FAIREST: A Framework for Assessing Research Repositories

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dc.contributor.authord’Aquin, Mathieu
dc.contributor.authorKirstein, Fabian
dc.contributor.authorOliveira, Daniela
dc.contributor.authorSchimmler, Sonja
dc.contributor.authorUrbanek, Sebastian
dc.date.accessioned2023-09-19T15:41:56Z
dc.date.available2023-09-19T15:41:56Z
dc.date.issued2022
dc.description.abstractThe open science movement has gained significant momentum within the last few years. This comes along with the need to store and share research artefacts, such as publications and research data. For this purpose, research repositories need to be established. A variety of solutions exist for implementing such repositories, covering diverse features, ranging from custom depositing workflows to social media-like functions. In this article, we introduce the FAIREST principles, a framework inspired by the well-known FAIR principles, but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts. The goal is to support decision makers in choosing such a solution when planning for a repository, especially at an institutional level. The metrics included are therefore based on two pillars: (1) an analysis of established features and functionalities, drawn from existing dedicated, general purpose and commonly used solutions, and (2) a literature review on general requirements for digital repositories for research artefacts and related systems. 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.
dc.identifier.citationd’Aquin, M., Kirstein, F., Oliveira, D., Schimmler, S., & Urbanek, S. (2022). FAIREST: A Framework for Assessing Research Repositories. Data Intelligence 2023; 5 (1): 202–241. doi: https://doi.org/10.1162/dint_a_00159
dc.identifier.doihttps://doi.org/10.1162/dint_a_00159
dc.identifier.issn2641-435X
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/365
dc.language.isoeng
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleFAIREST: A Framework for Assessing Research Repositories
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.booktitleData Intelligence
dcterms.bibliographicCitation.doi10.1162/dint_a_00159
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleData Intelligence
dcterms.bibliographicCitation.pageend241
dcterms.bibliographicCitation.pagestart202
dcterms.bibliographicCitation.urlhttps://direct.mit.edu/dint/article/doi/10.1162/dint_a_00159/113179/FAIREST-A-Framework-for-Assessing-Research
dcterms.bibliographicCitation.volume5
local.researchgroupDigitalisierung und Öffnung der Wissenschaft
local.researchtopicOrganisation von Wissen
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