Qualitative user research is a human-intensive approach that draws upon ethnographic methods from social sciences to develop insights about work practices to inform software design and development. Recent advances in data science, and in particular, natural language processing (NLP), enables the derivation of machine-generated insights to augment existing techniques. Our work describes our prototype framework based in Jupyter, a software tool that supports interactive data science and scientific computing, that leverages NLP techniques to make sense of transcribed texts from user interviews. This work also serves as a starting point for incorporating data science techniques in the qualitative analyses process.

Over the last several years, a growing body of work has examined the nature of large-scale virtual organizations for data-intensive cooperative science. These projects, known as Cyberinfrastructures (CI) in the United States, are established realms of inquiry for the eScience and Computer Supported Cooperative Work (CSCW) communities. Scholarship in these communities extends technology focused inquiries to investigate the sociotechnical concerns to such infrastructure creation and maintenance. In this paper we present findings from our qualitative study of a federated cyberinfrastructure organization known as GENI. We contribute to this body of scholarship by investigating how stakeholders in the GENI project position existing, and newly created, resources for use in educational settings. We examine how stakeholders acquaint new potential stakeholders with this CI in order to draw them into the community, and the ways in which stakeholder's roles evolve over time. Our findings illustrate several ways stakeholders leverage and align existing relationships and resources to expand the CI project's user base. Finally, this paper suggests avenues of further inquiry and implications for organizing future CI projects.

Software is an integral element of the work of science yet it is not commonly an object of inquiry in studies of scientific infrastructures. This paper presents findings from an ethnographic study of a cosmology group's collaborative scientific software production. We demonstrate how these cosmologists use plots to simultaneously test their software and analyze data while interrogating multiple layers of infrastructural components. We broaden perspectives on scientific software development using a sociotechnical, software studies lens to examine this work of scientific discovery as a creative and embodied, yet exacting and methodical, activity that requires a 'human in the loop'. We offer a new reading of scientific software practices to convey how creating scientific software is often really the act of doing science itself--an intervention we believe is necessary to more successfully support scientific software sharing and infrastructure production.

The CSCW community is reliant upon technology-centric models of groupware and collaboration that frame how we examine and design for cooperative work. This paper both reviews the CSCW literature to examine existing models of collaborative work and proposes a new, expanded conceptual model: the Model of Coordinated Action (MoCA). MoCA is a broader framework for describing complex collaborative situations and environments including, but not limited to, collaborations that have diverse, high-turnover memberships or emerging practices. We introduce MoCA's seven dimensions of coordinative action and illustrate their connection to past and current CSCW research. Finally, we discuss some ramifications of MoCA for our understanding of CSCW as a sociotechnical design space.

The production and use of software pipelines is a key component of much modern scientific research. We present emerging findings from our qualitative, social science study of a radio astronomy group developing software pipelines as they produce a data processing infrastructure. This paper examines how these researchers co-produce data products and software pipelines to enact their research infrastructure. We investigate the work of co-producing data and software to illustrate that to better support data-intensive science, we need to understand the practices that enable and produce data products, software, and ultimately infrastructures.

In recent years, the engineering education community has invested significant energy and resources in the creation of a rigorous empirical knowledge base to support the transformation of engineering education practice. In this work, we address the specific question: When researchers identify practical implications in their research publications, what specific actions are they suggesting be done, and who is expected to take these actions? We answer this question by identifying "implication for action" sentences in a sample of sixteen articles across two issues of the Journal of Engineering Education. We find that the actions offered are infrequently cognitive (15% in our dataset) and frequently related to courses (83% in our dataset); and the identification of an actor happens just under half of the time (43% in our dataset). We discuss these results through a pragmatic perspective and a critical perspective.

Coordinating Centers (CCs) are central bodies tasked with the work of coordination and operations management of a virtual organization whose purpose is to conduct multi-site research projects. We call these organizations Coordinating Center Enabled Networks (CCENs). This qualitative, interview-based study followed two CCs in the field of cancer epidemiology over seven months to answer the question: How does a CC facilitate the work of networked science in a CCEN? In order to answer the question of how CCs facilitate work, we first describe the complex ecology of CCEN work practices. We further discuss how various stakeholders engage in different work practices to facilitate scientific progress. Finally, we use the conceptual lenses of local articulation work and metawork together with the diversity of work practices to better understand what practices CCs actually coordinate.

Middleware software, which provides an abstraction layer between low-level computational services and domain-specific applications, is a key component of cyberinfrastructure. This paper presents a qualitative study of how cyberinfrastructure middleware development is accomplished in two supercomputing centers. Our investigation highlights key development phases in the lives of middleware projects. Middleware development is typically undertaken as part of collaborations between technologists and domain scientists, and middleware developers must balance the pressure to meet specific scientific needs and the desire to explore their own R&D agendas. We explore how developers work to sustain an ongoing development trajectory by aligning their own work with particular domain science projects and funding streams. However, we find that the key transition from being a component in a domain-specific project to a stand-alone system that is useful across domains is particularly challenging for middleware development. We provide organizational and national policy implications for how to better support this transition.

While previous CSCW research has noted that computer scientists have their own research interests pertaining to cyberinfrastructure development projects, most have focused on the research imperatives of scientists. This qualitative, interview-based study investigates the perspective of computer scientists developing middleware software for cyberinfrastructures at two supercomputing centers. This paper examines how technologists develop and sustain middleware applications over time by leveraging expertise and partnering with different research domains in order to achieve long-term infrastructural goals.

The objective of this work is to explore how implications for educational practice are represented in the scholarly literature of engineering education. This work is motivated by the increasing urgency to understand how research can be used to transform educational practice. Because scholarly writing (such as conference papers and journal articles) represents one place where researchers articulate ideas about how their research can be used (i.e., educational implications), an analysis of how such implications are represented can provide insight into ideas about the uses we imagine for our research. Once we have characterized the vision of use, we can step back and think about whether the collective vision we present may provide clues to the more general issue of transforming education.