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 Light plays an important role in human health and well-being, which necessitates the study of the effects of personal light exposure in real-world settings, measured by means of wearable devices. A growing number of studies incorporate  these kinds of data to assess associations between light and health outcomes.  Yet with few or missing standards, guidelines, and frameworks, setting up measurements, analysing the data, and comparing outcomes between studies is challenging, especially considering the significantly more complex time series data from wearable light loggers compared to controlled stimuli used in laboratory studies. In this paper, we introduce `LightLogR`, a novel resource to facilitate these research efforts in the form of an open-source, GPL-3.0-licenced software package for the statistical software R. As part of a developing software ecosystem, `LightLogR` is built with common challenges of current and future datasets in mind. The package standardizes many tasks for importing and processing personal light exposure data, provides quick as well as detailed insights into the datasets through summary and visualization tools, and incorporates major metrics commonly used in the field (61 metrics across 17 metric families), while embracing an inherently hierarchical, participant-based data structure.
 
-# Statement of need
-
 ![LightLogR logo \label{fig:one}](logo.png){width="25%"}
 
+# Statement of need
+
 Personalized luminous exposure data is progressively gaining importance across various domains, including research, occupational affairs, and lifestyle tracking. Data are collected through a proliferating selection of wearable light loggers and dosimeters, varying in size, shape, functionality, and output format [@hartmeyer2023]. Despite or potentially because of numerous use cases, the field still lacks a unified framework for collecting, validating, and analyzing the accumulated data [@hartmeyer2023; @spitschan2022].  This issue increases the time and expertise necessary to handle such data and also compromises the FAIRness (findability, accessibility, interoperability, reusability) [@wilkinson2016] of the results, especially for meta-analyses [@devries2024].
 
 `LightLogR` (\autoref{fig:one}) was designed to be used by researchers who deal with personal light exposure data collected from wearable devices. These data are of interest for various disciplines, including chronobiology, sleep research, vision science and epidemiology, as well as for post-occupancy evaluations in  architecture and lighting design. The package is intended to streamline the process of importing,  processing, and analysing these data in a reproducible and transparent manner. The package is available on GitHub (@repo) and CRAN (@cran), has a dedicated website for documentation and tutorials (@documentation), and releases are archived on Zenodo (@zenodo).