Citation analysis and discourse analysis of 369 R01 NIH proposals are used to discover possible predictors of proposal success. We focused on two issues: the Matthew effect in science-Merton's claim that eminent scientists have an inherent advantage in the competition for funds-and quality of writing or clarity. Our results suggest that a clearly articulated proposal is more likely to be funded than a proposal with lower quality of discourse. We also find that proposal success is correlated with a high level of topical overlap between the proposal references and the applicant's prior publications. Implications associated with the analysis of proposal data are discussed.

Portfolio analysis is a fundamental practice of organizational leadership and is a necessary precursor of strategic planning. Successful application requires a highly detailed model of research options. We have constructed a model, the first of its kind, that accurately characterizes these options for the biomedical literature. The model comprises over 18 million PubMed documents from 1996–2019. Document relatedness was measured using a hybrid citation analysis + text similarity approach. The resulting 606.6 million document-to-document links were used to create 28,743 document clusters and an associated visual map. Clusters are characterized using metadata (e.g., phrases, MeSH) and over 20 indicators (e.g., funding, patent activity). The map and cluster-level data are embedded in Tableau to provide an interactive model enabling in-depth exploration of a research portfolio. Two example usage cases are provided, one to identify specific research opportunities related to coronavirus, and the second to identify research strengths of a large cohort of African American and Native American researchers at the University of Michigan Medical School.

Translational research as a discipline has experienced explosive growth over the last decade as evidenced by significant federal investment and the exponential increase in related publications. However, narrow project-focused or process-based measurement approaches have resulted in insufficient techniques to measure the translational progress of institutions or large-scale networks. A shift from traditional industrial engineering approaches to systematic investigation using the techniques of scientometrics and network science will be required to assess the impact of investments in translational research.

Purpose The analysis of existing institutional research proposal databases can provide novel insights into science funding parity. The purpose of this study was to analyze the relationship between race/ethnicity and extramural research proposal and award rates across a medical school faculty and to determine whether there was evidence that researchers changed their submission strategies because of differential inequities across submission categories. Method The authors performed an analysis of 14,263 biomedical research proposals with proposed start dates between 2010–2022 from the University of Michigan Medical School, measuring the proposal submission and award rates for each racial/ethnic group across 4 possible submission categories (R01 & Equivalent programs, other federal, industry, and non-profit). Results Researchers from each self-identified racial/ethnic group (Asian, Black/African American, Hispanic/Latino) pursued a different proposal submission strategy than the majority group (White). The authors found that Black/African American researchers experienced negative award rate differentials across all submission categories, which resulted in the lowest R01 & Equivalent and Other Federal submission rates of any racial/ethnic group and the highest submission rate to non-profit sources. The authors did not find support for the hypothesis that researchers changed submission strategies in response to award rate inequalities across submission categories. Conclusions Biomedical researchers from different racial/ethnic groups follow markedly different proposal submission strategies within the University of Michigan Medical School. There is also a clear relationship between race/ethnicity and rates of proposal award. Black/African American and Asian researchers appear disadvantaged across all submission categories relative to White researchers. This study can be easily replicated by other academic research institutions, revealing opportunities for positive intervention.

Using a model of the literature indexed in Scopus, we have increased the accuracy of our ability to predict which of 20,747 research communities would achieve exceptional growth from 32.2 to 39.6 using double exponential smoothing of inertial indicators and by doing predictions in each of 26 fields rather than across the entire model. Each field nominated two (out of a possible 123) indicators as ‘best predictors’ following the procedure described in our previous studies. Significant diversity was found in which indicators performed best in each field, suggesting that field effects should be accounted for in predictive analytics. Nevertheless, there were groupings of contiguous fields with a surprising level of homogeneity in predictive indicators. Possible reasons for the similarities and differences are discussed.