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Found 11 result(s)
The CDAWeb data system enables improved display and coordinated analysis of multi-instrument, multimission data bases of the kind whose analysis is critical to meeting the science objectives of the ISTP program and the InterAgency Consultative Group (IACG) Solar-Terrestrial Science Initiative. The system combines the client-server user interface technology of the World Wide Web with a powerful set of customized IDL routines to leverage the data format standards (CDF) and guidelines for implementation adopted by ISTP and the IACG. The system can be used with any collection of data granules following the extended set of ISTP/IACG standards. CDAWeb is being used both to support coordinated analysis of public and proprietary data and better functional access to specific public data such as the ISTP-precursor CDAW 9 data base that is formatted to the ISTP/IACG standards. Many data sets are available through the Coordinated Data Analysis Web (CDAWeb) service and the data coverage continues to grow. These are largely, but not exclusively, magnetospheric data and nearby solar wind data of the ISTP era (1992-present) at time resolutions of approximately a minute. The CDAWeb service provides graphical browsing, data subsetting, screen listings, file creations and downloads (ASCII or CDF). Public data from current (1992-present) space physics missions (including Cluster, IMAGE, ISTP, FAST, IMP-8, SAMPEX and others). Public data from missions before 1992 (including IMP-8, ISIS1/2, Alouette2, Hawkeye and others). Public data from all current and past space physics missions. CDAWeb ist part of "Space Physics Data Facility" (https://www.re3data.org/repository/r3d100010168).
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.
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The Research Data Center (RDC) “International Survey Programs“ provides researchers with data, services, and consultation on a number of important international study series which are under intensive curation by GESIS. They all cover numerous countries and, quite often, substantial time spans. The RDC provides optimal data preparation and access to a wide scope of data and topics for comparative analysis.
The KNB Data Repository is an international repository intended to facilitate ecological, environmental and earth science research in the broadest senses. For scientists, the KNB Data Repository is an efficient way to share, discover, access and interpret complex ecological, environmental, earth science, and sociological data and the software used to create and manage those data. Due to rich contextual information provided with data in the KNB, scientists are able to integrate and analyze data with less effort. The data originate from a highly-distributed set of field stations, laboratories, research sites, and individual researchers. The KNB supports rich, detailed metadata to promote data discovery as well as automated and manual integration of data into new projects. The KNB supports a rich set of modern repository services, including the ability to assign Digital Object Identifiers (DOIs) so data sets can be confidently referenced in any publication, the ability to track the versions of datasets as they evolve through time, and metadata to establish the provenance relationships between source and derived data.
The Society of American Archivists (SAA) Dataverse is an SAA data service that was established to support the needs and interests of SAA’s members and the broader archives community. The SAA Dataverse supports the reuse of datasets for purposes of fostering knowledge, insights, and a deeper understanding of archival organizations, the status of archivists, and the impact of archives and archival work on the broader society. Deposited datasets should be “actionable” in that they should support direct analysis and interpretation. The SAA Dataverse welcomes deposits of collections of quantitative or qualitative data and associated documentation. SAA membership is not required to deposit or use data in the SAA Dataverse.
The Arctic Data Center is the primary data and software repository for the Arctic section of NSF Polar Programs. The Center helps the research community to reproducibly preserve and discover all products of NSF-funded research in the Arctic, including data, metadata, software, documents, and provenance that links these together. The repository is open to contributions from NSF Arctic investigators, and data are released under an open license (CC-BY, CC0, depending on the choice of the contributor). All science, engineering, and education research supported by the NSF Arctic research program are included, such as Natural Sciences (Geoscience, Earth Science, Oceanography, Ecology, Atmospheric Science, Biology, etc.) and Social Sciences (Archeology, Anthropology, Social Science, etc.). Key to the initiative is the partnership between NCEAS at UC Santa Barbara, DataONE, and NOAA’s NCEI, each of which bring critical capabilities to the Center. Infrastructure from the successful NSF-sponsored DataONE federation of data repositories enables data replication to NCEI, providing both offsite and institutional diversity that are critical to long term preservation.
Biological collections are replete with taxonomic, geographic, temporal, numerical, and historical information. This information is crucial for understanding and properly managing biodiversity and ecosystems, but is often difficult to access. Canadensys, operated from the Université de Montréal Biodiversity Centre, is a Canada-wide effort to unlock the biodiversity information held in biological collections.
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.