LightLogR Development Community Survey |
Welcome to the LightLogR survey
LightLogR is a free, open-source R software package to facilitate the analysis of personal light exposure data collected from wearable devices. It is developed by the Translational Sensory & Circadian Neuroscience Unit at the Technical University of Munich. LightLogR is part of a broader effort to standardize research using wearable light loggers in the EU project MeLiDos (Metrology for wearable light loggers and optical radiation dosimeters), funded by EURAMET. Now, we need your help to decide where to focus during the further development of LightLogR.
LightLogR is already entirely usable today and well-documented. It offers accessible import functions for the most common wearable light loggers (10 devices and counting). Data exploration to find and handle gaps and irregularities in the time series, aggregating the data, and enriching it with auxiliary data, e.g., from sleep diaries, has never been easier for personal light exposure. Powerful visualization functions allow explorative dives but use standard libraries to be tweaked for publications (such as the graph on this site, showing personal light exposure compared to the daylight potential and comparing it to the Brown et al. 2022 recommendations). Finally, 60 personal light exposure metrics across 16 metric families have already been integrated, and we support new metrics as they are adopted in the field.
LightLogR is already entirely usable today and well-documented. It offers accessible import functions for the most common wearable light loggers (10 devices and counting). Data exploration to find and handle gaps and irregularities in the time series, aggregating the data, and enriching it with auxiliary data, e.g., from sleep diaries, has never been easier for personal light exposure. Powerful visualization functions allow explorative dives but use standard libraries to be tweaked for publications (such as the graph on this site, showing personal light exposure compared to the daylight potential and comparing it to the Brown et al. 2022 recommendations). Finally, 60 personal light exposure metrics across 16 metric families have already been integrated, and we support new metrics as they are adopted in the field.
We have many things in store for the future development of LightLogR over the following years. Still, we want to make the software as relevant and impactful as possible among the research community. That is why we need your help – what are you looking for most when analyzing personal light exposure data? What are common hurdles, and which tasks do you most often perform? Let us know in this survey, and we will focus on the most relevant and sought-after aspects of data analysis for personal light exposure.
The survey will take about 15 minutes and is entirely anonymous. What do you get out of this survey?
- Your feature requests as part of LightLogRs roadmap
- Option to have an introductory consultation on LightLogR to make the software work best for you
- A free LightLogR sticker
- The chance to win a complimentary copy of the book “Circadian and Visual Neuroscience”
Thank you for your contribution. It is essential for the future development of LightLogR!
The LightLogR team
Johannes Zauner (TUM), Steffen Hartmeyer (EPFL), and Manuel Spitschan (TUM/MPI)
If you have questions regarding LightLogR or this survey, please message us at Johannes.zauner@tum.de.