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      WORCS: A workflow for open reproducible code in science

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          Abstract

          Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

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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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            SCIENTIFIC STANDARDS. Promoting an open research culture.

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              Measuring the prevalence of questionable research practices with incentives for truth telling.

              Cases of clear scientific misconduct have received significant media attention recently, but less flagrantly questionable research practices may be more prevalent and, ultimately, more damaging to the academic enterprise. Using an anonymous elicitation format supplemented by incentives for honest reporting, we surveyed over 2,000 psychologists about their involvement in questionable research practices. The impact of truth-telling incentives on self-admissions of questionable research practices was positive, and this impact was greater for practices that respondents judged to be less defensible. Combining three different estimation methods, we found that the percentage of respondents who have engaged in questionable practices was surprisingly high. This finding suggests that some questionable practices may constitute the prevailing research norm.
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                Author and article information

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                Journal
                Data Science
                DS
                IOS Press
                24518492
                24518484
                May 21 2021
                May 21 2021
                : 4
                : 1
                : 29-49
                Affiliations
                [1 ]Department of Methodology & Statistics, Utrecht University, The Netherlands and Open Science Community Utrecht, The Netherlands. E-mail: c.j.vanlissa@uu.nl
                [2 ]Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany and London, UK.
                [3 ]Open Science Community Utrecht, The Netherlands and University Medical Center, Utrecht University, The Netherlands.
                [4 ]Open Science Community Utrecht, The Netherlands and Department of Information and Computing Sciences, Utrecht University, The Netherlands.
                [5 ]Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
                [6 ]Open Science Community Utrecht, The Netherlands and Utrecht Institute of Linguistics OTS, Utrecht University, The Netherlands.
                [7 ]University Library, Utrecht University, The Netherlands and Netherlands eScience Center, The Netherlands.
                Article
                10.3233/DS-210031
                1e90f4f5-611a-4255-88e7-0260b059a77e
                © 2021

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

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