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M4.9 Report on Fair Data Assessment Mechanisms to Develop Pragmatic Concepts for Fairness Evaluation at the Dataset Level

Anusuriya Devaraju; Mustapha Mokrane; Linas Cepinskas; Robert Huber; Patricia Herterich; Jerry de Vries; Vesa Akerman; Joy Davidson; Hervé L'Hour; Michael Diepenbroek


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    <dct:title>M4.9 Report on Fair Data Assessment Mechanisms to Develop Pragmatic Concepts for Fairness Evaluation at the Dataset Level</dct:title>
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    <dcat:keyword>FAIR Principles</dcat:keyword>
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    <dcat:keyword>FAIR Metrics</dcat:keyword>
    <dcat:keyword>FAIRsFAIR</dcat:keyword>
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    <dct:description>&lt;p&gt;This report is a milestone of the FAIRsFAIR project. It&amp;nbsp;includes two main results on FAIR assessment at the dataset level:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;The FAIRsFAIR Data Object Assessment Metrics (v0.3) specification contains 15 metrics proposed by FAIRsFAIR to evaluate the FAIRness of research data objects in Trustworthy Digital Repositories (TDRs). We improved the metrics based on a focus group&amp;#39;s feedback and the RDA-endorsed FAIR data maturity model guidelines and specification. A total of 33 FAIR stakeholders, such as research communities, data service providers, standard bodies, and coordination fora participated in the focus group.&lt;/li&gt; &lt;li&gt;A preprint of the journal article titled &amp;lsquo;From Conceptualization to Implementation: FAIR Assessment of Research Data Objects&amp;rsquo;, submitted to CODATA Data Science Journal Special collection on RDA. The article summarizes the metrics development, and its two applications: an awareness-raising self-assessment tool, and a tool for automated assessment of research data FAIRness. The article also covers the initial results of testing the tools with researchers and data repositories, and future improvements including the next steps to enable FAIR data assessment in the broader research data ecosystem.&lt;/li&gt; &lt;/ul&gt;</dct:description>
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