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NIH Initiative on Enhancing Research Reproducibility and Transparency

Revised September 2016

About the Initiative

To improve the quality of NIDA-sponsored research, NIDA issued NOT-DA-14-007 “Improving Reporting of Research Methods and Results in Translational Addiction Research Involving Animals is a NIDA Commitment”  By doing so, NIDA joins NINDS (NOT-NS-11-023), NIMH (NOT-MH-14-004), and NICHD (PAR-13-195) in efforts to enhance the reporting of research methods and results.

NIDA's mission is to advance science on the causes and consequences of drug use and addiction and to apply that knowledge to improve individual and public health. To make meaningful and powerful research progress, foundational data upon which new advances will hinge must be reliable and reproducible. This is especially important in order to enable translation of preclinical findings into human applications intended to facilitate the development of new therapies. Toward this end, NIDA is committed to the support of translational studies involving animals, which are marked by transparency in reporting on the design, conduct and analysis of experiments. NIDA encourages the investigators proposing translational studies involving animals to address a core set of research parameters/reporting standards, as listed below.

Points to Consider for NIDA Grant Applications Involving Preclinical Animal Research

NIDA believes that it is important for investigators and reviewers to consider the following points in study design and to address those that are appropriate.  Keep in mind that many of the issues will be addressed in the Research Strategy section, others in the Vertebrate Animals section, and possibly others elsewhere in the grant application.

Experimental design:

  • Rationale for the selected models, including species, strain, housing, diet, sex, age and weight
  • Rationale for endpoints selected
  • Adequacy of the controls
  • Route, dosing and timing of treatment
  • Sample size estimates, including power calculation
  • Statistical methods to be used in analysis and interpretation of results, including justification for assuming normalized distribution if parametric statistics to be used

Minimizing bias:

  • Methods of blinding (allocation concealment and blinded assessment of outcome)
  • Inclusion and exclusion criteria
  • Strategies for randomization and/or stratification
  • Procedures for dealing with missing data due to attrition or exclusion
  • Reporting of all results (negative and positive)


  • Reporting of independent validation/replication, if available
  • Robustness and reproducibility of the observed results, including whether replicates involved different lots of animals, drugs, reagents, experimenters
  • Dose-response results
  • Verification that interventional drug or biologic reached and engaged the target

Applicants are encouraged to include the statements about sample size estimates, blinding, and randomization procedures, even if those procedures were not used.

Training Modules to Enhance Rigor and Reproducibility in Neuroscience Research

Addressing the need for systematic, formal training for graduate students and postdoctoral fellows, NIDA joins NIGMS (RFA-GM-15-006) and supported the Society of Neuroscience (SfN) project "Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience" (the Grant Number 1R25DA041326-01).

SfN has partnered with leading neuroscientists to offer the series of webinar-based training modules and the workshops that address scientific, technical, and interpersonal skills necessary to tackle issues of scientific rigor in neuroscience:

Webinar 1: http://neuronline.sfn.org/Articles/Professional-Development/2016/Improving-Experimental-Rigor-and-Enhancing-Data-Reproducibility-in-Neuroscience

Webinar 2: http://neuronline.sfn.org/Articles/Professional-Development/2016/Minimizing-Bias-in-Experimental-Design-and-Execution

Webinar 3: http://neuronline.sfn.org/Articles/Professional-Development/2016/Best-Practices-in-Post-Experimental-Data-Analysis

Webinar 4: http://neuronline.sfn.org/Articles/Professional-Development/2016/Best-Practices-in-Data-Management-and-Reporting

Workshop 1: http://neuronline.sfn.org/Articles/Professional-Development/2016/Tackling-Challenges-in-Scientific-Rigor


This page was last updated September 2016