Update paper.md

tscnlab · Oct 4, 2024 · c354b6a · c354b6a
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@@ -17,7 +17,7 @@ tags:
 - wearable sensors
 - wearables
 - "rstats-package"
-date: "01 October 2024"
+date: "04 October 2024"
 output: pdf_document
 authors:
 - name: Johannes Zauner
@@ -56,7 +56,7 @@ Light plays an important role in human health and well-being, which necessitates
 
 # 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].
+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` 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. Key features include: