This page should give you a (non-exclusive) overview over the use cases covered by mzQC:
It is easy with mzQC to get relevant QC info, easy to put your data into context (of measurement realities). That makes it a preferred medium to handover quality information. Read more about it in mzQC at a glance and explore a small mzQC example.
With JSON at its core, mzQC follows a ‘works online, works everywhere’ approach. Even for single spectra, as we show with the universal spectrum identifier example.
The format is an optimal QC tool for the analytical chemist and instrument operators keeping track (and archive) instrument performance. Read on with an introduction to mzQC for anlytical chemists or explore our QC sample example. You can even embed mzQC in mzML, should you choose to. View an example here.
With mzQC for archival, quality reports, and as handover format, mzQC can serve as a common currency for data repositories, journals, and collaborators.
Bridge the *omics Gap: Metabolomics
A technology agnostic design makes QC with mzQC easy in other fields as well! Assuming you make per sample measurements (runs in mzQC parlance) and multiple groups of runs in a study (sets), then all you need is to define your metrics to open up the mzQC ‘ecosystem’ to your field. Explore in this example, how easy you can include QC data from quite different instruments, employ advanced QC concepts like batch correction, and use metrics on expansive datasets.
Keep track of your study’s runs as a whole
With mzQC you can keep track of the quality of all your study’s runs. We show in this example how you can apply metrics to a whole set of runs. There are of course more scenarios in which you want to consider the quality of multiple runs, and you can read more here.