Literature DB >> 22401659

Opportunities and challenges for the life sciences community.

Eugene Kolker1, Elizabeth Stewart, Vural Ozdemir.   

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, www.delsall.org ) was formed to become a Digital Commons for the life sciences community.

Mesh:

Year:  2012        PMID: 22401659      PMCID: PMC3300061          DOI: 10.1089/omi.2011.0152

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  21 in total

1.  A vision for 21st century U.S. Policy to support sustainable advancement of scientific discovery and technological innovation.

Authors:  Eugene Kolker
Journal:  OMICS       Date:  2010-08

2.  Bioinformatics and data-intensive scientific discovery in the beginning of the 21st century.

Authors:  Roger Barga; Bill Howe; David Beck; Stuart Bowers; William Dobyns; Winston Haynes; Roger Higdon; Chris Howard; Christian Roth; Elizabeth Stewart; Dean Welch; Eugene Kolker
Journal:  OMICS       Date:  2011-04

3.  Communication and data-intensive science in the beginning of the 21st century.

Authors:  Jack Faris; Evelyne Kolker; Alex Szalay; Leon Bradlow; Ewa Deelman; Wu Feng; Judy Qiu; Donna Russell; Elizabeth Stewart; Eugene Kolker
Journal:  OMICS       Date:  2011-04

4.  Special issue on data-intensive science.

Authors:  Eugene Kolker
Journal:  OMICS       Date:  2011-04

5.  Classifying proteins into functional groups based on all-versus-all BLAST of 10 million proteins.

Authors:  Natali Kolker; Roger Higdon; William Broomall; Larissa Stanberry; Dean Welch; Wei Lu; Winston Haynes; Roger Barga; Eugene Kolker
Journal:  OMICS       Date:  2011 Jul-Aug

6.  The case for cloud computing in genome informatics.

Authors:  Lincoln D Stein
Journal:  Genome Biol       Date:  2010-05-05       Impact factor: 13.583

Review 7.  Computational solutions to large-scale data management and analysis.

Authors:  Eric E Schadt; Michael D Linderman; Jon Sorenson; Lawrence Lee; Garry P Nolan
Journal:  Nat Rev Genet       Date:  2010-09       Impact factor: 53.242

8.  Cloud computing and the DNA data race.

Authors:  Michael C Schatz; Ben Langmead; Steven L Salzberg
Journal:  Nat Biotechnol       Date:  2010-07       Impact factor: 54.908

9.  Hybrid cloud and cluster computing paradigms for life science applications.

Authors:  Judy Qiu; Jaliya Ekanayake; Thilina Gunarathne; Jong Youl Choi; Seung-Hee Bae; Hui Li; Bingjing Zhang; Tak-Lon Wu; Yang Ruan; Saliya Ekanayake; Adam Hughes; Geoffrey Fox
Journal:  BMC Bioinformatics       Date:  2010-12-21       Impact factor: 3.169

10.  Scientific software development is not an oxymoron.

Authors:  Susan M Baxter; Steven W Day; Jacquelyn S Fetrow; Stephanie J Reisinger
Journal:  PLoS Comput Biol       Date:  2006-09-08       Impact factor: 4.475

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  9 in total

1.  Why share data? Lessons learned from the fMRIDC.

Authors:  John Darrell Van Horn; Michael S Gazzaniga
Journal:  Neuroimage       Date:  2012-11-13       Impact factor: 6.556

Review 2.  A network perspective on unraveling the role of TRP channels in biology and disease.

Authors:  Jung Nyeo Chun; Jin Muk Lim; Young Kang; Eung Hee Kim; Young-Cheul Shin; Hong-Gee Kim; Dayk Jang; Dongseop Kwon; Soo-Yong Shin; Insuk So; Ju-Hong Jeon
Journal:  Pflugers Arch       Date:  2013-05-16       Impact factor: 3.657

3.  Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Authors:  Eugene Kolker; Vural Özdemir; Lennart Martens; William Hancock; Gordon Anderson; Nathaniel Anderson; Sukru Aynacioglu; Ancha Baranova; Shawn R Campagna; Rui Chen; John Choiniere; Stephen P Dearth; Wu-Chun Feng; Lynnette Ferguson; Geoffrey Fox; Dmitrij Frishman; Robert Grossman; Allison Heath; Roger Higdon; Mara H Hutz; Imre Janko; Lihua Jiang; Sanjay Joshi; Alexander Kel; Joseph W Kemnitz; Isaac S Kohane; Natali Kolker; Doron Lancet; Elaine Lee; Weizhong Li; Andrey Lisitsa; Adrian Llerena; Courtney Macnealy-Koch; Jean-Claude Marshall; Paola Masuzzo; Amanda May; George Mias; Matthew Monroe; Elizabeth Montague; Sean Mooney; Alexey Nesvizhskii; Santosh Noronha; Gilbert Omenn; Harsha Rajasimha; Preveen Ramamoorthy; Jerry Sheehan; Larry Smarr; Charles V Smith; Todd Smith; Michael Snyder; Srikanth Rapole; Sanjeeva Srivastava; Larissa Stanberry; Elizabeth Stewart; Stefano Toppo; Peter Uetz; Kenneth Verheggen; Brynn H Voy; Louise Warnich; Steven W Wilhelm; Gregory Yandl
Journal:  OMICS       Date:  2014-01

Review 4.  Toward a Literature-Driven Definition of Big Data in Healthcare.

Authors:  Emilie Baro; Samuel Degoul; Régis Beuscart; Emmanuel Chazard
Journal:  Biomed Res Int       Date:  2015-06-02       Impact factor: 3.411

5.  SCALEUS-FD: A FAIR Data Tool for Biomedical Applications.

Authors:  Arnaldo Pereira; Rui Pedro Lopes; José Luís Oliveira
Journal:  Biomed Res Int       Date:  2020-08-26       Impact factor: 3.411

6.  Mass digitization of scientific collections: New opportunities to transform the use of biological specimens and underwrite biodiversity science.

Authors:  Reed S Beaman; Nico Cellinese
Journal:  Zookeys       Date:  2012-07-20       Impact factor: 1.546

7.  Differential expression analysis for pathways.

Authors:  Winston A Haynes; Roger Higdon; Larissa Stanberry; Dwayne Collins; Eugene Kolker
Journal:  PLoS Comput Biol       Date:  2013-03-14       Impact factor: 4.475

8.  Big Data in Biology and Medicine: Based on material from a joint workshop with representatives of the international Data-Enabled Life Science Alliance, July 4, 2013, Moscow, Russia.

Authors:  O P Trifonova; V A Il'in; E V Kolker; A V Lisitsa
Journal:  Acta Naturae       Date:  2013-07       Impact factor: 1.845

9.  A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).

Authors:  Tsung-Jung Wu; Amirhossein Shamsaddini; Yang Pan; Krista Smith; Daniel J Crichton; Vahan Simonyan; Raja Mazumder
Journal:  Database (Oxford)       Date:  2014-03-25       Impact factor: 3.451

  9 in total

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