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This collaborative "essay of essays" begins with an introduction by a professor of human centered design and engineering who has been working concurrently with PhD students to study collaborative system design. We undertake widely scoped qualitative research studies, that we categorize as "extended studies," that cut across units of analysis, organizations, or time. Our research explores how people create new ways to enact systems that support the knowledge work of different stakeholders. In response to an anchor essay, the students have written reflections about the multifaceted experience of doing extended studies. Many of these studies began by focusing on a particular project to develop a particular system or information infrastructure, and associated standards. Over time the studies came to center on collaborative dynamics per se, and also how collaborative dynamics shifted the scope and functionality of products, sometimes also affecting programmatic and infrastructural level changes. 

Studies of research software development have focused on how to promote or encourage the adoption of software engineering practices, but we do not have a good empirical understanding of strategies that researchers have already begun to take in order to integrate those practices into research work in sustainable ways. We conduct a comparative case study of two research groups in different fields, and characterize two approaches that they have taken to get research software engineering work done: practice integration and differentiating expertise. From these findings we argue that examining outcomes of change in research software development practice is critical for understanding sustainability and the ramifications of such changes for scientific work. 

Along with a number of other computing technologies, cloud computing services are increasingly being promoted as a way of enabling openness, reproducibility, and the acceleration of scientific work. While there have been a variety of studies of the cloud in terms of computing performance, there has been little empirical attention to the changes going on around cloud computing at the level of work and practice. Through a qualitative, ethnographic study, we follow a cosmology research group’s transition from a shared high performance computing cluster to a cloud computing service, and examine the cloud service as a coordinative artifact being integrated into a larger ecology of existing practices and artifacts. We find that the transition involves both change and continuity in the group’s coordinative work and maintenance work, and point out some of the effects this adoption has on the group’s larger set of practices. Finally, we discuss practical implications this has for the broader adoption of cloud computing in university-based scientific work. 

Drawing from a longitudinal case study, we inspect the activities of an expanding team of scientists and their collaborators as they sought to develop a novel software pipeline that worked both for themselves and for their wider community. We argue that these two tasks - making the software work for themselves and also for their wider scientific community - could not be differentiated from each other at the beginning of the software development process. Rather, this division of labor and software capacities emerged, articulated by the actors themselves as they went about their tasks. The activities of making the novel software work at all, and the extra work of making that software repurposable or reusable could not be distinguished until near the end of the development process - rather than defined or structured in advance. We discuss implications for the trajectory of software development, and the practical work of making software repurposable. 

What is the relationship between Data Management Plans (DMPs), DMP guidance documents, and the reality of end-of-project data preservation and access? In this short paper we report on some preliminary findings of a 3-year investigation into the impact of DMPs on federally funded science in the United States. We investigated a small sample of publicly accessible DMPs (N=14) published using DMPTool. We found that while DMPs followed the National Science Foundation's guidelines, the pathways to the resulting research data are often obscure, vague, or not obvious. We define two “data pathways” as the search tactics and strategies deployed in order to find datasets. 

Recent CSCW research on the collaborative design and development of research infrastructures for the natural sciences has increasingly focused on the challenges of open data sharing. This qualitative study describes and analyzes how multidisciplinary, geographically distributed ocean scientists are integrating highly diverse data as part of an effort to develop a new research infrastructure to advance science. This paper identifies different kinds of coordination that are necessary to align processes of data collection, production, and analysis. Some of the hard work to integrate data is undertaken before data integration can even become a technical problem. After data integration becomes a technical problem, social and organizational means continue to be critical for resolving differences in assumptions, methods, practices, and priorities. This work calls attention to the diversity of coordinative, social, and organizational practices and concerns that are needed to integrate data and also how, in highly innovative work, the process of integrating data also helps to define scientific problem spaces themselves. 

Interest in data science, especially within the context of grad- uate education, is exploding. In this study we present initial results from an ongoing qualitative study of an interdisciplinary cyberinfrastructure- focused NSF-funded graduate data science education workshop hosted at an iSchool in the US. The complexity of the workshop curriculum, the participants' and instructors' disparate disciplinary backgrounds, and the technical tools employed are particularly suited to qualitative meth- ods which can synthesize all of these aspects from rich observational, ethnographic, and trace data collected as part of the authors' role on the grant's qualitative evaluation team. The success of the workshop in equipping participants to do reproducible computational science was in part due to the successful acculturation process, whereby participants comprehended, altered, and enacted new norms amongst themselves. At the same time, we observed potential challenges for data science in- struction resulting from the rhetorical framing of the technologies as inescapably new. This language, which mirrors that of a successful grant proposal, tends to obscure the deeply embedded and contingent history of the command-line technologies required to preform computational sci- ence, many of which are decades old. We conclude by describing our on- going work, future theoretical sampling plans from this and future data, and the contributions that our ndings can provide to graduate data science curriculum development and pedagogy. 

This poster reports on ongoing research into the National Science Foundation’s Data Management Plan guidelines and its impact on science data lifecycles. We ask two research questions (RQs): 1) How does guidance about the formulation of DMPs vary across different research areas? And 2) How has guidance about the management of data changed since the first DMP policies were published in 2011? To this end, we collected, examined, and compared 37 DMP guidance policies from 15 different research areas. We identify the following three themes during document analysis: 1) Responsibility for the future of data; 2) Data maintenance changes over time; and 3) The use of data repositories. Based on these preliminary findings we believe that National Science Foundation guidance policies represent a unique view into changes in data management practices over the last decade.