In this Newsletter issue, we introduce helpful resources for educational and training purposes, which were recently published and which all address data integrity and reproducibility issues:
1. The American Physiological Society (APS) has provided access (full audio and slides) to the 2017 symposium ‘Why Scientific Rigor Matters and Ways to Improve It’. All presenters were asked to suggest specific action items that served as the basis for creating a Toolbox that can be referred to for issues regarding research rigor. This toolbox includes Data Visualization Resources for Small Studies, Textbooks on Responsible Conduct of Research, Statistics Courses and a short Introduction to the NC3Rs Experiment Design Assistant.
Other APS links include
– a Reproducibility Journal Club featuring publications to gain insight into the challenges of improving scientific rigor;
– the ‘Best Practices for Publishing Your Research’ APS Training Course on Publication Ethics and
– Shai Silberberg’s Video on Unconscious Bias & Publications Bias, introducing key contributors to irreproducibility and offering suggestions for mitigating these factors.
2. The NIH has developed training modules to enhance data reproducibility for graduate students, postdoctoral fellows and early stage investigators as the primary audiences. These modules focus on integral aspects of rigor and reproducibility in the research endeavor, such as bias, blinding and exclusion criteria.
3. Harvard University offers an introduction to basic statistical concepts and R programming necessary for analyzing data in the life sciences. These online courses teach the basics of statistical inference in order to understand and compute p-values and confidence intervals, and analyzing data with R. Case studies requiring R programming will be used to test understanding and ability to implement basic data analyses. By using R scripts to analyze data, these courses will help to learn the basics of conducting reproducible research.