Literature DB >> 33816816

Towards computational reproducibility: researcher perspectives on the use and sharing of software.

Yasmin AlNoamany1, John A Borghi2.   

Abstract

Research software, which includes both source code and executables used as part of the research process, presents a significant challenge for efforts aimed at ensuring reproducibility. In order to inform such efforts, we conducted a survey to better understand the characteristics of research software as well as how it is created, used, and shared by researchers. Based on the responses of 215 participants, representing a range of research disciplines, we found that researchers create, use, and share software in a wide variety of forms for a wide variety of purposes, including data collection, data analysis, data visualization, data cleaning and organization, and automation. More participants indicated that they use open source software than commercial software. While a relatively small number of programming languages (e.g., Python, R, JavaScript, C++, MATLAB) are used by a large number, there is a long tail of languages used by relatively few. Between-group comparisons revealed that significantly more participants from computer science write source code and create executables than participants from other disciplines. Differences between researchers from computer science and other disciplines related to the knowledge of best practices of software creation and sharing were not statistically significant. While many participants indicated that they draw a distinction between the sharing and preservation of software, related practices and perceptions were often not aligned with those of the broader scholarly communications community. ©2018 AlNoamany and Borghi.

Entities:  

Keywords:  Code; Finding software; Reproducibility; Research software; Sharing software; Software sustainability

Year:  2018        PMID: 33816816      PMCID: PMC7924683          DOI: 10.7717/peerj-cs.163

Source DB:  PubMed          Journal:  PeerJ Comput Sci        ISSN: 2376-5992


  22 in total

1.  Research priorities. Shining light into black boxes.

Authors:  A Morin; J Urban; P D Adams; I Foster; A Sali; D Baker; P Sliz
Journal:  Science       Date:  2012-04-13       Impact factor: 47.728

2.  Toward standard practices for sharing computer code and programs in neuroscience.

Authors:  Stephen J Eglen; Ben Marwick; Yaroslav O Halchenko; Michael Hanke; Shoaib Sufi; Padraig Gleeson; R Angus Silver; Andrew P Davison; Linda Lanyon; Mathew Abrams; Thomas Wachtler; David J Willshaw; Christophe Pouzat; Jean-Baptiste Poline
Journal:  Nat Neurosci       Date:  2017-05-25       Impact factor: 24.884

3.  Computational science. Troubling trends in scientific software use.

Authors:  Lucas N Joppa; Greg McInerny; Richard Harper; Lara Salido; Kenji Takeda; Kenton O'Hara; David Gavaghan; Stephen Emmott
Journal:  Science       Date:  2013-05-17       Impact factor: 47.728

Review 4.  What does research reproducibility mean?

Authors:  Steven N Goodman; Daniele Fanelli; John P A Ioannidis
Journal:  Sci Transl Med       Date:  2016-06-01       Impact factor: 17.956

5.  Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability.

Authors:  Brian A Nosek; Jeffrey R Spies; Matt Motyl
Journal:  Perspect Psychol Sci       Date:  2012-11

6.  Ten simple rules for reproducible computational research.

Authors:  Geir Kjetil Sandve; Anton Nekrutenko; James Taylor; Eivind Hovig
Journal:  PLoS Comput Biol       Date:  2013-10-24       Impact factor: 4.475

7.  Good enough practices in scientific computing.

Authors:  Greg Wilson; Jennifer Bryan; Karen Cranston; Justin Kitzes; Lex Nederbragt; Tracy K Teal
Journal:  PLoS Comput Biol       Date:  2017-06-22       Impact factor: 4.475

8.  A quick guide to software licensing for the scientist-programmer.

Authors:  Andrew Morin; Jennifer Urban; Piotr Sliz
Journal:  PLoS Comput Biol       Date:  2012-07-26       Impact factor: 4.475

9.  Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide.

Authors:  Carol Tenopir; Elizabeth D Dalton; Suzie Allard; Mike Frame; Ivanka Pjesivac; Ben Birch; Danielle Pollock; Kristina Dorsett
Journal:  PLoS One       Date:  2015-08-26       Impact factor: 3.240

10.  Choice of transcripts and software has a large effect on variant annotation.

Authors:  Davis J McCarthy; Peter Humburg; Alexander Kanapin; Manuel A Rivas; Kyle Gaulton; Jean-Baptiste Cazier; Peter Donnelly
Journal:  Genome Med       Date:  2014-03-31       Impact factor: 11.117

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  2 in total

1.  Understanding experiments and research practices for reproducibility: an exploratory study.

Authors:  Sheeba Samuel; Birgitta König-Ries
Journal:  PeerJ       Date:  2021-04-21       Impact factor: 2.984

2.  Ten simple rules on writing clean and reliable open-source scientific software.

Authors:  Haley Hunter-Zinck; Alexandre Fioravante de Siqueira; Váleri N Vásquez; Richard Barnes; Ciera C Martinez
Journal:  PLoS Comput Biol       Date:  2021-11-11       Impact factor: 4.475

  2 in total

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