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Digging deeper into data citations: recognizing and rewarding data work
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AI
DOI：10.1093/reseval/rvag008.png)
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Abstract
En 中文
Citations and metrics are central features in evaluating academic careers. As researchers increasingly engage in open science, data citations have emerged as potential mechanisms for evaluating and rewarding data sharing and reuse in academic assessments. Despite this, we still lack critical information about the data citation practices and motivations of researchers themselves, information which is needed to contextualize the use of such metrics. Here, we present the results of a semi-structured interview study with researchers across disciplines exploring their data referencing practices and motivations, as well as how they would like their ‘data work’ (including data sharing) to be rewarded and evaluated. As a whole, our findings confirm a lack of standard practices for referencing data and provide new insights into the social and scientific reasons motivating data referencing. While our results show an overall skepticism toward the use of citation-based metrics in evaluations, they also suggest that researchers are caught between traditional and emergent modes of assessment for recognizing data work. Furthermore, we find that rather than valuing data citations as rewards, our participants value creating data objects which are useful for their (often small) research communities. Ultimately, we conclude that data work is a cornerstone of research practice which needs to be evaluated and considered, but one which also requires context-aware approaches.
Keywords:
data citations
research evaluation
open science
data sharing
academic metrics
Journal
IF:
2.5
Papers:
114
Citations:
1.9K
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