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Found 12 result(s)
SCEC's mission includes gathering data on earthquakes, both in Southern California and other locales; integrate the information into a comprehensive understanding of earthquake phenomena; and communicate useful knowledge for reducing earthquake risk to society at large. The SCEC community consists of more than 600 scientists from 16 core institutions and 47 additional participating institutions. SCEC is funded by the National Science Foundation and the U.S. Geological Survey.
The UC San Diego Library Digital Collections website gathers two categories of content managed by the Library: library collections (including digitized versions of selected collections covering topics such as art, film, music, history and anthropology) and research data collections (including research data generated by UC San Diego researchers).
Country
DataverseNO (https://dataverse.no) is a curated, FAIR-aligned national generic repository for open research data from all academic disciplines. DataverseNO commits to facilitate that published data remain accessible and (re)usable in a long-term perspective. The repository is owned and operated by UiT The Arctic University of Norway. DataverseNO accepts submissions from researchers primarily from Norwegian research institutions. Datasets in DataverseNO are grouped into institutional collections as well as special collections. The technical infrastructure of the repository is based on the open source application Dataverse (https://dataverse.org), which is developed by an international developer and user community led by Harvard University.
Country
OCTOPUS is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic 10Be and 26Al, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms and archaeological records, along with associated geospatial (vector and raster) data layers.
Country
depositar — taking the term from the Portuguese/Spanish verb for to deposit — is an online repository for research data. The site is built by the researchers for the researchers. You are free to deposit, discover, and reuse datasets on depositar for all your research purposes.
OSGeo's mission is to support the collaborative development of open source geospatial software, in part by providing resources for projects and promoting freely available geodata. The Public Geodata Repository is a distributed repository and registry of data sources free to access, reuse, and re-distribute.
Country
The interdisciplinary data platform INPTDAT provides easy access to research data and information from all fields of applied plasma physics and plasma medicine. It aims to support the findability, accessibility, interoperability and re-use of data for the low-temperature plasma physics community.
Country
The Climate Change Centre Austria - Data Centre provides the central national archive for climate data and information. The data made accessible includes observation and measurement data, scenario data, quantitative and qualitative data, as well as the measurement data and findings of research projects.
Ag Data Commons provides access to a wide variety of open data relevant to agricultural research. We are a centralized repository for data already on the web, as well as for new data being published for the first time. While compliance with the U.S. Federal public access and open data directives is important, we aim to surpass them. Our goal is to foster innovative data re-use, integration, and visualization to support bigger, better science and policy.
OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.