BIO-INFORMATICS RESEARCH NETWORK COORDINATING CENTER (BIRN-CC)

生物信息学研究网络协调中心（BIRN-CC）

• 批准号: 7961187
• 负责人: Carl Kesselman
• 金额: $ 64.27万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-23 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7961187
• 关键词: Address; Behavioral; Bioinformatics; Biomedical Informatics Research Network; Biomedical Research; Clinical; Collaborations; Communities; Complex; Computer Retrieval of Information on Scientific Projects Database; Computers; Data; Data Analyses; Data Sources; Databases; Development; Discipline; Documentation; Engineering; Evidence Based Medicine; Funding; Grant; Information Technology; Institution; Link; Modeling; National Center for Research Resources; Neurosciences; Ontology; Participant; Peer Review; Policies; Positioning Attribute; Procedures; Process; Publications; Research; Research Infrastructure; Research Personnel; Resources; Retrieval; Scientist; Services; Site; Solutions; Source; Structure; Techniques; United States National Institutes of Health; base; bioimaging; cluster computing; data management; data sharing; distributed data; interoperability; public health relevance; tool; virtual

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. DESCRIPTION (provided by applicant): To respond to the needs for advanced infrastructure for collaborative biomedicine, the NCRR Biomedical Informatics Research Network (BIRN) was established to develop a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The network of BIRN participants initially involved three testbeds with a neuroscience emphasis. The BIRN Coordinating Center (BIRN CC) is an important piece in the overall BIRN network, serving to develop, implement, and support the information technology infrastructure necessary to achieve distributed collaborations and data sharing between BIRN users. The current BIRN CC consists of a partnership between computer scientists, neuroscientists, and engineers based at the San Diego Super Computing Center (SDSC). In this proposal, we address issues that have hindered collaborative research effort. We will evolve BIRN with new capabilities, using proven techniques that are already being utilized in other disciplines to automate the data publication process and to form "virtual data warehouses" that provides unified query interfaces over disparate and distributed data sources Specifically: 1) we will transition the BIRN CC to a new site (ISI at USC); 2) migrate the core infrastructure to a virtual machine based deployment strategy; 3) leverage cyberinfrastructure tools to provide stability, scalability and interoperability; 4) replace Storage Resource Broker-based data solution with a service-based federated data management infrastructure, 4) move the BIRN Data Repository to storage hosted at ISI/USC, 5) migrate and support the most mature and extensible tools currently used by BIRN user base new services framework; 6) create an infrastructure allowing other developers to integrate tools into BIRN, 7) implement pragmatic data curation procedures focusing on creating a framework for using multiple ontologies. Finally, we propose a new. governance structure that will be more responsive to the needs of the growing BIRN community. This new model will allow expansion of the BIRN user base and domains and will position BIRN at the forefront of new efforts to store primary data linked to peer-reviewed publication, taking advantage of new NIH open-access policies. Public Health Relevance: Biomedical research benefits greatly from the integration of biomedical imaging, clinical, and behavioral data from multiple independent institutions. This project will provide a data sharing infrastructure that will enable researchers to investigate complex data-driven problems, otherwise impossible. The impact will be dramatic as multi-model data analysis has become fundamental to the development of evidence-based medicine.

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