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  • Victoria Stodden
Four simple recommendations to encourage best practices in research software
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CC BY
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Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Subject:
Computer Science
Information Science
Material Type:
Reading
Provider:
F1000Research
Author:
Alejandra Gonzalez-Beltran
Allegra Via
Andrew Treloar
Bérénice Batut
Bernard Pope
Björn GrüningJonas Hagberg
Brane Leskošek
Carole Goble
Daniel S. Katz
Daniel Vaughan
David Mellor
Federico López Gómez
Ferran Sanz
Harry-Anton Talvik
Horst Pichler
Ilian Todorov
Jon Ison
Josep Ll. Gelpí
Leyla Garcia
Luis J. Oliveira
Maarten van Gompel
Madison Flannery
Manuel Corpas
Maria V. Schneider
Martin Cook
Mateusz Kuzak
Michelle Barker
Mikael Borg
Monther Alhamdoosh
Montserrat González Ferreiro
Nathan S. Watson-Haigh
Neil Chue Hong
Nicola Mulder
Petr Holub
Philippa C. Griffin
Radka Svobodová Vařeková
Radosław Suchecki
Rafael C. Jiménez
Robert Pergl
Rob Hooft
Rowland Mosbergen
Salvador Capella-Gutierrez
Simon Gladman
Sonika Tyagi
Steve Crouchc
Victoria Stodden
Xiaochuan Wang
Yasset Perez-Riverol
Date Added:
08/07/2020
Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals
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CC BY
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Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.

Subject:
Computer Science
Information Science
Material Type:
Reading
Provider:
PLOS ONE
Author:
Peixuan Guo
Victoria Stodden
Zhaokun Ma
Date Added:
08/07/2020
An empirical analysis of journal policy effectiveness for computational reproducibility
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CC BY-NC-ND
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A key component of scientific communication is sufficient information for other researchers in the field to reproduce published findings. For computational and data-enabled research, this has often been interpreted to mean making available the raw data from which results were generated, the computer code that generated the findings, and any additional information needed such as workflows and input parameters. Many journals are revising author guidelines to include data and code availability. This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request. We assess the effectiveness of such a policy by (i) requesting data and code from authors and (ii) attempting replication of the published findings. We chose a random sample of 204 scientific papers published in the journal Science after the implementation of their policy in February 2011. We found that we were able to obtain artifacts from 44% of our sample and were able to reproduce the findings for 26%. We find this policy—author remission of data and code postpublication upon request—an improvement over no policy, but currently insufficient for reproducibility.

Subject:
Social Science
Material Type:
Reading
Author:
Jennifer Seiler
Victoria Stodden
Zhaokun Ma
Date Added:
11/13/2020