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      A data citation roadmap for scholarly data repositories

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          Abstract

          This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH-funded BioCADDIE ( https://biocaddie.org) project. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories. We describe the early adoption of these recommendations 18 months after they have first been published, looking specifically at implementations of machine-readable metadata on dataset landing pages.

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          Software citation principles

          Software is a critical part of modern research and yet there is little support across the scholarly ecosystem for its acknowledgement and citation. Inspired by the activities of the FORCE11 working group focused on data citation, this document summarizes the recommendations of the FORCE11 Software Citation Working Group and its activities between June 2015 and April 2016. Based on a review of existing community practices, the goal of the working group was to produce a consolidated set of citation principles that may encourage broad adoption of a consistent policy for software citation across disciplines and venues. Our work is presented here as a set of software citation principles, a discussion of the motivations for developing the principles, reviews of existing community practice, and a discussion of the requirements these principles would place upon different stakeholders. Working examples and possible technical solutions for how these principles can be implemented will be discussed in a separate paper.
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            A data citation roadmap for scientific publishers

            This article presents a practical roadmap for scholarly publishers to implement data citation in accordance with the Joint Declaration of Data Citation Principles (JDDCP), a synopsis and harmonization of the recommendations of major science policy bodies. It was developed by the Publishers Early Adopters Expert Group as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE program. The structure of the roadmap presented here follows the “life of a paper” workflow and includes the categories Pre-submission, Submission, Production, and Publication. The roadmap is intended to be publisher-agnostic so that all publishers can use this as a starting point when implementing JDDCP-compliant data citation. Authors reading this roadmap will also better know what to expect from publishers and how to enable their own data citations to gain maximum impact, as well as complying with what will become increasingly common funder mandates on data transparency.
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              Achieving human and machine accessibility of cited data in scholarly publications

              Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.
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                Author and article information

                Contributors
                twc8q@virginia.edu
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                10 April 2019
                10 April 2019
                2019
                : 6
                : 28
                Affiliations
                [1 ]GRID grid.475826.a, DataCite, ; Hannover, Germany
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Institute for Quantitative Social Science, , Harvard University, ; Cambridge, MA USA
                [3 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, University of California San Diego, ; La Jolla, CA USA
                [4 ]ISNI 0000 0001 0742 0364, GRID grid.168645.8, University of Massachusetts Medical School, ; Worcester, MA USA
                [5 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Bioinformatics Institute (EMBL-EBI), , European Molecular Biology Laboratory, ; Hinxton, Cambridgeshire UK
                [6 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Oxford e-Research Centre, , University of Oxford, ; Oxford, UK
                [7 ]Standard Analytics, New York, NY USA
                [8 ]ISNI 0000 0001 2189 1568, GRID grid.264484.8, Qualitative Data Repository, , Syracuse University, ; Syracuse, NY USA
                [9 ]ISNI 0000 0000 9136 933X, GRID grid.27755.32, University of Virginia School of Medicine, ; Charlottesville, VA 22903 USA
                Author information
                http://orcid.org/0000-0001-5212-7052
                http://orcid.org/0000-0002-9377-0797
                http://orcid.org/0000-0001-8479-0262
                http://orcid.org/0000-0002-1731-5346
                http://orcid.org/0000-0003-4060-7360
                Article
                31
                10.1038/s41597-019-0031-8
                6472386
                30971690
                af2135c4-85ed-4a29-bd5f-3ce0455f3c84
                © The Author(s) 2019

                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/.

                History
                : 2 October 2017
                : 12 March 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000780, European Commission (EC);
                Award ID: EC Grant Agreement 654039
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: U24HL126127
                Award ID: U24HL126127
                Award ID: U24HL126127
                Award ID: U24HL126127
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                databases,computational platforms and environments,data publication and archiving

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