Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

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Abstract

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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The operated Markov´s chains in economy (discrete chains of Markov with the income)

Grigoriy Sokolov (2015)

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The FAIR Guiding Principles for scientific data management and stewardship

Mark Wilkinson, Michel Dumontier, IJsbrand Aalbersberg … (2016)

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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Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

Dominique G. Roche, Loeske E. B. Kruuk, Robert Lanfear … (2015)

Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies.

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Affiliations

[a ] Leiden University Medical Centre , Leiden, The Netherlands. E-mail: b.mons@ 123456lumc.nl

[b ] Dutch Techcentre for Life Sciences , Utrecht, The Netherlands

[c ] Netherlands eScience Centre , Amsterdam, The Netherlands

[d ]Centre for Culture and Technology, Curtin University , Perth, Western Australia

[e ] Independent Open Access Publishing Consultant , Guildford, United Kingdom

[f ]Institute for Data Science, Maastricht University , Maastricht, The Netherlands

[g ] Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

[h ] Centre for Plant Biotechnology and Genomics U.P.M. – I.N.I.A. , Madrid, Spain

Author notes

[* ]Corresponding author: Barend Mons, Einthovenweg 20, 2333 ZC Leiden, P.O. Box 9600, 2300 RC Leiden, The Netherlands. Tel.: +31624879779; E-mail: b.mons@ 123456lumc.nl .

Journal

Journal ID (publisher-id): ISU

Title: Information Services & Use

Publisher: IOS Press (Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands )

ISSN (Electronic): 1875-8789

ISSN (Print): 0167-5265

Publication date (Electronic): 17 February 2017

Publication date (Electronic): 7 March 2017

Publication date (Electronic): 2017

Volume: 37

Issue: 1

Pages: 49-56

ID: ISU824 ID: 10.3233/ISU-170824

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Self URI (journal page): http://www.iospress.nl/journal/information-services-use/

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

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Abstract

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The FAIR data principles

Most cited references 5

The operated Markov´s chains in economy (discrete chains of Markov with the income)

The FAIR Guiding Principles for scientific data management and stewardship

Public Data Archiving in Ecology and Evolution: How Well Are We Doing?

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