The Office of Portfolio Analysis is excited to announce the publication of three data-driven studies aimed at supporting NIH decision-making and funding priorities

Topic choice contributes to the lower rate of NIH awards to African-American/black scientists

In a study published in the journal Science Advances, researchers with the NIH Office of Portfolio Analysis (OPA) found that African-American/black (AA/B) scientists that apply for NIH R01 funding are more likely to study topics with lower funding rates. Topic choice alone accounted for 20% of the observed funding gap after controlling for multiple variables. Community and population-level research tend to receive more AA/B applicants compared with fundamental/mechanistic topics, the latter tend to have higher award rates. These findings can be used to assist with the direction of future funding priorities within the NIH.

Full article access is available here

Predicting translational progress in biomedical research

Fundamental research can take decades to translate into clinical outcomes. To reduce this time interval and speed up the discovery of human therapeutics, a machine learning model was created by OPA researchers to accurately predict whether a scientific publication will have an impact on clinical research. These studies, recently published in PLOS Biology, demonstrate that with as little as two years of post-publication citation data, OPA scientists were able to accurately predict whether an article will eventually be cited by a clinical article (clinical trial or guideline). This article-level metric, the Approximate Potential for Translation (APT), can be used by decision-makers hoping to identify research with a high likelihood to contribute to clinical outcomes and is available to the public in the Translation module of the OPA-created iCite 2.0 tool.

Full article access is available here

Link to iCite 2.0

The NIH Open Citation Collection: A public access, broad coverage resource

Bibliometric analyses focused on determining the impact of a portfolio of grants or publications rely on accurate, reliable citation data. Historically, citation data have remained locked behind restrictive licensing agreements, hampering the ability of researchers to identify reference linkages between scientific articles. To address this barrier, the NIH Open Citation Collection (NIH-OCC) was created using unrestricted data sources and full-text articles that have been made freely available on the internet. This dataset underlies the updated version of iCite and has been made publicly available in the Open Cites module. These data can be used to perform reproducible, trustworthy bibliometric analyses.

Methodology used in to create the NIH-OCC was published in PLOS Biology. Full article access is available here

Link to iCite 2.0

Measuring Research Outputs – iCite 2.0 workshop

Have you already taken OPA Bibliometrics or Translational Science training? Are you interested in finding out more about iCite 2.0 released on Oct 10th and the recently published papers: Predicting translational progress in biomedical research and The NIH Open Citation Collection: A public access, broad coverage resource? If you answered yes to these questions, we are holding a workshop on Tuesday November 19th in Natcher (2:00PM-4:00PM). For more details and to register see the event registration page which can also be accessed from the OPA training page.

For those who are not familiar with the tools OPA offered previously, we have combined the material to create a new class – Measuring Research Outputs (iCite – RCR and Translation). The next class with spaces is on Thursday December 12th. You can find registration details on the OPA training page.

 

 

This page last reviewed on October 23, 2019