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A design framework and exemplar metrics for FAIRness

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      The FAIR Guiding Principles for scientific data management and stewardship

      There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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        Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency

        Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.
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          Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

           Cameron Neylon,  Barend Mons (corresponding) ,  Jan Velterop (2017)
          The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not.
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            Author and article information

            Affiliations
            [1 ]Centro de Biotecnología y Genómica de Plantas UPM – INIA , Madrid, Spain
            [2 ]Oxford e-Research Centre, Department of Engineering Science, University of Oxford , Oxford, UK
            [3 ]Dutch Techcentre for Life Sciences , Utrecht, The Netherlands
            [4 ]Data Archiving and Networked Services , Den Haag, The Netherlands
            [5 ]GO FAIR International Support and Coordination Office , Leiden, The Netherlands
            [6 ]Leiden University Medical Centre , Leiden, The Netherlands
            [7 ]Institute of Data Science, Maastricht University , Maastricht, The Netherlands
            Author notes
            Journal
            Sci Data
            Sci Data
            Scientific Data
            Nature Publishing Group
            2052-4463
            26 June 2018
            2018
            : 5
            29944145 6018520 sdata2018118 10.1038/sdata.2018.118
            Copyright © 2018, The Author(s)

            Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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