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--></style></head><body lang=en-DE link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal><i><span lang=EN-US style='mso-ligatures:none'>Now with functioning registration link...<o:p></o:p></span></i></p><p class=MsoNormal><span style='mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><span style='mso-ligatures:none'><img width=657 height=200 style='width:6.8437in;height:2.0833in' id="Picture_x0020_1" src="cid:image001.png@01DC296D.945F7450"></span><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>Dear colleagues,<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>We invite you to join the <i>Open and reproducible analysis of light exposure and visual experience data</i> online course—a free, live series for researchers analyzing wearable light and visual experience data in R with the open-source <a href="https://tscnlab.github.io/LightLogR/">LightLogR</a> package.<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>The series offers two tracks (Beginner and Advanced), each delivered as a two-part course via Zoom. To accommodate global time zones, every session will be offered twice on the same day.<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><b><span lang=EN-US style='font-family:"Arial",sans-serif'><a href="https://tum-conf.zoom.us/webinar/register/WN_xGoHi1i_Sz2flC6-qGcLoQ#/registration">Register here</a></span></b><o:p></o:p></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p style='margin:0cm'><b><span style='font-family:"Arial",sans-serif'>Beginner Track<o:p></o:p></span></b></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>Tuesday, 7 October 2025<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>Wednesday, 4 March 2026</span><span lang=EN-US style='font-family:"Arial",sans-serif'><o:p></o:p></span></p><p style='margin:0cm'><i><span style='font-family:"Arial",sans-serif'>Topics: tidy data workflows, preprocessing, metric calculation, visualization, merging auxiliary data, and photoperiod analysis</span></i><i><span lang=EN-US style='font-family:"Arial",sans-serif'><o:p></o:p></span></i></p><h3 style='margin:0cm'><span style='font-size:11.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></h3><h3 style='margin:0cm'><span style='font-size:11.0pt;font-family:"Arial",sans-serif'>Advanced Track<o:p></o:p></span></h3><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>Tuesday, 9 December 2025<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>Wednesday, 6 May 2026<o:p></o:p></span></p><p style='margin:0cm'><i><span style='font-family:"Arial",sans-serif'>Topics: merging light with sleep and auxiliary streams, advanced visualizations, conditional metrics, time-zone handling, and spectral/distance measures<o:p></o:p></span></i></p><h3 style='margin:0cm'><span style='font-size:11.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></h3><h3 style='margin:0cm'><span style='font-size:11.0pt;font-family:"Arial",sans-serif'>Format<o:p></o:p></span></h3><p class=MsoNormal><span style='font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-GB'>90-minute live webinars (introduction, demonstration, Q&A)<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-GB'>Offered twice daily: 08:30–10:00 and 18:00–19:30 CET/CEST<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-GB'>Dedicated Q&A with the package </span><span lang=EN-US style='font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-GB'>developer and maintainer<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-GB'>Certificate of participation (micro-credential) awarded upon completion of both parts in a track<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'>These tutorials are </span><strong><span style='font-family:"Arial",sans-serif'>practical, reproducible, and interactive</span></strong><span style='font-family:"Arial",sans-serif'>, with ready-to-use code snippets and example datasets. Detailed learning outcomes are provided in the attached PDF.<o:p></o:p></span></p><p style='margin:0cm'><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><b><span lang=EN-US style='font-family:"Arial",sans-serif'>About the developer / instructor<o:p></o:p></span></b></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'>Johannes Zauner is a postdoctoral researcher in circadian data science at the<a href="https://www.tscnlab.org/"> Translational Sensory & Circadian Neuroscience Uni<span lang=EN-US>t (MPS/TUM/TUMCREATE)</span></a> at the Technical University of Munich and the Max Planck Institute for Biological Cybernetics. He holds a PhD in Human Biology and is currently focused on developing software tools for analyzing data from wearable light loggers.<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-family:"Arial",sans-serif'>Best wishes,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-family:"Arial",sans-serif'>Manuel Spitschan PhD & Johannes Zauner PhD<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>