Badges for sharing data and code at
 Biostatistics: an observational study

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Abstract

Background: Reproducible research includes sharing data and code. The reproducibility policy at the journal Biostatistics rewards articles with badges for data and code sharing. This study investigates the effect of badges at increasing reproducible research, specifically, data and code sharing, at Biostatistics.

Methods: The setting of this observational study is the Biostatistics and Statistics in Medicine (control journal) online research archives. The data consisted of 240 randomly sampled articles from 2006 to 2013 (30 articles per year) per journal, a total sample of 480 articles. Data analyses included: plotting probability of data and code sharing by article submission date, and Bayesian logistic regression modelling to test for a difference in the probability of making data and code available after the introduction of badges at Biostatistics.

Results: The probability of data sharing was higher at Biostatistics than the control journal but the probability of code sharing was comparable for both journals. The probability of data sharing increased by 3.5 times (95% credible interval: 1.4 to 7.4 times, p-value probability that sharing increased: 0.996) after badges were introduced at Biostatistics. On an absolute scale, however, this difference was only a 7.3% increase in data sharing (95% CI: 2 to 14%, p-value: 0.996). Badges did not have an impact on code sharing at the journal (mean increase: 1.1 times, 95% credible interval: 0.45 to 2.14 times, p-value probability that sharing increased: 0.549).

Conclusions: The effect of badges at Biostatistics was a 7.3% increase in the data sharing rate, 5 times less than the effect of badges on data sharing at Psychological Science (37.9% badge effect). Though the effect of badges at Biostatistics did not impact code sharing, and was associated with only a moderate effect on data sharing, badges are an interesting step that journals are taking to incentivise and promote reproducible research.

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Most cited references 15

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Reproducible research in computational science.

Roger Peng (2011)

Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

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Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency

Mallory Kidwell, Ljiljana Lazarević, Erica Baranski … (2016)

Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.

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A systematic review of barriers to data sharing in public health

Willem G. van Panhuis, Proma Paul, Claudia Emerson … (2014)

Background In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy. Methods We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions. Results Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing. Conclusions The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good. Electronic supplementary material The online version of this article (doi:10.1186/1471-2458-14-1144) contains supplementary material, which is available to authorized users.

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Author and article information

Contributors

Anisa Rowhani-Farid: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – Original Draft Preparation

ORCID: https://orcid.org/0000-0003-3637-2423

Adrian G. Barnett: Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – Review & Editing

ORCID: https://orcid.org/0000-0001-6339-0374

Journal

Journal ID (nlm-ta): F1000Res

Journal ID (iso-abbrev): F1000Res

Journal ID (pmc): F1000Research

Title: F1000Research

Publisher: F1000 Research Limited (London, UK )

ISSN (Electronic): 2046-1402

Publication date (Electronic): 19 January 2018

Publication date Collection: 2018

Volume: 7

Electronic Location Identifier: 90

Affiliations

[1 ]Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, 4001, Australia

[1 ]Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

[2 ]Open Humans (openhumans.org), Frankfurt am Main, Germany

Queensland University of Technology, Australia

[1 ]Stress Research Institute, Stockholm University, Stockholm, Sweden

[2 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

Queensland University of Technology, Australia

Author notes

[a ] anisa.rowhanifarid@ 123456hdr.qut.edu.au

No competing interests were disclosed.

Competing interests: No competing interests were disclosed.

Competing interests: No competing interests

Competing interests: I am the former chair of the Open Science Badges Committee.

Competing interests: No competing interests.

Author information

Anisa Rowhani-Farid https://orcid.org/0000-0003-3637-2423

Adrian G. Barnett https://orcid.org/0000-0001-6339-0374

Article

DOI: 10.12688/f1000research.13477.1

PMC ID: 5843843

SO-VID: f4065bbb-3b34-4bc6-87a9-25e7424f0d51

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date accepted : 11 January 2018

Funding

Funded by: Queensland University of Technology

This study was supported in kind by the Institute of Health and Biomedical Innovation at the Queensland University of Technology.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Badges for sharing data and code at Biostatistics: an observational study

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Good practices in (cog-neuro) science and science communication

Most cited references 15

Reproducible research in computational science.

Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency

A systematic review of barriers to data sharing in public health

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