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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW.

Fathoni A Musyaffa1, Kirsten Rapp1, Holger Gohlke1,2

  • 1Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.

Journal of Chemical Information and Modeling
|September 29, 2023
PubMed
Summary

LISTER is a new method to automatically extract metadata from experimental documentation, making research data FAIR (findable, accessible, interoperable, reusable). This streamlines data management and enhances reproducibility in life sciences.

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Area of Science:

  • Life Sciences
  • Computational Biophysical Chemistry
  • Protein Biochemistry
  • Molecular Biology

Background:

  • Reproducibility in science relies on accessible methods, code, and data.
  • FAIR data principles (findable, accessible, interoperable, reusable) necessitate metadata annotation.
  • Current metadata creation is labor-intensive for researchers.

Purpose of the Study:

  • To develop an automated solution for metadata extraction from experimental documentation.
  • To minimize researcher effort in metadata generation for FAIR data compliance.
  • To integrate seamlessly with existing research data management platforms.

Main Methods:

  • Developed LISTER, a methodological and algorithmic solution for metadata extraction.
  • Utilized eLabFTW as an electronic lab notebook with customized entries and a 'container' concept.
  • Employed an ISA (Investigation, Study, Assay) model as the data framework.
  • Created a Python-based application for semi-automated metadata extraction.

Main Results:

  • LISTER efficiently extracts metadata from annotated, template-based documentation.
  • Outputs metadata in machine-readable (.json) and human-readable (.xlsx) formats.
  • Generates Material and Methods descriptions in .docx format for publications.
  • Facilitates the creation and extension of ontologies for enhanced data value.

Conclusions:

  • LISTER significantly reduces the effort required for metadata creation and extraction.
  • Enhances research data FAIRness and reproducibility.
  • Applicable to various life science fields and extendable to other domains.