Urology Scholarly Culture and Accountability Plan

November 4, 2024

Duke University is committed to maintaining the highest quality and integrity for all its scientific enterprises. The Duke University School of Medicine allows individual departments to adopt their own policies and procedures to ensure scientific accountability and integrity. Recognizing this, the Department of Urology has prepared the following Scholarly Culture & Accountability Plan (SCAP). The Department of Urology is committed to ensuring that policies and procedures exist to encourage the highest professional conduct and to promote a culture in which scientific results are critically reviewed and accountability for data integrity is clearly delineated. In addition, Department policy allows concerns about data integrity to be raised without hesitation and provides mechanisms by which these concerns can be addressed fairly and expeditiously.

There are several reasons for formalizing this plan. First, science is funded publicly and its credibility in the public’s eyes is vital to continuation and expansion of funding. We must do all that we can to eliminate misconduct. Second, research builds on previous reports in the pursuit of accurate knowledge. We must be confident of the truth in prior publications to further the scientific enterprise. Third, proper research standards avoid wasting time or money following up on inaccurate or erroneous reports. We must keep the scientific enterprise moving forward. Finally, science drives translational and clinical research. We must conduct and report science properly to avoid exposing patients to harmful or ineffective therapies.

Data provenance and integrity ensure that the knowledge we report is supported by the primary data, and that the primary data are retained in a form that allows us to be certain of the veracity of our knowledge. Scientific rigor ensures the proper application of the scientific method using the highest standards in the field. Scientific rigor is essential to conduct of the scientific enterprise. We recognize this requires the active participation of all parties in the research mission, from trainees to the department chair.

Principles

The Department of Urology has prepared a Scholarly Culture and Accountability Plan following the guidelines provided.  This plan reflects these important principles:

  • We foster an environment where scientific integrity is the highest priority.
  • We emphasize high-quality, reproducible data and results.
  • We value constructive critiques of research.
  • We encourage open discussion of any concerns regarding research conduct or integrity.

I. Recommended practices for improving the culture of scientific accountability within individual laboratories

  1. The principles above provide guidance for ensuring the integrity of data and for maintaining compliance with the SCAP. In recognition that no one size fits all, each laboratory should establish its own specific plan for scientific accountability and scientific rigor, according to established standards of its field, integrating other perspectives when appropriate. Lab directors should discuss these expectations with their research team, and develop explicit processes within the lab to monitor compliance with these policies. Scientific accountability and scientific rigor should be a frequent discussion between the laboratory head and the laboratory staff, to establish a sense of common purpose and a shared goal to discover the truth.
  2. Principal investigators set the example for their team through honest and open discussion of results and through emphasis on scientific integrity and data quality over positive results. Lab directors should not, in any way, encourage or put pressure on lab personnel to obtain specific results. They should make it clear at all times that their highest priority is to obtain the true result of all studies, irrespective of the effect such a result may have on the overall project, grant funding, or manuscript. They should make it clear that they have a zero-tolerance policy with respect to data manipulation, alteration, or falsification.
  3. Most of us rely on our trust of and judgment in others to ensure the integrity of our data. However, this alone is not sufficient. Examples exist where even the most seemingly trustworthy people have manipulated data. While you should continue to put your faith in others, you should reinforce this with specific practices and processes to ensure that your data are managed responsibly. Cross-train lab personnel so that one person can independently verify the results of another, and so that no one person is alone in providing data or analysis.
  4. Labs must ensure that all investigators regularly present their research findings to other investigators in a forum that allows open and critical discussion of the data and its analysis. In order to monitor compliance, we suggest maintaining logs of participation in these conferences.
  5. To ensure reproducibility, the lab director or a designate should consider re-analyzing all or part of the most critical studies, such as for major clinical trials. When practicable, study design and analysis may be conducted in collaboration with one of the independent statisticians affiliated with the Department.
  6. Lab directors should develop a process (e.g., PI notebook) in which they document critical results, the date they learned of them, their interpretation of these results, and conclusions or discoveries that these results imply. In addition, they should document procedures they have taken to confirm the validity of these results, such as their own review of the raw data and their re-analysis and conclusions. These records will allow the lab director to document the intellectual progress of specific projects to develop future hypotheses or research plans, and if needed, to support intellectual property claims. Furthermore, if questions of data integrity were to arise, such a notebook would serve to document that he/she verified all critical studies to the full extent possible.
  7. Labs must ensure that all lab investigators and personnel engaged in scientific research complete all required institutional training modules. In addition, individuals should also take advantage of programs offered through the SOM that are designed to address research integrity.
  1. Labs must implement a Data Management Plan (DMP) and ensure that all lab investigators abide by those policies. Duke provides guidance to help develop the DMP. The DMP should be reviewed at least once a year by the lab chief or an appropriate representative of the lab chief. The DMP should cover the 4 basic components of data management:
    • How are the data collected and stored?
    • How are notes taken and stored?
    • How is analysis done, tracked and, if intermediate steps are saved, stored?
    • How are figures or tables made and linked to both the analysis steps and the original data?
  2. Laboratory heads must minimize incentives or pressures (or the appearance thereof) that drive their staff to perform for reasons other than pursuit of truth. It is critical to avoid the real danger that staff will respond to the laboratory head’s concerns about academic promotions, choice of publication venue, or competition with other labs.
  3. If you have concerns about the integrity of someone’s data, be it in your lab or someone else’s, you should feel comfortable voicing your concerns. This is true whether you think a certain analytic method needs to be better validated or if you suspect scientific misconduct. Raising concerns about data integrity is not the same thing as accusing someone of scientific misconduct. The Department of Urology wishes to promote a culture in which all aspects of scientific findings are critically reviewed. This includes all steps in the scientific process, from study design to data acquisition to methods of analysis to the formulation of conclusions. Raising and responding to questions about data integrity should be a routine part of the critical review process – in other words, it need not be reserved solely for cases of suspected scientific misconduct. It is through this process that we all can work together to ensure the highest possible quality of science at Duke.
  4. Labs must identify a Lab Integrity Coordinator, to whom any concern or question about research data management can be taken by any individual inside or outside the lab. Unless otherwise designated, this contact person is the lab director. Staff should also be aware of the Duke Integrity Line to report concerns anonymously.
  5. Lab chiefs are encouraged to create a Research Mentoring Expectations Document, which describes mutual expectations around issues including professionalism, communication, and responsible conduct of research. The document is not intended to be used as a contract, but rather to establish a mutually beneficial framework for faculty to work effectively with their students/staff. Learn more about mentoring expectations documents in the Office of Research Mentoring Resources.

