Open Source for the Life Sciences (OS4LS)

Grants to support open source software projects that underpin AI and data-intensive research in the life sciences.

The Open Source for Science Fund is a programme by the US-based charitable organisation Renaissance Philanthropy. It pools capital across philanthropy, government, and industry to invest in scientific open source and make it ready for the AI era, with funding mechanisms built for maintainers.

With seed funding from Biohub and Wellcome, and support from the Kavli Foundation, the organisation has launched the inaugural call of the Open Source for Science Fund under the headline Open Source for the Life Sciences (OS4LS). The call is targeted at open source software that underpins data-intensive research and AI-driven discovery in the life sciences.

Applications are invited for work that addresses significant technical bottlenecks or delivers critical capabilities in these software projects. The focus is on proposals that target a clear technical challenge and serve critical needs for a research audience. This must include a realistic plan of work aligned with the project’s own roadmap, and genuine buy-in from the core maintainer community about the proposed fundable activities.  

The call is divided into two funding tracks:

Track 1: Domain-specific tools.

Applications are accepted from various areas of the life sciences, including but not limited to the following:

• Foundational biology: Molecular biology and genomics; Structural and chemical biology; Cell and developmental biology; Evolutionary biology; Immunology.
• Enabling technologies: Biological and Biomedical imaging: Synthetic biology; Bioinformatics; Computational biology; Predictive modelling.
• Translational/application areas: Neuroscience; Infectious disease; Therapeutics discovery and development.

Track 2: Foundational Libraries and Ecosystem Initiatives

Funding is offered for:

• Open source libraries that serve as core dependencies of scientific applications across multiple domains in the life sciences.
• Collaborative proposals developing shared interoperability, integrations, or common interfaces and capabilities across a set of related tools within the same software ecosystem. Applications in this track may propose coordinated work spanning multiple software projects.

Priority will be given to applications from software tools that enable large-scale data analysis and AI-driven applications and workflows, including but not limited to:

• Representing, managing, curating, and structuring scientific data for use in model training.
• Developing benchmarks and standards, endpoints, or protocols that unlock the use of open source tools in agentic workflows.
• Scalability and performance improvements, including support for hardware acceleration.
• Interoperability frameworks that make tools composable in AI-driven pipelines.