Do usage counts of scientific data make sense? An investigation of the Dryad repository

Abstract

Purpose The purpose of this paper is to evaluate the impact of scientific data in order to assess the reliability of data to support data curation, to establish trust between researchers to support reuse of digital data and encourage researchers to share more data. Design/methodology/approach The authors compared the correlations between usage counts of associated data in Dryad and citation counts of articles in Web of Science in different subject areas in order to assess the possibility of using altmetric indicators to evaluate scientific data. Findings There are high positive correlations between usage counts of data and citation counts of associated articles. The citation counts of article’s shared data are higher than the average citation counts in most of the subject areas examined by the authors. Practical implications The paper suggests that usage counts of data could be potentially used to evaluate scholarly impact of scientific data, especially for those subject areas without special data repositories. Originality/value The study examines the possibility to use usage counts to evaluate the impact of scientific data in a generic repository Dryad by different subject categories.

Zenodo in the Spotlight of Traditional and New Metrics

“…Our case study follows the vein of works that describe novel platforms and their potential for bibliometric studies, for example, Kraker et al (2015), Peters et al (2015Peters et al ( , 2016, and Thelwall and Kousha (2016), who studied Figshare, or Robinson-García et al (2017) who investigated DataCite, or He and Han (2017) and He and Nahar (2016) who studied Dryad.…”

“…In a follow-up study, He and Han (2017) analyzed the citedness of data records from the Dryad repository and the relation between their usage counts (i.e., number of downloads) and their citations. Citation counts from the Web of Science (WoS) were obtained for those 9,333 articles from Dryad whose DOIs were found in the WoS.…”

Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis

“…During the past decade, as the quantity of research output has risen precipitously and digital data objects have become more important for scientific research and scientists, datasets have also started to become direct research objects in scientometric studies. Under this line of scholarship, researchers have traced the quantitative scientific impact of specific datasets (Apai et al 2010 ; Belter 2014 ; He and Han 2017 ; He and Nahar 2016 ; Mayo et al 2016 ; Peters et al 2015 , 2016 ). A related topic that has recently attracted substantial interest is the quantification of the impact on original papers, typically measured in increased citations, after a paper’s dataset has been made openly available (e.g., Dorch 2012 ; Gleditsch et al 2003 ; Henneken and Accomazzi 2011 ; Ioannidis et al 2009 ; Pienta et al 2010 ; Piwowar et al 2007 ; Piwowar and Vision 2013 ).…”

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

“…Some fields facilitate sharing with specialised data repositories and/or standards for recording complex data (e.g., [22]). For evolutionary biology, the Dryad repository and journal data sharing mandates have combined to make data sharing almost universal in the top journals [23] (see also [24]). Multidisciplinary generic data sharing policies from publishers can also work reasonably well, with a study of PLoS ONE finding that most articles shared data, albeit with large disciplinary differences in format and sharing method [25].…”