diff --git a/DESCRIPTION b/DESCRIPTION
index 4ea93ba..dbe3d68 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -8,8 +8,8 @@ Authors@R: c(
     person("Manuel", "Spitschan", 
       email = "manuel.spitschan@tum.de", role = "aut",
       comment = c(ORCID = "0000-0002-8572-9268")),
-    person("MeLiDos", role = "fnd"),
-    person("EURAMET", role = "fnd"),
+    person("EURAMET", role = "fnd", comment = "European Association of National Metrology Institutes. Website: www.euramet.org. Grant Number: 22NRM05 MeLiDos. Grant Statement: The project (22NRM05 MeLiDos) has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States."),    
+    person("European Union", role = "fnd", comment = "Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EURAMET. Neither the European Union nor the granting authority can be held responsible for them."),
     person("TSCN-Lab", comment = c(URL = "www.tscnlab.org"), role = "cph"))
 Description: LightLogR is a package under development as part of the MeLiDos 
     project aimed at developing a standard workflow for wearable light logger 
diff --git a/R/filter_Datetime.R b/R/filter_Datetime.R
index f599803..9c094c1 100644
--- a/R/filter_Datetime.R
+++ b/R/filter_Datetime.R
@@ -161,7 +161,7 @@ filter_Datetime <- function(dataset,
       dataset %>% 
       dplyr::filter(
         {{ Datetime.colname }} >= lubridate::as_datetime(start, tz = tz),
-        {{ Datetime.colname }} <= lubridate::as_datetime(end, tz = tz)
+        {{ Datetime.colname }} < lubridate::as_datetime(end, tz = tz),
         )
     #possible extra filter step
     if(!is.null(filter.expr)) {
@@ -220,6 +220,7 @@ filter_Date <- function(...,
 #'   to be quoted with [quote()] or [rlang::expr()].
 #' @param filter_function The function to be used for filtering, either
 #'   `filter_Datetime` (the default) or `filter_Date`
+#' @param ... Additional arguments passed to the filter function
 #'
 #' @return A dataframe with the filtered data
 #' @export
@@ -236,9 +237,10 @@ filter_Date <- function(...,
 #'  gg_overview(Id.colname = Source)
 filter_Datetime_multiple <- function(dataset, 
                                      arguments, 
-                                     filter_function = filter_Datetime) {
+                                     filter_function = filter_Datetime,
+                                     ...) {
   
   purrr::reduce(arguments, function(dataset, params) {
-    do.call({{ filter_function }}, c(list(dataset = dataset), params))
+    do.call({{ filter_function }}, c(list(dataset = dataset), params, ...))
   }, .init = dataset)
 }
\ No newline at end of file
diff --git a/R/gg_day.r b/R/gg_day.r
index 29fe019..5ff58a2 100644
--- a/R/gg_day.r
+++ b/R/gg_day.r
@@ -164,7 +164,8 @@ gg_day <- function(dataset,
   if(jco_color) {
     jco_color_scheme <- 
       list(
-        ggsci::scale_color_jco()
+        ggsci::scale_color_jco(),
+        ggsci::scale_fill_jco()
       )
   }
   
diff --git a/R/import_LL.R b/R/import_LL.R
index eee5a14..b089d0a 100644
--- a/R/import_LL.R
+++ b/R/import_LL.R
@@ -155,7 +155,11 @@ imports <- function(device,
                    dplyr::mutate(!!ID.colname :=
                                    basename(file.name) %>%
                                    tools::file_path_sans_ext() %>%
-                                   stringr::str_extract(auto.id),
+                                   stringr::str_extract(
+                                     auto.id,
+                                     group =  
+                                       if(stringr::str_detect(auto.id, "\\(")) 1
+                                     ),
                                  .before = 1)},
                "FALSE" =
                  {tmp <- tmp %>%
@@ -238,7 +242,8 @@ import_arguments <- list(
             skip = rows_to_skip,
             locale=locale,
             id = "file.name",
-            show_col_types = FALSE
+            show_col_types = FALSE,
+            col_types = c("iDtfdfccccfdf")
             ),
             dots)))
           
