The stricter the threshold, the fewer false positives generated by the resulting bioNerDS system (though there is always the inevitable trade-off in recall to consider). Once each mention count is divided by the total mentions of the top 100 resources in each case, this provides us with an indication of the relative usage of the resource within each field, and in particular, how stable that usage is within the top 100 resources. This is in contrast to both our bioinformatics and biology corpora where it has seen continued growth, though the growth is more substantial within bioinformatics. Our survey shows that the bioinformatics resource profile is dynamic, with resources being replaced by others and there is much innovation around a slowly changing core. A “propagation” phase is then applied, which helps propagate document level matches to the mention level. As such, the y-vector may segregate journals by the range of resources contained within them; PLoS ONE has many resources with many mentions, whereas Acta Crystallography has few resources with many mentions (and few other resource mentions).  Data contents include gene sequences, textual descriptions, attributes and ontology classifications, citations, and tabular data. If you continue with this browser, you may see unexpected results. If we instead sort by document level mentions, we again get PLoS ONE and Nucleic Acids Research (with 255,538 and 64,249 mentions), but BMC Bioinformatics is replaced by BMC Genomics (with 44,528 and 50,302 mentions respectively). For complete details of the original bioNerDS system, please refer to . https://doi.org/10.1371/journal.pone.0157989.g006. Funding: GD is funded by a studentship from the Biotechnology and Biological Sciences Research Council (BBSRC) to GN, DLR and RS. Wrote the paper: GD GN MF DLR RS. PLoS ONE 11(6): https://doi.org/10.1371/journal.pone.0157989.t009, https://doi.org/10.1371/journal.pone.0157989.t010, https://doi.org/10.1371/journal.pone.0157989.g009. In particular, GO, GEO and R have seen significant growth in relative usage over the last ten years within bioinformatics, becoming core resources in patterns of database and software use . For example, only the full PMC corpus included mentions from Nucleic Acids Research as it has “Nucleic Acids” as an associated MeSH term (under “Chemicals and Drugs Category”), which is not a sub-term of biology, medicine or bioinformatics (under “Disciplines and Occupations Category”). We combine the results and discussion sections into a single category as they are often grouped together within journal articles. We characterise various sub-domains (medicine, biology and bioinformatics) by splitting the corpus into these three sub-corpora. The lower resources (R and SMART) are split from the others (SPSS, GenBank, BLAST), and are instead arranged close to some mass-spectroscopy (protein structure analysis) tools (e.g., Xcalibur). Macquarie University, AUSTRALIA, Received: October 13, 2015; Accepted: June 8, 2016; Published: June 22, 2016. Our results enable us to see that a few well-established resources account for a large fraction of the total mentions, while many resources towards the end of the graph (the “long-tail”) are rarely (if at all) mentioned after their initial introduction. Finally, we ordered the journals in decreasing order of the proportion of mentions to documents, to see which journals were more resource rich, but ignored journals with fewer than 1000 articles to maintain a reasonable sample size. The … PubMed, developed by the National Library of Medicine, provides access to bibliographic citations to biomedical journal articles, including MEDLINE , and to additional life sciences journals. 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