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Scholarly Works (19 results)

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Article

The Challenges of Reproducibility in Data-Scarce Fields

Presentations (2017)

Reproducing scientific results is difficult in fields whose research is characterized by large volumes of data, computationally intensive methods, and high degrees of standardization. In data scarce fields, which characterizes much of the earth sciences, reproducing results is even more challenging. Among the factors that limit reproducibility are data access, collection, curation, and accessibility. We discuss the extent to which reproducibility is an appropriate goal in data scarce domains and the tradeoffs between data reuse and reproducibility.

Cover page: The Challenges of Reproducibility in Data-Scarce Fields

Creative Commons 'BY-NC-ND' version 4.0 license

Article
Peer Reviewed

Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere

Publications (2016)

Background

An increasing array of scientific fields face a “data deluge.” However, in many fields data are scarce, with implications for their epistemic status and ability to command funding. Consequently, they often attempt to develop infrastructure for data production, management, curation, and circulation. A component of a knowledge infrastructure may serve one or more scientific domains. Further, a single domain may rely upon multiple infrastructures simultaneously. Studying how domains negotiate building and accessing scarce infrastructural resources that they share with other domains will shed light on how knowledge infrastructures shape science.

Methods

We conducted an eighteen-month, qualitative study of scientists studying the deep subseafloor biosphere, focusing on the Center for Dark Energy Biosphere Investigations (C-DEBI) and the Integrated Ocean Drilling Program (IODP) and its successor, the International Ocean Discovery Program (IODP2). Our methods comprised ethnographic observation, including eight months embedded in a laboratory, interviews (n = 49), and document analysis.

Results

Deep subseafloor biosphere research is an emergent domain. We identified two reasons for the domain’s concern with data scarcity: limited ability to pursue their research objectives, and the epistemic status of their research. Domain researchers adopted complementary strategies to acquire more data. One was to establish C-DEBI as an infrastructure solely for their domain. The second was to use C-DEBI as a means to gain greater access to, and reconfigure, IODP/IODP2 to their advantage. IODP/IODP2 functions as infrastructure for multiple scientific domains, which creates competition for resources. C-DEBI is building its own data management infrastructure, both to acquire more data from IODP and to make better use of data, once acquired.

Discussion

Two themes emerge. One is data scarcity, which can be understood only in relation to a domain’s objectives. To justify support for public funding, domains must demonstrate their utility to questions of societal concern or existential questions about humanity. The deep subseafloor biosphere domain aspires to address these questions in a more statistically intensive manner than is afforded by the data to which it currently has access. The second theme is the politics of knowledge infrastructures. A single scientific domain may build infrastructure for itself and negotiate access to multi-domain infrastructure simultaneously. C-DEBI infrastructure was designed both as a response to scarce IODP/IODP2 resources, and to configure the data allocation processes of IODP/IODP2 in their favor.

Cover page: Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere

Article
Peer Reviewed

Collaborative Qualitative Research at Scale:Reflections on 20 years of Acquiring Global Data and Making Data Global

Publications (2020)

A 5-year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the ‘team science’ approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards’s model of ‘making global data’ – collecting signals via consistent methods, technologies, and policies – to ‘make data global’ – comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.

Cover page: Collaborative Qualitative Research at Scale:Reflections on 20 years of Acquiring Global Data and Making Data Global

Creative Commons 'BY-NC' version 4.0 license

Article
Peer Reviewed

Library cultures of data curation: Adventures in astronomy

UCLA Previously Published Works (2020)

Article

Collaborative Ethnography at Scale: Reflections on 20 years of data integration

Publications (2020)

A 5-year STS project in geography, starting in 1999, evolved into 20 years of data collection about scientific data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the ‘team science’ approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards’s model of ‘making global data’ – collecting signals via consistent methods, technologies, and policies – to ‘make data global’ – comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.

Cover page: Collaborative Ethnography at Scale: Reflections on 20 years of data integration

Article
Peer Reviewed

Open Data in Scientific Settings: From Policy to Practice

Publications (2016)

Open access to data is commonly required by funding agencies, journals, and public policy, despite the lack of agreement on the concept of “open data.” We present findings from two longitudinal case studies of major scientific collaborations, the Sloan Digital Sky Survey in astronomy and the Center for Dark Energy Biosphere Investigations in deep subseafloor biosphere studies. These sites offer comparisons in rationales and policy interpretations of open data, which are shaped by their differing scientific objectives. While policy rationales and implementations shape infrastructures for scientific data, these rationales also are shaped by pre-existing infrastructure. Meanings of the term “open data” are contingent on project objectives and on the infrastructures to which they have access.

Cover page: Open Data in Scientific Settings: From Policy to Practice

Article

Our knowledge of knowledge infrastructures: Lessons learned and future directions

Publications (2020)

The Knowledge Infrastructures Workshop conducted at UCLA in February 2020, and funded by the Alfred P. Sloan Foundation, revisited the goals and findings of the 2012 workshop held at the University of Michigan. Thirty scholars, from a diverse array of disciplines and backgrounds, charted a course for the next decade of KI research. Such infrastructures are increasingly fragile, and often brittle, in the face of open data and open source, the demise of gatekeepers, and shifting public and private boundaries that redistribute power. Participants identified new methods and new opportunities for studying KI. Among the many scholarly products they proposed are publications, grant proposals, conference sessions, and workshops on the role of libraries in data services, the death and afterlives of KI, misinformation and disinformation in KI, KI in the Anthropocene, “N simplish rules” to grow and sustain KI, university capacities for KI, designing sustainable KI, and inclusion of underrepresented groups in the design of KI. The report, position papers, and other materials will be maintained at the KI workshop site, http://knowledgeinfrastructures.org.

Cover page: Our knowledge of knowledge infrastructures: Lessons learned and future directions

Article

The Durability and Fragility of Knowledge Infrastructures: Lessons Learned from Astronomy

Presentations (2016)

Article
Peer Reviewed

Library Cultures of Data Curation: Adventures in Astronomy

Publications (2020)

University libraries are partnering with disciplinary data producers to provide long-term digital curation of research datasets. Managing dataset producer expectations and guiding future development of library services requires understanding the decisions libraries make about curatorial activities, why they make these decisions, and the effects on future data reuse. We present a study, comprising interviews (n=43) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy dataset. The two libraries made different choices of the materials to curate and associated services, which resulted in different reuse possibilities. Each of the libraries offered partial solutions to the SDSS leaders’ objectives. The libraries’ approaches to curation diverged due to contextual factors, notably the extant infrastructure at their disposal (including technical infrastructure, staff expertise, values and internal culture, and organizational structure). The Data Transfer Process case offers lessons in understanding how libraries choose curation paths and how these choices influence possibilities for data reuse. Outcomes may not match data producers’ initial expectations but may create opportunities for reusing data in unexpected and beneficial ways.

Article

The Ups and Downs of Knowledge Infrastructures in Science: Implications for Data Management (slides)

Presentations (2014)