Forthcoming biotherapeutics and biotechnologies require sophisticated upstream process development to identify the suitable genetic and protein variation, cell line and strains, culture media, and their combinations for, e.g., industrial bioreactor applications. Improvements in lab automation, DNA synthesis, and Synthetic Biology have accelerated upstream process development. Microfluidics and lab automation have enabled the quick design, construction, and testing of multiple biomaterial candidates and production conditions. In addition, advances in machine learning and other computation approaches use upstream data sets to drive process decision-making and cross-program learning. In this data collection, the first aim is to exchange automation hardware and computational data, techniques, and workflows that can boost upstream throughput processes of gene discovery and development. Such procedures are expected to increase discovery operations' quality, resilience, dependability, and reproducibility. Next, it focuses on sharing innovative, high-throughput microfluidics platforms for data collection and storage of biomaterials. Lastly, it aims to cover AI breakthroughs for accurate property landscape sampling, analytical characterization, and rational design of biomaterials and their functions.