Search (9 results, page 1 of 1)

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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

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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

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.

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Abstract

Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990-2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments.

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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.

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.

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Abstract

Research in user behavior on online catalogs is in its early stages, but preliminary findings suggest that users encounter many of the same problems identified in behavioral studies of other types of bibliographic retrieval systems. Much can be learned from comparing the results of user behavior studies on these two types of systems. Research on user problems with both the mechanical aspects and the conceptual aspects of system use is reviewed, with the conclusion that more similiratiy exists across types of systems in conceptual than in mechanical problems. Also discussed are potential sources of the problems, due either to individual characteristics or to system variables. A series of research questions is proposed and a number of potential interim solutions ae suggested for alleviating some of the problems encountered by users of information systems

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Abstract

When developing university-based research data curation services, libraries face critical decisions around organization and sustainability that can affect dataset producers' satisfaction with these services. We present a study, involving interviews (n = 43) and ethnographic observation, of how two libraries partnered with the Sloan Digital Sky Survey (SDSS) to curate a significant astronomy dataset. Each library took different decisions: one library assigned activities to a unit specializing in digital curation, while the other distributed activities across its existing units. Neither approach proved a silver bullet. While library staff members felt the outcomes largely met their expectations, SDSS leaders expressed mixed opinions. We identify three factors that contributed to these differences in perspective: differing strategic motivations for undertaking this Data Transfer Process, SDSS leaders' misperceptions about libraries, and organizational mismatches. These factors contributed to four differences in perspective between SDSS leaders and library staff: provenance as technical information or as information about social context, dataset as a live research object or as a static object to be preserved, systems and services tailored to the dataset or easily adaptable to other datasets, and obstacles as setbacks or as opportunities. Only those differences that emerged when SDSS collaboration members and library staff communicated frequently were resolved.

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Source

IFLA journal. 22(1996) no.2, S.121-127

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Date

3. 6.2005 20:40:22