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Scholarly Works (14 results)

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Article

Beyond Privacy: The Emerging Ethics of Data Reuse

Pasquetto, Irene V.

Presentations (2018)

The workshop will explore the meaning of “Informed Consent” and its implication for reusing human subject open data from and for biomedical research. Patients and volunteers donate their data in the context of research designs that are vetted and approved by ethics committees. When research data are released in open access – especially observational data - these can be reused to explore a number of new hypotheses. As we know from previous studies, biomedical data can be reused in many unpredictable ways – new research communities are formed around pre-existing data, and the free availability of research data increases innovation, knowledge integration, and reproducibility. At the same time, openness of data could also expose donors to surveillance and discriminatory research practices that not only have ethical implications, but also were never agreed upon by the donors at the moment of data collection. In this workshop, cases of both successful and controversial data reuse practices will be presented and discusses. We further discuss: Can and should we – and if yes how – filter the kinds of hypotheses that can be tested on a human subject dataset? Can and should we – and if yes how – permanently include ethical concerns in the provenance records of human subject datasets? How can we keep data donors actively “informed” about unpredictable reuses of research data?

Cover page: Beyond Privacy: The Emerging Ethics of Data Reuse

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

Article

How and why do scientists reuse others’ data to produce new knowledge?

Presentations (2018)

Thesis
Peer Reviewed

From Open Data to Knowledge Production: Biomedical Data Sharing and Unpredictable Data Reuses

PASQUETTO, IRENE V.
Advisor(s): Borgman, Christine L.

UCLA Electronic Theses and Dissertations (2018)

Using a US consortium for data sharing as the primary field site, this three-year ethnographic research project examines the socio-technical, epistemic, and ethical challenges of making biomedical research data openly available and reusable. Public policy arguments for releasing scientific data for reuse by others include increasing trust in science and leveraging public investments in research. In most types of scientific research, data release occurs in parallel with associated publications, after peer-review. In the consortium studied for this project, datasets may also be released independently without an associated publication. Such research datasets are conceptualized as “hypothesis free” resources from which novel knowledge can be extracted indefinitely. Among the findings of this project are that biomedical researchers do not download and re-analyze “hypothesis free” research data from open repositories as a regular practice. Data reuse is a complex, delicate, and often time-consuming process. Metadata and ontology schemas appear to be necessary but not sufficient for data reuse processes. For scientists to test new hypotheses on “old” data, they depend on access to peer-reviewed primary analyses, pre-existing trusted relationships with the data creators, and shared research agendas. Data donors (patients, study participants, etc.), on the other hand, retain little control over how open research data are reused. Findings suggest that, in practice, it is impossible to predict – and consequently to regulate – how datasets might be reused once made openly available. Unintended consequences of reusing this consortium’s open data already are emerging, to the concern of some participants.

Cover page: From Open Data to Knowledge Production: Biomedical Data Sharing and Unpredictable Data Reuses

Article

Why Data Sharing and Reuse Are Hard To Do

Presentations (2017)

Researchers are producing an unprecedented amount of data by using new methods and instrumentation. By accessing and reusing these data, scientists can answer complex research problems that need systemic approaches to knowledge discovery. However, research data are often not readily available, and even when data are shared, they cannot be reused outside their original context of production. Based on our studies of data practices in science, we compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. Data sharing difficulties include determining what to release, when, in what format, and by what means. Data reuse challenges include determining what data could be reused, by whom (expertise required), with whom (collaborative environments), under what conditions (issues of data quality and curation), why (needs for data integration, control a nd comparison), and to what effects (types of analysis).

Cover page: Why Data Sharing and Reuse Are Hard To Do

Article

Big Data, Little Data, or No Data? Systematic Reviews in an Age of Open Data

Presentations (2018)

Article
Peer Reviewed

On the Reuse of Scientific Data

Publications (2017)

While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data. Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs between collecting new data and reusing existing data? How do motivations for data collection influence the ability to reuse data? How do standards and formats for data release influence reuse opportunities? We conclude by summarizing the implications of these questions for science policy and for investments in data reuse.

Cover page: On the Reuse of Scientific Data

Article
Peer Reviewed

Exploring Openness in Data and Science: What is “Open,” to Whom, When, and Why?

Publications (2015)

Peer Reviewed

Using the Jupyter Notebook as a tool for open science: An empirical study

Publications (2017)

As scientific work becomes more computational and data-intensive, research processes and results become more difficult to interpret and reproduce. In this poster, we show how the Jupyter notebook, a tool originally designed as a free version of Mathematica notebooks, has evolved to become a robust tool for scientists to share code, associated computation, and documentation.

Cover page: Using the Jupyter Notebook as a tool for open science: An empirical study

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