Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77819
Title: To what extent is researchers' data-sharing motivated by formal mechanisms of recognition and credit?
Authors: Dorta González, Pablo 
González Betancor, Sara María 
Dorta González,María Isabel 
UNESCO Clasification: 570106 Documentación
5302 Econometría
Keywords: Data Citation
Data Reuse
Data Sharing
Open Data
Open Science, et al
Issue Date: 2021
Journal: Scientometrics 
Abstract: Data sharing by researchers is a centerpiece of Open Science principles and scientific progress. For a sample of 6019 researchers, we analyze the extent/frequency of their data sharing. Specifically, the relationship with the following four variables: how much they value data citations, the extent to which their data-sharing activities are formally recognized, their perceptions of whether sufficient credit is awarded for data sharing, and the reported extent to which data citations motivate their data sharing. In addition, we analyze the extent to which researchers have reused openly accessible data, as well as how data sharing varies by professional age-cohort, and its relationship to the value they place on data citations. Furthermore, we consider most of the explanatory variables simultaneously by estimating a multiple linear regression that predicts the extent/frequency of their data sharing. We use the dataset of the State of Open Data Survey 2019 by Springer Nature and Digital Science. Results do allow us to conclude that a desire for recognition/credit is a major incentive for data sharing. Thus, the possibility of receiving data citations is highly valued when sharing data, especially among younger researchers, irrespective of the frequency with which it is practiced. Finally, the practice of data sharing was found to be more prevalent at late research career stages, despite this being when citations are less valued and have a lower motivational impact. This could be due to the fact that later-career researchers may benefit less from keeping their data private.
URI: http://hdl.handle.net/10553/77819
ISSN: 0138-9130
DOI: 10.1007/s11192-021-03869-3
Source: Scientometrics[ISSN 0138-9130], (Enero 2021)
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