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Measuring the evolution and output of cross-disciplinary collaborations within the nci physical sciences-oncology centers network

机译:评估nci物理科学-肿瘤学中心网络内跨学科合作的演变和输出

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Development of effective quantitative indicators and methodologies to assess the outcomes of cross-disciplinary collaborative initiatives has the potential to improve scientific program management and scientific output. This article highlights an example of a prospective evaluation that has been developed to monitor and improve progress of the National Cancer Institute Physical Sciences-Oncology Centers (PS-OC) program. Study data, including collaboration information, was captured through progress reports and compiled using the web-based analytic database: Interdisciplinary Team Reporting, Analysis, and Query Resource. Analysis of collaborations was further supported by data from the Thomson Reuters Web of Science database, MEDLINE database, and a web-based survey. Integration of novel and standard data sources was augmented by the development of automated methods to mine investigator pre-award publications, assign investigator disciplines, and distinguish cross-disciplinary publication content. The results highlight increases in cross-disciplinary authorship collaborations from pre- To post-award years among the primary investigators and confirm that a majority of cross-disciplinary collaborations have resulted in publications with cross-disciplinary content that rank in the top third of their field. With these evaluation data, PS-OC Program officials have provided ongoing feedback to participating investigators to improve center productivity and thereby facilitate a more successful initiative. Future analysis will continue to expand these methods and metrics to adapt to new advances in research evaluation and changes in the program.
机译:开发有效的定量指标和方法以评估跨学科合作计划的成果,有可能改善科学计划管理和科学产出。本文重点介绍了一个前瞻性评估的例子,该评估旨在监视和改善美国国家癌症研究所物理科学-肿瘤学中心(PS-OC)计划的进度。通过进度报告捕获包括协作信息在内的研究数据,并使用基于Web的分析数据库进行编译:跨学科团队报告,分析和查询资源。汤森路透Web of Science数据库,MEDLINE数据库和基于网络的调查数据进一步支持了合作分析。通过开发自动化方法来挖掘新颖和标准的数据源,从而挖掘研究人员的预先授予的出版物,分配研究人员的学科并区分跨学科的出版物内容。结果突出显示了主要研究人员从授予前到授予后跨学科作者合作的增加,并确认大多数跨学科合作导致出版物的跨学科内容在其领域中排名前三。有了这些评估数据,PS-OC计划官员就不断向参与调查的人员提供反馈,以提高中心的生产率,从而促进更成功的计划。未来的分析将继续扩展这些方法和指标,以适应研究评估和计划变更的新进展。

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