for a successful application in all kinds of regions. Nevertheless, local reference data remains crucial for crop type mapping and an ideal approach should include all types of crops. You can follow the approach to create your own crop type mapping system, utilize the pipeline to fetch satellite imagery for various purposes, get inspired by the use and fine-tuning of the geospatial foundational model and apply it to a different task. You can also use the collected reference data for your own model.", "provenance": "curated" }, "model_characteristics": { "text": "Machine learning pipeline for crop type classification in Rwanda using Sentinel-1 and Sentinel-2 satellite imagery. Training scripts include Random Forest and climate-corrected models. Ground reference data covers four districts (Nyagatare, Musanze, Nyabihu, Ruhango) with field observations of maize, beans, rice, and Irish potato collected May-June 2025. The ground truth dataset contains GPS-located farm plots with crop attributes, management practices, and directional photographs. Code: Python scripts for satellite data preparation, image download, and model training at github.com/crop-type-mapping/ML. License: MIT (code), CC-BY 4.0 (ground reference data).\n\nSource: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/O7IDTD, https://github.com/crop-type-mapping", "provenance": "auto-enriched" }, "how_to_use": { "text": "The mapping method developed is freely accessible and fully documented. Training courses with the Rwandan Space Agency and the Office of Statistics ensure that local experts can continue to develop the system independently.\r\nThe digital maps do not replace observations and measurements in the field or local knowledge. They complement these to create an improved basis for data-driven decisions, more targeted use of resources and forward-looking risk management using digital tools – for agriculture that offers prospects for the future even under climate pressure.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_60/images/placeholder_image.jpeg" }, { "id": "ui_61", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_61-better_language_translation_for_more_training/", "aliases": [ "better_language_translation_for_more_training" ], "title": "Better language translation for more training content in Kinyarwanda - education-specific machine translation for the Moodle Learning Management System", "description": { "text": "Enabling language translation capabilities on the Moodle LMS platform, through a collaboration with Atingi; the use case explores 3 modes of translation. The first mode is navigation based, simply translating static messages such as buttons and notifications, secondly it explores full content translation, lastly, in-line translation. The project uses the NLLB-200 model for translation, other models could be experimented to explore feasibility of additional features such as QA or summarization, leveraging the plugin built by the team.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 4" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "Permissive", "spdx": null, "url": null }, "organizations": { "provided_by": { "text": "Digital Umuganda", "links": [] }, "catalyzed_by": { "text": "Atingi, \"FAIR Forward - AI for All\" (GIZ)", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Digital Umuganda (samuel@digitalumuganda.com), FAIR Forward Rwanda", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: kinyarwanda-english-machine-trans…", "url": "https://huggingface.co/datasets/DigitalUmuganda/kinyarwanda-english-machine-translation-dataset" }, { "label": "Hugging Face: Kinyarwanda_English_parallel_data…", "url": "https://huggingface.co/datasets/mbazaNLP/Kinyarwanda_English_parallel_dataset" }, { "label": "Hugging Face: NMT_Education_parallel_data_en_kin", "url": "https://huggingface.co/datasets/mbazaNLP/NMT_Education_parallel_data_en_kin" } ], "usecase": [ { "label": "Hugging Face: Quantized_Nllb_Finetuned_Edu_En_K…", "url": "https://huggingface.co/mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit" }, { "label": "Hugging Face: Nllb_finetuned_general_en_kin", "url": "https://huggingface.co/mbazaNLP/Nllb_finetuned_general_en_kin" }, { "label": "Hugging Face: Finetuned-NLLB", "url": "https://huggingface.co/DigitalUmuganda/Finetuned-NLLB" } ], "additional": [ { "label": "Moodlemoot Global Day 3 (moodle.com)", "url": "https://moodle.com/news/moodlemoot-global-day-3/" }, { "label": "aiopeneducation.pubpub.org", "url": "https://aiopeneducation.pubpub.org/pub/7npwm2su/release/5?readingCollection=06969c6d" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Parallel datasets can be used to finetune LLMs to specific languages or domains; the example is for English-Kinyarwanda, to improve NLLB translation performance", "provenance": "curated" }, "model_characteristics": { "text": "Open source model NLLB-200, some variations quantized to minimize compute resource usage", "provenance": "curated" }, "how_to_use": { "text": "Integrate preferred machine translation model with Moodle plugin: https://github.com/Digital-Umuganda/moodle-translate_courses . Need to update for compatibility with newer Moodle updates, this was implemented during Moodle version 3.0", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_61/images/placeholder_image.jpeg" }, { "id": "ui_62", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_62-preventing_sexual_and_genderbased_violence_/", "aliases": [ "preventing_sexual_and_genderbased_violence_" ], "title": "Preventing Sexual and Gender-Based Violence - a featurephone-based information chatbot (*350#)", "description": { "text": "Enabling people to access essential information around Sexual and Gender-Based Violence free of charge and anonymously. This innovative tool requires only a feature phone, with no internet connectivity needed. A collaborative effort between HDI-Rwanda and GIZ-Rwanda. As of November 27 (2024), the chatbot has reached an impressive 343,475 unique users, up from 160,000 on October 1st, with 1,991,660 interactions recorded today compared to 1,170,000 interactions in October. Update of stats pending", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 5" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": { "name": "CC0 1.0", "spdx": "CC0-1.0", "url": null }, "organizations": { "provided_by": { "text": "Health Development Initiative (HDI) Rwanda", "links": [] }, "catalyzed_by": { "text": "GIZ (Sexualised and Gender-Based Violence (P-SGBV) Project), GIZ ZFD SIGA Refugee Programme, FAIR Forward - AI for All (GIZ)", "links": [ { "name": "GIZ)", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Digital Umuganda - Samuel (samuel@digitalumuganda.com), HDI-Rwanda (Louange Twahirwa Gutabarwa)", "access_note": { "kind": "info", "markdown": "The dataset was not made public (FAIR Forward status: experiment)" }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_62/images/placeholder_image.jpeg" }, { "id": "ui_63", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_63-from_maps_to_meals_ml_for/", "aliases": [ "enabling_geoscientists_to_use_machine_learning", "from_maps_to_meals_ml_for", "ml4eo_for_precision_agriculture" ], "title": "From Maps to Meals: ML for Precision Ag - Enabling geo-scientists to use Machine Learning for Precision Agriculture of geospatial data", "description": { "text": "Development and implementation of a training program to enable practitioners in the field of Earth Observation in South Africa to use machine learning. \nField data collected on small holder agricultural farm land (crops) with raw datasets + applied use cases of classification of land use cover using ML (based on individual projects presented)", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13", "SDG 11" ], "data_types": [ "Drone Imagery", "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "1R7Iawhmentrju Kmor1Iz26Aii15Elit (drive.google.com)", "url": "https://drive.google.com/drive/folders/1R7iawHmEnTRJu_kmoR1iz26aii15ELIT?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Types of Data:\n1. Satellite-Based Data: Sentinel-1 (SAR), \nSentinel-2 (optical), Sentinel-2 (Vis), Sentinel-2 (imagery)\n2. Drone-Based Data: Drone imagery, multispectral\n3. Ground-Based / In-situ Data\n4. Spectral Data Types: Hyperspectral, Thermal IR\nData Formats: Images, Tabular\n\nField-level reflectance, chlorophyll-related vegetation indices (VIs), Spectral response to stress; Sentinel-2, NIR bands, Low-res images, spectral variability for phosphorus deficiency & MSV, Spatial mapping of weeds vs. crops, Red-edge based chlorophyll estimates, Red-edge bands, REIP indices, Thermal + multispectral image fusion, Temporal NDVI, vegetation change monitoring, Crop separability via bands 7, 8, 8A, 11, Correlation of VIs to LAI", "provenance": "curated" }, "model_characteristics": { "text": "Please see the other South Africa projects from Dr. Meena Lysko - this is the consolidated dataset source for the sub projects", "provenance": "curated" }, "how_to_use": { "text": "Please see the other South Africa projects from Dr. Meena Lysko - this is the consolidated dataset source for the sub projects", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_63/images/drone_crop.jpg" }, { "id": "ui_64", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_64-data_for_detecting_and_assessing_tomato/", "aliases": [ "data_for_detecting_and_assessing_tomato", "detecting_and_assessing_tomato_stress_using" ], "title": "Data for Detecting and Assessing Tomato Stress", "description": { "text": "Investigates methods for detecting and assessing stress in tomato plants using ASD measurements and drone data, focusing on Project Munei Holding Investment in Ha-Mphaila, Limpopo", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11" ], "data_types": [ "Geospatial/Remote Sensing", "Images" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1t3KiZiEQFprZMb8AYI01HrbSsSrfUwlb/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Field-level reflectance, chlorophyll-related vegetation indices (VIs). The data comes in various forms: Hyperspectral, UAV imagery, SPAD, Images, Tabular", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest; used for stress classification; Software/Application: Random Forest + UAV analytics", "provenance": "curated" }, "how_to_use": { "text": "Train RF using spectral indices; cost includes UAV flights, ASD rentals; open-source RF models or GEE usable", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_64/images/placeholder_image.jpeg" }, { "id": "ui_65", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_65-the_internet_of_crops_ioc/", "aliases": [ "leaf_area_index_estimation_of_tomato", "the_internet_of_crops_ioc" ], "title": "The internet of Crops (IOC)", "description": { "text": "Investigates the use of Sentinel-2 satellite imagery and a random forest (RF) machine learning algorithm to estimate the Leaf Area Index (LAI) of tomato crops in the Ha-Mphaila irrigation scheme, Limpopo Province, South Africa. The research aimed to determine key wavebands and vegetation indices for LAI estimation, compare the performance of RF models with stepwise multiple linear regression (SMLR) models, and map the spatial distribution of LAI.