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Updated: Dec 31, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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The OpenScience Slovenia metadata dataset.

Mladen Borovič1, Marko Ferme1, Janez Brezovnik1

  • 1University of Maribor, Slovenia.

Data in Brief
|January 1, 2020
PubMed
Summary

The OpenScience Slovenia metadata dataset offers a unified collection of public domain academic documents. This resource is valuable for text mining, classification, and developing recommender systems.

Keywords:
MetadataNatural language processingReal-world dataText classificationText dataText mining

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Area of Science:

  • Bibliometrics
  • Information Science
  • Computer Science

Background:

  • Slovenian public domain academic documents were fragmented across separate library systems.
  • The establishment of the Slovenian Open-Access Infrastructure necessitated a unified approach to document collection and cataloguing.
  • Existing disparate library systems lacked a cohesive metadata scheme for academic content.

Purpose of the Study:

  • To create a comprehensive metadata dataset of Slovenian public domain academic documents.
  • To facilitate unified document collection and cataloguing for Slovenian universities.
  • To enable advanced data analysis and system development using academic metadata.

Main Methods:

  • Data collection from multiple pre-existing Slovenian library systems.
  • Implementation of metadata deduplication and merging techniques.
  • Creation of a single, unified metadata scheme for diverse academic document types.

Main Results:

  • A consolidated metadata dataset encompassing undergraduate and postgraduate theses, research articles, and professional articles.
  • Inclusion of key document attributes such as titles, keywords, abstracts, typologies, authors, and issue years.
  • Provision of unique identifiers including URL and Universal Decimal Classification (UDC).

Conclusions:

  • The OpenScience Slovenia metadata dataset is a valuable resource for text mining and text classification.
  • The dataset can be utilized for the development and benchmarking of content-based recommender systems.
  • This unified dataset supports research and innovation in information retrieval and academic data analysis.