POSE: Phase II: RepLab: Open Source Hardware for Laboratory Automation

About This Grant

This Pathways to Enable Open-Source Ecosystems (POSE) project enables automation in laboratory science workflows. Laboratory automation increases the precision and efficiency of science experiments, enabling the collection of large data sets and automating feedback loops. However, scientific experiments are highly varied and require deep domain expertise and specialized equipment. There are no one-size fits all approach to establishing a self-driving lab. This project addresses this opportunity by advancing RepLab, an Open-Source Ecosystem (OSE) for open-source laboratory automation. RepLab will support scientists in adopting automation in their workflows to increase their efficiency and accelerate their progress while training a generation of scientists with new skills. This ecosystem will vet open-source laboratory automation technologies by testing, benchmarking, and validating their performance and reliability. This solution will lower the barrier to replicating the technologies by conducting design for distributed production and supply chain assessments. Enabling automation in laboratory science workflows will foster U.S. innovation and accelerate technology development and translation by supporting industry and small businesses in establishing competitive science workflows. This POSE project establishes an OSE for laboratory automation. The team will validate and support open-source infrastructure that underpins self-driving labs, enabling scientists to close the loop on their experiments and accelerate their progress supported by automation and computing-enabled discovery. The OSE increases user trust in vetted open-source hardware and control software, and encourages scientists to incorporate optimization in their approaches. Users will be able to replicate laboratory automation infrastructure across different sites. As ecosystems for open-source hardware are also less well-established than their open-source software counterparts, this solution will improve collaboration and co-creation mechanisms and may become valuable to others facing similar challenges stemming from distributed manufacturing, trustworthy hardware, and quality control. RepLab will support onboarding by developing extensive documentation and training materials for scientists from various domains seeking to automate their laboratory processes. In support of these efforts, the OSE will convene several avenues for community engagement, including online and in-person workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.