The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

Abstract

Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-intensive science because it makes complex data pipelines possible, studies reproducible, and data reusable. Yet the complexities of the hands-on, technical, and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of data curators invisible but also hides the impact that curators' work has on the later usability, reliability, and reproducibility of data. To better understand the work and impact of data curation, we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). We asked: What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulated accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. While we identified numerous curatorial actions performed by ICPSR curators, we also found that curators rely on a number of craft practices to perform their jobs. The reality of their work practices defies the rote sequence of events implied by many life cycle or workflow models. Further, we show that craft practices are needed to enact data curation best practices and standards. The craft that goes into data curation is often invisible to end users, but it is well recognized by ICPSR curators and their supervisors. Explicitly acknowledging and supporting data curators as craftspeople is important in creating sustainable and successful curatorial infrastructures.

In the Academy, Data Science Is Lonely: Barriers to Adopting Data Science Methods for Scientific Research

“…This pattern is consistent with a perspective recently voiced by on the essential role of skilled, long-term data professionals in maintaining the technical infrastructure required for data-centric research. Ethnographic research has established that there can be considerable invisible labor-labor in which the work, worker, or both are somehow obscured involved in 'cleaning' and 'curating' data sets for scientific use (Plantin, 2018;Thomer et al, 2022). While less attention has been paid to the invisible labor of statistical programming for data analysis or maintaining computational infrastructure in research groups, these endeavors surely also involve skilled work that remains underspecified and undervalued.…”