OBJECTIVES: The NIH All of Us Research Program (All of Us) is engaging a diverse community of more than 10 000 registered researchers using a robust engagement ecosystem model. We describe strategies used to build an ecosystem that attracts and supports a diverse and inclusive researcher community to use the All of Us dataset and provide metrics on All of Us researcher usage growth. MATERIALS AND METHODS: Researcher audiences and diversity categories were defined to guide a strategy. A researcher engagement strategy was codeveloped with program partners to support a researcher engagement ecosystem. An adapted ecological model guided the ecosystem to address multiple levels of influence to support All of Us data use. Statistics from the All of Us Researcher Workbench demographic survey describe trends in researchers' and institutional use of the Workbench and publication numbers. RESULTS: From 2022 to 2024, some 13 partner organizations and their subawardees conducted outreach, built capacity, or supported researchers and institutions in using the data. Trends indicate that Workbench registrations and use have increased over time, including among researchers underrepresented in the biomedical workforce. Data Use and Registration Agreements from minority-serving institutions also increased. DISCUSSION: All of Us built a diverse, inclusive, and growing research community via intentional engagement with researchers and via partnerships to address systemic data access issues. Future programs will provide additional support to researchers and institutions to ameliorate All of Us data use challenges. CONCLUSION: The approach described helps address structural inequities in the biomedical research field to advance health equity.

Genetic studies indicate that more than 50% of women are genetically tetrachromatic, expressing four distinct types of color photoreceptors (cone cells) in the retina. At least one functional tetrachromat has been identified in laboratory tests. We hypothesize that there is a large latent group in the population capable of fundamentally richer color experience, but we are not yet aware of this group because of a lack of tetrachromatic colors in the visual environment. This paper develops theory and engineering practice for fabricating tetrachromatic colors and potentially identifying tetrachromatic color vision in the wild. First, we apply general d -dimensional color theory to derive and compute all the key color structures of human tetrachromacy for the first time, including its 4D space of possible object colors, 3D space of chromaticities, and yielding a predicted 2D sphere of tetrachromatic hues. We compare this predicted hue sphere to the familiar hue circle of trichromatic color, extending the theory to predict how the higher dimensional topology produces an expanded color experience for tetrachromats. Second, we derive the four reflectance functions for the ideal tetrachromatic inkset, analogous to the well-known CMY printing basis for trichromacy. Third, we develop a method for prototyping tetrachromatic printers using a library of fountain pen inks and a multi-pass inkjet printing platform. Fourth, we generalize existing color tests - sensitive hue ordering tests and rapid isochromatic plate screening tests - to higher-dimensional vision, and prototype variants of these tests for identifying and characterizing tetrachromacy in the wild.

Recent high-profile fire incidents in cities around the world have highlighted gaps in fire risk reduction efforts, as cities grapple with fewer resources and more properties to safeguard. To address this resource gap, prior work has developed machine learning frameworks to predict fire risk and prioritize fire inspections. However, existing approaches were limited by not including time-varying data, never deploying in real-time, and only predicting risk for a small subset of commercial properties in their city. Here, we have developed a predictive risk framework for all 20,636 commercial properties in Pittsburgh, based on time-varying data from a variety of municipal agencies. We have deployed our fire risk model on Pittsburgh Bureau of Fire's (PBF), and we have developed preliminary risk models for residential property fire risk prediction. Our commercial risk model outperforms the prior state of the art with a kappa of 0.33 compared to their 0.17, and is able to be applied to nearly 4 times as many properties as the prior model. In the 5 weeks since our model was first deployed, 58% of our predicted high-risk properties had a fire incident of any kind, while 23% of the building fire incidents that occurred took place in our predicted high or medium risk properties. The risk scores from our commercial model are visualized on an interactive dashboard and map to assist the PBF with planning their fire risk reduction initiatives. This work is already helping to improve fire risk reduction in Pittsburgh and is beginning to be adopted by other cities.

We investigated the role of domain structure, in designing for software usefulness and usability. We ran through the whole application development cycle, in miniature, from needs analysis through design, implementation, and evaluation, for planning needs of one NASA Mission Control group. Based on our needs analysis, we developed prototype software that matched domain structure better than did the legacy system. We compared our new prototype to the legacy application in a laboratory, high-fidelity analog of the natural planning work. We found large performance differences favoring the prototype, which better captured domain structure. Our research illustrates the importance of needs analysis (particularly Domain Structure Analysis), and the viability of the design process that we are exploring.