What do the teams that produce science and the networks in which they are included look like? How is credit allocated within them? Heretofore, our knowledge of these topics has largely been shaped by analyses performed using co-authorship data. However, not all people who make important contributions to research projects appear as coauthors on all articles and some author positions are more prestigious than others. Evidence suggests that women and members of underrepresented racial and ethnic groups are disadvantaged in terms of author position. The same is likely true for research staff. This project will combine UMETRICS data covering over 23 million payroll transactions to over 175 thousand people paid on NIH projects at 31 universities covering over one-third of federally funded, academic R&D with Torvik and Smalheiser?s updated Author-ity database of publications by biomedical researchers. The combined data will allow us to analyze scientific collaboration networks, as distinct from coauthorships, and shed new light on relatively marginal populations in biomedicine. The interdisciplinary team combines emerging and established scholars and has successfully developed, analyzed, and distributed both datasets.
We will begin by describing the composition of the teams and networks supported on research projects, answering questions such as: How large are the teams that actually conduct research? What factors relate to the size of teams? What types of people work on them in terms of gender, race, ethnicity and age, and in what capacities? How are they each positioned in collaboration networks? We further study how researcher characteristics such as gender, race, ethnicity, age, and job title are related to the credit that they receive for their work. Lastly, we will study how these relationships depend on the gender, race, ethnicity, and age of the Principal Investigator (PI) and on funding mechanisms ? for example, whether women and younger PIs are more inclusive and egalitarian than men or some mechanisms are better in terms of inclusion (e.g. Fs versus Rs). These questions are increasingly urgent as scientific teams expand but require information on who actually works on research projects beyond data on authorship alone.