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Found 21 result(s)
ICRISAT performs crop improvement research, using conventional as well as methods derived from biotechnology, on the following crops: Chickpea, Pigeonpea, Groundnut, Pearl millet,Sorghum and Small millets. ICRISAT's data repository collects, preserves and facilitates access to the datasets produced by ICRISAT researchers to all users who are interested in. Data includes Phenotypic, Genotypic, Social Science, and Spatial data, Soil and Weather.
In 1984 the establishment of the Vitis International Variety Catalogue (VIVC) took place at the Institute for Grapevine Breeding Geilweilerhof. The concept of a database on grapevine genetic resources was supported by IBPGR (today called Bioversity) and the International Organisation of Vine and Wine (OIV). Today VIVC is an encyclopedic database with around 23000 cultivars, breeding lines and Vitis species, existing in grapevine repositories and/or described in bibliography. It is an information source for breeders, researchers, curators of germplasm repositories and interested wine enthusiasts. Besides cultivar specific passport data, SSR-marker data, comprehensive bibliography and photos are to be found.
The Agri-Environmental Research Data Repository includes datasets from several studies conducted by researchers at the University of Guelph. This repository includes data on topics such as crop yield, soil moisture, weather and agroforestry.
DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) is an information management system that allows you to discover long-term ecosystem research sites around the globe, along with the data gathered at those sites and the people and networks associated with them. DEIMS-SDR describes a wide range of sites, providing a wealth of information, including each site’s location, ecosystems, facilities, parameters measured and research themes. It is also possible to access a growing number of datasets and data products associated with the sites. All sites and dataset records can be referenced using unique identifiers that are generated by DEIMS-SDR. It is possible to search for sites via keyword, predefined filters or a map search. By including accurate, up to date information in DEIMS, site managers benefit from greater visibility for their LTER site, LTSER platform and datasets, which can help attract funding to support site investments. The aim of DEIMS-SDR is to be the globally most comprehensive catalogue of environmental research and monitoring facilities, featuring foremost but not exclusively information about all LTER sites on the globe and providing that information to science, politics and the public in general.
In keeping with the open data policies of the U.S. Agency for International Development (USAID) and Bill & Melinda Gates Foundation, the Cereal Systems Initiative for South Asia (CSISA) has launched the CSISA Data Repository to ensure public accessibility to key data sets, including crop cut data- directly observed, crop yield estimates, on-station and on-farm research trial data and socioeconomic surveys. CSISA is a science-driven and impact-oriented regional initiative for increasing the productivity of cereal-based cropping systems in Bangladesh, India and Nepal, thus improving food security and farmers’ livelihoods. CSISA generates data that is of value and interest to a diverse audience of researchers, policymakers and the public. CSISA’s data repository is hosted on Dataverse, an open source web application developed at Harvard University to share, preserve, cite, explore and analyze research data. CSISA’s repository contains rich datasets, including on-station trial data from 2009–17 about crop and resource management practices for sustainable future cereal-based cropping systems. Collection of this data occurred during the long-term, on-station research trials conducted at the Indian Council of Agricultural Research – Research Complex for the Eastern Region in Bihar, India. The data include information on agronomic management for the sustainable intensification of cropping systems, mechanization, diversification, futuristic approaches to sustainable intensification, long-term effects of conservation agriculture practices on soil health and the pest spectrum. Additional trial data in the repository includes nutrient omission plot technique trials from Bihar, eastern Uttar Pradesh and Odisha, India, covering 2012–15, which help determine the indigenous nutrient supplying ability of the soil. This data helps develop precision nutrient management approaches that would be most effective in different types of soils. CSISA’s most popular dataset thus far includes crop cut data on maize in Odisha, India and rice in Nepal. Crop cut datasets provide ground-truthed yield estimates, as well as valuable information on relevant agronomic and socioeconomic practices affecting production practices and yield. A variety of research data on wheat systems are also available from Bangladesh and India. Additional crop cut data will also be coming online soon. Cropping system-related data and socioeconomic data are in the repository, some of which are cross-listed with a Dataverse run by the International Food Policy Research Institute. The socioeconomic datasets contain baseline information that is crucial for technology targeting, as well as to assess the adoption and performance of CSISA-supported technologies under smallholder farmers’ constrained conditions, representing the ultimate litmus test of their potential for change at scale. Other highly interesting datasets include farm composition and productive trajectory information, based on a 20-year panel dataset, and numerous wheat crop cut and maize nutrient omission trial data from across Bangladesh.