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1rqnCZTZ2b4MSp9C0nkFhU3A1Xhk4CwIe/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Correlation of VIs to LAI, Sentinel-2 + LAI ground truth.", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest Regression; Software/Application: Sentinel-2 + RF", "provenance": "curated" }, "how_to_use": { "text": "Free Sentinel-2 + fieldwork required; ML code free (e.g., Python)", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_65/images/placeholder_image.jpeg" }, { "id": "ui_66", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_66-artificial_intelligence_for_maize_aim/", "aliases": [ "artificial_intelligence_for_maize_aim", "characterizing_maize_stress_using_uav_remote" ], "title": "Artificial Intelligence for Maize (AIM)", "description": { "text": "Investigates the use of remote sensing and machine learning to characterize maize stress in the Limpopo Province, South Africa. The study concludes that integrating field spectra and remotely sensed reflectance provides valuable information for maize stress investigation, supporting the development of mitigation and prevention strategies to ensure food security.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11" ], "data_types": [ "Geospatial/Remote Sensing", "Images" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1cMBiijRJUyz8MVAuudrDnK2kkxbnoT6J/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Spectral response to stress; Sentinel-2, NIR bands.", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest, SVM; Software/Application: Sentinel-2 + field spectral data", "provenance": "curated" }, "how_to_use": { "text": "Access Sentinel-2 (free); field spectrometer needed for calibration", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_66/images/placeholder_image.jpeg" }, { "id": "ui_67", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_67-data_for_detecting_and_assessing_maize/", "aliases": [ "data_for_detecting_and_assessing_maize", "monitoring_zea_mays_maize_disease_stress" ], "title": "Data for Detecting and Assessing Maize Stress", "description": { "text": "Assesses the capabilities of Earth observation and machine learning algorithms, specifically Random Forest and Support Vector Machines, in detecting maize disease stress using drone data. The study was conducted on a small-scale farm in Limpopo, South Africa, focusing on healthy maize, maize with streak virus, and maize with phosphorus deficiency.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Drone Imagery" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/13tu-8UnI1FYk7DLkI_Z1qmprWW3EWlfp/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Low-res images, (hyper)spectral variability for phosphorus deficiency & MSV.", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest, SVM; <80% accuracy; Software/Application: Drone + ML (RF, SVM)", "provenance": "curated" }, "how_to_use": { "text": "Requires high-quality imagery, better spectral resolution; GEE and drone access recommended", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_67/images/placeholder_image.jpeg" }, { "id": "ui_68", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_68-weeties/", "aliases": [ "precision_weed_mapping_and_management_in", "weeties" ], "title": "Weeties", "description": { "text": "Drone high-resolution images were used with a semi-automated random forest (RF) classifier algorithm in Google Earth Engine to classify bare soil, weeds, and tomatoes. The findings can serve as a reference for agricultural researchers and advise farmers on crop management, potentially reducing costs and herbicide use.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11", "SDG 12" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Drone Imagery" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1uFKAZ9CCniykTKRSsh2-nxKW2f3Wkpbb/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Spatial mapping of weeds vs. crops using RGB/Multispectral data in addition to Drone imagery and Tabular obseravations.", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest; high training accuracy (89%); Software/Application: GEE + Random Forest", "provenance": "curated" }, "how_to_use": { "text": "Drone + GEE for processing; reduce herbicide usage", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_68/images/placeholder_image.jpeg" }, { "id": "ui_69", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_69-chlorophyllbusters/", "aliases": [ "chlorophyllbusters", "estimation_of_chlorophyll_contents_of_crops" ], "title": "Chlorophyll-Busters", "description": { "text": "Explores using the Random Forest Regression (RFR) machine learning algorithm with Sentinel-2 and drone imagery to estimate relative chlorophyll values in tomato and maize crops within South Africa's Ha-Mphaila farming area. The research concludes that both Sentinel-2 imagery (10m and 20m spatial resolution) and high-resolution drone images are valuable for rapidly assessing crop chlorophyll content across significant areas, offering useful insights for precision agriculture.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11", "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Drone Imagery" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1dfgcycIgFy7SKUHJcZ2qFERbQTbehxVh/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Red-edge based chlorophyll estimates & Sentinel-2 data", "provenance": "curated" }, "model_characteristics": { "text": "Random Forest Regression; Software/Application: Sentinel-2 + drone + RF", "provenance": "curated" }, "how_to_use": { "text": "Sentinel free; drone + SPAD sensor may be costly unless shared", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_69/images/placeholder_image.jpeg" }, { "id": "ui_70", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_70-the_agroinnovators/", "aliases": [ "evaluating_the_performance_of_machine_learning", "the_agroinnovators" ], "title": "The Agro-Innovators", "description": { "text": "Evaluates the performance of machine learning algorithms for estimating chlorophyll content in tomatoes using Sentinel-2 satellite data.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11", "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Drone Imagery" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1QYB5PBhQVaDQ1J-YP0ZfOAsLXznla_Kr/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Red-edge bands, REIP indices, Sentinel-2, SPAD chlorophyll", "provenance": "curated" }, "model_characteristics": { "text": "Bagging, Boosting, ANN, SVR; Software/Application: Chlorophyll estimation ML", "provenance": "curated" }, "how_to_use": { "text": "Train model on SPAD + satellite indices; uses open-source ML tools", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_70/images/placeholder_image.jpeg" }, { "id": "ui_71", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_71-agrothermography/", "aliases": [ "agrothermography", "integration_of_groundbased_vegetation_parameters_t" ], "title": "Agro-thermography", "description": { "text": "Explores the integration of ground-based vegetation parameters, thermal infrared data from handheld cameras, and UAV multispectral data to map crop canopy temperature at high resolution. The research, conducted at the Ha-Mphaila irrigation scheme in Limpopo, South Africa, focused on tomato crops.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 11", "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1GMpDiMlY-wYXTB6T5woikRpFbgQ21hhp/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Thermal IR and multispectral image fusion and field data + Drone Data", "provenance": "curated" }, "model_characteristics": { "text": "Regression model; canopy temperature estimation; Software/Application: Thermal camera + UAV + regression models", "provenance": "curated" }, "how_to_use": { "text": "Needs UAV & thermal cam; costly setup unless shared or borrowed", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_71/images/placeholder_image.jpeg" }, { "id": "ui_72", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_72-discovering_agriculture_insurance/", "aliases": [ "discovering_agriculture_insurance", "remote_sensing_and_machine_learning_applications" ], "title": "Discovering Agriculture Insurance", "description": { "text": "Investigates how remote sensing and machine learning can be used to improve Agricultural Index Insurance (AII) for smallholder farmers in South Africa, who often lack access to affordable insurance. Key recommendations include supporting further research and development in these areas and implementing educational campaigns to build farmer trust and awareness of index insurance benefits.