{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:21:21Z","timestamp":1764872481342,"version":"3.38.0"},"reference-count":6,"publisher":"China Science Publishing & Media Ltd.","issue":"1-2","content-domain":{"domain":["www.mitpressjournals.org"],"crossmark-restriction":true},"short-container-title":["Data Intellegence"],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:p> The FAIR principles were received with broad acceptance in several scientific communities. However, there is still some degree of uncertainty on how they should be implemented. Several self-report questionnaires have been proposed to assess the implementation of the FAIR principles. Moreover, the FAIRmetrics group released 14, general-purpose maturity for representing FAIRness. Initially, these metrics were conducted as open-answer questionnaires. Recently, these metrics have been implemented into a software that can automatically harvest metadata from metadata providers and generate a principle-specific FAIRness evaluation. With so many different approaches for FAIRness evaluations, we believe that further clarification on their limitations and advantages, as well as on their interpretation and interplay should be considered. <\/jats:p>","DOI":"10.1162\/dint_a_00051","type":"journal-article","created":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T20:43:34Z","timestamp":1572641014000},"page":"285-292","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":17,"title":["Considerations for the Conduction and Interpretation of FAIRness                     Evaluations"],"prefix":"10.3724","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7641-6446","authenticated-orcid":true,"given":"Ricardo","family":"de Miranda Azevedo","sequence":"first","affiliation":[{"name":"Institute of Data Science, Maastricht University, Universiteitssingel 60,                         Maastricht 6229 ER, The Netherlands"}]},{"given":"Michel","family":"Dumontier","sequence":"additional","affiliation":[{"name":"Institute of Data Science, Maastricht University, Universiteitssingel 60,                         Maastricht 6229 ER, The Netherlands"}]}],"member":"2026","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1038\/sdata.2016.18"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1002\/leap.1234"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.3233\/ISU-170824"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1038\/sdata.2018.118"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1038\/485564a"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1093\/nar\/gkr469"}],"container-title":["Data Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/dint_a_00051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T07:41:12Z","timestamp":1741938072000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciengine.com\/doi\/10.1162\/dint_a_00051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":6,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["10.1162\/dint_a_00051"],"URL":"https:\/\/doi.org\/10.1162\/dint_a_00051","relation":{},"ISSN":["2641-435X"],"issn-type":[{"type":"electronic","value":"2641-435X"}],"subject":[],"published":{"date-parts":[[2020,1]]},"assertion":[{"value":"2020-01-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}