The craft and coordination of data curation: complicating "workflow" views of data science

TL;DR

This paper explores the complex, craft-based nature of data curation at a social science data repository, highlighting how curatorial work is often invisible and intertwined with best practices, impacting data usability and reproducibility.

Contribution

It provides a detailed, empirical analysis of data curation practices, emphasizing the craft and coordination involved, which challenges traditional workflow models.

Findings

Curatorial work involves craft practices that defy simple workflow models.

Best practices and craft practices are deeply intertwined.

The work of curators significantly impacts data usability and reproducibility.

Abstract

Data curation is the process of making a dataset fit-for-use and archiveable. It is critical to data-intensive science because it makes complex data pipelines possible, makes studies reproducible, and makes data (re)usable. 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 the data curators invisible; it also makes it harder to tease out the impact curators' work has on the later usability, reliability, and reproducibility of data. To better understand the specific work of data curation -- and thereby, explore ways of showing curators' impact -- we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium of 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 triangulate accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. We find that curators describe a number of craft practices needed to perform their work, which defies the rote sequence of events implied by many lifecycle or workflow models. Further, we show how best practices and craft practices are deeply intertwined.