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1rGoapVNyVsVwMxDfBrRfdVoyRCpjwnIy/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Temporal NDVI and chlorophyll indices, vegetation change monitoring, Sentinel-2 based", "provenance": "curated" }, "model_characteristics": { "text": "ML for index-insurance mapping; Software/Application: Dynamic World + ML", "provenance": "curated" }, "how_to_use": { "text": "Integrate indices into insurance design; Sentinel-2 free; ML libraries (e.g., Scikit-learn)", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_72/images/placeholder_image.jpeg" }, { "id": "ui_73", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_73-sar_busters/", "aliases": [ "crop_type_mapping_in_mphaila_irrigation", "sar_busters" ], "title": "SAR Busters", "description": { "text": "Explores the use of Sentinel-1 and Sentinel-2 satellite data for crop type mapping in smallholder farming areas. The study focuses on improving classification accuracy by identifying optimal spectral bands and evaluating machine learning algorithms like decision tree and random forest classifiers", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Geospatial/Remote Sensing", "Images", "Tabular" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Move Beyond Consulting\nImplementing partners: MBC, WITS, CSIR, ARC", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Dr Meena Lysko (mbc.mlysko@gmail.com)", "access_note": null, "links": { "dataset": [ { "label": "Google Drive: 1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5", "url": "https://drive.google.com/drive/folders/1P_bW8Is1Qa1HJIznVbq_vsQwH3ejKUa5?usp=drive_link" } ], "usecase": [], "additional": [ { "label": "View (drive.google.com)", "url": "https://drive.google.com/file/d/1c54f9Lh5IV8T_XLrlf_8ItAitrBEjTVZ/view?usp=drive_link" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Crop separability via bands 7, 8, 8A, 11. Sentinel-1 (SAR) and Sentinel-2 (optical) based", "provenance": "curated" }, "model_characteristics": { "text": "Decision Tree, Random Forest; Software/Application: Crop mapping using ML", "provenance": "curated" }, "how_to_use": { "text": "Sentinel data free; GEE platform usable", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_73/images/placeholder_image.jpeg" }, { "id": "ui_74", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_74-voices_of_mzansi__making_all/", "aliases": [ "nlp_grant_localising_common_voice_for", "voices_of_mzansi__making_all" ], "title": "Voices of Mzansi - Making all official languages of South Africa AI-ready: translating the Common-Voice interface & enabling Open-source text-to-speech dataset collection", "description": { "text": "The \"Voices of Mzansi\" project aimed to get South Africa's languages launched on the Mozilla Common Voice platform. To achieve this aim the Common Voice website had to be translated from English into the other official languages and at least 5 000 sentences had to be collected for each language. The collected sentences have to be available in the open domain under CC-0 licensing. Once a languages is launched, the sentences are used as prompts for speech data collection through the Common Voice platform. This unlocks open-source text-to-speech dataset collection and thereby ultimately facilitates access to information in mother tongue, including by illiterate persons and in rural settings.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 10" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": { "name": "CC0 1.0", "spdx": "CC0-1.0", "url": null }, "organizations": { "provided_by": { "text": "Stellenbosch University", "links": [] }, "catalyzed_by": { "text": "South African Digital Language Resources (SADILAR), FAIR Forward - AI for All, GIZ, Mozilla Foundation, UNESCO decade of indigenous languages", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Prof. Febe de Wet (febe.dewet@gmail.com); Mozilla Data Collective (support@mozilladatacollective.com)", "access_note": null, "links": { "dataset": [ { "label": "commonvoice.mozilla.org: languages", "url": "https://commonvoice.mozilla.org/de/languages" } ], "usecase": [], "additional": [ { "label": "4437 (upjournals.up.ac.za)", "url": "https://upjournals.up.ac.za/index.php/dhasa/article/view/4437" } ], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": { "text": "## How to Use This Resource\n\nThe Voices of Mzansi project collects speech data for South Africa's official languages through the Mozilla Common Voice platform. This is a community-driven effort, and the most immediate way to contribute is by recording your voice or validating other people's recordings at [commonvoice.mozilla.org](https://commonvoice.mozilla.org).\n\nThe current status across South Africa's 11 official languages reveals a significant need for contributions. Setswana leads with 4.3 validated hours from 18 speakers, followed by Afrikaans with 0.6 validated hours from 67 speakers. The remaining languages -- isiZulu, Sesotho, siSwati, isiXhosa, Tshivenda, Sepedi, isiNdebele, and Xitsonga -- have little to no validated audio despite having hundreds or thousands of sentences already prepared for recording. For example, Sesotho has 2,339 sentences ready and Sepedi has 2,247, but both have almost no recorded audio yet.\n\nThis gap represents a concrete opportunity for development practitioners, language advocates, and community organisations in South Africa. Organising recording sessions with native speakers, particularly for the languages that already have substantial sentence pools waiting to be read aloud, is the single most impactful action anyone can take right now. No technical expertise is required -- the Common Voice platform handles everything through a web browser.\n\nOnce enough validated audio has been collected, Common Voice datasets are released periodically and can be downloaded from [commonvoice.mozilla.org/languages](https://commonvoice.mozilla.org/languages). These datasets are provided as MP3 audio files with corresponding text transcriptions, released under a CC0-1.0 license, making them freely usable for building speech recognition systems, voice-enabled applications, and other language technologies for South African languages.\n\nSource: https://commonvoice.mozilla.org/en/languages (language statistics via Common Voice API)", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_74/images/voice_data_2.jpg" }, { "id": "ui_75", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_75-crop_type_identification_from_satellite_imagery/", "aliases": [ "crop_type_identification_from_satellite_imagery", "south_africa_crop_type_competition", "spot_the_crop" ], "title": "Crop Type Identification from Satellite Imagery in Western Cape, South Africa", "description": { "text": "This dataset and AI model was produced as part of the Radiant Earth Spot the Crop Challenge (https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "South Africa", "iso2": "ZA" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13" ], "data_types": [ "Tabular" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Radiant Earth, Western Cape Department of Agriculture", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Radiant Earth (ml@radiant.earth)", "access_note": null, "links": { "dataset": [ { "label": "beta.source.coop: description", "url": "https://beta.source.coop/repositories/radiantearth/south-africa-crops-competition/description/" } ], "usecase": [ { "label": "GitHub: spot-the-crop-challenge", "url": "https://github.com/radiantearth/spot-the-crop-challenge" } ], "additional": [ { "label": "Radiant Earth Spot The Crop Challenge (zindi.africa)", "url": "https://zindi.africa/competitions/radiant-earth-spot-the-crop-challenge" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The dataset contains a time-series of satellite imagery and labels for crop type that have been collected through aerial and ground survey. Labels are derived from the survey conducted by the Western Cape Department of Agriculture. Satellite data including multispectral Sentinel-2 are then matched with corresponding labels.", "provenance": "curated" }, "model_characteristics": { "text": "This repository contains the winning models from the Zindi data competition \"Spot the Crop\" in which participants used time series of Sentinel-2 multispectral imagery as input for crop type classification.", "provenance": "curated" }, "how_to_use": { "text": "The agricultural sector makes a substantial contribution to GDP and livelihoods across the developing world. However, regular and reliable agricultural data remains difficult and expensive to collect on the ground. As a result, policy-makers usually don’t have access to updated data for implementing policies or supporting farmers. Earth observation satellites provide a wealth of multispectral image data that can be used for developing agricultural monitoring tools. These tools support farmers and policy-makers across Africa and the developing world. With this dataset and AI model you can use time-series of Sentinel-2 multi-spectral data to classify crops in the Western Cape of South Africa.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_75/images/crop.png" }, { "id": "ui_76", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_76-ai_for_agricultural_advisory_and_financial/", "aliases": [ "ai_for_agricultural_advisory_and_financial", "kiswahili_text_and_voice_recognition_platform" ], "title": "AI for Agricultural Advisory and Financial Services for Smallholder Farmers in Tanzania", "description": { "text": "A majority of smallholder farmers in Tanzania are only able to communicate through the Kiswahili spoken language and its dialects. A text and voice-based platform made available in the language of the underserved (i.e., Kiswahili) would be key to wide access, adoption, and usage of digital agricultural advisory and financial services in Tanzania. The objective is to develop a text and voice recognition platform that will offer smallholder farmers in the Tanzanian Maize Value Chain personalized digital financial and non-financial automated services based on location, agro-ecological zones, and crop cycle. Based on gender-disaggregated data from the pilot phase, it is anticipated that the majority of participants will be women. This was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Tanzania", "iso2": "TZ" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2", "SDG 5" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Duniacom Group LLC", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "Duniacom Group LLC (info@duniacom.com)", "access_note": { "kind": "info", "markdown": "Publishing status unclear. Please contact the