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 :)
I like your comments regarding OWL/RDF. I will have to look into that. I know of a few Chemical / Polymer mfg. companies who are still using their homegrown LIMS-slash-PLM systems to manage new materials research.
The thing I love about this field the most is working along side very intelligent chemists and engineers and helping them automate their labs. The appreciation factor is through the roof when you can remove manual data entry and help them advance their laboratory throughput.
Definitely Niche. I've been doing it for about 15 years and I keep seeing the same consultants over and over again.
Here's the deal. Most manufacturing companies (think Big Pharma, Petrochemical, Chemical, Widget manufacturing, anything really) measure quality. Those quality measurements are probably done in a laboratory. Every laboratory generates data. All that data needs to be managed well so that the laboratory can maintain its accredidation.
Basically, LIMS systems manage samples and lab result data. In addition, they probably integrate with Instruments and instrument software to automate some of that data entry.
Other industries besides manufacturing are Govt (waste and wastewater treatment labs), Third-party labs (labs that do testing for other companies), Healthcare (clinical labs), Forensic Labs, and more.
Bottom line, there are tons of laboratories and they all need to manage their data.
The players in this space are downright stuck in the last decade. There is so much opportunity here for a few new players to come in and make a step-change.
I'm currently working on creating a modern LIMS to manage Laboratory data.