Consequential, reproducible scientific research has led to better health worldwide many times and increasing scientific productivity of biomedical research is a frequent topic of discussions. However, there is a shortage of evidence on what strategies can make the most meaningful difference. The objective of this research is to identify pathways and outputs that distinguish fast-growing research institutions and clusters of laboratories from their comparable peers. Studying best practices and entrepreneurial initiatives of the most dynamic clusters should elucidate the mechanisms by which biomedical research can expand and improve. This project will compile data from a variety of research performance databases, including funded awards, publications, citations, clinical trials, submissions to data repositories, patenting, and commercialization, and many others. It puts emphasis on the use of non-publication research productivity measures at the laboratory level as they can be more up to date and cover broader range of activities. A variety of outcome metrics will be assessed for reliability and validity in association with fast growth. Subsequently, the proposed study will look into the driving factors, qualities of fast-growing clusters of laboratories, like workforce and diversity, behind outstanding outcomes. A central indicator of growth will be the size and competence of the cohort of principal investigators in comparison to their peers. In analyzing the value of gender, racial, ethnic and professional diversity and inclusiveness, this project will be focused not only on numeric representations but also on the level of participation in decision-making. Based on strong associations with success, best practices will be highlighted and recommended for consideration by research institutions and laboratories. The latest developments in the availability and granularity of information about research performance create unprecedented opportunities for analysis of scientific productivity at the level of research laboratories and clusters. The proposed project will focus on a less well-studied unit of measurement, the research laboratory. It will be based on a novel delineation of best practice cases (axiomatic superior outcomes) and controls (everyone else). The project will integrate new data sources and measures of scientific productivity and extensively use newer, non-publication research productivity measures. It will synthesize up-to-date, real-world evidence and leverage information about ongoing projects from multiple sources. Results of the proposed project should lead to better understanding of the relationships among various outcomes and the role of important qualities in promoting greater progress in reproducible and consequential biomedical research.