@@ -255,11 +260,12 @@ import_arguments <- list(
       dplyr::mutate(
         Datetime = lubridate::ymd_hms(Datetime),
         dplyr::across(
-          dplyr::where(is.character) & 
+          dplyr::where(is.character) &
             dplyr::where(~ any(stringr::str_detect(.x, ","), na.rm = TRUE)),
-          ~ stringr::str_replace(.x, ",", ".") %>% 
+          ~ stringr::str_replace(.x, ",", ".") %>%
               as.numeric()
-        ))
+        )
+        )
   })
 
 )
diff --git a/R/import_helper.r b/R/import_helper.r
index 310a5c7..1edb8f3 100644
--- a/R/import_helper.r
+++ b/R/import_helper.r
@@ -24,8 +24,8 @@ import.info <- function(tmp, device, tz, ID.colname) {
         "The system timezone is ",
         Sys.timezone(),
         ". Please correct if necessary!\n")},
-    "Start: ", format(tmp$Datetime[1]), "\n",
-    "End: ", format(max(tmp$Datetime)), "\n",
+    "Start: ", format(min.time), "\n",
+    "End: ", format(max.time), "\n",
     "Timespan: " , diff(c(min.time, max.time)) %>% format(digits = 2), "\n",
     "Observation intervals: \n",
     sep = "")
diff --git a/README.Rmd b/README.Rmd
index 0d9cee5..4dded31 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -21,12 +21,13 @@ knitr::opts_chunk$set(
 
 <!-- badges: end -->
 
-
 Personalized luminous exposure data is progressively gaining importance in various sectors, including research, occupational affairs, and fitness tracking. Data are collected through a proliferating selection of wearable loggers and dosimeters, varying in size, shape, functionality, and output format. Despite or maybe because of numerous use cases, the field lacks a unified framework for collecting, validating, and analyzing the accumulated data. This issue increases the time and expertise necessary to handle such data and also compromises the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the results, especially in meta-analyses.
 
-##### Please note that LightLogR is work in progress! If you are interested in the project and want to know more, please give us a [message](mailto:johannes.zauner@tum.de)
+::: {style="text-align:center"}
+![Light logger data can powerfully convey insights into personal light exposure](man/figures/Day.png){width="90%"}
+:::
 
-**LightLogR** is a package under development as part of the [*MeLiDos*](https://www.melidos.eu) project to address these issues. MeLiDos is a joint, [EURAMET](https://www.euramet.org)-funded project involving sixteen partners across Europe, aimed at developing a metrology and a standard workflow for wearable light logger data and optical radiation dosimeters. Its primary contributions towards fostering FAIR data include the development of a common file format, robust metadata descriptors, and an accompanying open-source software ecosystem. **LightLogR** aims to provide tools for:
+**LightLogR** is a package under development as part of the [*MeLiDos*](https://www.melidos.eu) project to address these issues. The package aims to provide tools for:
 
 -   Generation of data and metadata files
 
@@ -42,8 +43,22 @@ Personalized luminous exposure data is progressively gaining importance in vario
 
 -   Integration of data into a unified database for cross-study analyses
 
+##### Please note that LightLogR is work in progress! If you are interested in the project and want to know more, please give us a [message](mailto:johannes.zauner@tum.de)
+
+Have a look at the **Example** section down below to get started, or dive into the [Articles](https://tscnlab.github.io/LightLogR/articles/index.html) to get more in depth information about how to work with the package and generate images such as the one above.
+
+## About the creation and funding of LightLogR
+
 **LightLogR** is developed by the [*Translational Sensory & Circadian Neuroscience*](https://www.tscnlab.org) lab, a joint group from the [Technical University of Munich](https://www.tum.de/en/) and the [Max Planck Institute for Biological Cybernetics](https://www.mpg.de/152075/biological-cybernetics).
 
