Reproducibility Working Group
Conducting rigorous research that is both reliable and reproducible should be the goal of every researcher. Studies that are not designed to yield robust results waste animals and research resources and lead to publication of erroneous findings. Properly designed experiments will increase R&D productivity and improve decision making by decreasing false positive and false negative rates. While various guidelines and tools have been developed to help researchers improve experimental design and analysis, many are still not commonly utilized for impact.
Mission and Objectives
The mission of the 3Rs Reproducibility Working Group (WG) is to identify barriers and propose strategies to improve in vivo research rigor and reproducibility, and to facilitate scientific effectiveness and ethical research practices by promoting cultural changes across the research community. The team objectives include: 1) determining how we can support improved decision making through integration of statistical expertise for scientific reasoning, rigorous design and experimental methods, and data analysis and interpretation; 2) promoting collaboration with experts early in the research planning process and maintaining engagement during experiments, to assure that rigorous statistical methods are utilized that account for optimal study conduct, reproducibility, reporting and publication.
- Develop a brief survey using the Theory of Planned Behavior to gather data from IQ member scientists to identify the barriers to various strategies proposed for improved reproducibility of animal studies.
- Write a position paper to present results of survey on industry practice and discuss barriers to reproducibility related to experimental design. Based on survey results and WG discussion, provide suggestions for proactive engagement of statistical resources. Present strategies and resources supporting experimental design and data analysis for internal and external studies, to increase rigor and reproducibility of animal studies to improve decision making.
Updated September 2021