Speaker
Description
Knowing your data is trustworthy brings confidence and peace of mind to the subsequent data analysis step(s). This is reliant on the provision of thorough, well-configured data correction steps. Over the last decade, we have achieved this through implementation of a modular data correction system, which allows a comprehensive data correction graph to be constructed for a given instrument or experiment type. The first implementation in Java in the Data Analysis WorkbeNch (DAWN) has long been producing vast quantities of data on a range of instruments, using a correction graph that is universally applicable to all types of samples. It is now time for a refactored version.
With the involvement of several interested parties, the foundation for the new Python-based implementation has been laid and a functional prototype is available. Features include:
- native support for propagating multiple uncertainty estimates
- units-aware operations
- command-line and web-based APIs
- a full traceability chain can be included in the processed files
- graph output and interfaces are available for visualisation and (future) graphical configuration tools
- minimal dependencies on external libraries
This talk will introduce MoDaCor and encourage collaboration and adoption.
