Basner Jodi E, Theisz Katrina I, Jensen Unni S, Jones C David, Ponomarev Ilya, Sulima Pawel, Jo Karen, Eljanne Mariam, Espey Michael G, Franca-Koh Jonathan, Hanlon Sean E, Kuhn Nastaran Z, Nagahara Larry A, Schnell Joshua D, Moore Nicole M
Discovery Logic, a Thomson Reuters business, Kelly Government Solutions, Rockville, MD 20852 and Office of Physical Sciences - Oncology, Center for Strategic Scientific Initiatives, Office of the Director, National Cancer Institute, Bethesda, MD 20892, USA.
Res Eval. 2013 Dec;22(5):285-297. doi: 10.1093/reseval/rvt025.
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.
开发有效的定量指标和方法来评估跨学科合作项目的成果,有可能改善科研项目管理和科研产出。本文重点介绍了一个前瞻性评估的例子,该评估旨在监测和改善美国国立癌症研究所物理科学-肿瘤学中心(PS-OC)项目的进展。研究数据,包括合作信息,通过进展报告获取,并使用基于网络的分析数据库:跨学科团队报告、分析和查询资源进行汇编。来自汤森路透科学网数据库、医学文献数据库和一项基于网络的调查的数据进一步支持了对合作的分析。通过开发自动化方法来挖掘研究者获奖前的出版物、确定研究者的学科并区分跨学科出版物内容,增强了新数据来源与标准数据来源的整合。结果突出显示了主要研究者从获奖前到获奖后几年跨学科作者合作的增加,并证实大多数跨学科合作产生了跨学科内容的出版物,这些出版物在其领域排名前三分之一。有了这些评估数据,PS-OC项目官员向参与的研究者提供了持续反馈,以提高中心的生产力,从而推动该项目取得更成功的成果。未来的分析将继续扩展这些方法和指标,以适应研究评估的新进展和项目的变化。