​​​​​​​II. Recommended safeguards to ensure data integrity within laboratories

The Department of Urology believes that the proper accountable unit for ensuring data integrity is the laboratory. For this reason, all labs within the Department of Urology should follow the key principles and steps outlined in this SCAP. Although this document outlines the best practices with regards to scientific accountability, we also recognize that within each lab not all steps, or the same set of activities, may be appropriate. Ultimately, the Department requires that the lab chiefs maintain evidence of compliance with the SCAP on behalf of their lab. 

A. Best practices in experimental design

  1. High-quality research begins with careful planning and study design. Engage appropriate collaborators, statisticians, and other relevant team members for constructive input before actual experiments or clinical studies begin. Having well-defined study goals protects against fraud and improves the quality of results. Frame your research questions in ways that allow negative and positive results to be interesting and useful to your lines of inquiry; that is, refrain from expectations that one type of result is more valuable than another. Plan for multiple methods, techniques, or analytic approaches for reproducing and comparing results from your experiments.
  2. Employ systematic random sampling for data collection, including selection of subjects, body area, cells, or cell parts.
  3. Strive to eliminate bias in experimental procedures and analysis. If practical, experimenters should be blinded to treatment. The timing of experiments might be balanced to account for sources of bias over time (e.g., evolution of scanner technology, fatigue, change in personnel, test-order effects, circadian rhythms in experimental animals).
  4. Use positive and negative controls.
  5. Use replicate samples (including both technical and biologic replicates) for experimental groups, when appropriate.
  6. Use validated and/or well-characterized reagents (such as antibodies and pharmacological agents), or conduct full validation.
  7. Consider limitations of behavioral, animal, or cellular models including possible contributions of genetic background and gender.
  8. Obtain and study the raw data for any results provided by shared research cores.
  9. To the extent possible, independently replicate study results. For example, have different people perform experimental procedures and experimental readouts.

B. Best practices in data analysis and statistics

  1. Determine sample size by pre-experiment power analyses, when possible. Identify stopping points a priori to avoid testing to a foregone conclusion.
  2. Repeat experiments when possible and put bounds on the size of effects.
  3. Use care in pooling of data across experiments done at different times, multiple time points, or different experimental groups.
  4. Avoid data exclusion. If it is necessary, define and report objective procedures for dealing with attrition or other missing data and data exclusion. Unless there is a compelling, transparent reason to exclude data, include all runs of each experimental procedure. This applies to exclusion of individual points or complete data sets.
  5. Take advantage of professional statistical expertise, e.g., the Biostatistics collaboration service.
  6. Perform statistical tests to validate what is seen in the data, rather than to reveal effects that may be statistically significant but functionally non-significant.
  7. Select appropriate statistical tests, including testing of statistical assumptions, such as adherence of data to a normal distribution.
  8. Control for multiple comparisons.
  9. Avoid “significance chasing” such as interpreting the data in different ways so that it passes the statistical test of significance or analyzing different measures until finding one on which groups differ.
  10. For data science projects, use independent testing sets that are used only at the end of the experiment.

