Open source and research are intricately linked, with the foundational principles of open source—such as sharing, peer review, and collaboration—mirroring the core values of the research community. Despite the prevalent use of open source tools in data science, including Python, R, notebooks, and the development of a wide array of open source softwares and libraries, these contributions often go unrecognized. Research institutions frequently overlook the significance of software production and maintenance when evaluating research impacts and career advancement for research professionals. Simultaneously, technology transfer centers struggle to harness economic and commercial value from open source projects effectively.

Research groups committed to open source face challenges in gaining institutional support and fear potential roadblocks from technology transfer centers when attempting to initiate discussions. This reality prompts a reevaluation of the current situation and calls for enhancements to more closely align research practices with institutional goals.

In this presentation, we will examine the state of open source practices in research, spotlighting the barriers to recognition and the ongoing and proposed strategies for overcoming these hurdles. We will explore solutions, including the integration of open source with other components like hardware and data, and underscore the significance of the citation of software in publications. For technology transfer centers, we will discuss the potential of digital commons and the necessity for specialized training on the nuances of open source intellectual property and community-based approaches, and the role to play by the open source ecosystem.