The citation advantage of linking publications to research data

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 have become common by now, yet statements containing a link to a repository are still just a fraction of the total. We also find that articles with these statements, in particular, can have up to 25.36% higher citation impact on average: an encouraging result for all publishers and authors who make the effort of sharing their data. All our data and code are made available in order to reproduce and extend our results.

The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018

“…existing public or released with publication) was associated with 60.8% more citations per year than their private-data-only counterparts (95% CI: 28.1% -110.2%). Such a large effect is highly surprising, considering that prior studies in other fields [13,6,3] have found that releasing one's data is associated with only a 10%-30% increase in citations, where our study found a much larger effect from simply using public data. In our view, this illustrates the outsized importance of data in machine learning research, and suggests that the medical image computing field is highly catalyzed by the public release of imaging data.…”

“…Very recently, Colavizza et al [3] conducted a text mining and citation analysis of more than half a million papers published by PLOS and BMC that were also part of the PubMed Open Access Collection. These journals are interesting cases because they each recently enacted policies requiring authors to include a Data Availability Statement (DAS) belonging to one of three categories: (1) "data is available on request", (2) "data is contained within the article or supplementary material", and (3) "data is in a public repository and here is the link".…”

ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

“…An advantage of our approach compared to other studies that focus on specific journals that have a dedicated data availability statement (Federer et al 2018;Colavizza et al 2020) is the broader applicability. As our algorithm screens all parts of a publication, Open Data statements can be detected anywhere in the text.…”

“…On the level of individual researchers, Open Data can lead to increased visibility, resource efficiency (Pronk 2019;Milham et al 2018), and possibly a citation advantage (Colavizza et al 2020;Piwowar and Vision 2013). However, there are also limits to the open sharing of research data in the biomedical domain, especially when it comes to patient data that are subject to data protection regulations.…”

“…This splits the literature into journals in which data availability is relatively easy to check and journals where data availability is not reported in a standardized way. Current studies on data sharing mostly focus on specific journals with mandatory data availability statements (Federer et al 2018;Colavizza et al 2020). Open Data may also be shared together with the publication, e.g.…”