Responding to several studies that have shown that a substantial amount of basic and preclinical research results cannot be reproduced by other laboratories under the conditions described in publications, the National Institutes of Health (NIH) is seeking applications for creative educational activities that have a primary focus of developing courses for skills development, specifically training modules for graduate students, postdoctoral fellows, and beginning investigators designed to enhance data reproducibility.
NIH emphasizes that the lack of reproducibility is not due primarily to intentional fabrication or falsification of data, but in many cases is due to a lack of awareness or adherence to “sufficiently high standards in planning and execution of scientific experiments, and in transparency in the reporting of science.”
The NIH Research Education Program has a goal to support educational activities that complement and/or enhance the training of a workforce to meeting the nation’s biomedical, behavioral, and clinical research needs. The agency proposes to initiate a program of grants to develop exportable training modules in areas with the potential to enhance data reproducibility and to provide for communication and coordination of their development and deployment via a funding opportunity announcement (Training Modules to Enhance Data Reproducibility, RFA-GM-15-006). The proposed modules are expected to identify deficiencies and teach best laboratory practices in one or more of four general areas: experimental design, laboratory practices, analysis and reporting, and culture of science.
The participating NIH Institutes and Centers (ICs) include:
- General Medical Sciences (NIGMS)
- Cancer (NCI)
- Eye (NEI)
- Aging (NIA)
- Alcohol Abuse and Alcoholism (NIAAA)
- Dental and Craniofacial (NIDCR)
- Drug Abuse (NIDA)
- Neurological Disorders and Stroke (NINDS)
- Center for Complementary and Alternative Medicine (NCCAM)
- Office of Research Infrastructure Programs (ORIP).
The ICs have committed to supporting modules that cover a wide range of issues related to enhancing data reproducibility. Letters of intent are due October 20, 2014. Applications are due November 20, 2014.