Evidence map›Paper›PMID 27331905›Full record

ArticlePloS one2016

A Survey of Bioinformatics Database and Software Usage through Mining the Literature.

Geraint Duck, Goran Nenadic, Michele Filannino, Andy Brass, David L Robertson, Robert Stevens

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021
    Review
  9. Article
  10. Article
  11. Article
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  13. Article
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  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Matchmaking in Bioinformatics.F1000Research · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Geraint DuckSchool of Computer Science, The University of Manchester, Manchester, United Kingdom.ORCID http://orcid.org/0000-0002-1002-4458
Goran NenadicSchool of Computer Science, The University of Manchester, Manchester, United Kingdom.
Michele FilanninoSchool of Computer Science, The University of Manchester, Manchester, United Kingdom.
Andy BrassSchool of Computer Science, The University of Manchester, Manchester, United Kingdom.
David L RobertsonComputational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom.
Robert StevensSchool of Computer Science, The University of Manchester, Manchester, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer-based resources are central to much, if not most, biological and medical research. However, while there is an ever expanding choice of bioinformatics resources to use, described within the biomedical literature, little work to date has provided an evaluation of the full range of availability or levels of usage of database and software resources. Here we use text mining to process the PubMed Central full-text corpus, identifying mentions of databases or software within the scientific literature. We provide an audit of the resources contained within the biomedical literature, and a comparison of their relative usage, both over time and between the sub-disciplines of bioinformatics, biology and medicine. We find that trends in resource usage differs between these domains. The bioinformatics literature emphasises novel resource development, while database and software usage within biology and medicine is more stable and conservative. Many resources are only mentioned in the bioinformatics literature, with a relatively small number making it out into general biology, and fewer still into the medical literature. In addition, many resources are seeing a steady decline in their usage (e.g., BLAST, SWISS-PROT), though some are instead seeing rapid growth (e.g., the GO, R). We find a striking imbalance in resource usage with the top 5% of resource names (133 names) accounting for 47% of total usage, and over 70% of resources extracted being only mentioned once each. While these results highlight the dynamic and creative nature of bioinformatics research they raise questions about software reuse, choice and the sharing of bioinformatics practice. Is it acceptable that so many resources are apparently never reused? Finally, our work is a step towards automated extraction of scientific method from text. We make the dataset generated by our study available under the CC0 license here: http://dx.doi.org/10.6084/m9.figshare.1281371.

Indexed as

Databases, GeneticData MiningPubMedSoftwareBiologyCluster AnalysisComputational BiologyMedicineModels, TheoreticalPeriodicals as TopicReproducibility of ResultsTime Factors

Identifiers

PMID27331905
PMCPMC4917176

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.