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This dataset extracts food packaging composition and gas permeability data from scientific articles. It aids in training natural language processing (NLP) models for specialized entity recognition in experimental fields.

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Area of Science:

  • Materials Science
  • Data Science
  • Natural Language Processing

Background:

  • Scientific literature contains valuable data on food packaging composition and gas permeability.
  • Extracting this information manually is time-consuming and prone to errors.
  • Standardized datasets are crucial for developing robust computational tools.

Purpose of the Study:

  • To create a structured dataset of symbolic and quantitative entities related to food packaging composition and gas permeability.
  • To facilitate the training and evaluation of Natural Language Processing (NLP) models for scientific text analysis.
  • To support advancements in automated information extraction from experimental research.

Main Methods:

  • Compilation of 50 scientific articles from international journals available on ScienceDirect.
  • Annotation of HTML files using the WebAnno server by three independent experts.
  • Development of an annotation guideline based on an Ontological and Terminological Resource (OTR).

Main Results:

  • A dataset containing recognized entities for packaging permeability measures and composition.
  • Expert-validated annotations enabling the training of NLP models.
  • The dataset supports tasks like specialized entity recognition and binary relation extraction.

Conclusions:

  • The developed dataset provides a valuable resource for NLP applications in materials science and food packaging research.
  • It enables the evaluation of NLP models for extracting complex scientific information.
  • This resource can accelerate research by automating data extraction and analysis.