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      A study of the impact of data sharing on article citations using journal policies as a natural experiment

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          Abstract

          This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.

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          Sharing Detailed Research Data Is Associated with Increased Citation Rate

          Heather A. Piwowar, Roger S. Day, Douglas Fridsma (2007)
          Background Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. Principal Findings We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. Significance This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.
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            Identification of Causal Effects Using Instrumental Variables

            Joshua Angrist, Guido Imbens, Donald B Rubin (1996)
            Article 0 comments Cited 252 times – based on 0 reviews     Review now
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              Data reuse and the open data citation advantage

              Heather A. Piwowar, Todd J. Vision (2013)
              Background. Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the “citation benefit”. Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results. Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion. After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered. We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.
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                Author and article information

                Contributors
                Garret Christensen:
                ORCID: http://orcid.org/0000-0003-2558-1609
                Role: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Allan Dafoe: Role: ConceptualizationRole: Formal analysisRole: Writing – review & editing
                Edward Miguel: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Don A. Moore: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Andrew K. Rose: Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Florian Naudet: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 18 December 2019
                Publication date Collection: 2019
                Volume: 14
                Issue: 12
                Electronic Location Identifier: e0225883
                Affiliations
                [1 ] U.S. Census Bureau, Washington, DC, United States of America
                [2 ] University of Oxford, Oxford, England, United Kingdom
                [3 ] University of California, Berkeley, California, United States of America
                University of Rennes 1, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-2558-1609
                Article
                Publisher ID: PONE-D-19-18102
                DOI: 10.1371/journal.pone.0225883
                PMC ID: 6919593
                PubMed ID: 31851689
                SO-VID: abe5670b-b552-404c-a346-8b343aceeb2a
                License:

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                Date received : 27 June 2019
                Date accepted : 14 November 2019
                Page count
                Figures: 3, Tables: 4, Pages: 13
                Funding
                GC, EM Laura and John Arnold Foundation, grant number 040951. http://www.arnoldventures.org Publication made possible in part by support from the Berkeley Research Impact Initiative (BRII) sponsored by the UC Berkeley Library. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Citation Analysis
                Subject: Research and Analysis Methods
                Subject: Scientific Publishing
                Subject: Social Sciences
                Subject: Political Science
                Subject: Science Policy
                Subject: Science Policy
                Subject: Science Policy and Economics
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Instrumental Variable Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics
                Subject: Statistical Methods
                Subject: Instrumental Variable Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics
                Subject: Statistical Data
                Subject: Science Policy
                Subject: Science and Technology Workforce
                Subject: Careers in Research
                Subject: Scientists
                Subject: People and Places
                Subject: Population Groupings
                Subject: Professions
                Subject: Scientists
                Custom metadata
                Data Availability All data files are available on the Open Science Framework: https://osf.io/pxdch/ and https://osf.io/cdt8y/.

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                ScienceOpen disciplines: Uncategorized

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