Peer review at National Institutes of Health: Small steps forward

2008 ◽  
Vol 64 (5) ◽  
pp. A15-A17 ◽  
Author(s):  
S. Claiborne Johnston ◽  
Stephen L. Hauser
Keyword(s):  
Peer Review ◽  
National Institutes Of Health

Outcomes of National Institutes of Health Peer Review of Clinical Grant Applications

2006 ◽  
Vol 54 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Theodore A. Kotchen ◽  
Teresa Lindquist ◽  
Anita Miller Sostek ◽  
Raymond Hoffmann ◽  
Karl Malik ◽  
...  
Keyword(s):  
Peer Review ◽  
National Institutes Of Health ◽  
Grant Applications

The National Institutes of Health is going high‐tech in peer review

1992 ◽  
Vol 6 (7) ◽  
pp. 2384-2385
Author(s):  
Mushtaq A. Khan ◽  
Johnny W. Wortham ◽  
Nathan Watzman ◽  
Jerome G. Green
Keyword(s):  
Peer Review ◽  
National Institutes Of Health ◽  
High Tech

scholarly journals NIH peer review: Criterion scores completely account for racial disparities in overall impact scores

2020 ◽  
Vol 6 (23) ◽  
pp. eaaz4868 ◽  
Author(s):  
Elena A. Erosheva ◽  
Sheridan Grant ◽  
Mei-Ching Chen ◽  
Mark D. Lindner ◽  
Richard K. Nakamura ◽  
...  
Keyword(s):  
Racial Disparities ◽  
Peer Review ◽  
Multilevel Models ◽  
National Institutes Of Health ◽  
Career Stage ◽  
Review Criterion ◽  
Key Variables ◽  
Funding Disparities ◽  
Funding Gap ◽  
Final Impact

Previous research has found that funding disparities are driven by applications’ final impact scores and that only a portion of the black/white funding gap can be explained by bibliometrics and topic choice. Using National Institutes of Health R01 applications for council years 2014–2016, we examine assigned reviewers’ preliminary overall impact and criterion scores to evaluate whether racial disparities in impact scores can be explained by application and applicant characteristics. We hypothesize that differences in commensuration—the process of combining criterion scores into overall impact scores—disadvantage black applicants. Using multilevel models and matching on key variables including career stage, gender, and area of science, we find little evidence for racial disparities emerging in the process of combining preliminary criterion scores into preliminary overall impact scores. Instead, preliminary criterion scores fully account for racial disparities—yet do not explain all of the variability—in preliminary overall impact scores.

scholarly journals Research Synergy and Drug Development: Bright Stars in Neighboring Constellations

2017 ◽  
Author(s):  
Samet Keserci ◽  
Eric B. Livingston ◽  
Lingtian Wan ◽  
Alexander R. Pico ◽  
George Chacko
Keyword(s):  
Peer Review ◽  
Cancer Therapeutics ◽  
Scientific Activity ◽  
National Institutes Of Health ◽  
Scientific Publications ◽  
Societal Benefit ◽  
Core Set ◽  
Anti Cancer ◽  
History Of

AbstractDrug discovery and subsequent availability of a new breakthrough therapeutic or ‘cure’ is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. As a proof of principle, we have conducted a case study of five anti-cancer therapeutics. We have linked the work of roughly 237,000 authors in 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. We have enriched the content of networks of these therapeutics by annotating them with information on research awards as well as peer review that preceded these awards. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks as well as awards from the National Institutes of Health that supported this research. Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

scholarly journals Characterization of the Peer Review Network at the Center for Scientific Review, National Institutes of Health

2014 ◽  
Author(s):  
Kevin Boyack ◽  
Mei-Ching Chen ◽  
George Chacko
Keyword(s):  
Peer Review ◽  
Evaluation System ◽  
National Institutes Of Health ◽  
System Level ◽  
Graph Visualization ◽  
Structural Reorganization ◽  
Scientific Review ◽  
Grant Applications ◽  
Monitoring Tools ◽  
Visualization Techniques

The National Institutes of Health (NIH) is the largest source of funding for biomedical research in the world. This funding is largely effected through a competitive grants process. Each year the Center for Scientific Review (CSR) at NIH manages the evaluation, by peer review, of more than 55,000 grant applications. A relevant management question is how this scientific evaluation system, supported by finite resources, could be continuously evaluated and improved for maximal benefit to the scientific community and the taxpaying public. Towards this purpose, we have created the first system-level description of peer review at CSR by applying text analysis, bibliometric, and graph visualization techniques to administrative records. We identify otherwise latent relationships across scientific clusters, which in turn suggest opportunities for structural reorganization of the system based on expert evaluation. Such studies support the creation of monitoring tools and provide transparency and knowledge to stakeholders

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