responsible authors for an update and access to the data: info@duniacom.com" }, "links": { "dataset": [], "usecase": [], "additional": [ { "label": "Awards (mozillafoundation.org)", "url": "https://www.mozillafoundation.org/en/what-we-fund/programs/common-voice-kiswahili-awards/awards/" }, { "label": "Supporting Maize Farmers In Tanzania (foundation.mozilla.org)", "url": "https://foundation.mozilla.org/blog/supporting-maize-farmers-in-tanzania/" } ], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_76/images/placeholder_image.jpeg" }, { "id": "ui_77", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_77-kiazi_bora__informing_vulnerable_women/", "aliases": [ "kiazi_bora__informing_vulnerable_women" ], "title": "Kiazi Bora - informing vulnerable women in Tanzania on the nutritional values of Orange Fleshed Sweet Potatoes", "description": { "text": "Kiazi Bora, “Quality Potatoes’’ in Swahili, uses a voice enabled application that informs vulnerable women living in rural areas and marginalized communities of Tanzania on the nutritional values of Orange Fleshed Sweet Potatoes (OFSP), farming skills for better yields, and detailed market availability for raw or processed OFSP food products, all through a voice data set app. \n\nThis was part of a Mozilla innovation challenge supporting people and projects across East Africa who leverage Common Voice’s open-source voice data set to unlock social and economic opportunities. These grants help to advance the use of open-source voice data for products that support community participation and engagement.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Tanzania", "iso2": "TZ" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2", "SDG 5" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "SEE Africa", "links": [] }, "catalyzed_by": { "text": "Mozilla Foundation & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "Gates Foundation & BMZ", "links": [] } }, "contact": "SEE Africa (info@seeafricatz.org)", "access_note": { "kind": "info", "markdown": "Publishing status unclear. Please contact the responsible authors for an update and access to the data: https://seeafricatz.org/contact-us/" }, "links": { "dataset": [], "usecase": [], "additional": [ { "label": "Growing Skills Confidence And Quality Potatoes In Tanzania (foundation.mozilla.org)", "url": "https://foundation.mozilla.org/blog/growing-skills-confidence-and-quality-potatoes-in-tanzania/" } ], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_77/images/placeholder_image.jpeg" }, { "id": "ui_78", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_78-croppie_helping_smallholder_coffee_producers_to/", "aliases": [ "croppie_coffee_yield_prediction", "croppie_helping_smallholder_coffee_producers_to" ], "title": "Croppie- helping smallholder coffee producers to plan sales, estimate yields, get loans, and trace coffee - AI powered coffee yield prediction", "description": { "text": "Contributing almost a third of foreign export earnings, coffee is one of the main cash crops in Uganda. Nowadays, many small-holder farmers, who rely on coffee farming for their livelihood, are facing challenges such as unpredictable weather and volatile market prices. Traditionally, they have relied on manual yield estimates, but these are time-intensive to perform and not always reliable, leaving many farmers unable to safely plan their harvests or manage their resources effectively. On top, unpredictable weather conditions and price fluctuations pose major challenges for the coffee farmers. \r\n\r\nThe platform and app 'Croppie' supports them with targeted cultivation recommendations and precise crop forecasts—using AI-supported image recognition of coffee plants. As a digital public good based on open-source technology, it can be adapted and further developed locally. The basic user interface is provided through a mobile application. Croppie’s core functionality is to allow for AI-supported coffee yield estimation. This is done by guiding a selective sampling for a statistical yield estimation service based on branch count and coffee branch picture taking, alongside the integration of additional data provided by the user.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 2", "SDG 13" ], "data_types": [ "Tabular" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "CC-BY-SA 4.0", "spdx": "CC-BY-SA-4.0", "url": null }, "organizations": { "provided_by": { "text": "Producers Direct, M-Omulimisa, Alliance of Bioversity International and CIAT, Tecnicafé", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Croppie - Romain Gautron (r.gautron@cgiar.org), Producers Direct (info@producersdirect.org), M-Omulimisa, CIAT", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: croppie_coffee_ug", "url": "https://huggingface.co/datasets/rgautroncgiar/croppie_coffee_ug" } ], "usecase": [ { "label": "Hugging Face: croppie_coffee_ug", "url": "https://huggingface.co/rgautroncgiar/croppie_coffee_ug" } ], "additional": [ { "label": "Ai Coffee Intelligent Awake (bmz-digital.global)", "url": "https://www.bmz-digital.global/en/ai-coffee-intelligent-awake/" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The Data is fully open-sourced and can be accessed on Hugging Face\n • License: CC BY SA 4.0\n • Data Type: Tabular\n • Key Variables: Coffee cherry color (5), coffee cherry size\n • Appr. No of observations: 8333\n • Format: dbf, csv\n \n The data was collected over the span of 1.5 years covering different crop cycles between 2022 and 2024. The Data Collection was conducted by Alliance Bioversity & CIAT and M-Omolumisa via on-the-ground visits of the farms.\n\nFor this dataset / use case, a responsible AI Assessment was undertaken to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "Processed Data:\n The dataset is made of a mix of Arabica and Robusta coffee tree parts (with and without a background isolation element) with individual bounding boxes around all coffee cherries. These RGB pictures were on-farm collected with smartphones with the collaboration of smallholder farmers. \n Code for Crop Classification Model:\n Code to create the training data and for training the models for crop mapping can be accessed here: \n - Dataset\n - Model\n Application:\n The dataset/model can be used for automated cherry count or coffee ripeness assessment.", "provenance": "curated" }, "how_to_use": { "text": "This resource can be of use to anyone interested to automate the task of manual cherry counts (the traditional approach to harvest estimation) and to estimate coffee harvest on a plot level. This is done by processing pictures through AI-supported image recognition software along with a manual branch count (= tree level estimation) and a tree-per-ha estimation (or recall data) (scaling to plot level). Thus, this system is useful for anyone interested in building a central system for managing yield data at the farmer level. It also provides the option to share this data with external users; with for example cooperatives having the possibility to feed information from the application in an integrated database of their member’s performance and farm characteristics, provided they obtained prior consent to do so. This would then scale the Croppie system to have an aggregated data view of multiple users in a region, e.g. as a cooperative level dashboard.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_78/images/croppie.png" }, { "id": "ui_79", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_79-finding_good_spots_for_decentralized_green/", "aliases": [ "bridging_the_energy_gap_machine_learning", "finding_good_spots_for_decentralized_green", "scaling_mlbased_site_identification_for_minigrids" ], "title": "Finding good spots for decentralized green energy grids in Uganda - AI based site identification for MiniGrids", "description": { "text": "The Site Identification tool is an AI-driven tool to enhance renewable energy planning. This tool utilizes machine learning and satellite imagery to identify optimal sites for renewable energy deployment, particularly in remote regions. A successful pilot in Lamwo District demonstrated its potential to improve energy access by recommending suitable electrification strategies for villages. This initiative aligns with Uganda’s National Electrification Strategy, which seeks to connect 10 million households to the most optimal energy source by 2030.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Tabular" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Sunbird.ai", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Sunbird.ai (emwebaze@sunbird.ai)", "access_note": null, "links": { "dataset": [ { "label": "GitHub: lamwo-electrification-project", "url": "https://github.com/SunbirdAI/lamwo-electrification-project/tree/main" } ], "usecase": [ { "label": "lamwo.sunbird.ai", "url": "https://lamwo.sunbird.ai/" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "-• Data Type: Geospatial and tabular \n• Key Variables: Solar Irradiance,Biomass Resources,Wind Speed and Consistency,Biomass Resources,Hydropower Potential,\nPopulation Density,Proximity to Existing Grids,Road Networks,Land Availability,Protected Areas,Cost of Energy Delivery\n• The data adheres to FAIR (Findable, Accessible, Interoperable, Reusable) principles and includes anonymization measures to protect sensitive information.\n\nFor this dataset / use case, a responsible AI Assessment was undertaken to help AI developers and project managers to identify, assess and mitigate potential harms and biases in AI. For methodology, see https://www.bmz-digital.global/en/news/ethical-crash-test-for-ai-how-to-navigate-the-road-to-responsible-innovation/", "provenance": "curated" }, "model_characteristics": { "text": "This web application is a decision support tool. It is built as a prototype for a large-scale decision support tool for determining electrification strategies for different geographical areas. Currently implemented in Lamwo district electrification.", "provenance": "curated" }, "how_to_use": { "text": "This web application is a prototype decision support tool designed to guide electrification strategies across various geographic areas. It offers an initial village-level assessment through a color-coded map, with the legend explaining each category. Users can explore detailed analyses at the village level—the smallest unit for assessing electrification needs. The tool evaluates both demand and supply factors and suggests the most suitable energy solutions, including biomass, wind, solar home systems, mini-grids, or grid extension. It is intended to support government officials, energy sector stakeholders, and development partners in making informed renewable energy decisions.