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Research data and metadata curation as institutional issues.

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Title: Research data and metadata curation as institutional issues.
Authors: Mayernik, Matthew S.1 mayernik@ucar.edu
Source: Journal of the Association for Information Science & Technology. Apr2016, Vol. 67 Issue 4, p973-993. 21p. 2 Charts, 1 Graph.
Subjects: Climatology, Ecology, Information resources management, Information retrieval, Metadata, Ethnology research
Abstract: Research data curation initiatives must support heterogeneous kinds of projects, data, and metadata. This article examines variability in data and metadata practices using 'institutions' as the key theoretical concept. Institutions, in the sense used here, are stable patterns of human behavior that structure, legitimize, or delegitimize actions, relationships, and understandings within particular situations. Based on prior conceptualizations of institutions, a theoretical framework is presented that outlines 5 categories of 'institutional carriers' for data practices: (a) norms and symbols, (b) intermediaries, (c) routines, (d) standards, and (e) material objects. These institutional carriers are central to understanding how scientific data and metadata practices originate, stabilize, evolve, and transfer. This institutional framework is applied to 3 case studies: the Center for Embedded Networked Sensing ( CENS), the Long Term Ecological Research ( LTER) network, and the University Corporation for Atmospheric Research ( UCAR). These cases are used to illustrate how institutional support for data and metadata management are not uniform within a single organization or academic discipline. Instead, broad spectra of institutional configurations for managing data and metadata exist within and across disciplines and organizations. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Research data and metadata curation as institutional issues.
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  Data: <searchLink fieldCode="AR" term="%22Mayernik%2C+Matthew+S%2E%22">Mayernik, Matthew S.</searchLink><relatesTo>1</relatesTo><i> mayernik@ucar.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Association+for+Information+Science+%26+Technology%22">Journal of the Association for Information Science & Technology</searchLink>. Apr2016, Vol. 67 Issue 4, p973-993. 21p. 2 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Ecology%22">Ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Information+resources+management%22">Information resources management</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnology+research%22">Ethnology research</searchLink>
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  Data: Research data curation initiatives must support heterogeneous kinds of projects, data, and metadata. This article examines variability in data and metadata practices using 'institutions' as the key theoretical concept. Institutions, in the sense used here, are stable patterns of human behavior that structure, legitimize, or delegitimize actions, relationships, and understandings within particular situations. Based on prior conceptualizations of institutions, a theoretical framework is presented that outlines 5 categories of 'institutional carriers' for data practices: (a) norms and symbols, (b) intermediaries, (c) routines, (d) standards, and (e) material objects. These institutional carriers are central to understanding how scientific data and metadata practices originate, stabilize, evolve, and transfer. This institutional framework is applied to 3 case studies: the Center for Embedded Networked Sensing ( CENS), the Long Term Ecological Research ( LTER) network, and the University Corporation for Atmospheric Research ( UCAR). These cases are used to illustrate how institutional support for data and metadata management are not uniform within a single organization or academic discipline. Instead, broad spectra of institutional configurations for managing data and metadata exist within and across disciplines and organizations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2016
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