Large, complex datasets are fundamental to life science research, and extracting meaningful insights from them is essential for developing new products and services. This requires robust analytical workflows and sustainably maintained, professional-grade software. These tools enable data aggregation, analysis and infrastructure support, while promoting reproducibility, portability, trust and productivity, often in alignment with FAIR principles. While open source software (OSS) has been an important pillar for research and development for decades, the increasing ubiquity of machine learning (ML) has further emphasised the importance of workflows, as manual tasks increasingly shift towards to algorithm-driven processes. Life scientists in public and private institutions use open source software and workflows (OSSW) to build analytical pipelines tailored to specific research purposes, including clinical studies and the development of diagnostic or therapeutic applications. In turn, these scientists often contribute expertise to design, validate and enrich open workflows, fostering a community of practice and a wealth of open knowledge with diverse applications and methodologies. Such collective efforts produce invaluable assets for research across industry and academia. Investigating current practices in OSSW is crucial for understanding trends and developing best practices for sharing and using open software and workflows. To this end, this report examines contribution and sharing patterns on GitHub – the most widely-used platform for OSS – with a focus on selected life science companies and on Galaxy, a major research infrastructure project. The findings underscore the importance of OSSW in driving innovation, fostering collaboration and enhancing transparency in research.