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

Abstract

PurposeThis 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.Design/methodology/approachThis 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.FindingsBased 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).Originality/valueThe 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.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0079.

Integrated but Isolated: Implications from a Systematic Review of the Access Control Ecosystem for Individual Participant Data in Clinical Studies

“…Applying the concept of KI, a strong theoretical framework that helps to establish attributes for infrastructures in data sharing research (Jeng & He, 2022), we identified nine key aspects of IPD access control mechanisms. In Figure 1, we present four major components in the access control ecosystem for IPD, namely the clinical trial team (T), its sponsors (S), the data requesters (R) intending to access IPD, and the access control mechanisms (A).…”

“…As we view data sharing as a knowledge sharing practice, we apply the concept of knowledge infrastructure (KI) as a guidance framework to break down and able to depict the complex and interwoven ecosystem of IPD sharing. KI was defined as “robust networks that generate, share, and maintain specific knowledge about the human and natural worlds” (Edwards, 2010, as cited in Borgman, 2015, p. 33), of which we consider its seven elements a good fit for reflecting the dynamic and static IPD flow: people, artifacts, built technologies, institutions, policies, shared norms and values, and routines and practices (Edward et al, 2013, as cited in Jeng & He, 2022).…”

The utilisation of open research data repositories for storing and sharing research data in higher learning institutions in Tanzania

“…It also improves the return on investment for researchers' institutions and communities (Donaldson and Koepke, 2022). To improve access to and reuse of such research data, it is necessary to store and exchange it utilising open platforms such as Open RDRs (Sicilia et al, 2017Bezuidenhout, 2019Boyd, 2021;Donaldson and Koepke, 2022;Jeng and He, 2022;Mauthner and Parry, 2013;Tenopir et al, 2015).…”

Factors Influencing Criminologists' Open Research Data Practices: Trust, Contract, and Value

“…This trend has undoubtedly blown over to the social sciences as well. Researchers in social science often handle very complex data (Jeng & He, 2022). Taking criminology as an example, a researcher may involve primary data, secondary data, and data from vulnerable populations (e.g., prisoners) (Thanos, 2011).…”

“…The study's goal was to capture the perspectives and considerations of researchers in criminology on how they use and share data for their research within a data-intensive paradigm. Specifically, we investigated their willingness, experiences, and considerations for sharing and reusing research data and anticipates for data curation professionals to establish an ideal research data repository based on their needs to better build the research data infrastructure and foster the research data sharing and reuse in social science (Jeng & He, 2022).…”