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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">From Big Data to Knowledge in the Social Sciences</title><author><name sortKey="Hesse, Bradford W" sort="Hesse, Bradford W" uniqKey="Hesse B" first="Bradford W." last="Hesse">Bradford W. Hesse</name></author><author><name sortKey="Moser, Richard P" sort="Moser, Richard P" uniqKey="Moser R" first="Richard P." last="Moser">Richard P. Moser</name></author><author><name sortKey="Riley, William T" sort="Riley, William T" uniqKey="Riley W" first="William T." last="Riley">William T. Riley</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26294799</idno><idno type="pmc">4539961</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539961</idno><idno type="RBID">PMC:4539961</idno><idno type="doi">10.1177/0002716215570007</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000448</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">From Big Data to Knowledge in the Social Sciences</title><author><name sortKey="Hesse, Bradford W" sort="Hesse, Bradford W" uniqKey="Hesse B" first="Bradford W." last="Hesse">Bradford W. Hesse</name></author><author><name sortKey="Moser, Richard P" sort="Moser, Richard P" uniqKey="Moser R" first="Richard P." last="Moser">Richard P. Moser</name></author><author><name sortKey="Riley, William T" sort="Riley, William T" uniqKey="Riley W" first="William T." last="Riley">William T. Riley</name></author></analytic><series><title level="j">The Annals of the American Academy of Political and Social Science</title><idno type="ISSN">0002-7162</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the <italic>Big Data to Knowledge</italic> or <italic>BD2K</italic> initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">7505202</journal-id><journal-id journal-id-type="pubmed-jr-id">572</journal-id><journal-id journal-id-type="nlm-ta">Ann Am Acad Pol Soc Sci</journal-id><journal-id journal-id-type="iso-abbrev">Ann Am Acad Pol Soc Sci</journal-id><journal-title-group><journal-title>The Annals of the American Academy of Political and Social Science</journal-title></journal-title-group><issn pub-type="ppub">0002-7162</issn></journal-meta><article-meta><article-id pub-id-type="pmid">26294799</article-id><article-id pub-id-type="pmc">4539961</article-id><article-id pub-id-type="doi">10.1177/0002716215570007</article-id><article-id pub-id-type="manuscript">NIHMS714645</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>From Big Data to Knowledge in the Social Sciences</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hesse</surname><given-names>Bradford W.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Moser</surname><given-names>Richard P.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Riley</surname><given-names>William T.</given-names></name></contrib></contrib-group><pub-date pub-type="nihms-submitted"><day>11</day><month>8</month><year>2015</year></pub-date><pub-date pub-type="ppub"><day>1</day><month>5</month><year>2015</year></pub-date><pub-date pub-type="pmc-release"><day>18</day><month>8</month><year>2015</year></pub-date><volume>659</volume><issue>1</issue><fpage>16</fpage><lpage>32</lpage><pmc-comment>elocation-id from pubmed: 10.1177/0002716215570007</pmc-comment>
<abstract><p id="P1">One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the <italic>Big Data to Knowledge</italic> or <italic>BD2K</italic> initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.</p></abstract><kwd-group><kwd>big data</kwd><kwd>data visualization</kwd><kwd>integrative data analysis</kwd><kwd>informatics</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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