Integrating curation into scientific publishing to train AI models.

ABSTRACT:

Motivation

High-throughput extraction and structured labeling of data from academic articles are crucial for enabling downstream machine learning applications and secondary analyses. Current approaches lack integration with the publishing process and comprehensive annotation of experimental roles and methodologies alongside bioentity recognition.

Results

We embedded multimodal data curation into the academic publishing process to annotate segmented figure panels and captions, combining natural language processing with authors' feedback to increase annotation accuracy. The resulting dataset, SourceData-NLP, comprises over 620 000 annotated biomedical entities, curated from 18 689 figures in 3223 articles in molecular and cell biology. Annotations include eight classes of bioe

SUBMITTER: Abreu-Vicente J 

PROVIDER: S-EPMC12836429 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature