About the CoMSES Model Library more info
Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging norms that facilitate software citation, archival, interoperability, and reuse. Model authors can also request that their model code be peer reviewed to receive a DOI.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model archive tutorial or contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
Displaying 2 of 2 results biological models clear
FNNR-ABM
Judy Mak | Published Thursday, February 28, 2019 | Last modified Saturday, December 07, 2019FNNR-ABM is an agent-based model that simulates human activity, Guizhou snub-nosed monkey movement, and GTGP-enrolled land parcel conversion in the Fanjingshan National Nature Reserve in Guizhou, China.
Quick-start guide:
1. Install Python and set environmental path variables.
2. Install the mesa, matplotlib (optional), and pyshp (optional) Python libraries.
3. Configure fnnr_config_file.py.
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A-KinGDom: A Kinship, Grooming and Dominance Model for Primate Societies
Ruth Dolado Francesc S Beltran Vicenc Quera | Published Thursday, July 11, 2013 | Last modified Wednesday, July 17, 2013A-KinGDom simulates the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions which allow us to define four different attack and affiliative strategies.