C. Best practices in data management

  1. Implement a policy of best practices with respect to research records, including basic laboratory notebooks or clinical research records. Lab directors should make this policy clear to all lab personnel and enforce it, ensuring that entries are being made in a way that conforms to lab policy. Utilize electronic lab notebooks such as LabArchives that automatically record date and timestamps of entries and data changes. We recommend lab directors review people’s lab notebooks any time they meet with them to discuss data. Additionally, consider performing periodic audits of laboratory notebooks to ensure that a third-party reviewer would be satisfied with the level of documentation provided for an experiment. With regards to clinical research, maintain all appropriate documents in accordance with all regulatory and IRB requirements.
  2. Rather than create your own individual clinical research or laboratory database, leverage institutional resources such as the Duke Biobank, REDCap databases, Pedigene or other similar centralized infrastructure that limits users, directly obtains input from source elements, and tracks all changes in the data elements or samples.
  3. For any data that are generated by instruments, require personnel to include the specific instrument, its location, and the date and time the analysis was performed in their lab notebook. This will make it easier to match the lab notebook with instrument-generated raw data. In addition, maintain frequent monitoring, calibration, and validation of all laboratory equipment.
  4. According to the Research Policy Manual, Chapter 5, “Duke University expects research personnel to retain, via archives and/or placement in established repositories, research data and outputs for a minimum of six years after the final reporting or publication of a project.” That retention period may be longer if required by an external sponsor, law, rule or regulation.
  5. Alterations and modifications of the primary data should be performed on copies of the data whenever possible, and should be tracked, dated, and described.
  6. When possible, data and software should be shared in accordance with NIH and other sponsor policies. Sharing of medical images typically requires following institution-approved procedures including deidentification and licensing. Images may then be shared on archives at Duke or public resources.
  7. Every figure of every paper should be cross-referenced with the location of the original data that contributed to the figure.
  8. Develop a mechanism to monitor provenance for any data that comes from shared equipment or core facilities within the Department, if and when this becomes an issue.
  9. The level of information security should be appropriate for the material, especially for human subject protection and PHI.
  10. Data should be accessible readily to all data owners, and available to outside investigators if necessary.
  11. Develop a plan with collaborators to ensure data integrity. For example, you may request a copy of the raw data generated by your collaborators for archiving in your own lab. Similar to data analysis performed for your laboratory-generated data, when possible, perform an independent analysis of data generated by collaborators to verify accuracy.

D. Best practices in publication

  1. Report full details on methods and experimental design, including technical and biological replicates, methods for randomization and blinding, and self-replication efforts.
  2. Report complete results of all analyses done as part of an experiment, including statistical controls for multiple comparisons and identification of pre- and post-hoc analyses. Methods sections should be too long, rather than too short.
  3. Avoid “rushing” findings into publication without a full investigation and proper self-replication.
  4. Target appropriate venues for publication. Publish well-controlled negative, “uninteresting,” or “not novel” results in appropriate venues.
  5. Resist the emerging trend where the peer review process demands additional experiments on an abbreviated timeline, with the associated pressure for results to be interpreted to conform to previously reached conclusions.

​​​​​​​III. Departmental efforts to promote a culture of scientific accountability

The Department of Urology leadership will also take steps to support, guide, and ensure a culture of scientific integrity, including the actions listed below.

  1. We will continue to advocate proper scientific conduct at all levels: faculty meetings, lab meetings, and courses.
  2. We will strive to create a culture where we talk about the incentives for poor conduct openly, and try to address the pressures that create those incentives.
  3. We will endeavor to create a culture where people are evaluated on the basis of what they have done rather than metrics that may be weakly correlated with accomplishment.
  4. The laboratory’s Data Management Plan (DMP) must be documented with the Vice Chair for Research when it has been completed or revised.
  5. All research staff in all laboratories must read the Department’s Action Plan and the laboratory’s Data Management Plan, and sign an affirmation that they have done so.
  6. The Vice Chair for Research will serve as the Research Quality Officer to the School of Medicine. He/she will advise individuals or laboratories on all of the issues covered in this plan and will work with the school’s designated official for scientific integrity.
  7. Research integrity concerns should be raised and addressed initially to a lab chief, but in case of real or perceived conflict of interest, concerns may be raised to the Vice Chair for Research or to the Chair.
  8. Inform the faculty and staff of the Department about available resources and reporting mechanisms for scientific accountability, such as: