ArticleRoyal Society open science2020
Factors influencing taxonomic unevenness in scientific research: a mixed-methods case study of non-human primate genomic sequence data generation.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Phylogenetics and genomic variation of Hepatocystis isolated from shotgun sequencing of wild primate hosts.PLoS pathogens · 2025Article
- The diversity of ignorance and the ignorance of diversity: origins and implications of "shadow diversity" for conservation biology and extinction.Cambridge prisms. Extinction · 2024Review
- Evidence of selection in the uncoupling protein 1 gene region suggests local adaptation to solar irradiance in savannah monkeys (Proceedings. Biological sciences · 2022Article
- Broadening primate genomics: new insights into the ecology and evolution of primate gene regulation.Current opinion in genetics & development · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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;
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