The FAIR data principles aim to make scientific data more Findable, Accessible, Interoperable, and Reusable. In the field of traumatic stress research, FAIR data practices can help accelerate scientific advances to improve clinical practice and can reduce participant burden. Previous studies have identified factors that influence data sharing and re-use among scientists, such as normative pressure, perceived career benefit, scholarly altruism, and availability of data repositories. No prior study has examined researcher views and practices regarding data sharing and re-use in the traumatic stress field.
To investigate the perspectives and practices of traumatic stress researchers around the world concerning data sharing, re-use, and the implementation of FAIR data principles in order to inform development of a FAIR Data Toolkit for traumatic stress researchers.
A total of 222 researchers from 28 countries participated in an online survey available in seven languages, assessing their views on data sharing and re-use, current practices, and potential facilitators and barriers to adopting FAIR data principles.
The majority of participants held a positive outlook towards data sharing and re-use, endorsing strong scholarly altruism, ethical considerations supporting data sharing, and perceiving data re-use as advantageous for improving research quality and advancing the field. Results were largely consistent with prior surveys of scientists across a wide range of disciplines. A significant proportion of respondents reported instances of data sharing and re-use, but gold standard practices such as formally depositing data in established repositories were reported as infrequent. The study identifies potential barriers such as time constraints, funding, and familiarity with FAIR principles.
These results carry crucial implications for promoting change and devising a FAIR Data Toolkit tailored for traumatic stress researchers, emphasizing aspects such as study planning, data preservation, metadata standardization, endorsing data re-use, and establishing metrics to assess scientific and societal impact.