Opportunities and Challenges for the Life Sciences Community
Identifieur interne : 000374 ( Pmc/Curation ); précédent : 000373; suivant : 000375Opportunities and Challenges for the Life Sciences Community
Auteurs : Eugene Kolker ; Elizabeth Stewart ; Vural OzdemirSource :
- OMICS : a Journal of Integrative Biology [ 1536-2310 ] ; 2012.
Abstract
Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of data-enabled life sciences. Yet even more is required not only to keep up with the current developments, but also to pro-actively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the Final Report for Data-Intensive Science Workshops DISW1 and DISW2. The first NSF-funded Data Intensive Science Workshop (DISW1, Seattle, WA, September 19–20, 2010) overviewed the status of the data-enabled life sciences and identified their challenges and opportunities. This served as a baseline for the second NSF-funded DIS workshop (DISW2, Washington, DC, May 16–17, 2011). Based on the findings of DISW2 the following overarching recommendation to the NSF was proposed: establish a community alliance to be the voice and framework of the data-enabled life sciences. After this Final Report was finished, Data-Enabled Life Sciences Alliance (DELSA,
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DOI: 10.1089/omi.2011.0152
PubMed: 22401659
PubMed Central: 3300061
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<front><journal-meta><journal-id journal-id-type="nlm-ta">OMICS</journal-id><journal-id journal-id-type="publisher-id">omi</journal-id><journal-title-group><journal-title>OMICS : a Journal of Integrative Biology</journal-title></journal-title-group><issn pub-type="ppub">1536-2310</issn><issn pub-type="epub">1557-8100</issn><publisher><publisher-name>Mary Ann Liebert, Inc.</publisher-name><publisher-loc>140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22401659</article-id><article-id pub-id-type="pmc">3300061</article-id><article-id pub-id-type="publisher-id">10.1089/omi.2011.0152</article-id><article-id pub-id-type="doi">10.1089/omi.2011.0152</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Opportunities and Challenges for the Life Sciences Community</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Kolker</surname><given-names>Eugene</given-names></name><xref ref-type="aff" rid="aff1"><sup>1,</sup></xref><xref ref-type="aff" rid="aff2"><sup>2,</sup></xref><xref ref-type="aff" rid="aff3"><sup>3,</sup></xref><xref ref-type="author-notes" rid="fn1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Stewart</surname><given-names>Elizabeth</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Ozdemir</surname><given-names>Vural</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><aff id="aff1"><label><sup>1</sup></label>Bioinformatics & High-throughput Analysis Lab and High-Throughput Analysis Core,<institution>Seattle Children's Research Institute</institution>, Seattle, Washington.</aff><aff id="aff2"><label><sup>2</sup></label>Predictive Analytics,<institution>Seattle Children's Hospital</institution>, Seattle, Washington.</aff><aff id="aff3"><label><sup>3</sup></label>Departments of Biomedical Informatics & Medical Education and Pediatrics,<institution>University of Washington</institution>, Seattle, Washington.</aff><aff id="aff4"><label><sup>4</sup></label>Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine,<institution>McGill University</institution>, Montreal,<country>Canada</country>.</aff></contrib-group><author-notes><corresp>Address correspondence to: <italic>Eugene Kolker, Ph.D., Seattle Children's Research Institute, 1900 Ninth Avenue, C9S-9, Seattle, WA 98101. E-mail:</italic><email xlink:href="mailto:eugene.kolker@seattlechildrens.org">eugene.kolker@seattlechildrens.org</email></corresp><fn id="fn1" fn-type="other"><label><sup>*</sup></label><p>Corresponding author.</p></fn></author-notes><pub-date pub-type="ppub"><month>3</month><year>2012</year><pmc-comment>string-date: March 2012</pmc-comment>
</pub-date><volume>16</volume><issue>3</issue><fpage>138</fpage><lpage>147</lpage><permissions><copyright-statement>Copyright 2012, Mary Ann Liebert, Inc.</copyright-statement><copyright-year>2012</copyright-year></permissions><self-uri xlink:type="simple" xlink:href="omi.2011.0152.pdf"></self-uri><abstract><title>Abstract</title><p>Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of data-enabled life sciences. Yet even more is required not only to keep up with the current developments, but also to pro-actively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the Final Report for Data-Intensive Science Workshops DISW1 and DISW2. The first NSF-funded Data Intensive Science Workshop (DISW1, Seattle, WA, September 19–20, 2010) overviewed the status of the data-enabled life sciences and identified their challenges and opportunities. This served as a baseline for the second NSF-funded DIS workshop (DISW2, Washington, DC, May 16–17, 2011). Based on the findings of DISW2 the following overarching recommendation to the NSF was proposed: establish a community alliance to be the voice and framework of the data-enabled life sciences. After this Final Report was finished, Data-Enabled Life Sciences Alliance (DELSA, <uri xlink:type="simple" xlink:href="http://www.delsall.org">www.delsall.org</uri>) was formed to become a Digital Commons for the life sciences community.</p></abstract><counts><fig-count count="1"></fig-count><table-count count="3"></table-count><ref-count count="25"></ref-count><page-count count="10"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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