Grid-Enabled Measures Using Science 2.0 to Standardize Measures and Share Data

被引:31
|
作者
Moser, Richard P. [1 ]
Hesse, Bradford W. [1 ]
Shaikh, Abdul R. [1 ]
Courtney, Paul [2 ]
Morgan, Glen [1 ]
Augustson, Erik [1 ]
Kobrin, Sarah [1 ]
Levin, Kerry Y. [3 ]
Helba, Cynthia [3 ]
Garner, David [3 ]
Dunn, Marsha [3 ]
Coa, Kisha [3 ]
机构
[1] NCI, NIH, Bethesda, MD 20892 USA
[2] NCI Frederick, Clin Monitoring Res Program, SAIC Frederick Inc, Frederick, MD USA
[3] Westat Corp, Rockville, MD USA
基金
美国国家卫生研究院;
关键词
HEALTH-CARE; INFRASTRUCTURE; INFORMATION; SUPPORT; CYBERINFRASTRUCTURE; COLLABORATION; ASSOCIATION; ACCELERATE; SYSTEM;
D O I
10.1016/j.amepre.2011.01.004
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment-a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures. The first is accomplished by creating an online venue where a virtual community of researchers can collaborate together and come to consensus on measures by rating, commenting on, and viewing meta-data about the measures and associated constructs. The second is accomplished by connecting the constructs and measures to an ontological framework with data standards and common data elements such as the NCI Enterprise Vocabulary System (EVS) and the cancer Data Standards Repository (caDSR). This paper will describe the web 2.0 principles on which theGEMdatabase is based, describe its functionality, and discuss some of the important issues involved with creating the GEM database, such as the role of mutually agreed-on ontologies (i.e., knowledge categories and the relationships among these categories-for data sharing). (Am J Prev Med 2011;40(5S2):S134-S143) Published by Elsevier Inc. on behalf of American Journal of Preventive Medicine
引用
收藏
页码:S134 / S143
页数:10
相关论文
共 50 条
  • [41] The Self-organization of Distributed Heterogeneous Spatial Data Sources in Grid-enabled Spatial Data Infrastructure
    Guo, Li-Xia
    Li, Guo-Qing
    Yan, Yun-xuan
    JOURNAL OF PURE AND APPLIED MICROBIOLOGY, 2013, 7 : 159 - 166
  • [42] Grid-enabled high-performance quantitative aerosol retrieval from remotely sensed data
    Xue, Yong
    Ai, Jianwen
    Wan, Wei
    Guo, Huadong
    Li, Yingjie
    Wang, Ying
    Guang, Jie
    Mei, Linlu
    Xu, Hui
    COMPUTERS & GEOSCIENCES, 2011, 37 (02) : 202 - 206
  • [43] Using ESB and BPEL for Evolving Healthcare Systems Towards Pervasive, Grid-Enabled SOA
    Koufi, V.
    Malamateniou, F.
    Papakonstantinou, D.
    Vassilacopoulos, G.
    INFORMATION SYSTEMS DEVELOPMENT: TOWARDS A SERVICE PROVISION SOCIETY, 2009, : 167 - 175
  • [44] Observations in using Grid-enabled technologies for solving multi-objective optimization problems
    Luna, F.
    Nebro, A. J.
    Alba, E.
    PARALLEL COMPUTING, 2006, 32 (5-6) : 377 - 393
  • [45] Application of Grid-enabled technologies for solving optimization problems in data-driven reservoir studies
    Parashar, M
    Klie, H
    Catalyurek, U
    Kurc, T
    Bangerth, W
    Matossian, V
    Saltz, J
    Wheeler, MF
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2005, 21 (01): : 19 - 26
  • [46] Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies
    Parashar, M
    Klie, H
    Catalyurek, U
    Kurc, T
    Matossian, V
    Saltz, J
    Wheeler, MF
    COMPUTATIONAL SCIENCE - ICCS 2004, PT 3, PROCEEDINGS, 2004, 3038 : 805 - 812
  • [47] Grid-enabled OGC envirorment for EO data and services in support of Canada's forest applications
    Goodenough, David G.
    Chen, Hao
    Do, Liping
    Guan, Aimin
    Wei, Yaxing
    Dyk, Andrew
    Hobart, Geordie
    IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, : 4773 - +
  • [48] Grid-enabled bio-sample management application for data-intensive biomarker analysis
    Lickerman, Erik
    Bry, L.
    Herring, N.
    Pandelidis, Y.
    Afsheen, Nighat
    Sharif, A.
    19TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, 2006, : 751 - +
  • [49] Workload and task management of Grid-enabled quantitative aerosol retrieval from remotely sensed data
    Xue, Yong
    Ai, Jianwen
    Wan, Wei
    Li, Yingjie
    Wang, Ying
    Guang, Jie
    Mei, Linlu
    Xu, Hui
    Li, Qiang
    Bai, Linyan
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2010, 26 (04): : 590 - 598
  • [50] A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data
    Luca Corradi
    Marco Fato
    Ivan Porro
    Silvia Scaglione
    Livia Torterolo
    BMC Bioinformatics, 9