+[*MeLiDos*](https://www.melidos.eu) is a joint, [EURAMET](https://www.euramet.org)-funded project involving sixteen partners across Europe, aimed at developing a metrology and a standard workflow for wearable light logger data and optical radiation dosimeters. Its primary contributions towards fostering FAIR data include the development of a common file format, robust metadata descriptors, and an accompanying open-source software ecosystem.
+
+![](man/figures/Co-Funded-By-the-EU.png){width="288"}
+
+[![](man/figures/Metrology_Partnership_LOGO.jpg){width="282"}](https://www.euramet.org)
+
+The project (22NRM05 MeLiDos) has received funding from the European Partnership on Metrology, co-financed from the European Union's Horizon Europe Research and Innovation Programme and by the Participating States. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EURAMET. Neither the European Union nor the granting authority can be held responsible for them.
+
 ## Installation
 
 You can install the development version of LightLogR from [GitHub](https://github.com/) with:
@@ -65,50 +80,73 @@ library(dplyr)
 library(ggplot2)
 ```
 
-
 ### Import
+
 You can import a light logger dataset with ease. The import functions give quick, helpful feedback about the dataset.
 
 ```{r, out.width="50%", fig.align='center'}
 filename <- system.file("extdata/sample_data_LYS.csv", package = "LightLogR")
 dataset <- import$LYS(filename, tz = "Europe/Berlin")
 
-dataset %>% select(Datetime, lux, kelvin, MEDI) %>%  slice(8000:8005) %>% 
-  flextable() %>% autofit()
+dataset %>% ungroup() %>% select(Datetime, lux, kelvin, MEDI) %>%  
+  slice(8000:8005) %>% flextable() %>% autofit()
+```
+
+::: {style="color: white"}
+.
+:::
+
+For more complex data, there is the useful `gg_overview()` function to get an immediate grasp of your data. See it here in action for a dataset with multiple participants. It also indicates where data is missing, based on the measurement epochs found in the data.
+
+```         
+LLdata %>% gg_overview()
 ```
 
+::: {style="text-align:center"}
+![](man/figures/gg_overview2.png){width="50%"}
+:::
+
 ### Visualize
+
 Once imported, **LightLogR** allows you conveniently visualize the data.
 
-```{r, fig.retina=2}
-dataset %>% gg_overview()
+```{r, out.width= "50%", fig.align='center'}
 dataset %>% gg_day()
 ```
 
 There is a wide range of options to the `gg_day()` function to customize the output. Have a look at the reference page (`?gg_day`) to see all options. You can also override most of the defaults, e.g., for different `color`, `facetting`, `theme` options.
 
