Growing the Simulation Ecosystem: Introducing Mesa Data to Provide Transparent, Accessible and Extensible Data Pipelines for Simulation Development

TL;DR

This paper introduces Mesa Data, a set of transparent, accessible, and extensible data pipelines designed to improve data handling and reproducibility in agent-based modeling, demonstrated through crop yield and synthetic population examples.

Contribution

It presents a new ecosystem component that enhances data pipelines for simulation development, focusing on transparency, accessibility, and extensibility to support community collaboration.

Findings

Data pipelines from download to integration into ABM are demonstrated.

Pipelines are transparent, guiding users step-by-step.

Open-source pipelines facilitate community development.

Abstract

The Agent Based Model community has a rich and diverse ecosystem of libraries, platforms, and applications to help modelers develop rigorous simulations. Despite this robust and diverse ecosystem, the complexity of life from microbial communities to the global ecosystem still presents substantial challenges in making reusable code that can optimize the ability of the knowledge-sharing and reproducibility. This research seeks to provide new tools to mitigate some of these challenges by offering a vision of a more holistic ecosystem that takes researchers and practitioners from the data collection through validation, with transparent, accessible, and extensible subcomponents. This proposed approach is demonstrated through two data pipelines (crop yield and synthetic population) that take users from data download through the cleaning and processing until users of have data that can be integrated into an ABM. These pipelines are built to be transparent: by walking users step by step through the process, accessible: by being skill scalable so users can leverage them without code or with code, and extensible by being freely available on the coding sharing repository GitHub to facilitate community development. Reusing code that simulates complex phenomena is a significant challenge but one that must be consistently addressed to help the community move forward. This research seeks to aid that progress by offering potential new tools extended from the already robust ecosystem to help the community collaborate more effectively internally and across disciplines.