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Found 15 result(s)

DataON Repository

previous name: KORD

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DataON is Korea's National Research Data Platform. It provides integrated search of metadata for KISTI's research data and domestic and international research data and links to raw data. DataON allows users (researchers, policy makers, etc.) to perform the following tasks: Easily search for various types of research data in all scientific fields. By registering research results, research data can be posted and cited. Build a community among researchers and enable collaborative research. It provides a data analysis environment that allows one-stop analysis of discovered research data.

bonndata

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bonndata is the institutional, FAIR-aligned and curated, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn. The repository is fully embedded into the University IT and Data Center and curated by the Research Data Service Center (https://www.forschungsdaten.uni-bonn.de/en). The software that bonndata is based on is the open source software Dataverse (https://dataverse.org)

ETH Zürich Research Collection

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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.

melbourne.figshare.com

University of Melbourne data repository

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melbourne.figshare.com is a specialised service that has been tailored according to specific needs and requirements of the University and our community of researchers. The service offered at the University is free to use, provides 100GB of data, and stores all data on the University's storage system.

University of Strathclyde KnowledgeBase Datasets

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The KnowledgeBase of the University of Strathclyde includes research data from the staff and students of the university.

Wilfrid Laurier University Dataverse

WLU Dataverse

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The Wilfrid Laurier University Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers.

brainlife

brainlife.io

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Brainlife promotes engagement and education in reproducible neuroscience. We do this by providing an online platform where users can publish code (Apps), Data, and make it "alive" by integragrate various HPC and cloud computing resources to run those Apps. Brainlife also provide mechanisms to publish all research assets associated with a scientific project (data and analyses) embedded in a cloud computing environment and referenced by a single digital-object-identifier (DOI). The platform is unique because of its focus on supporting scientific reproducibility beyond open code and open data, by providing fundamental smart mechanisms for what we refer to as “Open Services.”

Open Research Data Online

ORDO

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The figshare service for The Open University was launched in 2016 and allows researchers to store, share and publish research data. It helps the research data to be accessible by storing metadata alongside datasets. Additionally, every uploaded item receives a Digital Object Identifier (DOI), which allows the data to be citable and sustainable. If there are any ethical or copyright concerns about publishing a certain dataset, it is possible to publish the metadata associated with the dataset to help discoverability while sharing the data itself via a private channel through manual approval.

UK Data Archive

The UK's largest collection of digital research data in the social sciences and humanities

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The UK Data Archive, based at the University of Essex, is curator of the largest collection of digital data in the social sciences and humanities in the United Kingdom. With several thousand datasets relating to society, both historical and contemporary, our Archive is a vital resource for researchers, teachers and learners. We are an internationally acknowledged centre of expertise in the areas of acquiring, curating and providing access to data. We are the lead partner in the UK Data Service (https://service.re3data.org/repository/r3d100010230) through which data users can browse collections online and register to analyse and download them. Open Data collections are available for anyone to use. The UK Data Archive is a Trusted Digital Repository (TDR) certified against the CoreTrustSeal (https://www.coretrustseal.org/) and certified against ISO27001 for Information Security (https://www.iso.org/isoiec-27001-information-security.html).

RUresearch Data Portal

Rutgers University Research Data Portal

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RUresearch Data Portal is a subset of RUcore (Rutgers University Community Repository), provides a platform for Rutgers researchers to share their research data and supplementary resources with the global scholarly community. This data portal leverages all the capabilities of RUcore with additional tools and services specific to research data. It provides data in different clusters (research-genre) with excellent search facility; such as experimental data, multivariate data, discrete data, continuous data, time series data, etc. However it facilitates individual research portals that include the Video Mosaic Collaborative (VMC), an NSF-funded collection of mathematics education videos for Teaching and Research. Its' mission is to maintain the significant intellectual property of Rutgers University; thereby intended to provide open access and the greatest possible impact for digital data collections in a responsible manner to promote research and learning.

Forschungsdatenzentren der Statistischen Ämter des Bundes und der Länder

Research Data Centres of the Federal Statistical Offices and of the Statistical Offices of the Federal States

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The basic goal of the research data centres of the statistical offices of the Federation and the Länder is to improve the accessibility and usability of microdata of official statistics by setting up various ways of using the data. Research data centres of the Federal Statistical Office (FDZ-Bund) and the statistical offices of the Länder (FDZ-Länder) provide collectively access to selected microdata of official statistics to researchers for scientific purposes.

University of Salford Data Repository

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Figshare is the University of Salford Data Repository for archiving and publishing research data, in accordance with EPSRC expectations, and to increase the impact of the research.

Stockholm University Figshare Repository

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Researchers at Stockholm University have access to a repository for data through the Figshare Services via su.figshare.com.

heiDATA

heiDATA Institutional Repository for Research Data of Heidelberg University

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heiDATA is Heidelberg University’s research data repository. It is managed by the Competence Centre for Research Data, a joint institution of the University Library and the Computing Centre. All researchers affiliated with Heidelberg University can use this service for archiving and publishing their data.

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.