FAIREST Metrics and Assessment Data
dc.contributor.author | d’Aquin, Mathieu | |
dc.contributor.author | Kirstein, Fabian | |
dc.contributor.author | Schimmler, Sonja | |
dc.contributor.author | Oliveira, Daniela | |
dc.contributor.author | Urbanek, Sebastian | |
dc.date.accessioned | 2023-10-11T17:28:54Z | |
dc.date.available | 2023-10-11T17:28:54Z | |
dc.date.issued | 2021-11-08 | |
dc.description.abstract | <p>This data supplements the article “FAIREST: A Framework for Assessing Research Repositories”. In the article, we introduce the FAIREST principles, an extension of the well-known FAIR principles. Along these principles, we provide comprehensive metrics for assessing and selecting solutions for building digital repositories for research artefacts. The metrics are based on two pillars:</p> <ol> <li>an analysis of established features and functionalities, drawn from existing solutions, </li> <li>a literature review on general requirements for digital repositories for research artefacts and related systems.</li> <ol> <br /> <p>We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed. The solutions are:</p> <ul> <li>– ResearchGate</li> <li>– Academia.edu</li> <li>– Zenodo</li> <li>– arXiv</li> <li>– Bibsonomy</li> <li>– Figshare</li> <li>– CKAN</li> <li>– DSpace</li> <li>– Invenio</li> <li>– Dataverse</li> <li>– EPrints</li> <ul> <br /> <h5>Overview of the data</h5> <p><b>01 FAIREST Assessment Metrics and Solutions (All-in-one).xlsx</b></p> <p>This Excel file includes both the assessment metrics and the results for the 11 solutions</p> <br /> <p><b>02 FAIREST Assessment Metrics.csv</b></p> <p>The assessment metrics as CSV</p> <br /> <p><b>XX FAIREST Assessment XXX.csv</b></p> <p>Assessment result for the respective solution</p> <br /> <p><b>14 FAIREST Assessment Template.xlsx</b></p> <p>A template to apply the metrics to an individual solution</p> <p>Note: Fill in your assessment in column F and get the result at the bottom of the sheet</p> | en |
dc.description.sponsorship | This research was supported by the Fundação para a Ciência e a Tecnologia through the LASIGE Research Unit, UIDB/00408/2020 and UIDP/00408/2020. This work was supported by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII12 ("Deutsches Internet-Institut"). | |
dc.identifier.citation | Mathieu d'Aquin, Fabian Kirsten, Daniela Oliveira, Sonja Schimmler, & Sebastian Urbanek. (2021). FAIREST Metrics and Assessment Data (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5282929 | |
dc.identifier.doi | https://doi.org/10.5281/zenodo.5282929 | |
dc.identifier.uri | https://zenodo.org/record/5282929 | |
dc.identifier.uri | https://beta.weizenbaum-library.de/handle/id/382 | |
dc.language.iso | eng | |
dc.publisher | zenodo | |
dc.relation.issupplementto | https://doi.org/10.1162/dint_a_00159 | |
dc.relation.issupplementto | https://www.weizenbaum-library.de/handle/id/365 | |
dc.rights | open access | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Research Repositories | |
dc.subject | FAIR Principles | |
dc.subject | Open Access | |
dc.subject | Research Data | |
dc.subject.ddc | 004 Informatik | |
dc.title | FAIREST Metrics and Assessment Data | |
dc.type | ResearchData | |
dc.type.status | publishedVersion | |
dcmi.type | Dataset | |
local.researchgroup | Digitalisierung der Wissenschaft | |
local.researchtopic | Mensch – Arbeit – Wissen |
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- 01_FAIREST_Assessment_Metrics_and_Solutions_(All-in-one).xlsx
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- 02_FAIREST_Assessment_Metrics.csv
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- 03_FAIREST_Assessment_ResearchGate.csv
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- 04_FAIREST_Assessment_Academia.edu.csv
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- 05_FAIREST_Assessment_Zenodo.csv
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