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The Question of Data Integrity in Article-Level Metrics.

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Article-level metrics (ALMs) are increasingly used. Ensuring trustworthy and reliable ALM data is critical for researchers, publishers, and institutions. Two case studies demonstrate methods for establishing data integrity.

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Area of Science:

  • Bibliometrics
  • Research Evaluation
  • Scholarly Communication

Background:

  • Article-level metrics (ALMs) are gaining traction within the global research community.
  • Researchers, publishers, funders, and institutions are increasingly utilizing ALMs for various purposes.
  • The growing adoption necessitates a focus on the integrity and trustworthiness of ALM data.

Purpose of the Study:

  • To highlight the growing importance of article-level metrics (ALMs) in research evaluation.
  • To address the critical need for secure, reliable, and trustworthy ALM data.
  • To present practical approaches for ensuring ALM data integrity through case studies.

Main Methods:

  • The study examines two distinct case studies.
  • Each case study illustrates a different strategy for establishing the integrity of article-level metrics data.
  • Qualitative analysis of data management and validation processes was employed.

Main Results:

  • The case studies reveal diverse methodologies for ensuring ALM data integrity.
  • Different approaches were effective in enhancing the trustworthiness of ALM data in varied contexts.
  • Key factors for successful data integrity were identified across the studies.

Conclusions:

  • Establishing robust data integrity is paramount for the effective and ethical use of ALMs.
  • The presented case studies offer valuable insights for stakeholders seeking to implement reliable ALM data practices.
  • Continued attention to data security and trustworthiness is essential as ALM usage expands.