CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. 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.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
This is the full repository to run the survival analysis (in R) and run the population viability model and its analysis (NetLogo + R) of the Northern Bald Ibis (NBI) presented in the study
On the road to self-sustainability: Reintroduced migratory European Northern Bald Ibises (Geronticus eremita) still need management interventions for population viability
by Sinah Drenske, Viktoriia Radchuk, Cédric Scherer, Corinna Esterer, Ingo Kowarik, Johannes Fritz, Stephanie Kramer-Schadt
The model represents migration of the green sea turtle, Chelonia mydas, between foraging and breeding sites in the Southwest Indian Ocean. The purpose of the model is to investigate the impact of local environmental conditions, including the quality of foraging sites and ocean currents, on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites.
Corresponding article to found here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.5552
The model is a combination of a spatially explicit, stochastic, agent-based model for wild boars (Sus scrofa L.) and an epidemiological model for the Classical Swine Fever (CSF) virus infecting the wild boars.
The original model (Kramer-Schadt et al. 2009) was used to assess intrinsic (system immanent host-pathogen interaction and host life-history) and extrinsic (spatial extent and density) factors contributing to the long-term persistence of the disease and has further been used to assess the effects of intrinsic dynamics (Lange et al. 2012a) and indirect transmission (Lange et al. 2016) on the disease course. In an applied context, the model was used to test the efficiency of spatiotemporal vaccination regimes (Lange et al. 2012b) as well as the risk of disease spread in the country of Denmark (Alban et al. 2005).
References: See ODD model description.
The purpose of the simulation was to explore and better understand the process of bridging between an analysis of qualitative data and the specification of a simulation. This may be developed for more serious processes later but at the moment it is merely an illustration.
This exercise was done by Stephanie Dornschneider (School of Politics and International Relations, University College Dublin) and Bruce Edmonds to inform the discussion at the Lorentz workshop on “Integrating Qualitative and Quantitative Data using Social Simulation” at Leiden in April 2019. The qualitative data was collected and analysed by SD. The model specification was developed as the result of discussion by BE & SD. The model was programmed by BE. This is described in a paper submitted to Social Simulation 2019 and (to some extent) in the slides presented at the workshop.
Inspired by the SKIN model, the basic concept here is to model the acceptance and implementation of supplier innovations. This model includes three types of agents comprising suppliers, manufacturers and applicators.