Project summary This SBIR proposal aims to address current challenges in data-driven neuroscience by implementing DataJoint SciOps: a commercial service to help research labs implement computational workflows for data-intensive science experiments. This turn-key service will organize secure data pipelines and automate analysis jobs based on scalable cloud infrastructure while keeping the entire process transparent and reproducible. Progress in neuroscience relies on analyzing vast amounts of complex data recorded by new generations of neurotechnologies. To analyze this data, research teams develop advanced algorithms and share them as open-source software. These software toolchains require advanced computing infrastructure and operations, posing a set of engineering hurdles to manage the particular experiment workflows for data entry, acquisition, analysis, sharing, and publishing. DataJoint SciOps is made possible by the DataJoint Elements program (NIH Grant U24 NS116470), which provides a collection of community-curated software modules for building standardized computational workflows. These designs integrate best-in-class open-source analysis software from leading research teams and provide integrations with neuroscience infrastructure projects. DataJoint SciOps will effectively serve as the commercial extension of DataJoint Elements by providing computing infrastructure, hosting, and a managed service with subject-matter expert support and customization services. This Direct-to-Phase II commercialization project will develop and validate a comprehensive managed service for executing data-centric neuroscience projects with robust automated processes for data management and analysis (Aim 1). A cloud-based software-as-a-service platform will streamline the service to enable scaling to hundreds of labs through standardization, self-service, and process automation (Aim 2). In the process, DataJoint will partner with Johns Hopkins University's Applied Physics Lab to integrate the platform with neuroinformatics resources and provide collaboration interfaces (Aim 3). Jointly, the teams will ensure the transparency and reproducibility of the managed workflows and integrate it with other data infrastructure programs in the U.S. and internationally. With several thousand neuroscience research groups seeking to adopt advanced neurotechnology instruments and analysis tools, the commercially operated DataJoint SciOps service will lower the technological and organizational barriers for efficient and reproducible research. 1