Julian Henn

CEO of the DataQ Intelligence company

Bayreuth, Bayern, Germany

JulianHenn@web.de

Short CV:

Julian Henn studied physics at the Julius-Maximilians Universität Würzburg, Germany, from 1993 to 2000, finishing with a university diploma degree. After a short research stay at Chalmers University of Technology, Göteborg, Sweden, he did his PhD in theoretical chemistry at the University of Würzburg from 2001 to 2004 with a doctoral fellowship of the ‘Graduiertenkolleg 690: Elektronendichte’ funded by the Deutsche Forschungsgemeinschaft (DFG) (Summa cum laude). He worked from 2005 to 2006 as a Feodor-Lynen research fellow of the German Alexander von Humboldt Stiftung at the School of Physics and Astronomy at the University of St. Andrews, Scotland, in a theoretical quantum optics research group. In 2006 he switched to the Georg-August Universität Göttingen, Germany, where he started developing fit quality indicators. In 2010 he moved to Bayreuth, Germany, for embarking on a habilitation in physics funded by the DFG at Bayreuth University. He finished his habilitation in natural sciences in 2016 with the topic ‘Quality Indicators for Fit Data with applications in crystallography’ under the scientific supervision of Prof. Peter Luger. In 2016 and 2020/2021 he worked 3 months/6months as a guest professor at Warsaw University, Poland, with Prof. Krzysztof Wozniak. He is CEO of the company DataQ intelligence, founded in 2022 together with Ph.D Slawomir Domagala and Petrick Nourd, and provides innovative metrics and data quality protocols as well as workshops and consulting. In his spare time he likes to be out in nature with friends for hiking, rock bouldering and sport climbing

Lecture 22: Julian Henn

 

Global data quality metrics and data quality filter systems

Julian Henn

CEO of the DataQ Intelligence company

Bayreuth, Bayern, Germany

JulianHenn@web.de

 

Abstract: A number of global data quality descriptors are presented and combined to a filter system in order to define a visual representation of the fit data quality for diffraction data. This representation is used to filter out particular good data sets. This draws the attention to particularly good practice in data acquisition, data processing and model refinement.