  Research with AI and ML technologies lives in a variety of settings with
often asynchronous goals and timelines: academic labs and government
organizations pursue open-ended research focusing on discoveries with long-term
value, while research in industry is driven by commercial pursuits and hence
focuses on short-term timelines and return on investment. The journey from
research to product is often tacit or ad hoc, resulting in technology
transition failures, further exacerbated when research and development is
interorganizational and interdisciplinary. Even more, much of the ability to
produce results remains locked in the private repositories and know-how of the
individual researcher, slowing the impact on future research by others and
contributing to the ML community's challenges in reproducibility. With research
organizations focused on an exploding array of fields, opportunities for the
handover and maturation of interdisciplinary research reduce. With these
tensions, we see an emerging need to measure the correctness, impact, and
relevance of research during its development to enable better collaboration,
improved reproducibility, faster progress, and more trusted outcomes. We
perform a case study of the Frontier Development Lab (FDL), an AI accelerator
under a public-private partnership from NASA and ESA. FDL research follows
principled practices that are grounded in responsible development, conduct, and
dissemination of AI research, enabling FDL to churn successful
interdisciplinary and interorganizational research projects, measured through
NASA's Technology Readiness Levels. We also take a look at the SpaceML Open
Source Research Program, which helps accelerate and transition FDL's research
to deployable projects with wide spread adoption amongst citizen scientists.
