Supporting data for "Quantifying research interests in 7,521 mammalian species with <i>h</i>-index: a case study"

Taxonomic bias is a known issue within the field of biology, causing scientific knowledge to be unevenly distributed across species. However, a systematic quantification of the research interest that the scientific community has allocated to individual species remains a big data problem. Scalable approaches are needed to integrate biodiversity datasets and bibliometric methods across large numbers of species. The outputs of these analyses are important for identifying understudied species and directing future research to fill these gaps. <br>In this study, we used the species <i>h</i>-index to quantify the research interest in 7,521 species of mammals. We tested factors potentially driving species <i>h</i>-index, by using a Bayesian phylogenetic generalised linear mixed model (GLMM). We found that a third of the mammals had a species <i>h</i>-index of zero, while a select few had inflated research interest. Further, mammals with higher species <i>h</i>-index had larger body masses, were found in temperate latitudes, had more humans uses, including domestication, and were in lower risk IUCN Red List categories. These results surprisingly suggested that critically endangered mammals are understudied. A higher interest in domesticated species suggested that human use rather than conservation drives mammalian scientific literature. <br>Our study has demonstrated a scalable workflow and systematically identified understudied species of mammals, as well as identified the likely drivers of this taxonomic bias in the literature. This case study can become a benchmark for future research that asks similar biological and meta-research questions for other taxa.