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Multidisciplinary data review and analysis to fulfill NIH data sharing requirements via S‐Plus server
Author(s) -
Nicholson C. E.,
Narayan M.,
Patel D.,
Vijayakumar S.,
Barrett J. S.
Publication year - 2005
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1016/j.clpt.2004.12.167
Subject(s) - computer science , data sharing , world wide web , java , web server , web application , operating system , process (computing) , software , web application development , database , web page , web development , the internet , medicine , alternative medicine , pathology
Background Collaborative, multidisciplinary research places needs on data sharing and analysis that requires preplanning and adequate IT resources. Current efforts must also accommodate cross platform, multi‐site environments. The details of such solutions are required in the current NIH mandate on data sharing as well. Methods Our solution involves the use of both open‐source and proprietary software, to permit user‐friendly data analysis and review with limited burden on system administration. The open source server (Red Hat Enterprise Linuz AS v3.0) and S‐Plus Serve v6.2 are combined to perform real‐time, web‐based analysis of clinical, PK/PD and epidemiologic data being generated in the Clinical Pharmacology & Therapeutics Division. The underlying hardware is a Dell PowerEdge Server 2600 with dual 2.8 GHz Intel Xeon Processors and 512 MB of system memory. The server also runs Apache web server and Apache Tomcat JSP Server. Security is managed at the website level. The overall process is as follows: data source (local or remote) is parsed and imported via S‐plus server. S‐plus performs requested analyses and passes results to Tomcat which formats results for display in a java graphlet embedded into a standards‐compliant HTML webpage that is returned to the requesting client. Results/Conclusions We will demonstrate the productive environment and provide examples of data sharing and analyses for ongoing clinical collaborations. Clinical Pharmacology & Therapeutics (2005) 77 , P72–P72; doi: 10.1016/j.clpt.2004.12.167