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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Assessing research productivity in addiction datasets using OpenAlex.

David Melero-Fuentes1, Cristina Rius2,3,4,5, Rut Lucas-Domínguez2,3,6

  • 1Adolescence, Addictions and Development Research Group, Faculty of Psychology, Catholic University of Valencia, Valencia, Spain.

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Summary

OpenAlex can retrieve health datasets for evaluating researchers, but data curation is needed. Improving information management infrastructure enhances Open Science accuracy in medical research.

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Area of Science:

  • Bibliometrics
  • Open Science
  • Health Informatics

Background:

  • Open Science promotes data sharing and new evaluation methods.
  • Information technology supports open archiving and researcher assessment.
  • Open alternatives to traditional databases are emerging for health data.

Purpose of the Study:

  • Analyze OpenAlex for retrieving research datasets in addictive substances.
  • Determine OpenAlex's utility in automatically evaluating researchers and institutions.
  • Assess the accuracy and completeness of dataset information within OpenAlex.

Main Methods:

  • Pilot study focusing on addictive substances datasets.
  • Utilized OpenAlex as the primary data retrieval tool.
  • Examined dataset types and completeness of metadata (author ID, affiliation).

Main Results:

  • Four repositories held 85.8% of 2782 addictive substance datasets.
  • Datasets comprised 30% of records; others included comments, monographs, and publications.
  • Significant missing metadata: 30.2% author ID, 69% affiliation.

Conclusions:

  • OpenAlex is valuable in the Open Science medical ecosystem.
  • Dataset accuracy and researcher/institution evaluation require improved information curation.
  • Verification via research information management and professional training can enhance OpenAlex's utility.