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Version 1

Raw data for Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Published:15 March 2024|Version 1|DOI:10.17632/3gh4bz52w2.1
Contributors:
,
,
,

Description

This dataset supports the manuscript 'Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence'. The codebase used in the manuscript can be found on Zenodo at https://zenodo.org/records/10611675. The codebase used to generate this data can be found on Zenodo at https://doi.org/10.5281/zenodo.10622156.

Steps to reproduce

The .xml input files specify the initial vasculature and cell populations for running simulations with the ARCADE framework, which can be found on Zenodo at https://doi.org/10.5281/zenodo.10622156. Specific instructions to run simulations can be found on GitHub at https://github.com/bagherilab/ARCADE using v2.4.

Institutions

University of Washington

Categories

Machine Learning, Agent-Based Modeling, Surrogate Modeling

Related Links

Licence

Creative Commons Attribution 4.0 International

Version 2

Simulation data for Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Published:18 March 2024|Version 2|DOI:10.17632/3gh4bz52w2.2
Contributors:
,
,
,

Description

This dataset supports the manuscript 'Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence'. The codebase used in the manuscript can be found on Zenodo at https://zenodo.org/records/10611675. The codebase used to generate this data can be found on Zenodo at https://doi.org/10.5281/zenodo.10622156.

Steps to reproduce

The .xml input files specify the initial vasculature and cell populations for running simulations with the ARCADE framework, which can be found on Zenodo at https://doi.org/10.5281/zenodo.10622156. Specific instructions to run simulations can be found on GitHub at https://github.com/bagherilab/ARCADE using v2.4.

Institutions

University of Washington

Categories

Machine Learning, Agent-Based Modeling, Surrogate Modeling

Related Links

Licence

Creative Commons Attribution 4.0 International