That's awesome. Would love to know more. I was (only tangentially, really it was my manager) involved with (one of many) LIMS evaluations at CSIRO. I'm under the impression that "Generic" LIMS have awful track records in Australia at universities and research orgs like CSIRO: despite custom software projects also having poor cost/performance outcomes generally, historically it seems there aren't any great examples of LIMS aimed at diverse multidisciplinary research environments which have delivered better outcomes at less cost than even the most horribly expensive over-budget bespoke systems places like this churn out. There is a trail of train wrecks that is failed LIMS projects in this sector, largely from manufacturing/forensics sector thinking their LIMS fits all...

The most inspiring thing I saw was work on automated high-throughput materials discovery (I believe the examples at the time were polymers). Instrument operation, experiment design and results capture was done with OWL/RDF... And off it goes: repeatable results where the software drives experiment parameters until a goal or properties are reached. Semantic web tech has a lot of failed promises to answer for but those guys really seemed to make this stuff sing. Seeing what they're modeling/capturing, graph data models might not be such a bad fit, after seeing the contortions we went through in genetic studies trying to capture every facet of every piece of data and its provenance and the provenance of its methods and materials and specimens and specimen preparation and specimen identification and splitting and cloning and so on ad nauseum

Better stop ranting. I do software defined radio at dayjob and some silly osint graph-db driven thing I'm playing with at home to help security audit all packages, binaries and hopefully one day in-memory processes in linux/containers :)