Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
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 publishing tutorial and feel free to 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 detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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This code simulates the WiFi user tracking system described in: Thron et al., “Design and Simulation of Sensor Networks for Tracking Wifi Users in Outdoor Urban Environments”. Testbenches used to create the figures in the paper are included.
The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.
This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.
The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.
This model simulates networking mechanisms of an empirical social network. It correlates event determinants with place-based geography and social capital production.
The model aims at simulating the car traffic. It allows to use either a macro or a micro sub-model for the simulation of the flow on the roads.
This is an agent-based model that simulates the structural evolution in food supply chain.
The model demonstrates how non-instantaneous sampling techniques produce bias by overestimating the number of counted animals, when they move relative to the person counting them.
The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.
The model simulates agents in a spatial environment competing for a common resource that grows on patches. The resource is converted to energy, which is needed for performing actions and for surviving.
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