Are Race, Ethnicity, and Medical School Affiliation Associated with NIH R01 Type Award Probability for Physician Investigators?

This is a non-final version of an article published in final form in Acad Med. 2012 November ; 87(11): 1516–1524. doi:10.1097/ACM.0b013e31826d726b.PURPOSE: To analyze the relationship among NIH R01 Type 1 applicant degree, institution type, and race/ethnicity, and application award probability. METHOD: The authors used 2000–2006 data from the NIH IMPAC II grants database and other sources to determine which individual and institutional characteristics of applicants may affect the probability of applications being awarded funding. They used descriptive statistics and probit models to estimate correlations between race/ethnicity, degree (MD or PhD), and institution type (medical school or other institution), and application award probability, controlling for a large set of observable characteristics. RESULTS: Applications from medical schools were significantly more likely than those from other institutions to receive funding, as were applications from MDs versus PhDs. Overall, applications from blacks and Asians were less likely than those from whites to be awarded funding; however, among applications from MDs at medical schools, there was no difference in funding probability between whites and Asians and the difference between blacks and whites decreased to 7.8 percentage points. The inclusion of human subjects significantly decreased the likelihood of receiving funding. CONCLUSIONS: Compared with applications from whites, applications from blacks have a lower probability of being awarded R01 Type 1 funding, regardless of the investigator’s degree. However, funding probability is increased for applications with MD investigators and for those from medical schools. To some degree, these advantages combine so that applications from black MDs at medical schools have the smallest difference in funding probability compared with those from whites

Using ORCID, DOI, and Other Open Identifiers in Research Evaluation

An evaluator's task is to connect the dots between program goals and its outcomes. This can be accomplished through surveys, research, and interviews, and is frequently performed post hoc. Research evaluation is hampered by a lack of data that clearly connect a research program with its outcomes and, in particular, by ambiguity about who has participated in the program and what contributions they have made. Manually making these connections is very labor-intensive, and algorithmic matching introduces errors and assumptions that can distort results. In this paper, we discuss the use of identifiers in research evaluation—for individuals, their contributions, and the organizations that sponsor them and fund their work. Global identifier systems are uniquely positioned to capture global mobility and collaboration. By leveraging connections between local infrastructures and global information resources, evaluators can map data sources that were previously either unavailable or prohibitively labor-intensive. We describe how identifiers, such as ORCID iDs and DOIs, are being embedded in research workflows across science, technology, engineering, arts, and mathematics; how this is affecting data availability for evaluation purposes: and provide examples of evaluations that are leveraging identifiers. We also discuss the importance of provenance and preservation in establishing confidence in the reliability and trustworthiness of data and relationships, and in the long-term availability of metadata describing objects and their inter-relationships. We conclude with a discussion on opportunities and risks for the use of identifiers in evaluation processes

Race, Ethnicity, and NIH Research Awards

This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on 2011 August 19; 333(6045): 1015–1019., DOI: 10.1126/science.1196783.We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant’s self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant’s educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black or African-American applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention

Race, Ethnicity, and NIH Research Awards

This is the author's accepted manuscript. The original is available at http://www.sciencemag.org/content/333/6045/1015.We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant’s self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant’s educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black or African-American applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention

Race, Ethnicity, and NIH Research Awards

We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant’s self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant’s educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention

Diversity in Academic Biomedicine: An Evaluation of Education and Career Outcomes with Implications for Policy

Currently, the U.S. population is undergoing major racial and ethnic demographic shifts that could affect the pool of individuals interested in pursuing a career in biomedical research. To achieve its mission of improving health, the National Institutes of Health must recruit and train outstanding individuals for the biomedical workforce. In this study, we examined the educational transition rates in the biomedical sciences by gender, race, and ethnicity, from high school to academic career outcomes. Using a number of educational databases, we investigated gender and racial/ethnic representation at typical educational and career milestones en route to faculty careers in biomedicine. We then employed multivariate regression methods to examine faculty career outcomes, using the National Science Foundation’s Survey of Doctorate Recipients. We find that while transitions between milestones are distinctive by gender and race/ethnicity, the transitions between high school and college and between college and graduate school are critical points at which underrepresented minorities are lost from the biomedical pipeline, suggesting some specific targets for policy intervention

Standards and Infrastructure for Innovation Data Exchange

This is the author's accepted manuscript. The original publication is available at http://www.sciencemag.org/content/338/6104/196.Economic growth relies in part on efficient advancement and application of research and development (R&D) knowledge. This requires access to data about science, in particular R&D inputs and outputs such as grants, patents, publications, and data sets, to support an understanding of how R&D information is produced and what affects its availability. But there is a cacophony of R&D-related data across countries, disciplines, data providers, and sectors. Burdened with data that are inconsistently specified, researchers and policy-makers have few incentives or mechanisms to share or interlink cleaned data sets. Access to these data is limited by a patchwork of laws, regulations, and practices that are unevenly applied and interpreted (1). A Web-based infrastructure for data sharing and analysis could help. We describe administrative and technical demands and opportunities to meet them. Data exchange standards are a first step

ORCID: connecting researchers and scholars with their works

Open Researcher and Contributor ID (ORCID) is a community-driven non-profit organization that provides an open registry of persistent identifiers for researchers and scholars. The mission of ORCID is to address the name ambiguity problem in research and scholarly communication. ORCID works with the research community to embed identifiers in research workflows: grant applications, manuscript submissions, association membership renewal, meeting abstract submission, everywhere that a person can be connected with a research or scholarly contribution. This article describes ORCID, and its adoption by the research community, and provides examples of how ORCID identifiers are being integrated by organizations throughout the community and becoming part of the metadata on a diverse set of documents. Ultimately, the goal of ORCID is to improve discoverability, reduce repetitive data entry, and thereby allow researchers and organizations more time to focus on research and scholarly pursuits