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The citation advantage of linking publications to research data
Open accessAbstract
Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository-rather than being available on request or included as supporting information files-are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.
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Metadata
- Authors
- Colavizza, G., Hrynaszkiewicz, I., Staden, I., Whitaker, K., & McGillivray, B.
- Affiliations
- The Alan Turing Institute (GB)
- Springer Nature (United Kingdom) (GB)
- Queen Mary University of London (GB)
- University of Cambridge (GB)
- Corresponding author
- McGillivray, Barbara
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-04-22
- Volume
- 15
- Issue
- 4
- Pages
- e0230416
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7176083
- OpenAlex
- W2955244474
- Cited by
- 335
- References
- 90
- Citations by year
- 2026: 27 · 2025: 48 · 2024: 47 · 2023: 58 · 2022: 71 · 2021: 51 · 2020: 25
- FWCI
- 48.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Apollo (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/N510129/1)
- Macmillan Education Ltd (RG92108)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- data availability statements, research data repositories, citation advantage, open science, reproducibility, PLOS, BMC, citation impact, data sharing
- MeSH terms
- Humans, Information Services, Publications, Research, Journal Impact Factor, Knowledge Discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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