ArticlePloS one2016
A Survey of Bioinformatics Database and Software Usage through Mining the Literature.
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.
What it found
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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.
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.
Who cites it
23 citing papers in PubMed.
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- The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central.PeerJ. Computer science · 2022Article
- Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation.Journal of medical Internet research · 2021Article
- Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical Database Inventory.JMIR medical informatics · 2021Article
- Taxonomic evaluation ofPeerJ · 2021Article
- MaRe: Processing Big Data with application containers on Apache Spark.GigaScience · 2020Article
- The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences.Bioinformatics (Oxford, England) · 2020Article
- Recognizing software names in biomedical literature using machine learning.Health informatics journal · 2020Article
- Screening and Identification of Differentially Expressed Genes Expressed among Left and Right Colon Adenocarcinoma.BioMed research international · 2020Article
- The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases.Genetics · 2019Review
- Bioinformatics in Jordan: Status, challenges, and future directions.PLoS computational biology · 2019Article
- Automated workflow composition in mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2019Article
- U-Index, a dataset and an impact metric for informatics tools and databases.Scientific data · 2018Article
- A large-scale analysis of bioinformatics code on GitHub.PloS one · 2018Article
- Matchmaking in Bioinformatics.F1000Research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.