What Difference Does Quantity Make? On the Epistemology of Big Data in Biology
Identifieur interne : 000605 ( Ncbi/Merge ); précédent : 000604; suivant : 000606What Difference Does Quantity Make? On the Epistemology of Big Data in Biology
Auteurs : Sabina LeonelliSource :
- Big data & society [ 2053-9517 ] ; 2014.
Abstract
Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.
Url:
PubMed: 25729586
PubMed Central: 4340542
Links toward previous steps (curation, corpus...)
- to stream Pmc, to step Corpus: 000445
- to stream Pmc, to step Curation: 000445
- to stream Pmc, to step Checkpoint: 000151
Links to Exploration step
PMC:4340542Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">What Difference Does Quantity Make? On the Epistemology of Big Data in Biology</title>
<author><name sortKey="Leonelli, Sabina" sort="Leonelli, Sabina" uniqKey="Leonelli S" first="Sabina" last="Leonelli">Sabina Leonelli</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">25729586</idno>
<idno type="pmc">4340542</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340542</idno>
<idno type="RBID">PMC:4340542</idno>
<date when="2014">2014</date>
<idno type="wicri:Area/Pmc/Corpus">000445</idno>
<idno type="wicri:Area/Pmc/Curation">000445</idno>
<idno type="wicri:Area/Pmc/Checkpoint">000151</idno>
<idno type="wicri:Area/Ncbi/Merge">000605</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">What Difference Does Quantity Make? On the Epistemology of Big Data in Biology</title>
<author><name sortKey="Leonelli, Sabina" sort="Leonelli, Sabina" uniqKey="Leonelli S" first="Sabina" last="Leonelli">Sabina Leonelli</name>
</author>
</analytic>
<series><title level="j">Big data & society</title>
<idno type="eISSN">2053-9517</idno>
<imprint><date when="2014">2014</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p id="P1">Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.</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">101648833</journal-id>
<journal-id journal-id-type="pubmed-jr-id">43393</journal-id>
<journal-id journal-id-type="nlm-ta">Big Data Soc</journal-id>
<journal-id journal-id-type="iso-abbrev">Big Data Soc</journal-id>
<journal-title-group><journal-title>Big data & society</journal-title>
</journal-title-group>
<issn pub-type="epub">2053-9517</issn>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25729586</article-id>
<article-id pub-id-type="pmc">4340542</article-id>
<article-id pub-id-type="manuscript">EMS61442</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>What Difference Does Quantity Make? On the Epistemology of Big Data in Biology</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Leonelli</surname>
<given-names>Sabina</given-names>
</name>
<aff id="A1">Department of Sociology, Philosophy and Anthropology & Exeter Centre for the Study of the Life Sciences (Egenis), University of Exeter, UK,<email>s.leonelli@exeter.ac.uk</email>
</aff>
</contrib>
</contrib-group>
<pub-date pub-type="nihms-submitted"><day>9</day>
<month>1</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="ppub"><day>1</day>
<month>6</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>25</day>
<month>2</month>
<year>2015</year>
</pub-date>
<volume>1</volume>
<issue>1</issue>
<elocation-id>10.1177/2053951714534395</elocation-id>
<self-uri xlink:href="http://bds.sagepub.com/content/1/1/2053951714534395"></self-uri>
<abstract><p id="P1">Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.</p>
</abstract>
<kwd-group><kwd>big data epistemology</kwd>
<kwd>data-intensive science</kwd>
<kwd>biology</kwd>
<kwd>databases</kwd>
<kwd>data infrastructures</kwd>
<kwd>data curation</kwd>
<kwd>model organisms</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Leonelli, Sabina" sort="Leonelli, Sabina" uniqKey="Leonelli S" first="Sabina" last="Leonelli">Sabina Leonelli</name>
</noCountry>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/CyberinfraV1/Data/Ncbi/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000605 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd -nk 000605 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Ticri/CIDE |area= CyberinfraV1 |flux= Ncbi |étape= Merge |type= RBID |clé= PMC:4340542 |texte= What Difference Does Quantity Make? On the Epistemology of Big Data in Biology }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/RBID.i -Sk "pubmed:25729586" \ | HfdSelect -Kh $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd \ | NlmPubMed2Wicri -a CyberinfraV1
![]() | This area was generated with Dilib version V0.6.25. | ![]() |