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Found 15 result(s)
The Carleton University Data Repository Dataverse is the research data repository for Carleton University. It is managed by the Data Services in the MacOdrum Library. The repository also houses the MacOdrum Library Dataverse Collection which contains numerous public opinion polls.
Genomic Expression Archive (GEA) is a public database of functional genomics data such as gene expression, epigenetics and genotyping SNP array. Both microarray- and sequence-based data are accepted in the MAGE-TAB format in compliance with MIAME and MINSEQE guidelines, respectively. GEA issues accession numbers, E-GEAD-n to experiment and A-GEAD-n to array design. Data exchange between GEA and EBI ArrayExpress is planned.
The University of Toronto Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers. Researchers can choose to make content available publicly, to specific individuals, or to restrict access.
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.
<<<!!!>>> NVO - National Virtual Observatory is closed now <<<!!! >>> The National Virtual Observatory (NVO) was the predecessor of the VAO. It was a research project aimed at developing the technologies that would be used to build an operational Virtual Observatory. With the NVO era now over, a new organization has been funded in its place, with the explicit goal of creating useful tools for users to take advantage of the groundwork laid by the NVO. To carry on with the NVO's goals, we hereby introduce you to the Virtual Astronomical Observatory http://www.usvao.org/
!!! <<< the repository is offline, please use: https://www.re3data.org/repository/r3d100011650 >>> !!! The USGODAE Project consists of United States academic, government and military researchers working to improve assimilative ocean modeling as part of the International GODAE Project. GODAE hopes to develop a global system of observations, communications, modeling and assimilation, that will deliver regular, comprehensive information on the state of the oceans, in a way that will promote and engender wide utility and availability of this resource for maximum benefit to the community. The USGODAE Argo GDAC is currently operational, serving daily data from the following national DACs: Australia (CSIRO), Canada (MEDS), China (2: CSIO and NMDIS), France (Coriolis), India (INCOIS), Japan (JMA), Korea (2: KMA and Kordi), UK (BODC), and US (AOML).
Western University's Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers. Researchers can choose to make content available publicly, to specific individuals, or to keep it locked.
The Abacus Data Network is a data repository collaboration involving Libraries at Simon Fraser University (SFU), the University of British Columbia (UBC), the University of Northern British Columbia (UNBC) and the University of Victoria (UVic).
Queen's University Dataverse is the institutional open access research data repository for Queen's University, featuring Queen's University Biological Station (QUBS) which includes research related to ecology, evolution, resource management and conservation, GIS, climate data, and environmental science.
PDBe is the European resource for the collection, organisation and dissemination of data on biological macromolecular structures. In collaboration with the other worldwide Protein Data Bank (wwPDB) partners - the Research Collaboratory for Structural Bioinformatics (RCSB) and BioMagResBank (BMRB) in the USA and the Protein Data Bank of Japan (PDBj) - we work to collate, maintain and provide access to the global repository of macromolecular structure data. We develop tools, services and resources to make structure-related data more accessible to the biomedical community.
SureChemOpen is a free resource for researchers who want to search, view and link to patent chemistry. For end-users with professional search and analysis needs, we offer the fully-featured SureChemPro. For enterprise users, SureChemDirect provides all our patent chemistry via an API or a data feed. The SureChem family of products is built upon the Claims® Global Patent Database, a comprehensive international patent collection provided by IFI Claims®. This state of the art database is normalized and curated to provide unprecedented consistency and quality.
For datasets from individual researchers or research groups affiliated with Stockholm University, who do not want set up a separate Dataverse for a project or institution. Metadata provisions for Geospatial, Social Science, Humanities, Astronomy, Astrophysics, Life Sciences and Journals (all optional, by choice) are included. Data curation help from Stockholm University Library possible on request.
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.