StreamLine: An AI-Enabled Open Source Publication Platform to Revolutionize Scientific Communication

The future of scientific communication is moving towards openness, fostering highly collaborative environments and extending beyond traditional publication artifacts with the rise of data-driven AI. This shift necessitates platforms that support open science and offer the customizations needed for current and future trends in scientific research. However, current systems are often rigid, tailored to specific workflows, and difficult to customize, falling short in managing diverse forms of scientific communication, such as datasets, algorithms, and other artifacts. Moreover, these systems fail to leverage recent advancements in Artificial Intelligence (AI), particularly the large language models (LLMs) that have driven the success of tools like ChatGPT. To address these gaps, Kitware and the University of Illinois Urbana-Champaign propose developing StreamLine, a novel AI-enabled publication platform with an open software infrastructure supporting modern scientific and technical communication. StreamLine will leverage LLMs and augment them with domain knowledge via Retrieval-Augmented Generation (RAG) to automate the inner workings of the publication process while creating a highly engaging environment for authors, reviewers, and other stakeholders through web interfaces. It uses Git for versioning of the contents and Postgres for indexing the metadata. A key feature of the platform will be the ability to support customizable publication workflows around a diverse range of content and artifact types. Towards this goal, in Phase I, we will 1) conduct a comprehensive survey of existing software and workflows adopted by current publication systems for geosciences, 2) build the Phase I StreamLine prototype using open-source standards and tools to drive the publication process for journal articles, 3) extend and integrate pre-trained open-source LLMs and augment them with domain knowledge to find and suggest related work, and 4) gather community and stakeholder feedback. In Phase II, we plan to iterate and enhance the software by expanding its capabilities to support a broader range of technical and scientific communication use cases and implementing complete end-to-end testing. We will collaborate closely with Naval Research, universities, and other scientific publication partners to further refine and improve the software. Our developed platform and models will be open source and freely available, subject to government permission, actively encouraging community review and contributions.