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Research data mismanagement - from questionable research practice to research misconduct.

Nicole Shu Ling Yeo-TehBor Luen Tang
Published in: Accountability in research (2022)
Good record keeping practice and research data management underlie responsible research conduct and promote reproducibility of research findings in the sciences. In many cases of research misconduct, inadequate research data management frequently appear as an accompanying finding. Findings of disorganized or otherwise poor data archival or loss of research data are, on their own, not usually considered as indicative of research misconduct. Focusing on the availability of raw/primary data and the replicability of research based on these, we posit that most, if not all, instances of research data mismanagement (RDMM) could be considered a questionable research practice (QRP). Furthermore, instances of RDMM at their worst could indeed be viewed as acts of research misconduct. Here, we analyze with postulated scenarios the contexts and circumstances under which RDMM could be viewed as a significant misrepresentation of research (ie. falsification), or data fabrication. We further explore how RDMM might potentially be adjudicated as research misconduct based on intent and consequences. Defining how RDMM could constitute QRP or research misconduct would aid the formulation of relevant institutional research integrity policies to mitigate undesirable events stemming from RDMM.
Keyphrases
  • electronic health record
  • big data
  • healthcare
  • primary care
  • public health
  • drug delivery
  • machine learning
  • artificial intelligence
  • quality improvement
  • tissue engineering