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_79/images/solar_grid.jpg" }, { "id": "ui_80", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_80-estimating_solar_irradiance_for_improved_solar/", "aliases": [ "development_of_unbiased_ai_models_for", "estimating_solar_irradiance_for_improved_solar" ], "title": "Estimating Solar Irradiance for Improved Solar Energy Planning in Sub-Saharan Africa through AI", "description": { "text": "The project successfully developed a machine learning model to predict daily Global Horizontal Irradiance (GHI) in Sub-Saharan Africa, with a specific focus on Uganda. The core objective was to correct the systematic bias found in readily available satellite-derived solar irradiation data (like NASA CERES and CAMS). Key activities included identifying five installation sites in Uganda for ground-truth data collection (Gulu, Mbale, Jinja, Kasese, Hoima), procuring pyranometers (though installation was hindered by lack of Ministry of Energy approval), and extensively collecting data from four satellite sources and partner ground stations (Cross Boundary Energy, Makerere University, Ministry of Energy) combined with local topological information (ie. altitude). A Random Forest model was trained on this integrated dataset, proving superior to other models (LSTMs, SVMs) by effectively adjusting the satellite predictions to align with the ground truth across all climatic regions, thus providing a significantly more reliable and bankable estimate of solar resources essential for rural electrification planning.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing", "Tabular", "Meterological" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Makerere University (Marconi Lab)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Jan Mikelson Andrew Katumba katraxuk@gmail.com", "access_note": null, "links": { "dataset": [ { "label": "GitHub: Solar_irradiation", "url": "https://github.com/Marconi-Lab/Solar_irradiation" } ], "usecase": [ { "label": "irradiation-portal-55883164704.europe-west1.run.app", "url": "https://irradiation-portal-55883164704.europe-west1.run.app/" }, { "label": "GitHub: Irradiation_Portal", "url": "https://github.com/Marconi-Lab/Irradiation_Portal" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Bias-corrected daily Global Horizontal Irradiance (GHI) data for Uganda, derived by correcting CAMS and NASA POWER satellite datasets against ground-truth measurements from 56 validation sites across Uganda. Training data includes measurements from 7 African countries. The correction addresses a 20% satellite overestimation of solar potential. Data is accessible via the Solar Irradiance Portal web interface and a RESTful API for application integration.\n\nSource: https://github.com/Marconi-Lab/Solar_irradiation, https://irradiation-portal-55883164704.europe-west1.run.app/", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Random Forest model (SuSSE -- Sub-Saharan Solar Estimation) that corrects satellite-derived solar irradiance estimates for Uganda. Input: CAMS and NASA POWER satellite GHI data. Output: bias-corrected daily GHI values. Validated against 56 ground-truth sites across Uganda, achieving R-squared of 0.86 and correcting a 20% overestimation in standard satellite products. Application: deployed as an interactive web portal with maps, charts, and a RESTful API for integration. Code: Python package at github.com/Marconi-Lab/Solar_irradiation (87.9% Jupyter Notebook, 12.1% Python).\n\nSource: https://github.com/Marconi-Lab/Solar_irradiation, https://irradiation-portal-55883164704.europe-west1.run.app/", "provenance": "auto-enriched" }, "how_to_use": { "text": "## How to Use This Resource\n\nThis resource addresses a critical problem for solar energy planning in Uganda: standard satellite data sources such as CAMS and NASA POWER systematically overestimate solar irradiance by roughly 20%, which reduces lifetime energy savings for consumers by 5-20% and creates financial risk through incorrect system sizing. The Irradiation Portal provides corrected Global Horizontal Irradiance (GHI) data by applying a machine learning model trained on ground-truth measurements from 56 validation sites across Uganda and 7 African countries, achieving an R-squared accuracy of 0.86.\n\nAnyone involved in solar project development, rural electrification planning, or energy policy in Uganda can access the corrected data through the [Irradiation Portal web application](https://irradiation-portal-55883164704.europe-west1.run.app/). The portal offers interactive data exploration with dynamic charts and maps, monthly irradiance measurements in kWh/m2/day, and historical data access -- all through a standard web browser. Technical documentation and user support are built into the portal.\n\nFor organisations that want to integrate the corrected solar data into their own planning tools or applications, a RESTful API is available through the portal. This enables automated data retrieval for feasibility studies, system design calculations, or monitoring dashboards.\n\nResearchers and developers looking to extend the underlying methodology can access the SuSSE (Sub-Saharan Solar Estimation) model code, which is openly available on [GitHub](https://github.com/Marconi-Lab/Solar_irradiation). The repository includes the full codebase with Jupyter Notebooks and Python scripts, along with testing and development tools. This opens possibilities for adapting the correction model to other Sub-Saharan African countries where similar satellite overestimation issues exist.\n\nSources:\n- https://github.com/Marconi-Lab/Solar_irradiation\n- https://irradiation-portal-55883164704.europe-west1.run.app/", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_80/images/placeholder_image.jpeg" }, { "id": "ui_81", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_81-ai_as_a_helping_hand_to/", "aliases": [ "ai_as_a_helping_hand_to", "nlp_use_case_strengthening_relevance_of" ], "title": "AI as a helping hand to understand audit reports - A conversational chatbot answering questions related to audit reports of the Auditor General Office of Uganda", "description": { "text": "In Uganda, the Civil Society and Budget Advocacy Group (CSBAG) in partnership with GIZ, has recognized the need for further enhancement in how audit reports are processed and understood. An AI-powered conversational assistant was designed to help anyone understand audit reports. It will answer questions by using Audit reports published by Auditor General Office, Uganda. The prototype tool leverages AI to offer capabilities such as summarizing complex texts, extracting thematic insights, and enabling interactive, user-friendly analysis through a chatbot interface. By making the audit reports more accessible, this aims to increase readership and utilization among stakeholders, which can lead to better accountability and improve service delivery.", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Civil Society and Budget Advocacy Group (CSBAG)", "links": [] }, "catalyzed_by": { "text": "GIZ Country Office Uganda, FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "CSBAG (csbag@csbag.org)", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "Hugging Face: audit_assistant", "url": "https://huggingface.co/spaces/GIZ/audit_assistant/tree/main" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Data comes from audit documents in Uganda and samples can be sourced from HuggingFace. Full implementation will come later.", "provenance": "curated" }, "model_characteristics": { "text": "The prototyped is linked - full implementation will come later", "provenance": "curated" }, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_81/images/placeholder_image.jpeg" }, { "id": "ui_82", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_82-monitoring_deforestation_predicting_landuse_and_la/", "aliases": [ "monitoring_deforestation_predicting_landuse_and_la" ], "title": "Monitoring Deforestation, predicting Landuse and Landcover changes and planning forest restoration in Uganda through AI-powered Remote Sensing", "description": { "text": "This project provides Uganda’s first openly accessible AI-ready satellite imagery dataset designed to predict land-use and land-cover change. It was created to address persistent deforestation and the lack of reliable, context-specific data needed to detect, forecast, and manage ecosystem degradation. Through an open-source replication kit including annotated Sentinel-2 and Hansen datasets, protocols, and inventory data, this products enables local innovators, forest agencies, and policymakers to build AI tools for monitoring forests, planning restoration, and supporting NDC and SDG15 actions. Its goal is to improve early detection of land-cover change, strengthen climate-smart decision-making, and empower local institutions with high-quality geospatial data.", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "Uganda", "iso2": "UG" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 15" ], "data_types": [ "Geospatial/Remote Sensing", "Images" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Makerere University in cooperation with the National Forestry Authority (NFA) of Uganda", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Makerere AI Lab - Dr. Joyce Nabende (joyce.nabende@mak.ac.ug), National Forestry Authority (NFA): https://nfa.go.ug/", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "1drv.ms: Es68F3WluONJjORAR_WvTloBdtisO9TGs…", "url": "https://1drv.ms/f/c/BFFEF9896BFF8791/Es68F3WluONJjORAR_WvTloBdtisO9TGsGyWR8Se5xNYDA?e=q0Afso" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "1. Landus Landcover Datasets (2015 -2023), \n2. ArcGIS Pro Project, ArcGIS DEEPLearning Model for Tree Species.