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      Reproducible Research Publication Workflow: A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output

      1 , 2 , 2 , 3
      Data Intelligence
      MIT Press

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          Abstract

          In this paper we present the Reproducible Research Publication Workflow (RRPW) as an example of how generic canonical workflows can be applied to a specific context. The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts (i.e., data, code, etc.) that underlie the scholarly claims in the manuscript. A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow. The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers, curators, and publishers. The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility, increase FAIR compliance of all research artefacts at all steps, and facilitate better exchange of annotated and machine-readable metadata.

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          Most cited references25

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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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            A manifesto for reproducible science

            Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.
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              SCIENTIFIC STANDARDS. Promoting an open research culture.

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                Author and article information

                Journal
                Data Intelligence
                MIT Press
                2641-435X
                2022
                April 01 2022
                2022
                April 01 2022
                April 01 2022
                : 4
                : 2
                : 306-319
                Affiliations
                [1 ]Institution for Social and Policy Studies, Yale University, Connecticut 06520, USA
                [2 ]Center for Empirical Research in Economics and Behavioral Sciences (CEREB), University of Erfurt, Thüringen 99089, Germany
                [3 ]Odum Institute for Research in Social Science, University of North Carolina System, North Carolina 27514-3916, USA
                Article
                10.1162/dint_a_00133
                06adabf6-6ccf-4104-ab4d-033fb9538abb
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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