ArticleJournal of biomedical semantics2015
Database citation in supplementary data linked to Europe PubMed Central full text biomedical articles.
Article in Journal of biomedical semantics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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
8 citing papers in PubMed.
- The academic impact of Open Science: a scoping review.Royal Society open science · 2025Review
- EMBL's European Bioinformatics Institute (EMBL-EBI) in 2024.Nucleic acids research · 2025Article
- Unlocking the potential of PubMed Central supplementary data files.Bioinformatics advances · 2025Article
- Assessing the use of supplementary materials to improve genomic variant discovery.Database : the journal of biological databases and curation · 2023Article
- Quantitative monitoring of nucleotide sequence data from genetic resources in context of their citation in the scientific literature.GigaScience · 2021Article
- tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.Bioinformatics (Oxford, England) · 2018Article
- Literature evidence in open targets - a target validation platform.Journal of biomedical semantics · 2017Article
- Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources.F1000Research · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundIn this study, we present an analysis of data citation practices in full text research articles and their corresponding supplementary data files, made available in the Open Access set of articles from Europe PubMed Central. Our aim is to investigate whether supplementary data files should be considered as a source of information for integrating the literature with biomolecular databases.
resultsUsing text-mining methods to identify and extract a variety of core biological database accession numbers, we found that the supplemental data files contain many more database citations than the body of the article, and that those citations often take the form of a relatively small number of articles citing large collections of accession numbers in text-based files. Moreover, citation of value-added databases derived from submission databases (such as Pfam, UniProt or Ensembl) is common, demonstrating the reuse of these resources as datasets in themselves. All the database accession numbers extracted from the supplementary data are publicly accessible from http://dx.doi.org/10.5281/zenodo.11771.
conclusionsOur study suggests that supplementary data should be considered when linking articles with data, in curation pipelines, and in information retrieval tasks in order to make full use of the entire research article. These observations highlight the need to improve the management of supplemental data in general, in order to make this information more discoverable and useful.
Indexed as
Identifiers
What OpenQuestion holds
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.