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Related Experiment Video

Updated: May 28, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Why shared data should not be acknowledged on the author byline.

T Rohlfing1, J-B Poline

  • 1SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. rohlfing@ieee.org

Neuroimage
|October 20, 2011
PubMed
Summary
This summary is machine-generated.

Adding data contributors to author lists undermines academic authorship standards. Alternative methods for crediting data sharing are recommended to maintain fair scientific evaluation.

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Area of Science:

  • Scientific Publishing
  • Academic Authorship Standards
  • Data Sharing Practices

Background:

  • Emerging practices involve including data contributors in author bylines.
  • This trend deviates from established academic authorship norms.
  • Concerns exist regarding the equitable recognition of contributions.

Purpose of the Study:

  • To analyze the compatibility of data contributor bylines with current authorship standards.
  • To evaluate the implications of including non-author contributors on published papers.
  • To propose alternative methods for acknowledging data sharing contributions.

Main Methods:

  • Analysis of current academic authorship guidelines.
  • Examination of the ethical and practical issues arising from byline acknowledgments for data.
  • Review of alternative credit mechanisms for data contributors.

Main Results:

  • The practice of including data contributors in author bylines is incompatible with established authorship standards.
  • This practice devalues authorship and raises concerns about guest authorship and equitable credit.
  • Deviation from standards can compromise fair scientific evaluations.

Conclusions:

  • Non-author contributors, including groups or individuals sharing data, should not be listed as authors.
  • Viable alternatives for crediting data contributors include dataset publications, acknowledgments, or appendix entries.
  • Maintaining robust authorship standards is crucial for the integrity of scientific evaluation.