LightLogR newsletter

July 2025, Edition #01

Author

LightLogR Team

Doi

We are excited to bring you light news from the team behind LightLogR!

Last year, many of you contributed to our community survey - you have been patient with us and we promise it was not for nothing… We have been busy building something big and implementing your feedback into the newest version of LightLogR 0.9.2 Sunrise.

Let us first express our sincere appreciation to all who contributed either through the survey or feedback in general, your insights and perspective help shape our research and guide our community activities, thank you.

Community survey insights

Diverse & engaged community

Over half of you are specialists in Chronobiology and Sleep Research, with strong representation across Neuroscience, Psychology, and Environmental Science, demonstrating our interdisciplinary strength.

Onboarding opportunities

A balanced mix of beginners, intermediates, and advanced users provides clear opportunities to support everyone, from detailed tutorials for newcomers to advanced customization for seasoned users.

Growing interest & adoption

Over 50% of respondents are using or planning to use LightLogR, confirming the importance of our next phase of improvements.

Top scientific fields represented

Typical dataset duration

10 unique scientific fields, with many researchers working across multiple domains.

LightLogR 0.9.2 Sunrise: your priorities implemented

Here are some of the highlights in LightLogR 0.9.2 Sunrise, with even more to be found in the changelog.

Photoperiod support

Simply provide the coordinates of your data collection to add information on sunrise, sunset, and the duration of the photoperiod to your analysis:

add_photoperiod() adds photoperiod information to your dataset
extract_photoperiod() provides a succinct summary for photoperiod on all relevant days contained in a dataset
gg_photoperiod() adds shaded areas for nighttimes in plots

Mind the gaps

Five new functions for complete and regular time series resolve frustrations many of you highlighted.

gap_table() for a clean summary of available and missing data
has_gaps(), has_irregulars(), and extract_gaps() for quick assessments
remove_partial_data() to remove groups with insufficient data
gg_gaps() to visualize where gaps and irregular data occur

Spectral analysis

Three new functions to reconstruct and integrate across light spectra for multi-spectral devices.

spectral_reconstruction() returns spectral irradiances with the help of a calibration matrix
spectral_integration() integrates over the spectrum, either fully or in parts, optionally with action spectra and weighing factors
normalize_counts() normalize raw counts based on sensor outputs and a gain value.

Rock-solid & reproducible

How many stars can you count in this newsletter? Well, ten times as many unit tests, 888 to be specific, ensure that the functions in LightLogR have consistent output, and remain so even with future changes.

Continuous integration, and three new tutorials ensure analysis with transparent, accessible, and standardized pipelines.

Insightful visuals, instantly

Publication-ready plots that spotlight gaps, states, and 24-h patterns (click to enlarge).

data |> gg_heatmap()

data |> 
  gg_days() |> 
  gg_photoperiod(coordinates)

data |> 
  gg_days() |> gg_state(sleep)

There are many more ways to visualize your data with LightLogR. Visit the tutorial on visualization to learn more.

Broader device support

We’ve added more import wrappers and are now supporting 18 different devices and counting. See the list of supported devices here. Let us know of any other device that should join the list.

More metrics

We are continuously expanding on the number of metrics we support with a consistent interface and have rewritten the tutorial on metrics because the new functionality of LightLogR makes metric summaries even easier and more flexible.

Notable additions are functions to mark up or extract special conditions from your dataset:

add_clusters() and extract_clusters() find clusters of data, where conditions hold for a specified duration.
extract_states() summarises states in your data
summarize_numeric()/summarise_numeric() is a quick way to calculate averages across many groups
mean_daily() calculates average values for weekdays, weekends, and a weekday/weeken-weighted average

Dealing with many zero values in measurements of light exposure data can also be a challenge for logarithmis transformation. The pair of zero-inflation functions log_zero_inflated() and exp_zero_inflated() help to deal with this issue in a principled way.

We are very happy to see so many researchers from so many disciplines utilising LightLogR in their work and we are eager to support you along this journey to further empower your data and build meaningful insights.

LightLogR training webinars

Whether you’re starting out or optimizing your workflow, we aim to support you at every step.

Live walkthroughs: Demonstrations of core features and new functions.
Tips & tricks: Processing time series, reconstructing spectra, generating publication-ready visuals.
Dedicated Q&A: Troubleshoot your specific use cases with our experts.
Tailored sessions: Content for all levels, from onboarding to advanced custom analysis.

First sessions launch in Q3 2025 across multiple time zones to support our global community.

Subscribe on our website to receive updates and registration links.

Community updates

There are many places for a LightLogR sticker around the office

Survey prize winner

Congratulations to the winner of our survey draw for a free copy of Circadian and Visual Neuroscience! Check your inbox.

LightLogR stickers

For those who missed out, we have mailed LightLogR stickers - place them somewhere fun and share a photo with us!

GLEE: a new chapter

The Translational Sensory & Circadian Neuroscience Unit (MPS/TUM/TUMCREATE) is launching GLEE, an open-source reimagining how light exposure data is collected, stored, processed, and analyzed.

The vision

GLEE will foster knowledge exchange and drive advancements beyond the capabilities of any single research group.

Community impact

With a focus on collaboration and engagement, GLEE aims to build a legacy of innovation and progress in understanding and managing the health impacts of global light exposure.

Join the GLEE journey

Be part of this groundbreaking initiative that will transform light exposure research. GLEE represents our commitment to open science and collaborative research methodologies that benefit the entire scientific community.

Global collaboration

Connect with researchers worldwide working on light exposure studies.

Open-source database

Access standardized light exposure data from diverse populations and environments.

Shared resources

Benefit from community-developed analysis tools and methodologies.

Join us on our journey and subscribe to stay up to date with GLEE.

Cite

If you want to cite this newsletter, please do so as:

APA:

Zauner, J., Tsukimori, E., & Spitschan, M. (2025, July). LightLogR newsletter (No. 01). https://doi.org/10.5281/zenodo.16308351

Harvard:

Zauner, J., Tsukimori, E. & Spitschan, M. 2025. LightLogR Newsletter, no. 1, July. Available at: https://doi.org/10.5281/zenodo.16308351 (Published 22 July 2025).