-```{r, fig.retina=2}
+```{r, out.width= "60%", fig.align='center'}
 dataset %>% 
-  gg_day(
-    aes_col = MEDI >= 250, scales = "fixed", size = 0.5) + 
-  scale_color_discrete(type = c("orange", "skyblue"))
+  gg_day(aes_col = MEDI < 250, size = 0.75) + 
+  theme(legend.position = "bottom")
 ```
 
 ### More than one dataset
 
 The built-in dataset `sample.data.environment` shows a combined dataset of light logger data and a second set of data - in this case unobstructed outdoor light measurements. Combined datasets can be easily visualized with `gg_day()`. The `col` parameter used on the `Source` column of the dataset allows for a color separation.
 
-```{r, fig.height= 6, fig.retina=2}
+```{r, out.width= "60%", fig.align='center', fig.height = 6}
 sample.data.environment %>% 
   gg_day(
     start.date = "2023-08-18",
     y.axis = `MELANOPIC EDI`,
     aes_col = Source,
     scales = "fixed",
-    geom = "line")
+    geom = "line") + theme(legend.position = "bottom")
 ```
+
+If you get a feeling for the data over the course of multiple days, the `gg_days()` function comes in handy. It works similar to `gg_day()`. It is also opinionated in terms of the scaling and linebreaks to only show whole days, all of which can be adjusted.
+
+```{r, fig.width = 10, warning=FALSE}
+sample.data.environment %>% group_by(Source) %>% 
+  gg_days(y.axis = `MELANOPIC EDI`, geom = "ribbon", alpha = 0.25, col = "black")
+```
+
+
 With the `cut_Datetime()` function, the data can further be broken up into arbitrary time intervals. This can be used to easily compare different datasets. Just put the function in between the dataset and `gg_day()`. This makes a new variable available for plotting: `Datetime.rounded`. Just make sure, that the `geom` parameter is set to *boxplot* and the `group` parameter uses both the info from the rounded time interval (`Datetime.rounded`) and the different datasets (`Source`). The `interaction` function can easily combine them. The default interval for `cut_Datetime()` is 3 hours.
-```{r, fig.retina=2}
+
+```{r, out.width= "60%", fig.align='center'}
 sample.data.environment %>% 
   cut_Datetime() %>% 
   gg_day(
@@ -121,3 +159,30 @@ sample.data.environment %>%
   theme(legend.position = "bottom")
 ```
 
+### Insights, Validation, and Processing
+
+**LightLogR** provides a range of functions to get insight into your light logger data. Most importantly, you can search for and eliminate implicit gaps.
+
+```{r}
+dataset %>% gap_finder()
+```
+
+The huge amount of gaps comes from the fact that the measurement intervals are somewhat irregular between 15 and 18 seconds in this case. This leaves very little intervals to start regularly. We got this information after import, but can still get to this info through `count.difftime()`.
+
+```{r}
+dataset %>% ungroup() %>% count.difftime()
+```
+
+We can eliminate this through teh `gap_handler()` function. This function will automatically fill in the gaps with NA values. As the most dominant interval in the dataset is now not 15 seconds anymore(because intermediate datapoints have been added), we need to specify the epoch for `gap_finder()`.
+
+```{r}
+dataset %>% gap_handler() %>% gap_finder(epoch = "15 sec")
+```
+
+If we want to force the data to be regular, we can use the `aggregate_Datetime()` function. This will aggregate the data to the specified epoch. There are sensible defaults on how to aggregate numeric, categorical, and logical data. You can also specify your own aggregation functions.
+
+```{r}
+dataset %>% aggregate_Datetime(unit = "15 sec") %>% gap_finder()
+```
+
+Now, very few gaps are left (every time the the lagged epochs lead to a completely skipped regular epoch). The function can also be used to conveniently change the interval to arbitrary values, e.g., `"5 mins"`, or `"1 hour"`.
\ No newline at end of file
diff --git a/README.md b/README.md
index 1fa6e58..6f34b90 100644
--- a/README.md
+++ b/README.md
@@ -20,17 +20,20 @@ time and expertise necessary to handle such data and also compromises
 the FAIRness (Findability, Accessibility, Interoperability, Reusability)
 of the results, especially in meta-analyses.
 
-##### Please note that LightLogR is work in progress! If you are interested in the project and want to know more, please give us a [message](mailto:johannes.zauner@tum.de)
+<div style="text-align:center">
+
+<figure>
+<img src="man/figures/Day.png" style="width:90.0%"
+alt="Light logger data can powerfully convey insights into personal light exposure" />
+<figcaption aria-hidden="true">Light logger data can powerfully convey
+insights into personal light exposure</figcaption>
+</figure>
+
+</div>
 
 **LightLogR** is a package under development as part of the
-[*MeLiDos*](https://www.melidos.eu) project to address these issues.
-MeLiDos is a joint, [EURAMET](https://www.euramet.org)-funded project
-involving sixteen partners across Europe, aimed at developing a
-metrology and a standard workflow for wearable light logger data and
-optical radiation dosimeters. Its primary contributions towards
-fostering FAIR data include the development of a common file format,
-robust metadata descriptors, and an accompanying open-source software
-ecosystem. **LightLogR** aims to provide tools for:
+[*MeLiDos*](https://www.melidos.eu) project to address these issues. The
+package aims to provide tools for:
 
 - Generation of data and metadata files
 
@@ -47,12 +50,42 @@ ecosystem. **LightLogR** aims to provide tools for:
 
 - Integration of data into a unified database for cross-study analyses
 
+##### Please note that LightLogR is work in progress! If you are interested in the project and want to know more, please give us a [message](mailto:johannes.zauner@tum.de)
+
+Have a look at the **Example** section down below to get started, or
+dive into the
+[Articles](https://tscnlab.github.io/LightLogR/articles/index.html) to
+get more in depth information about how to work with the package and
+generate images such as the one above.
+
+## About the creation and funding of LightLogR
+
 **LightLogR** is developed by the [*Translational Sensory & Circadian
 Neuroscience*](https://www.tscnlab.org) lab, a joint group from the
 [Technical University of Munich](https://www.tum.de/en/) and the [Max
 Planck Institute for Biological
 Cybernetics](https://www.mpg.de/152075/biological-cybernetics).
 
+[*MeLiDos*](https://www.melidos.eu) is a joint,
+[EURAMET](https://www.euramet.org)-funded project involving sixteen
+partners across Europe, aimed at developing a metrology and a standard
+workflow for wearable light logger data and optical radiation
+dosimeters. Its primary contributions towards fostering FAIR data
+include the development of a common file format, robust metadata
+descriptors, and an accompanying open-source software ecosystem.
+
+<img src="man/figures/Co-Funded-By-the-EU.png" width="288" />
+
+[<img src="man/figures/Metrology_Partnership_LOGO.jpg" width="282" />](https://www.euramet.org)
+
+The project (22NRM05 MeLiDos) has received funding from the European
+Partnership on Metrology, co-financed from the European Union’s Horizon
+Europe Research and Innovation Programme and by the Participating
+States. Views and opinions expressed are however those of the author(s)
+only and do not necessarily reflect those of the European Union or
+EURAMET. Neither the European Union nor the granting authority can be
+held responsible for them.
+
 ## Installation
 
 You can install the development version of LightLogR from
@@ -95,28 +128,40 @@ dataset <- import$LYS(filename, tz = "Europe/Berlin")
 #> 3 sample_data_LYS 17s              23 0.201% 
 #> 4 sample_data_LYS 18s              16 0.140%
 
-dataset %>% select(Datetime, lux, kelvin, MEDI) %>%  slice(8000:8005) %>% 
-  flextable() %>% autofit()
-#> Adding missing grouping variables: `Id`
+dataset %>% ungroup() %>% select(Datetime, lux, kelvin, MEDI) %>%  
+  slice(8000:8005) %>% flextable() %>% autofit()
 ```
 
