Collaborative Annotation for Scientific Data Discovery and Reuse.
Human classification alone, unable to handle the enormous quantity of project data, requires the support of automated machine-based strategies. In collaborative annotation, humans and machines work together, merging editorial strengths in semantics and pattern recognition with the machine strengths...
| Publicado en: | Bulletin of the Association for Information Science & Technology Vol. 39; no. 4; pp. 44 - 46 |
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| Formato: | Journal Article |
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Wiley-Blackwell
Apr/May2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104187582&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104187582 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23739223 H7HM jtl: Bulletin of the Association for Information Science & Technology issn: 23739223 maglogo: N pubinfo: dt: Apr/May2013 vid: 39 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104187582 2012169178 104187582 ppf: 44 ppct: 2 formats: fmt: @attributes: type: P tig: atl: Collaborative Annotation for Scientific Data Discovery and Reuse. aug: au: Borne, Kirk affil: Professor of astrophysics and computational science, George Mason University, Fairfax, Virginia sug: subj: Science Metadata Data Management Methods Collaboration Data Mining Astronomy Bibliography and References Classification ab: Human classification alone, unable to handle the enormous quantity of project data, requires the support of automated machine-based strategies. In collaborative annotation, humans and machines work together, merging editorial strengths in semantics and pattern recognition with the machine strengths of scale and algorithmic power. Discovery informatics can be used to generate common data models, taxonomies and ontologies. A proposed project of massive scale, the Large Synoptic Survey Telescope (LSST) project, will systematically observe the southern sky over 10 years, collecting petabytes of data for analysis. The combined work of professional and citizen scientists will be needed to tag the discovered astronomical objects. The tag set will be generated through informatics and the collaborative annotation efforts of humans and machines. The LSST project will demonstrate the development and application of a classification scheme that supports search, curation and reuse of a digital repository. pubtype: Periodical doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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