Search (3 results, page 1 of 1)

  • × author_ss:"Golshan, M.S."
  • × author_ss:"Borgman, C.L."
  1. Borgman, C.L.; Scharnhorst, A.; Golshan, M.S.: Digital data archives as knowledge infrastructures : mediating data sharing and reuse (2019) 0.03
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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
  2. Borgman, C.L.; Wofford, M.F.; Golshan, M.S.; Darch, P.T.: Collaborative qualitative research at scale : reflections on 20 years of acquiring global data and making data global (2021) 0.01
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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.
  3. Darch, P.T.; Sands, A.E.; Borgman, C.L.; Golshan, M.S.: Do the stars align? : Stakeholders and strategies in libraries' curation of an astronomy dataset (2021) 0.01
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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.