 <img src="man/figures/README-unnamed-chunk-3-1.png" width="50%" style="display: block; margin: auto;" />
 
-### Visualize
+<div style="color: white">
 
-Once imported, **LightLogR** allows you conveniently visualize the data.
+.
 
-``` r
-dataset %>% gg_overview()
-```
+</div>
+
+For more complex data, there is the useful `gg_overview()` function to
+get an immediate grasp of your data. See it here in action for a dataset
+with multiple participants. It also indicates where data is missing,
+based on the measurement epochs found in the data.
+
+    LLdata %>% gg_overview()
 
-<img src="man/figures/README-unnamed-chunk-4-1.png" width="100%" />
+<div style="text-align:center">
+
+<img src="man/figures/gg_overview2.png" style="width:50.0%" />
+
+</div>
+
+### Visualize
+
+Once imported, **LightLogR** allows you conveniently visualize the data.
 
 ``` r
 dataset %>% gg_day()
 ```
 
-<img src="man/figures/README-unnamed-chunk-4-2.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-4-1.png" width="50%" style="display: block; margin: auto;" />
 
 There is a wide range of options to the `gg_day()` function to customize
 the output. Have a look at the reference page (`?gg_day`) to see all
@@ -125,14 +170,11 @@ options. You can also override most of the defaults, e.g., for different
 
 ``` r
 dataset %>% 
-  gg_day(
-    aes_col = MEDI >= 250, scales = "fixed", size = 0.5) + 
-  scale_color_discrete(type = c("orange", "skyblue"))
-#> Scale for colour is already present.
-#> Adding another scale for colour, which will replace the existing scale.
+  gg_day(aes_col = MEDI < 250, size = 0.75) + 
+  theme(legend.position = "bottom")
 ```
 
-<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-5-1.png" width="60%" style="display: block; margin: auto;" />
 