The range of CIRAD's research has given rise to numerous datasets and databases associating various types of data: primary (collected), secondary (analysed, aggregated, used for scientific articles, etc), qualitative and quantitative. These "collections" of research data are used for comparisons, to study processes and analyse change. They include: genetics and genomics data, data generated by trials and measurements (using laboratory instruments), data generated by modelling (interpolations, predictive models), long-term observation data (remote sensing, observatories, etc), data from surveys, cohorts, interviews with players.
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Launched in November 1995, RADARSAT-1 provided Canada and the world with an operational radar satellite system capable of timely delivery of large amounts of data. Equipped with a powerful synthetic aperture radar (SAR) instrument, it acquired images of the Earth day or night, in all weather and through cloud cover, smoke and haze. RADARSAT-1 was a Canadian-led project involving the Canadian federal government, the Canadian provinces, the United States, and the private sector. It provided useful information to both commercial and scientific users in such fields as disaster management, interferometry, agriculture, cartography, hydrology, forestry, oceanography, ice studies and coastal monitoring. In 2007, RADARSAT-2 was launched, producing over 75,000 images per year since. In 2019, the RADARSAT Constellation Mission was deployed, using its three-satellite configuration for all-condition coverage. More information about RADARSAT-2 see https://mda.space/en/geo-intelligence/ RADARSAT-2 PORTAL see https://gsiportal.mda.space/gc_cp/#/map
The International Center for Tropical Agriculture (CIAT), a member of the CGIAR Consortium, believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. Research data produced by CIAT and its Partners is distributed freely whenever possible. Kindly note that these datasets require proper citation and citation information is included with the metadata for each dataset.
The CGIAR Research Program No. 6 (CRP6): Forests, Trees and Agroforestry: Livelihoods, Landscapes and Governance aims to enhance the management and use of forests, agroforestry and tree genetic resources across the landscape, from farms to forests.
The International Soil Carbon Network (ISCN) is a science-based network that facilitates data sharing, assembles databases, identifies gaps in data coverage, and enables spatially explicit assessments of soil carbon in context of landscape, climate, land use, and biotic variables. The ISCN Database is an evolving data resource. Because it is too large to present in one file, it is available as a set of documents, reports, and tables; because it changes over time, it is important to note the version date stamped in the upper-left corner of any dataset that you access.
The British Columbia Conservation Data Centre (CDC) collects and disseminates information on plants, animals and ecosystems at risk in British Columbia. The " BC Species and Ecosystems Explorer" is a source for authoritative conservation information on approximately 7400 plants and animals, and over 600 ecological communities (ecosystems)in British Columbia. Information includes conservation status, legal designation, and ecosection values for ecological communities.
The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty. In collaboration with institutions throughout the world, IFPRI is often involved in the collection of primary data and the compilation and processing of secondary data. The resulting datasets provide a wealth of information at the local (household and community), national, and global levels. IFPRI freely distributes as many of these datasets as possible and encourages their use in research and policy analysis. IFPRI Dataverse contains following dataverses: Agricultural Science and Knowledge Indicators - ASTI, HarvestChoice, Statistics on Public Expenditures for Economic Development - SPEED, International Model for Policy Analysis of Agricultural Commodities and Trade - IMPACT, Africa RISING Dataverse and Food Security Portal Dataverse.
UM Dataverse is part of the Dataverse Project conceived of by Harvard University. It is an open source repository to assist researchers in the creation, management and dissemination of their research data. UM Dataverse allows for the creation of multiple collaborative environments containing datasets, metadata and digital objects. UM Dataverse provides formal scholarly data citations and can help with data requirements from publishers and funders.