Department of Molecular Genetics and Microbiology
Science Culture and Accountability Plan
Duke University is committed to fostering and maintaining a culture that values the quality and integrity of its research enterprises at all levels. This requires that primary data be critically evaluated and retained in an unaltered and accessible form, and that acceptable standards of experimental rigor and reproducibility be strictly adhered to. The Department of Molecular Genetics and Microbiology (MGM) has developed a Science Culture and Accountability Plan (SCAP) that reflects the nature of the research conducted by its primary faculty. Because of our shared commitment to maintaining the highest standards for research programs conducted in the department, the SCAP extends to all members of the department – faculty, trainees, staff, and administrators. Concerns about data integrity or provenance should be brought to the immediate attention of the Chair (Dr. Heitman), Vice-Chairs (Drs. Jinks-Robertson or Luftig), or Research Quality Officer (Dr. Ko) and such concerns will be addressed in a fair and expeditious manner. Alternatively, a compliance violation or concern can be anonymously reported to the Duke Compliance and Fraud Hotline (800-826-8109).
General SCAP principles
- When and how primary data were generated and where they are stored must be recorded and known.
- If and how copies of data were modified from the original for presentation or any other purpose must be recorded and known.
- Data must adhere to discipline/field standards for rigor and reproducibility.
- Proper and appropriate procedures for data/statistical analysis must be documented and adhered to.
Best practices in research design
High quality research begins with careful planning and study design:
- Eliminate bias in experimental procedures and analysis, using blinded studies as appropriate and analyses by more than one individual.
- Account for possible sources of bias and variation over time (e.g., reagents, changes in personnel, circadian rhythms, and microbiomes in experimental animals).
- Include positive and negative controls.
- Perform both technical and biological replicates as appropriate.
- Use validated and/or well-characterized reagents (e.g., antibodies and pharmacological agents, strains or cell lines, mutants or mutant cell lines), or conduct full, independent validation.
- Consider limitations of behavioral, animal, or cellular models including possible contributions of genetic background, age, and gender.
- Determine sample size by pre-experiment power analysis when appropriate.
- Frame the research questions such that, when possible, both negative as well as positive results will be informative.
Best practices in laboratory management
- It should be made clear that the highest priority is to report accurate results, irrespective of the effect they might have on the overall project, grant submissions or manuscripts. The laboratory head should not allow his/her concerns about funding and/or publication to affect the attitudes, expectations, or behavior of laboratory members and staff.
- To establish a sense of common purpose and goals, the laboratory head should discuss basic principles of scientific accountability and rigor with lab members.
- Meetings with lab members should include primary data inspection, discussion of analysis procedures and notebook inspection.
- The laboratory head should develop a process (e.g. a PI notebook or written updates by lab members) that documents the date critical results were conveyed, the interpretation of results, and conclusions or discoveries that the results imply. Such records document the intellectual progress of specific projects and, if needed, support intellectual property claims.
- The laboratory head should implement a policy of best practices with respect to maintaining research records. Data notebooks should be accessible at all times and should be periodically audited. Utilization of electronic notebooks is encouraged as it timestamps entries and data changes and allows data sharing.
Best practices in data management and analysis
- All primary data should be stored, backed up as appropriate, and protected against alterations. The U.S. Department of Health and Human Services requires that project data be retained for at least 3 years after the funding period ends.
- Figures in publications should be cross-referenced with the location of the original data that contributed to the figure.
- Modifications of raw data should be performed only on copies of the original and should be tracked, dated, and documented fully.
- There will be a zero-tolerance policy with respect to data manipulation or alteration that leads to falsification of results.
- Care should be taken when pooling data from experiments done at different times, from multiple time points, or from different experimental groups.
- Unless there is a compelling and transparent reason to exclude data, all runs of each experiment should be included in analyses. If exclusion is deemed to be warranted, procedures for dealing with attrition or other missing data as well as data exclusion should be defined and reported.
- Data analysis should be performed using appropriate statistics and sample sizes.
- For data generated by instruments such as plate readers, scintillation counters, cameras, flow cytometers, etc., the specific instrument used, its location, and the date and time the analysis was performed should be recorded. Frequent monitoring, calibration, and validation of laboratory equipment should be done.
- Obtain and store the raw data for any results provided by shared research cores.
- Develop a plan with collaborators to ensure data integrity. When possible, perform an independent analysis of data generated by collaborators to verify accuracy.
Standard Operating Procedure for individual labs
Each laboratory head is required develop a Standard Operating Procedure (SOP) that provides guidelines for data acquisition, storage, and transparency. The SOP should be tailored to and reflect the specific research interests and experimental approaches used in the lab, and should minimally include the following components of data management:
- How are primary data collected and stored?
- How are notes taken and stored?
- How are analyses done, tracked, and stored?
- How are figures made and linked to both the analysis steps and the original data?
The laboratory SOP should be updated yearly, and a copy provided to the Chair and Research Quality Officer.
Departmental efforts to promote a culture of scientific accountability
- Research staff and trainees must read the lab SOP and the department SCAP yearly and attest that this has been done using the following survey link: http://bit.ly/SCAPattest2020. Staff are expected to adhere to the practices outlined in each.
- Individuals engaged in research will be required to complete relevant online training modules and should take advantage of SOM programs designed to address research integrity.
- An environment should be established and nurtured in which concerns about the integrity of another’s data can be comfortably voiced. Raising and responding to questions about data integrity should be a routine part of the critical review process and need not be reserved for cases of suspected scientific misconduct.
- Faculty, trainees, and staff should be educated about available resources and reporting mechanisms for scientific accountability, which include:
- Duke Office of Scientific Integrity (https://dosi.duke.edu/)
- The NIH Office of Research Integrity (http://ori.hhs.gov/)
- Guidelines for the Proper Handling of Digital Image Data (http://jcb.rupress.org/content/166/1/11.full)
- Online Learning Tool for Research Integrity and Image Processing (http://ori.hhs.gov/education/products/RIandImages/default.html)
- A suspected compliance violation or concern can be anonymously reported to the Duke Compliance and Fraud Hotline (800-826-8109).
Original: January 2018.
Revised: May 2020.