Reflectometry curves (XRR and NR) and corresponding fits for machine learning
Linus Pithan, Alessandro Greco, Alexander Hinderhofer, Alexander Gerlach, Stefan Kowarik, Nadine Rußegger, Ingrid Dax, Frank Schreiber · Zenodo (CERN European Organization for Nuclear Research) · 2022
This is a compiled dataset of raw X-ray reflectivity (XRR, reflectometry) measurements together with corresponding fit parameters, intentionally published to use as training or test data for machine learning models. (The authors aim to include NR data in further versions of this dataset and plan to include other substrates and materials for XRR. Contributions welcome!) An interactive documentation can be found in "README.html" or at https://schreiber-lab.github.io/reflectometry-dataset. Data structure All data is provided in an hdf5 file, following NeXus convention with respect to the provided metadata in the hdf5 attributes. Some datesets have been measured in-situ and therefore there are stacks of curves that correspond to the different layer thicknesses of the same material on top of SiOx. The measured data is provided under experimental and the corresponding fit parameters under fit. Additional information is collected in metadata. Where to find the dataset and how to contribute Have a look at github and zenodo. In case you wish to contribute further curves to this dataset or have ideas how to improve the dataset or where else to deposit it, please contact the authors at softmatter AT ifap.uni-tuebingen.de.