\n\nThe dataset provides georeferenced, annotated Sentinel-2 and Hansen imagery with clear landuse and landcover classes, groundtruth data, and standardized protocols. Users can train AI models for landuse and landcover classification, detect deforestation, validate predictions, and support restoration planning. Its high resolution, temporal coverage, and open accessibility enable accurate, scalable environmental monitoring.", "provenance": "curated" }, "model_characteristics": { "text": "The AI application analyzes historical satellite imagery gereated landuse and landcover maps (2015-2024) and predicts future landuse and land-cover change, offering simple inputs including Sentinel-2 and Hansen datasets of tree cover and outputs such as classified maps and simulations for 2025 and 2030. It identifies deforestation, farmland expansion, and urban growth patterns, supporting evidence-based land management. While highly accurate (ANN: 85.2% OA), limitations include data inconsistencies, sampling bias, and reliance on remote-sensing quality. Ethical use requires adherence to responsible-AI assessment, transparency, and proper citation. The model runs on standard GIS/AI software (QGIS, google colab, Jupyter-notebook) with no special hardware. All derived datasets and protocols are open for reuse under an open license; users must credit the original project to strengthen community knowledge and track downstream impact.", "provenance": "curated" }, "how_to_use": { "text": "One can immediately use this AI system to classify landuse and landcover types, detect deforestation, and generate LULC predictions for 2025–2030. Users can replicate the full workflow from preprocessing imagery, training ANN models, and simulating future change scenarios to support forest monitoring, restoration planning, district land-use decision-making, or environmental reporting. Researchers can extend the work by integrating socio-economic variables, testing hybrid models, adding additional years of imagery, or adapting the model to new districts. Replication requires awareness of limitations such as sampling imbalance, image quality differences, and potential classification bias; an ethical-AI review is recommended for all downstream use. Costs remain low because the tools (QGIS, GEE, SEPAL) and datasets are free; only compute for model training (standard PCs) and technical time are required. All datasets, protocols, and documentation form an open public-good resource, with opportunities for collaboration through Makerere AI Lab and NFA.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_82/images/placeholder_image.jpeg" }, { "id": "ui_83", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_83-preserving_privacy_and_avoiding_gender_bias/", "aliases": [ "datasets_marking_personal_identifiable_information", "preserving_privacy_and_avoiding_gender_bias" ], "title": "Preserving privacy and avoiding gender bias of AI systems in Luganda, Lumasaba, Hausa, and Kanuri - The Lacuna personally identifiable information Text Dataset", "description": { "text": "The Lacuna PII Multilingual Text Dataset contains annotated sentences with personally identifiable information (PII) in Luganda, Lumasaba, Hausa, and Kanuri. These four languages span Central and Eastern Uganda, Nigeria, Ghana, and Northern Cameroon. The dataset can help to anonymize and pseudonymize personally identifiable information in AI tasks. This can help organizations comply with legal requirements while still being able to analyze and use and share their data effectively. It can also be used to improve machine translation systems for low-resource languages, improve the performance of NLP applications in these languages, and support the extraction of specific information from text, such as automated form filling and information retrieval systems. It comprises 4000 Luganda sentences, 5000 Lumasaba sentences, 3000 Kanuri sentences, and 3000 Hausa sentences. The team aimed to curate a dataset that is gender inclusive. It was created by Makerere Artificial Intelligence Lab in collaboration with Marconi Lab and Clear Global.", "provenance": "curated" }, "kind": [ "dataset" ], "countries": [ { "name": "Uganda", "iso2": "UG" }, { "name": "Kenya", "iso2": "KE" }, { "name": "Nigeria", "iso2": "NG" } ], "regions": [], "sdgs": [ "SDG 13", "SDG 5", "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset", "tags": [ "dataset" ] }, "license": null, "organizations": { "provided_by": { "text": "Makerere University (Makerere AI Lab, Marconi Lab), Clear Global", "links": [] }, "catalyzed_by": { "text": "Lacuna-Fund / Meridian (Climate-call) & FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Andrew Katumba - Makerere University (katumba@mak.ac.ug)", "access_note": null, "links": { "dataset": [ { "label": "dataverse.harvard.edu: citation", "url": "https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/CGHWZE" } ], "usecase": [], "additional": [ { "label": "File.Xhtml (dataverse.harvard.edu)", "url": "https://dataverse.harvard.edu/file.xhtml?fileId=10577234&version=2.0" } ], "documents": [] }, "content": { "data_characteristics": { "text": "Text data:\n• Luganda: 4000 PII sentences with representation in entities of\nPerson, Date, National identity, tribal identity, location, and organization.\n• Lumasaaba: 5000 PII sentences with representation in entities\nof Person, Date, National identity, tribal identity, location, and\norganization.\n• Hausa: 3000 PII sentences with representation in entities of Person, Date, National identity, tribal identity, location, and organization.\n• Kanuri: 3000 PII sentences with representation in entities of Person, Date, National identity, tribal identity, location, and organization\n\nFor more, see data card here: https://dataverse.harvard.edu/file.xhtml?fileId=10577233&version=2.0", "provenance": "curated" }, "model_characteristics": null, "how_to_use": { "text": "This dataset of personally identifiable information (PII) can help any organization or initiative, that would like to comply with legal requirements while still being able to analyze and use and share data effectively in one of the languages in Luganda, Lumasaba, Hausa, and Kanuri. It can also be used to improve machine translation systems for these low-resource languages, improve the performance of NLP applications in these languages, and support the extraction of specific information from text, such as automated form filling and information retrieval systems.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_83/images/placeholder_image.jpeg" }, { "id": "ui_84", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_84-forest_carbon_stock_monitoring_for_climate/", "aliases": [ "carbonlens__forest_carbon_stock_monitoring", "forest_carbon_stock_monitoring_for_climate" ], "title": "Forest Carbon Stock Monitoring for Climate Accountability in Senegal", "description": { "text": "Senegal's forest monitoring agencies have long relied on costly, manual field surveys to estimate how much carbon its forests store — making it difficult to credibly track climate commitments or access carbon finance. This tool automates that process by combining satellite imagery (Sentinel-1, Sentinel-2, GEDI) with field data and machine learning to produce accurate, reproducible estimates of above-ground biomass at scale - using the high carbon stock approach. The models, datasets and workflows are fully open-source, meaning environmental agencies, researchers and development partners in other countries can adapt and deploy them without starting from scratch. For national governments, it strengthens NDC reporting; for carbon finance bodies, it provides the verifiable data needed to unlock results-based funding; and for the broader AI and climate community, it is a replicable blueprint for low-cost, satellite-driven forest monitoring across the Global South.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Senegal", "iso2": "SN" } ], "regions": [], "sdgs": [ "SDG 13" ], "data_types": [ "Geospatial/Remote Sensing" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": null, "organizations": { "provided_by": { "text": "Data 354, Ministère de l'Environnement et de la Transition Écologique (METE)- Senegal", "links": [] }, "catalyzed_by": { "text": "Data Economy, GIZ / FAIR Forward - AI for All, GIZ / The Estonian Centre for International Development (ESTDEV), African Union, D4D Hub — Data for Development Hub", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "data354 (gabriel.fonlladosa@data354.co)", "access_note": { "kind": "info", "markdown": "Not yet available\n\nNot yet available (still being worked on)" }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "The dataset combines satellite imagery with forest field plot data collected across Senegal. Its main limitation is geographic scope, it reflects Senegalese conditions and should be validated against local ground truth before use elsewhere. It will be published as an open digital public good, free to reuse and adapt, provided the original source is credited and derivative work is linked back to the project repository. Users are encouraged to contribute improvements back to the community where possible.", "provenance": "curated" }, "model_characteristics": { "text": "The model takes satellite imagery and forest field measurements as input and produces estimates of above-ground biomass across forest landscapes. No specialised hardware is required. The key limitation is geographic bias, it was trained on Senegalese forest conditions and will need retraining with local field data before being applied elsewhere. The tool and datasets are openly licensed for replication and adaptation, but users are asked to credit the original work and link any derivative products back to the source, this helps the community track how the tool develops over time.", "provenance": "curated" }, "how_to_use": { "text": "The replication kit, including satellite data pipelines, field plot datasets, trained models and processing workflows, is openly available