What factors motivate scientists to use data collected by other scientists?

What is it about?

The paper identifies factors that make scientists more likely to report reusing data that were collected by other scientists. It draw on the theory of reasoned action to develop a model that includes positive and negative attitudes towards data reuse (e.g., reusing data saves time or data may not be reliable) and perceived subjective norms about data reuse (e.g., data reuse is not accepted in my field). The model was tested using data from a survey of 595 scientists. Unsurprisingly, we found that belief in the efficiency and efficacy of data reuse predicts data reuse, as does perception of the importance of data reuse, while perceptions of norms against data reuse predicts less reuse. However, the last effect (norms) was not significant for those with more developed data management practices (which we proxied by reported use of metadata standards), suggesting that more experienced scientists find that the benefits of reuse outweigh perceived norms against it. Interestingly, agreement with concerns about the trustworthiness of data did not predict less reuse of data, suggesting that researchers feel they can overcome problems. There are some difference in data reuse based on the kind of data used. Finally, we find that reported data sharing and data reuse are only moderately correlated, suggesting that some scientists are sharers and others, reusers.

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Why is it important?

There is a growing expectation that scientists should share their research data to promote transparency in research and to facilitate follow-on studies. However, while the focus has been on sharing, little attention has been paid to data reuse, which our study suggests is a different behaviour. Knowledge of the factors related to data reuse is important to understand how to encourage the practice. The results suggest possible strategies to encourage reuse, such as demonstrations of the effectiveness of data reuse (e.g., examples of studies that have benefited) or tutorials on the processes of data reuse. Attention should also be paid to norms about data reuse, e.g., visible advocacy for reuse, prizes for exemplary papers that reuse data or support in tenure letters.

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