added experimentology

README.md

@@ -185,6 +185,7 @@ Getting into a field as complicated and challenging as cognitive neuroscience ca
 - [NBIS Tools for reproducible research](https://nbis-reproducible-research.readthedocs.io/en/course_2104/) A course ran by the National Bioinformatics Infrastructure Sweden (NBIS) on reproducibility covering Data management, Conda, Snakemake, Git, Jupyter, Markdown, Docker and Singularity.
 - [Computational and Inferential Thinking: The Foundations of Data Science](https://inferentialthinking.com/chapters/intro.html) A online course in data science originally developed for the UC Berkeley course Data 8: Foundations of Data Science by Ani Adhikari, John DeNero and David Wagner.
 - [Coding for data](https://lisds.github.io/textbook/intro.html) An introduction to data science by Matthew Brett, borrowing from the Berkeley textbook above.
+- [Experimentology: An Open Science Approach to Experimental Psychology Methods](https://experimentology.io/) Experimentology, as described by the authors is 'the set of practices, findings, and approaches that enable the construction of robust, precise, and generalizable experiments'. Created as the foundational course for incoming graduate students in the Stanford psychology department, it provides guidance for preregistration, project management, data sharing, and reproducible report writing. Authored by: Michael C. Frank, Mika Braginsky, Julie Cachia, Nicholas A. Coles, Tom E. Hardwicke, Robert D. Hawkins, Maya B. Mathur and Rondeline Williams.
 ### Software/tools
 - [Cookiecutter](https://drivendata.github.io/cookiecutter-data-science/) 'A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.'
 - [Docker](https://davetang.github.io/learning_docker/) A comprehensive introductory course to [Docker](https://docs.docker.com/) by [Dave Tang](https://davetang.org/).