Attitudes and norms affecting scientists’ data reuse

Abstract

The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour. To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves). Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations. We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value. We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.

Digital data archives as knowledge infrastructures: Mediating data sharing and reuse

“…Incentives among stakeholders also are misaligned, as costs and benefits may be distributed in ways that discourage data sharing. While many researchers recognize the importance of preserving data, others question the long-term value of their data to themselves or to others, or ask whether potential reusers can understand someone else's data (Borgman, 2015;Curty, Crowston, Specht, Grant, & Dalton, 2017;Frank, Yakel, & Faniel, 2015;Gregory, Cousijn, Groth, Scharnhorst, & Wyatt, 2018;Mayernik, 2011Mayernik, , 2016Piwowar, 2011;Tenopir, Palmer, Metzer, van der Hoeven, & Malone, 2011;Tsoukala et al, 2015;Wallis et al, 2013;Weber, Baker, Thomer, Chao, & Palmer, 2012;Zimmerman, 2008).…”

Open science, reproducibility, and transparency in ecology

“…Key aspects of computational reproducibility can be achieved using open source tools for tasks such as data integration, data wrangling, statistics and modeling, handling of multiple users, visualization, workflow pipelines (e.g., Kepler, noWorkflow for Python), creation of process metadata (e.g., RDataTracker), and version control. Such tools are now widely accessible (Jones et al 2006, Hampton et al 2015, Borregaard and Hart 2016, Ellison 2017, Rocchini et al 2017) and attitudes and norms increasingly favor data reuse (Curty et al 2017).…”

Motivación, barreras e incentivos para la compartición y reutilización de los datos de investigación. Visión de los investigadores

“…Respecto a las prácticas de reutilización de datos, tanto la eficiencia como la eficacia percibida, como la importancia que tiene para el avance de la disciplina en particular, son aspectos que incentivan su práctica (Gonçalves-Curty et al, 2017). Sin embargo, debido a los problemas de la multitud de formatos, a la propia generación de los datos de diversas disciplinas o a la falta de comprensión de temas de propiedad intelectual, no contribuyen a su implementación (Wallis; Rolando; Borgman, 2013).…”