Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework

This study develops a conceptual framework and a series of instruments for capturing researchers' data-sharing practices in the social sciences, by synergizing the theory of knowledge infrastructure and the theory of remote scientific collaboration.

This paper triangulates the results of three studies of data sharing across the social sciences, with 144 participants in total, and classifies the confusion, “frictions” and opportunities arising from such sharing into four overarching dimensions: data characteristics, technological infrastructure, research culture and individual drivers.

Based on the sample, the findings suggest that the majority of faculty and students in social science research do not share their data because many of them are unaware of the benefits and methods of doing so. Additional findings regarding social scientists' data-sharing behaviors include: (1) those who do share qualitative data in data repositories are more likely to share their research tools than their raw data; and (2) perceived technical support and extrinsic motivation are both strong predictors of qualitative data sharing (a previously underresearched subtype of social science data sharing).

The study confirms the previously hypothesized nature of “friction” in qualitative data sharing in the social sciences, arising chiefly from the time and labor intensiveness of ensuring data privacy.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0079.