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Found 29 result(s)
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
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Created and managed by the Library, DataSpace@HKUST is the data repository and workspace service for HKUST research community. Faculty members and research postgraduate students can use the platform to store, share, organize, preserve and publish research data. It is built on Dataverse, an open source web application developed at Harvard’s Institute for Quantitative Social Science. Using Dataverse architecture, the repository hosts multiple "dataverses". Each dataverse contains datasets; while each dataset may contain multiple data files and the corresponding descriptive metadata.
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Research Data Finder is QUT’s discovery service for research data created or collected by QUT researchers. Designed to promote the visibility of QUT research datasets, Research Data Finder provides descriptions about shareable, reusable datasets available via open or mediated access.
The WashU Research Data repository accepts any publishable research data set, including textual, tabular, geospatial, imagery, computer code, or 3D data files, from researchers affiliated with Washington University in St. Louis. Datasets include metadata and are curated and assigned a DOI to align with FAIR data principles.
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.
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.
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Swedish National Data Service (SND) is a research data infrastructure designed to assist researchers in preserving, maintaining, and disseminating research data in a secure and sustainable manner. The SND Search function makes it easy to find, use, and cite research data from a variety of scientific disciplines. Together with an extensive network of almost 40 Swedish higher education institutions and other research organisations, SND works for increased access to research data, nationally as well as internationally.
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GESIS preserves (mainly quantitative) social research data to make it available to the scientific research community. The data is described in a standardized way, secured for the long term, provided with a permanent identifier (DOI), and can be easily found and reused through browser-optimized catalogs (https://search.gesis.org/).
OLOS is a Swiss-based data management portal tailored for researchers and institutions. Powerful yet easy to use, OLOS works with most tools and formats across all scientific disciplines to help researchers safely manage, publish and preserve their data. The solution was developed as part of a larger project focusing on Data Life Cycle Management (dlcm.ch) that aims to develop various services for research data management. Thanks to its highly modular architecture, OLOS can be adapted both to small institutions that need a "turnkey" solution and to larger ones that can rely on OLOS to complement what they have already implemented. OLOS is compatible with all formats in use in the different scientific disciplines and is based on modern technology that interconnects with researchers' environments (such as Electronic Laboratory Notebooks or Laboratory Information Management Systems).
Atmosphere to Electrons (A2e) is a new, multi-year, multi-stakeholder U.S. Department of Energy (DOE) research and development initiative tasked with improving wind plant performance and mitigating risk and uncertainty to achieve substantial reduction in the cost of wind energy production. The A2e strategic vision will enable a new generation of wind plant technology, in which smart wind plants are designed to achieve optimized performance stemming from more complete knowledge of the inflow wind resource and complex flow through the wind plant.
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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 researcher´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.
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ResearchGate is a network where 15+ million scientists and researchers worldwide connect to share their work. Researchers can upload data of any type and receive DOIs, detailed statistics and real-time feedback. In Data discovery Section of ResearchGate you can explore the added datasets.
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Research Data Australia is the data discovery service of the Australian Research Data Commons (ARDC). The ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program. Research Data Australia helps you find, access, and reuse data for research from over one hundred Australian research organisations, government agencies, and cultural institutions. We do not store the data itself here but provide descriptions of, and links to, the data from our data publishing partners.
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PubData is Leuphana's institu­tional research data reposi­tory for the long-term preser­vation, documen­tation and publi­cation of research data from scienti­fic projects. PubData is main­tained by Leuphana's Media and Infor­mation Centre (MIZ) and is free of charge. The service is primarily aimed at Leuphana em­ployees and additionally at re­searchers from coope­ration partners con­tractually asso­ciated with Leuphana.
San Raffaele Open Research Data Repository (ORDR) is an institutional platform which allows to safely store, preserve and share research data. ORDR is endowed with the essential characteristics of trusted repositories, as it ensures: a) open or restricted access to contents, with persistent unique identifiers to enable referencing and citation; b) a comprehensive set of Metadata fields to enable discovery and reuse; c) provisions to safeguard integrity, authenticity and long-term preservation of deposited data.
LINDAT/CLARIN is designed as a Czech “node” of Clarin ERIC (Common Language Resources and Technology Infrastructure). It also supports the goals of the META-NET language technology network. Both networks aim at collection, annotation, development and free sharing of language data and basic technologies between institutions and individuals both in science and in all types of research. The Clarin ERIC infrastructural project is more focused on humanities, while META-NET aims at the development of language technologies and applications. The data stored in the repository are already being used in scientific publications in the Czech Republic. In 2019 LINDAT/CLARIAH-CZ was established as a unification of two research infrastructures, LINDAT/CLARIN and DARIAH-CZ.
CORE is a full-text, interdisciplinary, non-profit social repository designed to increase the impact of work in the Humanities. Commons Open Repository Exchange, a library-quality repository for sharing, discovering, retrieving, and archiving digital work. CORE provides Humanities Commons members with a permanent, open access storage facility for their scholarly output, facilitating maximum discoverability and encouraging peer feedback.
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TUdatalib is the institutional repository of the TU Darmstadt for research data. It enables the structured storage of research data and descriptive metadata, long-term archiving (at least 10 years) and, if desired, the publication of data including DOI assignment. In addition there is a fine granular rights and role management.
The Maine Dataverse Network is a cloud-based data repository intended to act as a long-term archive and to facilitate data sharing among the research community in accordance with NSF, NIH, NASA and other granting authority data management plan requirements. The Maine Dataverse Network offers a convenient and secure method of sharing and archiving data and is made available to the Maine research community at no cost.
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.