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Abstract

"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

BK

54.04 Ausbildung, Beruf, Organisationen Informatik

Classification

54.04 Ausbildung, Beruf, Organisationen Informatik

Content

Provocations -- What are data? -- Data scholarship -- Data diversity -- Data scholarship in the sciences -- Data scholarship in the social sciences -- Data scholarship in the humanities -- Sharing, releasing, and reusing data -- Credit, attribution, and discovery of data -- What to keep and why to keep them.

Footnote

Weitere Rez. in: JASIST 67(2016) no.3, S.751-753 (C. Tenopir).

LCSH

Communication in learning and scholarship / Technological innovations

Subject

Communication in learning and scholarship / Technological innovations

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Abstract

Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice.

Date

11. 6.2012 15:22:29

Series

Advances in information science

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Abstract

Digital archives are the preferred means for open access to research data. They play essential roles in knowledge infrastructures-robust networks of people, artifacts, and institutions-but little is known about how they mediate information exchange between stakeholders. We open the "black box" of data archives by studying DANS, the Data Archiving and Networked Services institute of The Netherlands, which manages 50+ years of data from the social sciences, humanities, and other domains. Our interviews, weblogs, ethnography, and document analyses reveal that a few large contributors provide a steady flow of content, but most are academic researchers who submit data sets infrequently and often restrict access to their files. Consumers are a diverse group that overlaps minimally with contributors. Archivists devote about half their time to aiding contributors with curation processes and half to assisting consumers. Given the diversity and infrequency of usage, human assistance in curation and search remains essential. DANS' knowledge infrastructure encompasses public and private stakeholders who contribute, consume, harvest, and serve their data-many of whom did not exist at the time the DANS collections originated-reinforcing the need for continuous investment in digital data archives as their communities, technologies, and services evolve.

Date

7. 7.2019 11:58:22

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Abstract

In the process of scientific research, many information objects are generated, all of which may remain valuable indefinitely. However, artifacts such as instrument data and associated calibration information may have little value in isolation; their meaning is derived from their relationships to each other. Individual artifacts are best represented as components of a life cycle that is specific to a scientific research domain or project. Current cataloging practices do not describe objects at a sufficient level of granularity nor do they offer the globally persistent identifiers necessary to discover and manage scholarly products with World Wide Web standards. The Open Archives Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these requirements. We demonstrate a conceptual implementation of OAI-ORE to represent the scientific life cycles of embedded networked sensor applications in seismology and environmental sciences. By establishing relationships between publications, data, and contextual research information, we illustrate how to obtain a richer and more realistic view of scientific practices. That view can facilitate new forms of scientific research and learning. Our analysis is framed by studies of scientific practices in a large, multidisciplinary, multi-university science and engineering research center, the Center for Embedded Networked Sensing.