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This S-STEM project addresses the national need for a well-educated engineering and computing workforce by supporting the retention and graduation of low-income students with demonstrated financial need and strong academic potential. The project focuses on creating pathways that allow students to progress from an associate's and bachelor's degree (at the regional campus) in technology to a bachelor's and possibly even a master's degree in engineering and computing at the main campus. This has been achieved by creating curricular pathways and providing infrastructure and support to encourage higher degree attainment by participating students while reducing graduation time. Over six years, this project aims to provide scholarships to 132 full-time students pursuing Associate, Bachelor's, and Master's degrees in Engineering, Computer Science, and related fields. So far, through this project, three cohorts of students have been recruited through a holistic review process, with recruitment strategies involving high school visits, outreach events, and collaborations with community colleges. As of Fall 2024, 45 students have been funded, with $256,125 in scholarships awarded. The diverse body of S-STEM scholars includes ~27% female, 11% African American/Black, 11% Asian, and ~7% Hispanic students. So far, ten students have graduated with a bachelor's degree who started with an associate's degree, and one student who started with an associate degree has completed a master's program. This supporting paper associated with the poster highlights the various aspects of this project, including recruitment strategies, curricular pathway development, cohort building, etc. We anticipate that this project will generate data on recruiting and retaining low-income, academically talented students, with findings related to fostering community and identity among scholarship recipients through mentoring and peer support, promoting excellent retention and workforce development. 

Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.