Process for Making a Model FAIR

Here, you will find guidance on how this initiative seeks to make models more FAIR (learn more about the FAIR principles here). This effort will contribute a public good to the modeling community, increasing transparency and building trust in many of the highly cited models that are frequently used (and built upon) in new research. While the initiative is primarily focused on the first two steps outlined below, it will open the door for replication and robustness checks and other associated opportunities.

Each publication (and associated model) originally selected for this initiative has been assessed based on the five FAIR criteria: (1) Publicly accessible code, (2) License for the code, (3) DOI for the code, (4) Good documentation, and (5) Clean code.

Steps in the process:

1. Original assessment of the FAIR criteria
   See Assess a Model for more information

   • This is done by the CoMSES team for the preliminary models for this initiative
   • See the Models page for each model’s assessment score
   
   

2. Make the model FAIR (across the five criteria)
   See How to Make FAIR for more information

   • Periodically re-assess the five FAIR criteria of the model you’re working on
   • Contact fair@comses.net and/or comment on the model’s issue within the coordination repository when this process has been completed
   
   

3. Replication check

   • Can you reproduce the originally-published results?
   
   

4. Robustness check

   • Sensitivity tests, parameter sweeps
   • Opportunities to utilize high throughput computing, etc.
   
   

5. Get your FAIR share

   • Once you have made a model FAIR, it would be useful to get credit for your hard work! How can you make this an item on your CV? There are different options, dependent on what was involved and the results of your efforts.
     • You will have the Github repository and the DOI of the model that you made FAIR. These are items you can put on your CV as outputs of your academic activities.
     • You want to share your experience and lessons learned with a broader audience. You could do this via a blog post in RofASSS (Review of Artificial Societies and Social Simulation).
     • You find something newsworthy that contributes to knowledge of the field. For example, the insights of the original model cannot be replicated, or the results are not robust when you do a more elaborate model analysis than could be done when the original model was published. In those cases, you should consider writing up a manuscript for a peer-reviewed journal, such as the Journal of Artificial Societies and Social Simulation or Socio-Environmental Systems Modeling. Examples of such types of articles are here, here, and here.

If you have any questions, please contact us at fair@comses.net.