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Data-Intensive Science and Research Integrity.

David B Resnik1, Kevin C Elliott2,3,4, Patricia A Soranno3

  • 1a National Institute for Environmental Health Sciences , National Institutes of Health , Research Triangle Park , North Carolina , USA.

Accountability in Research
|May 9, 2017
PubMed
Summary
This summary is machine-generated.

Research integrity in data-intensive science does not require new misconduct categories. Upholding core values like honesty and transparency, alongside education and policy, is key to ensuring trustworthy scientific data processing and analysis.

Keywords:
Data-intensive sciencedeceptioneducationethicsmisconductresearch integritytransparency

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Area of Science:

  • Data Science
  • Research Ethics

Background:

  • Data-intensive science presents unique challenges to research integrity.
  • Existing frameworks for scientific misconduct may not fully address data processing and analysis deception.

Purpose of the Study:

  • To evaluate the necessity of a distinct misconduct category for data-intensive science.
  • To propose strategies for upholding research integrity in data-intensive research.

Main Methods:

  • Commentary and scholarly discussion.
  • Analysis of existing research integrity principles.

Main Results:

  • Argues against creating a separate misconduct category for data-related deception.
  • Emphasizes the applicability of established ethical and epistemological values.

Conclusions:

  • Maintaining honesty, openness, transparency, and objectivity is sufficient for research integrity in data-intensive science.
  • Promoting education, policy development, and scholarly debate on statistical practices is crucial.