Currently, there are no predictive models of microbial growth under various conditions that utilize data generated specifically for machine learning applications, and that are collected under consistent experimental conditions. This project aims to develop an extensible experimental platform and standardized data ontology for collecting phenotypic measurements of microbes grown in various cultivation conditions. Our goal is to understand and predict how environmental conditions interact with microbial genotypes to affect phenotypes such as growth and function. To do this, we will generate a comprehensive, machine learning-ready dataset comprising growth data for 1,000 culturable microbial strains across 1,000 cultivation conditions, resulting in one million unique experiments. Each strain will be cultured under controlled conditions, and growth measurements will be systematically collected along with a suite of additional phenotypic assays. This dataset will provide a deeper understanding of microbial growth dynamics and phenotypes across diverse environmental conditions, ultimately enabling the development of robust predictive models

This version includes a clarification on the rationale for selecting an open-source phenotyping platform. A sentence was added to explain why commercial technologies (e.g., Biolog Phenotype Microarrays) were not suitable due to proprietary components. Additionally, numerical corrections were made in Section 2.4.1 and Table 1 to update the number of media factors under investigation from 149 to 147.