Division of Research

Research Misconduct Prevention

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Research Misconduct Prevention

It is crucial that University researchers maintain supportive and professional working environments in their laboratories. The Professionalism and Integrity in Research (P.I.) Program funded by the National Institutes of Health has created a useful checklist to assist with lab leadership and management.

Principal investigators are responsible for managing lab operations and leading research staff. They must also establish effective professional habits to support these activities. Using the following checklist to identify areas of strength and opportunities for improvement.

P.I. Program Checklist [PDF]

The Principal Investigator, mentor, and/or supervisor must convey to all researchers:

  • Appropriate standards of research conduct (through instruction, by example, and ideally, by written guidelines)
  • Authorship policies and other intellectual property issues currently used in their research group
  • Expected practices and standards for recording, storing, backing up and archiving primary and collateral data, including lab notebooks and electronic information that may reside on lab computers and personal laptops or other devices
  • Applicable journal guidelines for the preparation and presentation of figures when drafting or submitting a paper for publication

Additional Ways to Encourage Ethical Research

The following are additional strategies that researchers can use to promote integrity:

  1.  Data Integrity & Documentation
    • Standardize Recordkeeping: Develop written standards for data management to ensure every lab member follows the same protocols for naming and storage.
    • Use Electronic Lab Notebooks (ELNs): ELNs create a transparent, searchable trail of work that simplifies data ownership transfer to the PI.
    • Verify Image Metadata: Rigorously trace publication images back to raw files.
  2.  Accountability & Culture
    • Normalize Data Challenging: Foster a culture where sharing "unpolished" data in meetings is expected and team members feel comfortable questioning results.
    • Trust Your Instincts: Implement unannounced spot checks and investigate data that looks "too good to be true" or makes no sense.
    • Value Negative Results: Seek independent replication before rushing to publish and never discourage findings that don't fit the expected hypothesis.
  3.  Mentorship & Transitions
    • Prioritize Mental Health: Monitor lab sizes and mentor-mentee ratios to ensure no one is overwhelmed, which reduces the risk of cutting corners.
    • Secure Data Ownership: Before anyone leaves the lab, take possession of all organized data and sign formal, written authorship agreements for future projects.

Resources

Getting Ahead of Research Misconduct Allegations

Respond Promptly to Journal Queries & Clarifications

Journals are ramping up their use of plagiarism and image manipulation detection software, leading to more frequent inquiries. To protect your work and avoid unintended research misconduct allegations, ensure all journal clarifications are handled and resolved without delay. For more information, please visit Author Responsibilities for Preprints and Post-Publication Review.

Disclose Use of AI in Manuscripts and Grant Proposals

More publishers are now establishing guidelines and policies surrounding AI and its use in scholarly publishing. It is a good idea to check with a publisher before including AI generated content in work that you intend to submit for publication. Please note that if researchers use AI to write and generate data and ideas, the researcher remains accountable for the validity of data generated from AI and citations necessary for that work.

Please visit the Library’s Publisher Policies on Generative AI for more resources related to publisher statements on AI.

Just like publishers, agencies (such as the NIH or NSF) have strict AI policies. Always verify current guidelines and visit the following resources for more information:

Utilize iThenticate Software

Brown has chosen iThenticate as a tool for researchers to check their original work during the publication process. iThenticate can serve as a preventative measure to help ensure research manuscripts, grant applications, and scholarly documents have the appropriate citations and references of previously published work before submission to journals, funding agencies, and academic repositories. Most Brown faculty will have access to iThenticate to check their manuscripts. 

Please note that iThenticate is only to be used to check your own work.

Additional iThenticate information

It is important to note that iThenticate itself cannot identify plagiarism specifically. It will provide a “similarity report” that will highlight text that may be potentially copied or plagiarized. 

Please use Brown University Library’s iThenticate support request form for questions or requests for support.