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Found 13 result(s)

CaltechDATA

California Institute of Technology Research Data Repository

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CaltechDATA is an institutional data repository for Caltech. Caltech library runs the repository to preserve the accomplishments of Caltech researchers and share their results with the world. Caltech-associated researchers can upload data, link data with their publications, and assign a permanent DOI so that others can reference the data set. The repository also preserves software and has automatic Github integration. All files present in the repository are open access or embargoed, and all metadata is always available to the public.

UC San Diego Library Digital Collections

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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).

Duke Research Data Repository

Duke RDR

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The Duke Research Data Repository is a service of the Duke University Libraries that provides curation, access, and preservation of research data produced by the Duke community. Duke's RDR is a discipline agnostic institutional data repository that is intended to preserve and make public data related to the teaching and research mission of Duke University including data linked to a publication, research project, and/or class, as well as supplementary software code and documentation used to provide context for the data.

PRISM Dataverse

University of Calgary Data Repository

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PRISM Dataverse is the institutional data repository of the University of Calgary, which has its purpose in digital archiving and sharing of research data from researchers. PRISM Dataverse is a data repository hosted through Borealis, a service of the Ontario Council of University Libraries and supported by University of Calgary's Libraries and Cultural Resources. PRISM Dataverse enables scholars to easily deposit data, create data-specific metadata for searchability and publish their datasets.

Sloan Digital Sky Survey

SDSS

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The Sloan Digital Sky Survey (SDSS) is one of the most ambitious and influential surveys in the history of astronomy. Over eight years of operations (SDSS-I, 2000-2005; SDSS-II, 2005-2008; SDSS-III 2008-2014; SDSS-IV 2013 ongoing), it obtained deep, multi-color images covering more than a quarter of the sky and created 3-dimensional maps containing more than 930,000 galaxies and more than 120,000 quasars. DSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), Max-Planck-Institut für Astronomie (MPIA Heidelberg), National Astronomical Observatory of China, New Mexico State University, New York University, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Portsmouth, University of Utah, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.

DataverseNO

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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.

Merritt

UC3Merritt

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Merritt is a curation repository for the preservation of and access to the digital research data of the ten campus University of California system and external project collaborators. Merritt is supported by the University of California Curation Center (UC3) at the California Digital Library (CDL). While Merritt itself is content agnostic, accepting digital content regardless of domain, format, or structure, it is being used for management of research data, and it forms the basis for a number of domain-specific repositories, such as the ONEShare repository for earth and environmental science and the DataShare repository for life sciences. Merritt provides persistent identifiers, storage replication, fixity audit, complete version history, REST API, a comprehensive metadata catalog for discovery, ATOM-based syndication, and curatorially-defined collections, access control rules, and data use agreements (DUAs). Merritt content upload and download may each be curatorially-designated as public or restricted. Merritt DOIs are provided by UC3's EZID service, which is integrated with DataCite. All DOIs and associated metadata are automatically registered with DataCite and are harvested by Ex Libris PRIMO and Thomson Reuters Data Citation Index (DCI) for high-level discovery. Merritt is also a member node in the DataONE network; curatorially-designated data submitted to Merritt are automatically registered with DataONE for additional replication and federated discovery through the ONEMercury search/browse interface.

Kaggle

Your home for data science

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Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know beforehand which technique or analyst will be most effective.

NIH 3D Print Exchange

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The NIH 3D Print Exchange (the “Exchange”) is an open, comprehensive, and interactive website for searching, browsing, downloading, and sharing biomedical 3D print files, modeling tutorials, and educational material. "Biomedical" includes models of cells, bacteria, or viruses, molecules like proteins or DNA, and anatomical models of organs, tissue, and body parts. The NIH 3D Print Exchange provides models in formats that are readily compatible with 3D printers, and offers a unique set of tools to create and share 3D-printable models related to biomedical science.

depositar

研究資料寄存所

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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.

CCCA Data Server

Climate Change Centre Austria Data Centre

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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

USDA’s Open Research Data

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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

Open Machine Learning

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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.