 ### More than one dataset
 
@@ -149,20 +191,33 @@ sample.data.environment %>%
     y.axis = `MELANOPIC EDI`,
     aes_col = Source,
     scales = "fixed",
-    geom = "line")
+    geom = "line") + theme(legend.position = "bottom")
 #> Only Dates will be used from start.date and end.date input. If you also want to set Datetimes or Times, consider using the `filter_Datetime()` function instead.
 ```
 
-<img src="man/figures/README-unnamed-chunk-6-1.png" width="100%" /> With
-the `cut_Datetime()` function, the data can further be broken up into
-arbitrary time intervals. This can be used to easily compare different
-datasets. Just put the function in between the dataset and `gg_day()`.
-This makes a new variable available for plotting: `Datetime.rounded`.
-Just make sure, that the `geom` parameter is set to *boxplot* and the
-`group` parameter uses both the info from the rounded time interval
-(`Datetime.rounded`) and the different datasets (`Source`). The
-`interaction` function can easily combine them. The default interval for
-`cut_Datetime()` is 3 hours.
+<img src="man/figures/README-unnamed-chunk-6-1.png" width="60%" style="display: block; margin: auto;" />
+
+If you get a feeling for the data over the course of multiple days, the
+`gg_days()` function comes in handy. It works similar to `gg_day()`. It
+is also opinionated in terms of the scaling and linebreaks to only show
+whole days, all of which can be adjusted.
+
+``` r
+sample.data.environment %>% group_by(Source) %>% 
+  gg_days(y.axis = `MELANOPIC EDI`, geom = "ribbon", alpha = 0.25, col = "black")
+```
+
+<img src="man/figures/README-unnamed-chunk-7-1.png" width="100%" />
+
+With the `cut_Datetime()` function, the data can further be broken up
+into arbitrary time intervals. This can be used to easily compare
+different datasets. Just put the function in between the dataset and
+`gg_day()`. This makes a new variable available for plotting:
+`Datetime.rounded`. Just make sure, that the `geom` parameter is set to
+*boxplot* and the `group` parameter uses both the info from the rounded
+time interval (`Datetime.rounded`) and the different datasets
+(`Source`). The `interaction` function can easily combine them. The
+default interval for `cut_Datetime()` is 3 hours.
 
