Data in Climate Resilient Agriculture (DiCRA) is a collaborative digital public good which provides open access to key geospatial datasets pertinent to climate resilient agriculture. These datasets are curated and validated through collaborative efforts of hundreds of data scientists and citizen scientists across the world. The pattern detection and data insights emerging from DiCRA are aimed towards strengthening evidence-driven policy making for climate resilient food systems. DiCRA is guided by the digital public good principles of open access, open software, open code and open APIs.
The platform is facilitated by Government of Telangana and UNDP, in collaboration with Zero Huger Lab (Netherlands), JADS (Netherlands), ICRISAT, PJTSAU, and RICH. It is part of UNDP’s ‘Data for Policy’ initiative supported by Rockefeller Foundation.
Data collaboration and synchronization is vital for implementing this technology solution. This platform aims to incorporate data from Open data platforms, Non-Public Domain datasets through data partnerships and incorporate data from open APIs.
Following is a depiction of the dataflow proposed under the project -
The DiCRA Platform is an open data platform managed by the United Nations Development Programme. These Terms of Service (hereafter ‘Terms’ or ‘these Terms’) describe how DiCRA is managed and how the platform should be used. UNDP will update these Terms as needed, and will post notice of significant updates on our GitHub Page and through the DiCRA Platform. All organizations and individuals using this platform are bound by these Terms. If you do not agree with the Terms, you should discontinue use of DiCRA. If you have any questions or comments about these Terms or DiCRA, please leave a comment on the Discussions tab of our GitHub repository or send an email to acceleratorlab.in@undp.org
Disclaimer
The DiCRA online platform and its content are made available by the UNDP team. The content providers make no warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of this content or its online services, nor represent that its use may not potentially infringe property rights. UNDP makes no warranty that DiCRA online services and its content will be uninterrupted or error-free, nor that any defects will be corrected, nor that its online services or content will be free of viruses or other harmful components. UNDP assumes no liability for possible damages or implications which occur by direct or indirect use of DiCRA services or content.
Privacy
User contact details are only shared with the administrator of the DiCRA Platform if the user needs to download datasets.
UNDP upholds the highest standard of data protection for the personal data of DiCRA users and organization administrators. In case such personal data is exposed, UNDP will notify all affected individuals and remedy the incident.
UNDP continually seeks to understand the behavior of users on the DiCRA platform in order to make improvements. To do so, UNDP uses third-party analytics services, such as Google Analytics. This service use cookies stored on users’ devices to send encrypted information to Google Analytics about how users arrived at DiCRA, what pages they visited on DiCRA, and their actions within those pages. UNDP does not send identifying information (including names, usernames, or email addresses) to Google Analytics. Google Analytics’ use of the data collected from the DiCRA platform is governed by their respective Terms of Use. If you would like to disable the tracking described above, you can install the Google Analytics Opt-out Browser Add-on to disable Google Analytics tracking. The data collected by these tracking systems will be retained indefinitely in order to understand how user behavior is changing over time.
Data Licensing
Each of the datasets made available through the DiCRA PLatform have their own data use licensing and citation requirement. More information regarding this is made available at the following link. By downloading the data through DiCRA Platform, the user acknowledges the individual data source's data use agreements and have to cite the data source as per the citation format of the dataset.
| Title | Category | Description | Source | Citation | License |
|---|---|---|---|---|---|
| Normalized Difference Vegetation Index (NDVI) | Socio-economic | NDVI quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs) | GLAM NDVIDB | Didan, K. (2015). MOD13A1 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-04-12 from https://doi.org/10.5067/MODIS/MOD13A1.006 | All data distributed by the LP DAAC contain no restrictions on the data reuse |
| Relative Wealth Index | Socio-economic | The Relative Wealth Index predicts the relative standard of living within countries using privacy protecting connectivity data, satellite imagery, and other novel data sources. | Facebook Data for Good Relative Wealth Index | Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119 | Creative Commons Attribution-Non Commercial 4.0 International (CC BY-NC 4.0) |
| Population | Socio-economic | WorldPop produces different gridded population layers | WorldPop Population Counts | Christopher T. Lloyd, Heather Chamberlain, David Kerr, Greg Yetman, Linda Pistolesi, Forrest R. Stevens, Andrea E. Gaughan, Jeremiah J. Nieves, Graeme Hornby, Kytt MacManus, Parmanand Sinha, Maksym Bondarenko, Alessandro Sorichetta & Andrew J. Tatem (2019) Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets, Big Earth Data, 3:2, 108-139, DOI: 10.1080/20964471.2019.1625151 | Creative Commons Attribution 4.0 International License |
| Sentinel-2 10m Land Use/Land Cover Timeseries | Socio-economic | This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated from Impact Observatory’s deep learning AI land classification model used a massive training dataset of billions of human-labeled image pixels developed by the National Geographic Society. The global maps were produced by applying this model to the Sentinel-2 scene collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year. | Land Use/ Land Cover Map | Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. | Creative Commons by Attribution (CC BY 4.0) license |
| Daily Prices of Market Yard Commodities in Telangana | Socio-economic | This dataset contains information on the daily prices of all the commodities across all the market yards in the state of Telangana | Open Data Telangana | Department of Agriculture and Co-operation, 2022, Daily Prices of Market Yard Commodities, Open Government Data Platform Telangana | Open Government License, India |
| Soil Moisture | Environmental | The SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture, Version 6 product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). | NASA | O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell. 2019. SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/R50VUC07OM4W. [03-04-2022] | Free to use with Citation |
| Active Fire Data | Environmental | Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies / Fire locations - Collection 6/61 processed by NASA's Science Computing Facility (SCF) at the University of Maryland (UMD) and distributed by Fire Information for Resource Management System (FIRMS), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified | Fire Information for Resource Management Systems | This data set was provided by LANCE FIRMS operated by NASA ESDIS with funding provided by NASA Headquarters. See https://earthdata.nasa.gov/earth-observation-data/near-real-time/citation#ed-firms-citation | Citation, Acknowledgements and Disclaimer |
| Telangana Weather Data | Environmental | This dataset provides information about the cumulative rainfall, minimum & maximum temperature, humidity & wind speed across all 589 weather stations in the state of Telangana | Open Data Telangana | Telangana State Development Planning Society, 2022, Telangana Weather Data 2022, Open Government Data Platform Telangana | Open Government License, India |
| Telangana Warehouses Geolocation Data | Infrastructure | This dataset contains information about the details of individual warehouses maintained by the State with geo-locations, names, their address, type, capacities and other related information. | Open Data Telangana | Department of Agriculture and Co-operation, Telangana Warehouses Geolocation Data, Open Government Data Platform Telangana | Open Government License, India |