Conjunts de dades de producció científicaGestió d'Empreses

TOURSKAD: Tourism Skills, Competencies and Knowledge Dataset for Topic Modeling and Skill Extraction

  • Identification data

    Identifier:  PC:5033
    Authors:  Soria, Eddy; Valls, Aida; Moreno, Antonio; Hernández-Lara, Ana Beatriz
    Abstract:
    TOURSKAD is a processed textual dataset designed for topic modeling, dynamic topic modeling, and skill extraction in tourism research. The dataset contains 7,097 preprocessed excerpts derived from scientific publications in the tourism domain, originally retrieved from Scopus and Web of Science. The source corpus covers publications from 1960 to 2024 and was filtered to retain text related to skills, competencies, literacy, and knowledge in tourism. The deposited files include a training split, a validation split, and a complete source records summary. The training.csv file contains 5,677 rows and the validation.csv file contains 1,420 rows, both with a single text column compatible with OCTIS-based topic modeling workflows. The source_records_summary.csv file contains the complete processed corpus with id, year, split, and text fields, enabling temporal analyses such as dynamic topic modeling. The dataset was collected on 25 December 2024 and preprocessed between January and April 2025.
  • Others:

    Document type: info:eu-repo/semantics/other
    DOI: 10.34810/DATA2133
    Related publications: Is Supplement To: Soria, E., Valls, A., Moreno, A., & Hernández-Lara, A. B. (2025). LLM-Enhanced BERTopic vs. traditional topic models in tourism: Performance and temperature effects. Frontiers in Artificial Intelligence and Applications, 410, 47-56. https://doi.org/10.3233/FAIA250575 doi 10.3233/FAIA250575 https://doi.org/10.3233/FAIA250575
    Departament: Gestió d'Empreses
    Author: Soria, Eddy; Valls, Aida; Moreno, Antonio; Hernández-Lara, Ana Beatriz
    Repository ingest date: 2025-03-25
    Dataset publication year: 2026-07-16
    Subject matter: Business and Management; Computer and Information Science; Social Sciences
    Researcher identifier: https://orcid.org/0000-0001-9999-5850; https://orcid.org/0000-0003-3616-7809; https://orcid.org/0000-0003-3945-2314; https://orcid.org/0000-0002-8110-9328
    Language: en
    Published by (editorial): Universitat Rovira i Virgili (URV)
    Funding program: Generalitat de Catalunya: 2023 FI-1 00622
    Access rights: info:eu-repo/semantics/openAccess
    Abstract: TOURSKAD is a processed textual dataset designed for topic modeling, dynamic topic modeling, and skill extraction in tourism research. The dataset contains 7,097 preprocessed excerpts derived from scientific publications in the tourism domain, originally retrieved from Scopus and Web of Science. The source corpus covers publications from 1960 to 2024 and was filtered to retain text related to skills, competencies, literacy, and knowledge in tourism. The deposited files include a training split, a validation split, and a complete source records summary. The training.csv file contains 5,677 rows and the validation.csv file contains 1,420 rows, both with a single text column compatible with OCTIS-based topic modeling workflows. The source_records_summary.csv file contains the complete processed corpus with id, year, split, and text fields, enabling temporal analyses such as dynamic topic modeling. The dataset was collected on 25 December 2024 and preprocessed between January and April 2025.
  • Keywords:

    Tourism https://www.wikidata.org/wiki/Q49389 (Wikidata) https://www.wikidata.org/ Knowledge https://www.wikidata.org/wiki/Q9081 (Wikidata) https://www.wikidata.org/ Natural language processing https://www.wikidata.org/wiki/Q30642 (Wikidata) https://www.wikidata.org/ Topic modeling https://www.wikidata.org/wiki/Q96468792 (Wikidata) https://www.wikidata.org/ Skills https://www.wikidata.org/wiki/Q205961 (Wikidata) https://www.wikidata.org/ Competencies https://www.wikidata.org/wiki/Q572718 (Wikidata) https://www.wikidata.org/ Literacy https://www.wikidata.org/wiki/Q8236 (Wikidata) https://www.wikidata.org/
    Business and Management; Computer and Information Science; Social Sciences
  • Documents:

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