 ``` r
 sample.data.environment %>% 
@@ -178,4 +233,59 @@ sample.data.environment %>%
 #> Only Dates will be used from start.date and end.date input. If you also want to set Datetimes or Times, consider using the `filter_Datetime()` function instead.
 ```
 
-<img src="man/figures/README-unnamed-chunk-7-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-8-1.png" width="60%" style="display: block; margin: auto;" />
+
+### Insights, Validation, and Processing
+
+**LightLogR** provides a range of functions to get insight into your
+light logger data. Most importantly, you can search for and eliminate
+implicit gaps.
+
+``` r
+dataset %>% gap_finder()
+#> Found 10758 gaps. 761 Datetimes fall into the regular sequence.
+```
+
+The huge amount of gaps comes from the fact that the measurement
+intervals are somewhat irregular between 15 and 18 seconds in this case.
+This leaves very little intervals to start regularly. We got this
+information after import, but can still get to this info through
+`count.difftime()`.
+
+``` r
+dataset %>% ungroup() %>% count.difftime()
+#> # A tibble: 4 × 2
+#>   difftime       n
+#>   <Duration> <int>
+#> 1 15s        10015
+#> 2 16s         1367
+#> 3 17s           23
+#> 4 18s           16
+```
+
+We can eliminate this through teh `gap_handler()` function. This
+function will automatically fill in the gaps with NA values. As the most
+dominant interval in the dataset is now not 15 seconds anymore(because
+intermediate datapoints have been added), we need to specify the epoch
+for `gap_finder()`.
+
+``` r
+dataset %>% gap_handler() %>% gap_finder(epoch = "15 sec")
+#> No gaps found
+```
+
+If we want to force the data to be regular, we can use the
+`aggregate_Datetime()` function. This will aggregate the data to the
+specified epoch. There are sensible defaults on how to aggregate
+numeric, categorical, and logical data. You can also specify your own
+aggregation functions.
+
+``` r
+dataset %>% aggregate_Datetime(unit = "15 sec") %>% gap_finder()
+#> Found 97 gaps. 11422 Datetimes fall into the regular sequence.
+```
+
+Now, very few gaps are left (every time the the lagged epochs lead to a
+completely skipped regular epoch). The function can also be used to
+conveniently change the interval to arbitrary values, e.g., `"5 mins"`,
+or `"1 hour"`.
diff --git a/inst/CITATION b/inst/CITATION
index 70f1d10..e46822b 100644
--- a/inst/CITATION
+++ b/inst/CITATION
@@ -1,7 +1,7 @@
 citHeader("To cite LightLogR in publications use:")
 
-citEntry(
-  entry    = "Misc",
+bibentry(
+  bibtype  = "Misc",
   title    = "LightLogR: Working With Wearable Light Logger Data",
   author   = "Johannes Zauner, Manuel Spitschan",
   journal  = "",
@@ -11,6 +11,6 @@ citEntry(
   pages    = "",
   url      = "https://github.com/tscnlab/LightLogR",
   textVersion = paste(
-  "Zauner, J.; Spitschan, M. (2023): LightLogR: Working With Wearable Light Logger Data. R Package, Available on https://github.com/tscnlab/LightLogR"
+  "Zauner, J.; Spitschan, M. (2023): LightLogR: Working With Wearable Light Logger Data. R Package, Available on https://github.com/tscnlab/LightLogR. Funded by EURAMET, co-funded by the European Union, Grant#22NRM05 MeLiDos"
   )
 )
diff --git a/man/LightLogR-package.Rd b/man/LightLogR-package.Rd
index c4bee8f..6c01eaf 100644
--- a/man/LightLogR-package.Rd
+++ b/man/LightLogR-package.Rd
@@ -29,8 +29,8 @@ Authors:
 
 Other contributors:
 \itemize{
-  \item MeLiDos [funder]
-  \item EURAMET [funder]
+  \item EURAMET (European Association of National Metrology Institutes. Website: www.euramet.org. Grant Number: 22NRM05 MeLiDos. Grant Statement: The project (22NRM05 MeLiDos) has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.) [funder]
+  \item European Union (Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EURAMET. Neither the European Union nor the granting authority can be held responsible for them.) [funder]
   \item TSCN-Lab (www.tscnlab.org) [copyright holder]
 }
 
diff --git a/man/figures/Co-Funded-By-the-EU.png b/man/figures/Co-Funded-By-the-EU.png
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diff --git a/man/figures/Day.png b/man/figures/Day.png
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diff --git a/man/figures/Metrology_Partnership_LOGO.jpg b/man/figures/Metrology_Partnership_LOGO.jpg
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diff --git a/man/figures/README-unnamed-chunk-8-1.png b/man/figures/README-unnamed-chunk-8-1.png
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diff --git a/man/figures/gg_overview2.png b/man/figures/gg_overview2.png
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diff --git a/man/filter_Datetime_multiple.Rd b/man/filter_Datetime_multiple.Rd
index 6fc66a1..71178cf 100644
--- a/man/filter_Datetime_multiple.Rd
+++ b/man/filter_Datetime_multiple.Rd
@@ -4,7 +4,12 @@
 \alias{filter_Datetime_multiple}
 \title{Filter multiple times based on a list of arguments.}
 \usage{
-filter_Datetime_multiple(dataset, arguments, filter_function = filter_Datetime)
+filter_Datetime_multiple(
+  dataset,
+  arguments,
+  filter_function = filter_Datetime,
+  ...
+)
 }
 \arguments{
 \item{dataset}{A light logger dataset}
@@ -16,6 +21,8 @@ to be quoted with \code{\link[=quote]{quote()}} or \code{\link[rlang:expr]{rlang
 
 \item{filter_function}{The function to be used for filtering, either
 \code{filter_Datetime} (the default) or \code{filter_Date}}
+
+\item{...}{Additional arguments passed to the filter function}
 }
 \value{
 A dataframe with the filtered data
diff --git a/man/import.Dataset.Rd b/man/import.Dataset.Rd
index e1fd94b..9515d97 100644
--- a/man/import.Dataset.Rd
+++ b/man/import.Dataset.Rd
@@ -131,7 +131,7 @@ dplyr::slice(1500:1505) \%>\%
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 flextable::autofit()
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 }
 }
 
diff --git a/vignettes/articles/Styling.Rmd b/vignettes/articles/Day.Rmd
similarity index 100%
rename from vignettes/articles/Styling.Rmd
rename to vignettes/articles/Day.Rmd