This record includes training materials associated with the Australian BioCommons webinar ‘DOME - Machine Learning Best Practices & Recommendations’. This webinar took place on 5 December 2024. Event description As the adoption of Artificial Intelligence (AI) and Machine Learning (ML) accelerates across life science research, the demand for standardised practices has become crucial to ensure transparency, reproducibility, and adherence to FAIR principles. In response to these needs, DOME (Data Optimization Model Evaluation) has been developed as a key solution - a set of community-wide recommendations designed to guide supervised ML analysis reporting in biological studies. DOME offers broad, field-agnostic guidelines to enhance the impact of ML applications while ensuring reproducibility. This framework not only supports robust model evaluation but also serves as a valuable resource for training and capacity building in life sciences. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead Trainer: Dr Fotis Psomopoulos, Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Host: Dr. Giorgia Mori, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. DOME_Webinar (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://www.youtube.com/watch?v=ijFg3VbO2VM