Hi @AWGall
There have been a number of questions about which projects could be carried out mining the biological and/or environmental telemetry (CO2, RH, Temp, Radiation) data accessible and maximally open via OSDR-GeneLab
There also is a TON of interest lately in AI/ML approaches.
I urge AWGers to check out all the data, dashboard, viz & analysis tools, APIs, etc at the About page.
Then I suggest AWG members read this recent paper 
by Dupire et al 2026 on “ Fifteen challenges for generative AI applications to cell biology” — there are lots and lots of ideas on approaches to consider pursuing, and determining if these OSDR/GeneLab may be ripe to conduct a collaborative AWG investigation 
Summary
Generative AI (Gen-AI) has shown a remarkable impact in several biological research areas, from protein folding and de novo design to pathogenic mutation prediction. However, it remains unclear whether these molecular-level successes can translate to cellular and multicellular insights relevant to fields ranging from immunology to cancer and neurodegeneration. This arises from the intricate nature of the molecular mechanisms that determine cellular and organismal behavior, the lack of sufficient training data, and the multicellular nature of most pathophysiologic phenotypes. Novel Gen-AI frameworks are likely needed to integrate prior biological knowledge, such as molecular interaction networks, as well as guiding principles focusing the community’s attention on solving biologically and translationally relevant problems. Drawing inspiration from Hilbert’s list of 23 mathematical problems that have focused the mathematical community’s attention for more than a century, we propose fifteen grand AI challenges to focus the biomedical community’s attention on critically relevant questions, most of which still lack effective predictive methodologies.
Dupire L, Khan A, Karaletsos T, et al. Fifteen challenges for generative AI applications to cell biology. Cell, 2026; 0. https://doi.org/10.1016/j.cell.2026.07.004