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Found 48 result(s)

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.

Mendeley Data

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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.

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.

ScienceDB

Science Data Bank

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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.

Zenodo

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ZENODO builds and operates a simple and innovative service that enables researchers, scientists, EU projects and institutions to share and showcase multidisciplinary research results (data and publications) that are not part of the existing institutional or subject-based repositories of the research communities. ZENODO enables researchers, scientists, EU projects and institutions to: easily share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. display their research results and get credited by making the research results citable and integrate them into existing reporting lines to funding agencies like the European Commission. easily access and reuse shared research results.

figshare

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figshare allows researchers to publish all of their research outputs in an easily citable, sharable and discoverable manner. All file formats can be published, including videos and datasets. Optional peer review process. figshare uses creative commons licensing. figshare+ repository allows figshare users to share larger datasets, over 20GB up to many TBs, see: https://plus.figshare.com/

LINDAT/CLARIAH-CZ repository

LINDAT/CLARIN repository

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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.

Society of American Archivists Dataverse

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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.

Archiving BASIS

formerly: GESIS SowiDataNet|datorium

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<<<!!!<<< Archiving BASIS is part of the GESIS Data Services. This record is no longer updated. Please use https://www.re3data.org/repository/r3d100012496 >>>!!!>>>

CLARIN-LV repository

CLARIN Latvia repository

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CLARIN-LV is a national node of Clarin ERIC (Common Language Resources and Technology Infrastructure). The mission of the repository is to ensure the availability and long­ term preservation of language resources. The data stored in the repository are being actively used and cited in scientific publications.

FID move

Forschungsdaten-Repositorium des Fachinformationsdienstes Mobilitäts- und Verkehrsforschung

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The Research Data Repository of FID move is a digital long-term repository for open data from the field of transport and mobility research. All datasets are provided with an open licence and are assigned a persistent DataCite DOI (Digital Object Identifier). Both data search and archiving are free. The Specialised Information Service for Mobility and Transport Research (FID move) has been set up by the Saxon State and University Library Dresden (SLUB) and the German TIB – Leibniz Information Centre for Science and Technology as part of the DFG funding programme "Specialised Information Services".

MIDAS

Nacionalinis atviros prieigos mokslinių tyrimų duomenų archyvas

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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.

Scientific Data Repository

Data Repository. Interactive Analytics, Exploration & Visualization.

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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.

Natural History Museum, Data Portal

Explore and download the Natural History Museum’s research and collections data

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The Museum is committed to open access and open science, and has launched the Data Portal to make its research and collections datasets available online. It allows anyone to explore, download and reuse the data for their own research. Our natural history collection is one of the most important in the world, documenting 4.5 billion years of life, the Earth and the solar system. Almost all animal, plant, mineral and fossil groups are represented. These datasets will increase exponentially. Under the Museum's ambitious digital collections programme we aim to have 20 million specimens digitised in the next five years.

data.world

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A data repository and social network so that researchers can interact and collaborate, also offers tutorials and datasets for data science learning. "data.world is designed for data and the people who work with data. From professional projects to open data, data.world helps you host and share your data, collaborate with your team, and capture context and conclusions as you work."

DANS: Data Station Archaeology

DANS Archaeology

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A domain-specific repository for Archaeology and related data, primarily aimed at the Netherlands, but not exclusively.

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.

Research Compendia

Researchcompendia

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>>>>!!!<<< As stated 2017-06-27 The website http://researchcompendia.org is no longer available; repository software is archived on github https://github.com/researchcompendia >>>!!!<<< The ResearchCompendia platform is an attempt to use the web to enhance the reproducibility and verifiability—and thus the reliability—of scientific research. we provide the tools to publish the "actual scholarship" by hosting data, code, and methods in a form that is accessible, trackable, and persistent. Some of our short term goals include: To expand and enhance the platform including adding executability for a greater variety of coding languages and frameworks, and enhancing output presentation. To expand usership and to test the ResearchCompendia model in a number of additional fields, including computational mathematics, statistics, and biostatistics. To pilot integration with existing scholarly platforms, enabling researchers to discover relevant Research Compendia websites when looking at online articles, code repositories, or data archives.

PubData

Research Data Repository Leuphana University Lüneburg

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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.

OSF - Vrije Universiteit Amsterdam

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A Research Data Management Tool for the Vrije Universiteit Amsterdam Research Community.

Open Health Data Dataverse

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The Harvard Dataverse Repository is a free data repository open to all researchers from any discipline, both inside and outside of the Harvard community, where you can share, archive, cite, access, and explore research data. Each individual Dataverse collection is a customizable collection of datasets (or a virtual repository) for organizing, managing, and showcasing datasets.

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.

Genomic Observatories MetaDatabase

GEOME

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The Genomic Observatories Meta-Database (GEOME) is a web-based database that captures the who, what, where, and when of biological samples and associated genetic sequences. GEOME helps users with the following goals: ensure the metadata from your biological samples is findable, accessible, interoperable, and reusable; improve the quality of your data and comply with global data standards; and integrate with R, ease publication to NCBI's sequence read archive, and work with an associated LIMS. The initial use case for GEOME came from the Diversity of the Indo-Pacific Network (DIPnet) resource.