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Found 41 result(s)
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The German Neuroinformatics Node's data infrastructure (GIN) services provide a platform for comprehensive and reproducible management and sharing of neuroscience data. Building on well established versioning technology, GIN offers the power of a web based repository management service combined with a distributed file storage. The service addresses the range of research data workflows starting from data analysis on the local workstation to remote collaboration and data publication.
For datasets big and small; Store your research data online. Quickly and easily upload files of any type and we will host your research data for you. Your experimental research data will have a permanent home on the web that you can refer to.
RAVE (RAdial Velocity Experiment) is a multi-fiber spectroscopic astronomical survey of stars in the Milky Way using the 1.2-m UK Schmidt Telescope of the Anglo-Australian Observatory (AAO). The RAVE collaboration consists of researchers from over 20 institutions around the world and is coordinated by the Leibniz-Institut fĆ¼r Astrophysik Potsdam. As a southern hemisphere survey covering 20,000 square degrees of the sky, RAVE's primary aim is to derive the radial velocity of stars from the observed spectra. Additional information is also derived such as effective temperature, surface gravity, metallicity, photometric parallax and elemental abundance data for the stars. The survey represents a giant leap forward in our understanding of our own Milky Way galaxy; with RAVE's vast stellar kinematic database the structure, formation and evolution of our Galaxy can be studied.
<<<!!!<<< The repository is no longer available. further information and data see: Oxford University Research Archive: https://www.re3data.org/repository/r3d100011230 >>>!!!>>>
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The University of Chile Research Data Repository preserves, disseminates and provides access to the research data generated by its academics and researchers, in order to give visibility, guarantee its preservation and facilitate its access and reuse.
The Arizona State University (ASU) Research Data Repository provides a platform for ASU-affiliated researchers to share, preserve, cite, and make research data accessible and discoverable. The ASU Research Data Repository provides a permanent digital identifier for research data, which complies with data sharing policies. The repository is powered by the Dataverse open-source application, developed and used by Harvard University. Both the ASU Research Data Repository and the KEEP Institutional Repository are managed by the ASU Library to ensure research produced at Arizona State University is discoverable and accessible to the global community.
University of Alberta Dataverse is a service provided by the University of Albert Library to help researchers publish, analyze, distribute, and preserve data and datasets. Open for University of Alberta-affiliated researchers to deposit data.
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Science Data Bank is an open generalist data repository developed and maintained by the Chinese Academy of Sciences Computing and Network Information Center (CNIC). It promotes the publication and reuse of scientific data. Researchers and journal publishers can use it to store, manage and share science data.
The Henry A. Murray Research Archive is Harvard's endowed, permanent repository for quantitative and qualitative research data at the Institute for Quantitative Social Science, and provides physical storage for the entire IQSS Dataverse Network. Our collection comprises over 100 terabytes of data, audio, and video. We preserve in perpetuity all types of data of interest to the research community, including numerical, video, audio, interview notes, and other data. We accept data deposits through this web site, which is powered by our Dataverse Network software
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An institutional repository at Graz University of Technology to enable storing, sharing and publishing research data, publications and open educational resources. It provides open access services and follows the FAIR principles.
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The Research Data Center (RDC) ā€œInternational Survey Programsā€œ 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.
WDC for STP, Moscow collects, stores, exchanges with other WDCs, disseminates the publications, sends upon requests data on the following Solar-Terrestrial Physics disciplines: Solar Activity and Interplanetary Medium, Cosmic Rays, Ionospheric Phenomena, Geomagnetic Variations.
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Borealis, the Canadian Dataverse Repository, is a bilingual, multidisciplinary, secure, Canadian research data repository, supported by academic libraries and research institutions across Canada. Borealis supports open discovery, management, sharing, and preservation of Canadian research data. Borealis is available to researchers who are affiliated with a participating Canadian university or research organization and their collaborators. Borealis is a shared service provided in partnership with Canadian regional academic library consortia, institutions, research organizations, and the Digital Research Alliance of Canada, with technical infrastructure hosted by Scholars Portal and the University of Toronto Libraries.
The SURF Data Repository is a user-friendly web-based data publication platform that allows researchers to store, annotate and publish research datasets of any size to ensure long-term preservation and availability of their data. The service allows any dataset to be stored, independent of volume, number of files and structure. A published dataset is enriched with complex metadata, unique identifiers are added and the data is preserved for an agreed-upon period of time. The service is domain-agnostic and supports multiple communities with different policy and metadata requirements.
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JĆ¼lich DATA is a registry service to index all research data created at or in the context of Forschungszentrum JĆ¼lich. As an institutionial repository, it may also be used for data and software publications.
Content type(s)
A machine learning data repository with interactive visual analytic techniques. This project is the first to combine the notion of a data repository with real-time visual analytics for interactive data mining and exploratory analysis on the web. State-of-the-art statistical techniques are combined with real-time data visualization giving the ability for researchers to seamlessly find, explore, understand, and discover key insights in a large number of public donated data sets. This large comprehensive collection of data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive visual analytics engine that allows users to visualize and interactively explore the data in a free-flowing manner.
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In HilReDa, researchers can permanently secure their research data and make it publicly available (publish) in open access in a sustainable and quality-appropriate manner. The research data is given a persistent identifier when it is published.
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MIDAS is a national research data repository. The aim of MIDAS is to collect, process, store and analyse research data and other relevant information in all fields of knowledge, enabling free, easy and convenient access to the data via the Internet. MIDAS provides services for registered and unregistered users: students, listeners, academics, researchers, scientists, research administrators, other actors of the research and studies ecosystem, and all individuals interested in research data. MIDAS consists of the MIDAS portal and MIDAS user account. The MIDAS portal is a public space accessible to anyone interested in discovering and viewing published research Data and their metadata, whereas MIDAS user account is available to registered users only. MIDAS is managed by Vilnius University.
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ISTA Research Explorer is an online digital repository of multi-disciplinary research datasets as well as publications produced at IST Austria, hosted by the Library. ISTA researchers who have produced research data associated with an existing or forthcoming publication, or which has potential use for other researches, are invited to upload their dataset for sharing and safekeeping. A persistent identifier and suggested citation will be provided.
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RADAR4Culture is a low-threshold and easy-to use service for sustainable publication and preservation of cultural heritage research data. It offers free publication for any data type and format according to the FAIR principles, independent of the researcherĀ“s institutional affiliation. Through persistent identifiers (DOI) and a guaranteed retention period of at least 25 years, the research data remain available, citable and findable long-term. Currently, the offer is aimed exclusively at researchers at publicly funded research institutions and (art) universities as well as non-commercial academies, galleries, libraries, archives and museums in Germany. No contract is required and no data publication fees are charged. The researchers are responsible for the upload, organisation, annotation and curation of research data as well as the peer-review process (as an optional step) and finally their publication.
The Texas Data Repository is a platform for publishing and archiving datasets (and other data products) created by faculty, staff, and students at Texas higher education institutions. The repository is built in an open-source application called Dataverse, developed and used by Harvard University. The repository is hosted by the Texas Digital Library, a consortium of academic libraries in Texas with a proven history of providing shared technology services that support secure, reliable access to digital collections of research and scholarship. For a list of TDL participating institutions, please visit: https://www.tdl.org/members/.
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.