This repository provides the source-code for a practical guide to implementing Adaptive Preregistration for Model-Based Research that accompanies the paper:
Gould, E., Jones, C.S., Yen, J.D.L., Fraser, H.F., Wootton, H., Vivian, L., Good, M., Duncan, D.H., Rumpff, L., Fidler, F.M., et al. (in prep., 2024) "But I can't preregister my research: Improving the reproducibility and transparency of ecological modelling with adaptive preregistration".
The guide provides background on researcher degrees of freedom and the risk of quesitonable research practices in model-based research, and argues for the role in preregistration for improving the replicability and transparency of ecological modelling. The main components of the guide include an overview of adaptive preregistration, a practical guide on implementing it using git and GitHub, as well as a template that can be used to preregister ecological modelling studies.
Both the template and Adaptive Preregistration methodology are currently being tested and refined using a case study of environmental flows modelling in Victoria, Australia led by Chris Jones and Jian Yen at the Arthur Rylah Institute for Environmental Research. You can find the repository for that code at https://github.com/egouldo/VEFMAP_VEG_Stage6/tree/EG-prereg-updates (note, repository currently embargoed and set to private as of 12 March 2024).
The OSF repository for the Adaptive Preregistration project may be found at: https://osf.io/sq5j4/.