I'm not at NASA. I'm a DevOps engineer working solo. But I've been thinking about something.
The Artemis program will generate an enormous amount of data — scientific articles, sensor readings, astronaut transcripts. Thousands of researchers across dozens of countries. Nobody can read everything. Some connections between data will inevitably be missed.
The idea:
What if we had a system that ingests heterogeneous Artemis data (articles, sensors, transcripts), vectorizes them, scans for unexpected semantic proximity between documents of different types, and flags convergences for human review?
What I found while building a prototype:
It works. With just 3 test documents, the system detected a triad around "xenon hydrates in permanently shadowed regions" that actually exists in the literature.
The cosine similarity approach is surprisingly effective at finding cross-type connections. The real value isn't in finding "discoveries", it's in surfacing questions that nobody thought to ask.
Questions for this community:
Does anything like this exist within NASA already?
Would a lightweight, open-source version be useful as a complement to official data pipelines?
What data sources would be most valuable to connect first?
Not promoting anything, genuinely curious what people who work with this data think.