Evidence map›Paper›PMID 40861394›Full record

ArticleBioinformatics advances2025

Unlocking the potential of PubMed Central supplementary data files.

Julien Gobeill, Déborah Caucheteur, Alexandre Flament, Pierre-André Michel, Anaïs Mottaz, Emilie Pasche, Patrick Ruch

Abstract read
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Enhancing the SIB Literature Services (SIBiLS) with annotations to support biocuration.Database : the journal of biological databases and curation · 2026
    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

7 authors.

Julien GobeillSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.ORCID https://orcid.org/0000-0001-9809-7741
Déborah CaucheteurSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.ORCID https://orcid.org/0000-0003-0800-544X
Alexandre FlamentSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.
Pierre-André MichelSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.
Anaïs MottazSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.ORCID https://orcid.org/0000-0003-0080-9451
Emilie PascheSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.ORCID https://orcid.org/0000-0002-9118-5762
Patrick RuchSIB Text Mining Group, Swiss Institute of Bioinformatics, Geneva 1206, Switzerland.ORCID https://orcid.org/0000-0002-3374-2962

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Biocuration workflows often rely on comprehensive literature searches for specific biological entities. However, standard search engines such as MEDLINE and PubMed Central provide an incomplete picture of the scientific literature because they do not index the increasing amount of valuable information published in supplementary data files. Over two years, we addressed this gap by systematically extracting text from a large proportion (85%) of these files, resulting in 35 million searchable documents. To assess the information gain provided by supplementary data files beyond the manuscripts, we searched both for mentions of dozens of Global Core Biodata Resources (GCBRs), which are fundamental biological databases essential for the life sciences. We searched for mentions of GCBR names and accession numbers, which uniquely identify biological entities within these resources. Results: The recall gain from using the supplementary data files to search for articles mentioning resource names is 6%. In addition, 97% of all accession numbers identified were published in the supplementary data files, highlighting their increasing importance for highly specific topics or curation pipelines. We show that the number of accession numbers published in the supplementary data files is increasing year on year, but that 87% of these are published in Excel files. This format facilitates human readability and accessibility, but severely limits machine reusability and interoperability. We therefore discuss alternative and complementary approaches to the publication of research data. Availability and implementation: All extracted data are accessible and searchable as a collection on the BiodiversityPMC platform (https://biodiversitypmc.sibils.org/).

Identifiers

PMID40861394
PMCPMC12371329

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.