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Cited by 336Research Data Management Practicesscientometrics and bibliometrics researchAcademic Publishing and Open Access

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

The citation advantage of linking publications to research data

Open access
Colavizza, Giovanni, Hrynaszkiewicz, Iain, Staden, Isla, Whitaker, Kirstie, McGillivray, Barbara·2020·PLOS ONE
DOI 10.1371/journal.pone.0230416PMC PMC7176083
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Abstract

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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Fig 1 Data extraction and processing steps. We first downloaded the PubMed open access collection (1) and created a database with all articles with a known identifier and which contained at least one reference (2; N = 1, 969, 175). Next we identified and disambiguated authors of these papers (3; S = 4, 253, 172) and calculated citations for each author and each publication from within the collection (4). We used these citation counts to calculate a within-collection H-index for each author. Our analysis only focuses on PLOS and BMC publications as these publishers introduced mandated DAS, so we filtered the database for these articles and extracted DAS from each publication (5). We annotated a training dataset by labelling each of these statements into one of four categories (6) and used those labels to train a natural language processing classifier (7). Using this classifier we then categorised the remaining DAS in the database (8). Finally, we exported this categorised dataset of M = 531, 889 publications to a csv file (9) and archived it (see Data and code availability section below).
Fig 1 Data extraction and processing steps. We first downloaded the PubMed open access collection (1) and created a database with all articles with a known identifier and which contained at least one reference (2; N = 1, 969, 175). Next we identified and disambiguated authors of these papers (3; S = 4, 253, 172) and calculated citations for each author and each publication from within the collection (4). We used these citation counts to calculate a within-collection H-index for each author. Our analysis only focuses on PLOS and BMC publications as these publishers introduced mandated DAS, so we filtered the database for these articles and extracted DAS from each publication (5). We annotated a training dataset by labelling each of these statements into one of four categories (6) and used those labels to train a natural language processing classifier (7). Using this classifier we then categorised the remaining DAS in the database (8). Finally, we exported this categorised dataset of M = 531, 889 publications to a csv file (9) and archived it (see Data and code availability section below).CC BY · PubMed Central
Fig 2 Data availability statements over time. All the histograms above show the number of publications from specific subsets of the dataset and classify them into four categories: No DAS (0), Category 1 (data available on request), Category 2 (data contained within the article and supplementary materials), and Category 3 (a link to archived data in a public repository). The vertical solid line shows the date that the publisher introduced a mandated DAS policy. A dashed line indicates the date an encouraged policy was introduced. The groups of articles are as follows. A: all BMC articles, B: all PLOS articles, C: all BMC Series articles, D: PLOS One articles, E: PLOS articles not published in PLOS One, F: articles from the BMC Genomics journal (selected to illustrate a journal that had high uptake of an encouraged policy), G: articles from the Trials journal (published by BMC, selected to illustrate a journal that has a very high percentage of data that can only be made available by request to the authors), H: articles from the Parasites and Vectors journal (selected to illustrate a journal that has an even distribution of the three DAS categories). Articles are binned by publication year.
Fig 2 Data availability statements over time. All the histograms above show the number of publications from specific subsets of the dataset and classify them into four categories: No DAS (0), Category 1 (data available on request), Category 2 (data contained within the article and supplementary materials), and Category 3 (a link to archived data in a public repository). The vertical solid line shows the date that the publisher introduced a mandated DAS policy. A dashed line indicates the date an encouraged policy was introduced. The groups of articles are as follows. A: all BMC articles, B: all PLOS articles, C: all BMC Series articles, D: PLOS One articles, E: PLOS articles not published in PLOS One, F: articles from the BMC Genomics journal (selected to illustrate a journal that had high uptake of an encouraged policy), G: articles from the Trials journal (published by BMC, selected to illustrate a journal that has a very high percentage of data that can only be made available by request to the authors), H: articles from the Parasites and Vectors journal (selected to illustrate a journal that has an even distribution of the three DAS categories). Articles are binned by publication year.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. F Naudet, 2018, Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine, BMJ, k400, 10.1136/bmj.k400 · DOI · Paper page
  2. PubMed. Open Access Collection; 2019. https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist. Downloaded using the FTP service in February 2019.
  3. Archambault E, Beauchesne OH, Caruso J. Towards a multilingual, comprehensive and open scientific journal ontology. In: Proceedings of the 13th International Conference of the International Society for Scientometrics and Informetrics; 2011. p. 66–77.

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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
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-04-22
Volume
15
Issue
4
Pages
e0230416
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0230416
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
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

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