We are developing training modules, assessments, and certificates and badges on good practices for computational modeling. These include making models FAIR, how to use version control (Git/GitHub), performing robust sensitivity analyses over model outputs, and how to containerize your models for easier transitions from local development and testing to high performance compute, high throughput compute, or the ☁️.
We are collecting a diverse set of computational models from different domains with different input / output characteristics and manually augmenting them to be FAIR+ and to add containerization that supports execution on the Open Science Grid Consortium's OSPool.
If you'd like to help us pilot these materials and provide early feedback, or if you have any computational models you'd like us to consider for the codebase augmentation pilot project, please let us know!
Current candidates include all the models suggested by the Making Models FAIR Initiative as well as the following models:
- The example wolf sheep predation model from NetLogo
- The Communicating Hazard Information in the Modern Environment agent based model (NetLogo)
- A Julia ABM on coffee leaf rust
- R analysis on drought sensitivity https://doi.org/10.1038/s41467-022-30316-5
- The code used in Inverting topography for landscape evolution model process representation
- https://github.com/comses-education/hydrotrend
- https://github.com/comses-education/GIPL-BMI-Fortran
- Dakota SWASH Parameter Study: https://github.com/comses-education/dakota-swash-parameter-study
- TOPMODEL with BMI support (in progress) - https://csdms.colorado.edu/wiki/Model:TOPMODEL and https://github.com/NOAA-OWP/topmodel
- GeoClaw - https://github.com/clawpack/geoclaw
- ADRIA
Hydrological model candidates:
Template repositories and educational materials under development:
- Cookiecutter NetLogo OSG template - now available for testing!
- FAIR+ OSG template - still under active development
- Reusable Building Blocks - still under active development
- Carpentries' Good Enough Practices for Scientific Computing lesson
References and possible collabs: