Coordinative Entities: Forms of Organizing in Data Intensive Science

Abstract

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries.

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

“…By emergence [13], we mean the fluid nature of sensemaking practices and by resonance, we mean the relationship between our findings in AI/ML projects and the sensemaking practices of software teams in traditional projects. We have organized our empirical findings into three themes; these divisions are analytical and in practice, their boundaries may overlap, blur, merge, or cleave in different configurations of entities [15]. This presents both a challenge and an opportunity for those interested in developing SE process models of sensemaking -how and in what ways do software teams themselves understand their sensemaking practices?…”

Encoding Collective Knowledge, Instructing Data Reusers: The Collaborative Fixation of a Digital Scientific Data Set

“…Given the mutually complementary expertises of its members, assembling the MUWAGS team was a synergizing effort in Bietz et al's (2010) terms. Its organization was similar to what Paine and Lee (2021), in their typology of coordinative entities, call a Principal Group, the chief difference being that the principal investigator of MUWAGS did not have central control over monetary and human resources, making the collaboration a more egalitarian arrangement oriented to consensual decision-making.…”

“…Several threads of research in CSCW and Science and Technology Studies (STS) help to make sense of such an undertaking and its challenges. A team needs to structure and coordinate its activities (Bietz et al 2010;Paine and Lee 2021;Wulf 1993), and this involves articulation work (Strauss 1988), including the division, allocation, coordination, scheduling, meshing and interrelating of 'distributed individual activities' (Schmidt and Bannon 1992, p. 14). This is supported, shaped and challenged by infrastructures (Jirotka et al 2013).…”

Gender Segregation in the Borderlands of E-Science

“…New tools and applications of the rising, male-dominated data sciences (Boston Consulting Group Gamma Study, 2019), such as AI and machine learning (ML), have recently been assembled in e-science platforms without due attention to how gender figures within these. STS scholars who study e-science have started to shift their attention to data science (Ribes, 2019;Paine and Lee, 2020;Beaulieu and Leonelli, 2021;Mökander and Schroeder, 2021). With this shift of attention gendered asymmetries which are sunk into the infrastructure might become further invisibilised.…”