What is it?
The goal of this initiative is to provide capacity-building opportunities to improve the skill, practices, and protocols to make computational models findable, accessible, interoperable, and reusable (FAIR). We have selected a list of highly cited papers in different domains and developed a protocol for making those models FAIR. Our aim is to make over 100 models FAIR, with the help of the modeling community. We will stimulate activities to advance model analysis of those FAIR models using high throughput computing.
The initial development of this initiative has had contributors from the Network for Computational Modeling in Social and Ecological Sciences (CoMSES Net). Many other modeling organizations endorse this initiative and seek to stimulate participation across the community.
Why should I get involved?
The Making Models FAIR initiative may provide many opportunities for networking, paper publications, and training and learning. Community members may wish to collaborate on making a selection of highly-cited models FAIR in order to publish their work and share more widely with the scientific community. Or, they may seek to replicate findings or perform additional parameter sweeps and sensitivity tests for better understanding of some of the most classic models that are frequently re-used and referenced.
This initiative seeks to offer a straightforward way to engage with modeling communities such as CoMSES, while allowing room for the community itself to build and grow the initiative in ways that most suit their needs. For instance, additional models may be added to the initial list of publications / models, or new experiments with high throughput computing could be performed on newly FAIR models, etc.
Navigating the site
This website’s documentation pages provide information on the process involved in making a model FAIR, the currently-selected papers and their associated models, and the steps for getting involved with this initiative. Additionally, be sure check out the community discussion board on Github.
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