Archives des mots-clés : Christine L. Borgman

From Data Creator to Data Reuser: Distance Matters

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Authors : Christine L. Borgman, Paul T. Groth

Sharing research data is complex, labor-intensive, expensive, and requires infrastructure investments by multiple stakeholders. Open science policies focus on data release rather than on data reuse, yet reuse is also difficult, expensive, and may never occur. Investments in data management could be made more wisely by considering who might reuse data, how, why, for what purposes, and when.

Data creators cannot anticipate all possible reuses or reusers; our goal is to identify factors that may aid stakeholders in deciding how to invest in research data, how to identify potential reuses and reusers, and how to improve data exchange processes.

Drawing upon empirical studies of data sharing and reuse, we develop the theoretical construct of distance between data creator and data reuser, identifying six distance dimensions that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality.

These dimensions are primarily social in character, with associated technical aspects that can decrease – or increase – distances between creators and reusers. We identify the order of expected influence on data reuse and ways in which the six dimensions are interdependent.

Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies.

URL : From Data Creator to Data Reuser: Distance Matters

arXiv : https://arxiv.org/abs/2402.07926

The Future of Data in Research Publishing: From Nice to Have to Need to Have?

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Authors : Christine L. Borgman, Amy Brand

Science policy promotes open access to research data for purposes of transparency and reuse of data in the public interest. We expect demands for open data in scholarly publishing to accelerate, at least partly in response to the opacity of artificial intelligence algorithms.

Open data should be findable, accessible, interoperable, and reusable (FAIR), and also trustworthy and verifiable. The current state of open data in scholarly publishing is in transition from ‘nice to have’ to ‘need to have.’

Research data are valuable, interpretable, and verifiable only in context of their origin, and with sufficient infrastructure to facilitate reuse. Making research data useful is expensive; benefits and costs are distributed unevenly.

Open data also poses risks for provenance, intellectual property, misuse, and misappropriation in an era of trolls and hallucinating AI algorithms. Scholars and scholarly publishers must make evidentiary data more widely available to promote public trust in research.

To make research processes more trustworthy, transparent, and verifiable, stakeholders need to make greater investments in data stewardship and knowledge infrastructures.

DOI : https://doi.org/10.1162/99608f92.b73aae77

Uses and Reuses of Scientific Data: The Data Creators’ Advantage

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Authors : Irene V. Pasquetto, Christine L. Borgman, Morgan F. Wofford

Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers. We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves.

By conducting a qualitative meta-analysis of evidence on two long-term, distributed, interdisciplinary consortia, we found that scientists frequently sought data from public collections and from other researchers for comparative purposes such as “ground-truthing” and calibration.

When they sought others’ data for reanalysis or for combining with their own data, which was relatively rare, most preferred to collaborate with the data creators.

We propose a typology of data reuses ranging from comparative to integrative. Comparative data reuse requires interactional expertise, which involves knowing enough about the data to assess their quality and value for a specific comparison such as calibrating an instrument in a lab experiment.

Integrative reuse requires contributory expertise, which involves the ability to perform the action, such as reusing data in a new experiment. Data integration requires more specialized scientific knowledge and deeper levels of epistemic trust in the knowledge products.

Metadata, ontologies, and other forms of curation benefit interpretation for any kind of data reuse. Based on these findings, we theorize the data creators’ advantage, that those who create data have intimate and tacit knowledge that can be used as barter to form collaborations for mutual advantage.

Data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources, and institutions.

URL : Uses and Reuses of Scientific Data: The Data Creators’ Advantage

DOI : https://doi.org/10.1162/99608f92.fc14bf2d

The principles of tomorrow’s university

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Authors : Daniel S. Katz, Gabrielle Allen, Lorena A. Barba, Devin R. Berg, Holly Bik, Carl Boettiger, Christine L. Borgman, C. Titus Brown, Stuart Buck, Randy Burd, Anita de Waard, Martin Paul Eve, Brian E. Granger, Josh Greenberg, Adina Howe, Bill Howe, May Khanna, Timothy L. Killeen, Matthew Mayernik, Erin McKiernan, Chris Mentzel, Nirav Merchant, Kyle E. Niemeyer, Laura Noren, Sarah M. Nusser, Daniel A. Reed, Edward Seidel, MacKenzie Smith, Jeffrey R. Spies, Matt Turk, John D. Van Horn, Jay Walsh

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility.

In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled “Imagining Tomorrow’s University.”

Workshop attendees were charged with launching a new dialog around open research – the current status, opportunities for advancement, and challenges that limit sharing.

The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world.

During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society.

They expressed the results of these deliberations as a set of 22 principles of tomorrow’s university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies.

Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete.

These authors are also conducting efforts to implement these ideas, and to make changes in the university system.

Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented.

Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.

URL : The principles of tomorrow’s university

DOI : https://doi.org/10.12688/f1000research.17425.1

Text Data Mining from the Author’s Perspective: Whose Text, Whose Mining, and to Whose Benefit?

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Authors : Christine L. Borgman

Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.

URL : https://arxiv.org/abs/1803.04552

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

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Author : Christine L. Borgman

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property.

Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of ‘grey data’ about individuals in their daily activities of research, teaching, learning, services, and administration.

The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII.

Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them.

The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection.

This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.

URL : https://arxiv.org/abs/1802.02953

On the Reuse of Scientific Data

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Authors : Irene V. Pasquetto, Bernadette M. Randles, Christine L. Borgman

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.

URL : On the Reuse of Scientific Data

DOI : http://doi.org/10.5334/dsj-2017-008