for use. Forest agencies can deploy the existing models immediately to generate biomass estimates for their own landscapes; carbon finance practitioners can use the outputs directly to support NDC reporting or funding applications; and researchers can fine-tune the models for different forest types or regions using local field data. Extending the work is straightforward, the modular architecture allows individual components to be upgraded independently.", "provenance": "curated" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_84/images/placeholder_image.jpeg" }, { "id": "ui_85", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_85-enhancing_business_registration_in_kenya_through/", "aliases": [ "brschatbot", "enhancing_business_registration_in_kenya_through" ], "title": "Enhancing business registration in Kenya through a chatbot", "description": { "text": "The BRS-chatbot is an AI-powered chatbot that streamlines the business registration process in Kenya. It aimes to enhance access to information, simplify the registration process, improve user experience through Human-Centered Design principles, and promote compliance with post-registration requirements. The goal is to reduce reliance on intermediaries and make the registration process more user-friendly and efficient", "provenance": "curated" }, "kind": [ "usecase" ], "countries": [ { "name": "Kenya", "iso2": "KE" } ], "regions": [], "sdgs": [ "SDG 8" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case", "tags": [ "dataset", "model", "pilot", "usecase" ] }, "license": null, "organizations": { "provided_by": { "text": "Made by People / Tanasuk Technologies Africa, Kenya, CLEAR Global, Tech Innovators Network Kenya (THiNK), BRS - Business Registration Service (Kenya)", "links": [] }, "catalyzed_by": { "text": "Smart Development Fund, FAIR Forward - AI for All, Digital Transformation Centre Kenya, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Made by People, Kenya (hello@made.ke)", "access_note": null, "links": { "dataset": [], "usecase": [ { "label": "brs.go.ke", "url": "https://brs.go.ke/" } ], "additional": [ { "label": "Giz Kenya Fair Forward Ai Powered Chatbot (web.archive.org)", "url": "https://web.archive.org/web/20251116031001/https://made.ke/case-studies/giz-kenya-fair-forward-ai-powered-chatbot/" } ], "documents": [] }, "content": { "data_characteristics": null, "model_characteristics": null, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_85/images/placeholder_image.jpeg" }, { "id": "ui_86", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_86-kinycomet_automatic_evaluation_of_machine_translat/", "aliases": [ "kinycomet_automatic_evaluation_of_machine_translat" ], "title": "KinyCOMET: Automatic evaluation of machine translation for Kinyarwanda-English", "description": { "text": "Until now, the lack of automatic evaluation tools made Kinyarwanda-English machine translation development slow and expensive, as it required manual human review. We’ve bridged this gap by creating a new evaluation dataset and model. Both are now open-source to support the research community. Our methodology and results are detailed in our upcoming paper for LREC 2026.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 9", "SDG 10" ], "data_types": [ "Text" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "Apache 2.0", "spdx": "Apache-2.0", "url": null }, "organizations": { "provided_by": { "text": "Mbaza NLP community, German Research Center for Artificial Intelligence (DFKI)", "links": [] }, "catalyzed_by": { "text": "Regional AI Hub Rwanda, FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "Chris Prince Mazimpaka chrismazimpaka7@gmail.com", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: kinycomet_dataset", "url": "https://huggingface.co/datasets/chrismazii/kinycomet_dataset" } ], "usecase": [ { "label": "Hugging Face: kinycomet_unbabel", "url": "https://huggingface.co/chrismazii/kinycomet_unbabel" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "4000 rows of parallel sentences English - Kinyarwanda with annotated translation quality as Direct Assessment", "provenance": "curated" }, "model_characteristics": { "text": "Pretrained Model: KinyCOMET-Unbabel, a translation quality estimation model for Kinyarwanda-English (bidirectional). Fine-tuned on Unbabel/wmt22-comet-da using the COMET framework. Input: source text, machine translation, and human reference translation. Output: quality score from 0 to 1. Achieves 0.75 Pearson correlation with human judgments (vs. 0.30 for BLEU), Spearman 0.59, MAE 0.07. A second variant (KinyCOMET-XLM) based on XLM-RoBERTa-large achieves 0.73 Pearson. Trained on 4,323 human-annotated translation pairs scored by 15 annotators following WMT Direct Assessment standards. Install via pip install unbabel-comet; load with comet.load_from_checkpoint(\"chrismazii/kinycomet_unbabel\"). License: Apache 2.0.\n\nSource: https://huggingface.co/datasets/chrismazii/kinycomet_dataset, https://huggingface.co/chrismazii/kinycomet_unbabel", "provenance": "auto-enriched" }, "how_to_use": null }, "image": "https://fair-forward.github.io/datasets/projects/ui_86/images/kinycomet.png" }, { "id": "ui_87", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_87-tunga_agrichatbot_open_source_suite_/", "aliases": [ "tunga_agrichatbot_open_source_suite_", "tunga_chatbot_open_source_suite_" ], "title": "Tunga Agri-Chatbot Open Source Suite - A full system to build call center agent based voicebots in Kinyarwanda e.g. for Agriculture and other sectors", "description": { "text": "Voicebots acting as call center agents—accessible via telephone and capable of speaking local languages—hold immense potential for development cooperation. They provide a vital link to users in rural areas who may lack internet access, have limited literacy, do not speak English or do not own digital devices. We believe voice-driven interfaces are the most intuitive way to bridge the digital divide for these communities.\n\nAs part of the Tunga project, we have developed such a voicebot. To support further innovation in this space, we are releasing a comprehensive suite of foundational AI models and datasets:\n\nText-to-speech model\nCurrently the highest-quality synthetic voice for Kinyarwanda.\nhttps://huggingface.co/C4IR-RW/kinya-flex-tts\n\nKinyaCOLBERT Free\nThe first Kinyarwanda embedding/information retrieval model, essential for building RAG chatbots such as Tunga.\nhttps://huggingface.co/C4IR-RW/kiny-colbert-free\n\nVoice Dataset\nA dataset for training TTS and Speech-to-Text (STT) models.\nhttps://huggingface.co/datasets/C4IR-RW/kinya-ag-tts\n\nInformation Retrieval Dataset\nThe data used to train the KinyaColBERT model.\nhttps://huggingface.co/datasets/C4IR-RW/kinya-ag-retrieval\n\nKinyaBERT\nA foundational BERT-style model serving as the backbone for various Kinyarwanda NLP tasks.\nhttps://huggingface.co/C4IR-RW/kinyabert \n\nSource Code\nAll relevant code for the models and datasets mentioned above.\nhttps://github.com/c4ir-rw/ac-ai-models", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 2" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "Apache 2.0", "spdx": "Apache-2.0", "url": null }, "organizations": { "provided_by": { "text": "Centre for the Fourth Industrial Revolution Rwanda (C4IR), Kigali Natural Language Processing (KiNLP)", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "C4IR Rwanda (info@c4ir.rw)", "access_note": null, "links": { "dataset": [ { "label": "Hugging Face: kinya-ag-tts", "url": "https://huggingface.co/datasets/C4IR-RW/kinya-ag-tts" }, { "label": "Hugging Face: kinya-ag-retrieval", "url": "https://huggingface.co/datasets/C4IR-RW/kinya-ag-retrieval" } ], "usecase": [ { "label": "Hugging Face: kinya-flex-tts", "url": "https://huggingface.co/C4IR-RW/kinya-flex-tts" }, { "label": "Hugging Face: kiny-colbert-free", "url": "https://huggingface.co/C4IR-RW/kiny-colbert-free" }, { "label": "Hugging Face: kinyabert", "url": "https://huggingface.co/C4IR-RW/kinyabert" }, { "label": "GitHub: ac-ai-models", "url": "https://github.com/c4ir-rw/ac-ai-models" } ], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Two Kinyarwanda agricultural datasets. (1) TTS Dataset (kinya-ag-tts): 10,480 audio clips (5,242 female, 5,238 male) of voice actors reading agricultural content. Format: WAV mono at 24 kHz, with TSV metadata mapping clip IDs to text. Total size: 2.63 GB. (2) Retrieval Dataset (kinya-ag-retrieval): 984 agricultural passages in Kinyarwanda paired with 19,537 questions. Structured as query-positive-negative triplets for training retrieval models (1,901,200 training triplets, 19,600 dev, 32,900 test). Format: TSV. Includes morphologically parsed versions of passages and queries. Both datasets created by C4IR Rwanda and KiNLP. License: CC-BY 4.0.\n\nSource: https://huggingface.co/datasets/C4IR-RW/kinya-ag-tts, https://huggingface.co/datasets/C4IR-RW/kinya-ag-retrieval", "provenance": "auto-enriched" }, "model_characteristics": { "text": "Two pretrained models for a Kinyarwanda agricultural IVR system. (1) TTS Model (kinya-flex-tts): Multi-speaker text-to-speech based on MB-iSTFT-VITS2 architecture. Converts Kinyarwanda text to speech at 24 kHz with 3 speaker options (2 female, 1 male). Requires PyTorch; implementation via the DeepKIN-AgAI package. (2) Retrieval Model (kiny-colbert-free): Information retrieval model for RAG, fine-tuned from Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda. 0.2B parameters, F32 safetensors format. Trained on agricultural question-passage pairs. Usable via ragatouille library. Both models by C4IR Rwanda and KiNLP. License: CC-BY 4.0.\n\nSource: https://huggingface.co/C4IR-RW/kinya-flex-tts, https://huggingface.co/C4IR-RW/kiny-colbert-free", "provenance": "auto-enriched" }, "how_to_use": { "text": "## How to Use This Resource\n\nThe Tunga Agri-Chatbot Suite provides the building blocks for a Kinyarwanda-language agricultural advisory system delivered via Interactive Voice Response (IVR). It was built to address capacity gaps in Rwanda's agricultural extension services and is implemented by C4IR Rwanda and KiNLP, supported by GIZ and financed by BMZ.