Sprecher
Beschreibung
The smallest laboratories in our field often have the highest repetition-rate experiments and the opportunity for the fattest data pipelines. One shortcut to achieving high-repetition-rate data analysis and storage, while retaining high-fidelity of datasets for post-analysis, is to implement an automated global shot counter in the lab and stamp it on every bit of scientific data. Data can then be stored separately, without interdependency on other devices, and this data can be centralized and analyzed later. Small teams can also benefit from leveraging existent modular control systems such as EPICS, and self-describing data formats such as HDF5. For an iterative and decoupled approach to improving data infrastructure, "sidekick systems" mimicking laboratory infrastructure to various fidelity can be built at a very low cost. We have demonstrated construction of such data systems with undergraduates at California State University Channel Islands.
This work is supported by Lawrence Livermore National Laboratory, LLC under Subcontract No. B645313, and LDRD 21-ERD-015 and DOE Early Career SCW1651. LLNL is under Prime Contract No. DE-AC52-07NA27344 with the DOE/NNSA.
