Factors influencing taxonomic unevenness in scientific research: A mixed-methods case study of non-human primate genomic sequence data generation: Why do scientists study certain species?

Scholars have noted major disparities in the extent of scientific research conducted among taxonomic groups. Such trends may cascade if future scientists gravitate towards study species with more data and resources already available. As new technologies emerge, do research studies employing these technologies continue these disparities? Here, using non-human primates as a case study, we identified disparities in massively parallel genomic sequencing data and conducted interviews with scientists who produced these data to learn their motivations when selecting study species. We tested whether variables including publication history and conservation status were significantly correlated with publicly available sequence data in the NCBI Sequence Read Archive (SRA). Of the 179.6 terabases (Tb) of sequence data in SRA for 519 non-human primate species, 135 Tb (approx. 75%) were from only five species: rhesus macaques, olive baboons, green monkeys, chimpanzees and crab-eating macaques. The strongest predictors of the amount of genomic data were the total number of non-medical publications (linear regression; r 2 = 0.37; p = 6.15 × 10 -12) and number of medical publications (r 2 = 0.27; p = 9.27 × 10 -9). In a generalized linear model, the number of non-medical publications (p = 0.00064) and closer phylogenetic distance to humans (p = 0.024) were the most predictive of the amount of genomic sequence data. We interviewed 33 authors of genomic data-producing publications and analysed their responses using grounded theory. Consistent with our quantitative results, authors mentioned their choice of species was motivated by sample accessibility, prior published work and relevance to human medicine. Our mixed-methods approach helped identify and contextualize some of the driving factors behind species-uneven patterns of scientific research, which can now be considered by funding agencies, scientific societies and research teams aiming to align their broader goals with future data generation efforts.