\n\nThis suite is relevant for anyone working on agricultural extension, farmer advisory services, or voice-based information delivery in Rwanda or similar contexts. The core idea is that smallholder farmers can call a phone number and receive spoken agricultural guidance in Kinyarwanda -- covering topics such as pest and disease diagnosis, agro-climatic practices, and MINAGRI support programmes -- without needing internet access or literacy.\n\nThe text-to-speech component (kinya-flex-tts) can generate natural-sounding Kinyarwanda speech with three voice options (two female, one male), outputting audio at broadcast quality (24 kHz). A live demo is available at [huggingface.co/spaces/Professor/c4ir-rw-kinyarwandatts](https://huggingface.co/spaces/Professor/c4ir-rw-kinyarwandatts), where you can hear sample outputs before deciding whether to integrate the model. The underlying TTS training dataset contains 10,482 audio clips recorded by two voice actors, all licensed under CC-BY-4.0.\n\nThe passage retrieval component (kiny-colbert-free) matches farmer questions in Kinyarwanda to relevant agricultural knowledge passages. This is the engine that allows the system to find the right answer from a knowledge base when a farmer asks a question. The accompanying retrieval dataset contains 984 agricultural passages, 19,537 related questions, and nearly 2 million training triplets, also under CC-BY-4.0.\n\nThe suite also includes KinyaBERT, a morphology-aware language model for Kinyarwanda used for passage ranking, available in base (107M parameter) and large (365M parameter) variants. All model code, training scripts, and technical documentation are available in the [DeepKIN-AgAI package on GitHub](https://github.com/c4ir-rw/ac-ai-models/tree/main/DeepKIN-AgAI). All components require attribution to C4IR Rwanda and KiNLP.\n\nDevelopers and researchers can extend this work to other crops, regions, or languages, or integrate these components into existing agricultural advisory platforms. The individual models (TTS, retrieval, language understanding) can also be used independently for other Kinyarwanda language technology applications beyond agriculture. All datasets and models are hosted on Hugging Face under the [C4IR-RW organisation](https://huggingface.co/C4IR-RW).\n\nSources:\n- https://huggingface.co/datasets/C4IR-RW/kinya-ag-tts\n- https://huggingface.co/datasets/C4IR-RW/kinya-ag-retrieval\n- https://huggingface.co/C4IR-RW/kinya-flex-tts\n- https://huggingface.co/C4IR-RW/kiny-colbert-free\n- https://huggingface.co/C4IR-RW/kinyabert\n- https://github.com/c4ir-rw/ac-ai-models", "provenance": "auto-enriched" } }, "image": "https://fair-forward.github.io/datasets/projects/ui_87/images/agribot.png" }, { "id": "ui_88", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_88-rwanda_media_voice_bridge__aipowered/", "aliases": [ "c4ir_grant_rwanda_media_voice_bridge", "rwanda_media_voice_bridge__aipowered" ], "title": "Rwanda Media Voice Bridge - AI-powered voice transcription and translation solution for Rwanda's film and media industry", "description": { "text": "An AI-powered voice transcription and translation solution for Rwanda's film and media industry, focused on low-resource African languages. The initiative prioritises ethical AI development by upskilling local transcribers and annotators as active contributors to model improvement rather than replacing them, creating sustainable digital jobs in the creative economy.", "provenance": "curated" }, "kind": [], "countries": [ { "name": "Rwanda", "iso2": "RW" } ], "regions": [], "sdgs": [ "SDG 10", "SDG 9", "SDG 8" ], "data_types": [ "Text", "Voice" ], "maturity": { "stage": "Dataset > Model > Pilot", "tags": [ "dataset", "model", "pilot" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Centre for the Fourth Industrial Revolution Rwanda (C4IR), Duka Labs", "links": [] }, "catalyzed_by": { "text": "GIZ Regional AI Hub Rwanda, GIZ Film Made in Africa, FAIR Forward - AI for All, GIZ", "links": [] }, "financed_by": { "text": "BMZ", "links": [] } }, "contact": "C4IR, juwizeye@c4ir.com", "access_note": { "kind": "info", "markdown": "Dataset will include voice recordings from film and TV productions, paired with transcriptions in text format. Data sharing platform pending.\n\nModels used include off-the-shelf open-source Speech-to-Text (STT) and Machine Translation (MT) models, benchmarking report against the dataset to be shared. No training on copyright data." }, "links": { "dataset": [], "usecase": [], "additional": [], "documents": [] }, "content": { "data_characteristics": { "text": "Parallel voice-text corpus in low-resource East and West African languages, sourced from professional film and TV productions. Data quality reflects studio-grade audio. Responsible AI considerations include informed consent from content owners, a no-commercial-use clause, right-to-retract provisions, and no third-party sharing. Bias assessments should account for genre diversity, speaker demographics, and dialect representation across the corpus.", "provenance": "curated" }, "model_characteristics": { "text": "No training, only testing for benchmarks. The pipeline combines Badrex's Wav2Vec-BERT 2.0 for Kinyarwanda ASR, Nvidia's SortFormer streaming model for speaker diarisation (current state-of-the-art), and MbazaNLP's fine-tuned NLLB model for open-source English-Kinyarwanda translation, with Gemini 3.1 Pro serving as the best-performing option for context-aware translation. All models accept audio or text input and output transcriptions, speaker segments, and translations optimised for media dialogue and subtitling workflows.", "provenance": "curated" }, "how_to_use": { "text": "Users can access transcription and translation services through a web-based platform integrated into post-production subtitling workflows. The solution operates on a usage-based pricing model agreed between the product owner and local studios. Resources include technical documentation, community-maintained model improvements, and training materials for onboarding production teams. Computing costs scale with usage and can be optimised through cloud hosting or shared infrastructure.", "provenance": "curated" } }, "image": null }, { "id": "ui_89", "canonical_url": "https://fair-forward.github.io/datasets/projects/ui_89-datadriven_decisionmaking_for_farmers_to_increase/", "aliases": [ "datadriven_decisionmaking_for_farmers_to_increase", "digital_green__local_dataset_collection", "geospatial_dataset_of_wheat_and_rice" ], "title": "Data-driven decision-making for farmers to increase climate resilience in India", "description": { "text": "Smallholder farmers are crucial contributors to global food production, and in India often suffer most from poverty and malnutrition. These farmers face challenges such as limited access to modern agriculture, unpredictable weather, and resource constraints. To tackle this issue, Digital Green collected data via surveys, offering insights into farming practices, environmental conditions, and crop yields.\n\nThe project developed a scalable data collection model for JEEViKA’s frontline workers, integrating digital tools and geofencing, and empowered farmers with personalized climate-smart advice through Farmer Scorecards.\n\nThe project has contributed to Digital Greens current FarmerChat.", "provenance": "curated" }, "kind": [ "dataset", "usecase" ], "countries": [ { "name": "India", "iso2": "IN" } ], "regions": [], "sdgs": [ "SDG 2" ], "data_types": [ "Tabular" ], "maturity": { "stage": "Dataset > Model > Pilot > Use-Case > Use-Case with Business Model", "tags": [ "dataset", "model", "pilot", "usecase", "business" ] }, "license": { "name": "CC-BY 4.0", "spdx": "CC-BY-4.0", "url": null }, "organizations": { "provided_by": { "text": "Digital Green", "links": [] }, "catalyzed_by": { "text": "FAIR Forward - AI for All, GIZ", "links": [ { "name": "FAIR Forward - AI for All", "url": "https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/" } ] }, "financed_by": { "text": "BMZ and Sequoia Climate Fund, CISCO Foundation", "links": [ { "name": "BMZ", "url": "https://www.bmz-digital.global/en/digital-transformation-and-development-cooperation/" } ] } }, "contact": "Digital Green (info@digitalgreentrust.org), Viamo and Partners, Tech for Her, DynAg, Digifarm, Opportunity International", "access_note": null, "links": { "dataset": [ { "label": "GitHub: frame-templates", "url": "https://github.com/digitalgreenorg/frame-templates" } ], "usecase": [ { "label": "GitHub: frame-templates", "url": "https://github.com/digitalgreenorg/frame-templates" } ], "additional": [ { "label": "Giz Ff Dg Learning Report.Pdf (digitalgreen.org)", "url": "https://digitalgreen.org/wp-content/uploads/2024/10/GIZ-FF_DG_Learning-Report.pdf" } ], "documents": [] }, "content": { "data_characteristics": { "text": "The data was collected through a survey conducted across multiple districts in India. It consists of a variety of factors that could potentially impact the yield of rice crops. These factors include things like the type and amount of fertilizers used, the quantity of seedlings planted, methods of preparing the land, different irrigation techniques employed, among other features. The dataset comprises more than 5000 data points, each having more than 40 features.", "provenance": "curated" }, "model_characteristics": { "text": "Multiple models are available based on the shared data. Please refer to the GitHub repository.", "provenance": "curated" }, "how_to_use": { "text": "The possibilities include creating a machine learning solution to predict the crop yield per acre of rice or wheat crops in India. Our goal is to empower these farmers and break the cycle of poverty and malnutrition.\n\nThis use case also included the development of a business model and funding model for open source AI as a stepping stone towards financially viable operations. It was facilitated by Villgro Africa's and FAIR Forward's six-month mentorship programme on \"Creating sustainable business and funding models with open source AI\". See: https://www.bmz-digital.global/en/news/open-source-ai-business-impact/", "provenance": "curated" } }, "image": null } ] }