Data reuse and sensemaking among novice social scientists

Abstract

We know little about the data reuse practices of novice data users. Yet large scale data reuse over the long term depends in part on uptake from early career researchers. This paper examines 22 novice social science researchers and how they make sense of social science data. Novices are particularly interested in understanding how data: 1) are transformed from qualitative to quantitative data, 2) capture concepts not well-established in the literature, and 3) can be matched and merged across multiple datasets. We discuss how novice data users make sense of data in these three circumstances. We find that novices seek to understand the data producer's rationale for methodological procedures and measurement choices, which is broadly similar to researchers in other scientific communities. However we also find that they not only reflect on whether they can trust the data producers' decisions, but also seek guidance from members of their disciplinary community. Specifically, novice social science researchers are heavily influenced by more experienced social science researchers when it comes to discovering, evaluating, and justifying their reuse of other's data. Keywords Communities of practice, Data repositories, Data reuse LITERATURE REVIEWMuch of the data reuse literature has drawn from the concept of communities of practice to explain reuse behavior within and across disciplinary communities. The research suggests that data reuse is easier when data circulate within as opposed to outside of a disciplinary community, because members of a disciplinary community This is the space reserved for copyright notices.

Social scientists' data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories

“…Several recent studies have investigated data reuse practices and behaviors in the social sciences (Daniels, Faniel, Fear, & Yakel, 2012;Faniel, Kriesberg, & Yakel, 2012;Faniel, Kriesberg, & Yakel, 2016;Niu, 2009;Yoon, 2014bYoon, , 2016Yoon, , 2017 as part of a broader drive to understand data practices within the social sciences. While these studies have captured some of the contexts and characteristics of social science data reuse by focusing on specific aspects of data reuse practices, which may overlap with other disciplinary contexts, fewer studies have used theoretical approaches or models to explain social scientists' data reuse behaviors.…”

“…Yoon (2017) found that social science data reusers also sought external help from data reuser groups, repository staff, and data producers when they encountered problems. Faniel, Kriesberg, and Yakel (2012) reported that human scaffolding, particularly the use of faculty advisors, was an effective technique for novice social science data reusers to manage complex issues that arose during data reuse.…”

More Data, Less Process? The Applicability of MPLP to Research Data

“…This research will provide the empirical evidence and rationale for data archives' roles in curating data for IASSIST Quarterly reuse in accordance with the needs of our designated community. Although previous studies have already clearly demonstrated the importance of contextual information (Faniel & Jacobsen 2010;Faniel et al, 2012), additional research is needed to investigate: 1) the designated community's expectations of the archive's role in providing quality data; 2) how variable level reviews affect reuse; 3) how the presentation of contextual information affects use; 4) how users interact with contextual and variable level metadata; and 5) how specific data curation tasks performed by archives directly impact the data quality and satisfaction criteria discussed within the literature.…”

Red flags in data: Learning from failed data reuse experiences

“…Conditions of documentation can be another factor of this result, although this study could not examine the actual quality of documentation that the participants interacted with. However, the condition of documentation might be a real barrier for inexperienced researchers in data reuse, as novice data reusers made efforts to understand and work with data through different methods (Faniel et al, 2012).…”