This vignette demonstrates how the functions included in this package can be used to read and clean different data formats.
Write data in tempfile
# records and cameras in separate files ------------------------------------------
data(recordTableSample, package = "camtrapR")
data(camtraps, package = "camtrapR")
# Create subfolder
dir.create(paste0(tempdir(), "/csv"))
# Write files
recordfile <- paste0(tempdir(), "/csv/records.csv")
camtrapfile <- paste0(tempdir(), "/csv/camtraps.csv")
write.csv(recordTableSample, recordfile,
row.names = FALSE)
write.csv(camtraps, camtrapfile,
row.names = FALSE)
# records and cameras in same file ------------------------------------------
# Create file
recordcam <- recordTableSample |>
dplyr::left_join(camtraps, by = "Station")
# Create subfolder
dir.create(paste0(tempdir(), "/csvcam"))
# Write file
recordcamfile <- paste0(tempdir(), "/csvcam/recordcam.csv")
write.csv(recordcam, recordcamfile,
row.names = FALSE)Records and cameras in separate csv files
First, we see how data import and cleaning is performed with two csv files (records and cameras):
Read data
dat <- read_data(path_rec = recordfile,
path_cam = camtrapfile,
sep_rec = ",", sep_cam = ",")| Station | Species | DateTimeOriginal | Date | Time | delta.time.secs | delta.time.mins | delta.time.hours | delta.time.days | Directory | FileName |
|---|---|---|---|---|---|---|---|---|---|---|
| StationA | PBE | 2009-04-21 00:40:00 | 2009-04-21 | 00:40:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-21__00-40-00(1).JPG |
| StationA | PBE | 2009-04-22 20:19:00 | 2009-04-22 | 20:19:00 | 157140 | 2619 | 43.6 | 1.8 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-22__20-19-00(1).JPG |
| StationA | PBE | 2009-04-23 00:07:00 | 2009-04-23 | 00:07:00 | 13560 | 226 | 3.8 | 0.2 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-23__00-07-00(1).JPG |
| StationA | PBE | 2009-05-07 17:11:00 | 2009-05-07 | 17:11:00 | 1270920 | 21182 | 353.0 | 14.7 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-05-07__17-11-00(1).JPG |
| StationA | VTA | 2009-04-10 05:07:00 | 2009-04-10 | 05:07:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-04-10__05-07-00(1).JPG |
| StationA | VTA | 2009-05-06 19:06:00 | 2009-05-06 | 19:06:00 | 2296740 | 38279 | 638.0 | 26.6 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-05-06__19-06-00(1).JPG |
| Station | utm_y | utm_x | Setup_date | Retrieval_date | Problem1_from | Problem1_to |
|---|---|---|---|---|---|---|
| StationA | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationB | 606000 | 523000 | 03/04/2009 | 16/05/2009 | NA | NA |
| StationC | 607050 | 525000 | 04/04/2009 | 17/05/2009 | 12/05/2009 | 17/05/2009 |
The imported file is a list with one component $data
containing 2 dataframes:
-
$observationscontains the records -
$deploymentscontains the cameras information
Clean data
This step ensures all columns have the desired type. It will also move these columns to the beginning of the table.
To cast data to the appropriate type, this function has two
arguments, created below: rec_type (for the records table)
and cam_type (for the cameras table).
rec_type <- list(Station = "as.character",
Date = list("as_date",
format = "%Y-%m-%d"),
Time = "times",
DateTimeOriginal = list("as.POSIXct",
tz = "Etc/GMT-8"))
cam_type <- list(Station = "as.character",
Setup_date = list("as.Date",
format = "%d/%m/%Y"),
Retrieval_date = list("as.Date",
format = "%d/%m/%Y"))These lists contain the information about how to convert column types.
- Values contain the casting function to apply
(e.g.
"as.Date"will translate toas.Date(x)). Values cal also be lists to provide additional arguments: for instance,list("as.Date", format = "%d/%m/%Y")will translate toas.Date(x, format = "%d/%m/%Y"). - the names of the list give the corresponding column of the data that should be casted.
dat_clean <- clean_data(dat,
rec_type = rec_type,
cam_type = cam_type)| Station | Species | DateTimeOriginal | Date | Time | delta.time.secs | delta.time.mins | delta.time.hours | delta.time.days | Directory | FileName |
|---|---|---|---|---|---|---|---|---|---|---|
| StationA | PBE | 2009-04-21 00:40:00 | 2009-04-21 | 00:40:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-21__00-40-00(1).JPG |
| StationA | PBE | 2009-04-22 20:19:00 | 2009-04-22 | 20:19:00 | 157140 | 2619 | 43.6 | 1.8 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-22__20-19-00(1).JPG |
| StationA | PBE | 2009-04-23 00:07:00 | 2009-04-23 | 00:07:00 | 13560 | 226 | 3.8 | 0.2 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-23__00-07-00(1).JPG |
| StationA | PBE | 2009-05-07 17:11:00 | 2009-05-07 | 17:11:00 | 1270920 | 21182 | 353.0 | 14.7 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-05-07__17-11-00(1).JPG |
| StationA | VTA | 2009-04-10 05:07:00 | 2009-04-10 | 05:07:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-04-10__05-07-00(1).JPG |
| StationA | VTA | 2009-05-06 19:06:00 | 2009-05-06 | 19:06:00 | 2296740 | 38279 | 638.0 | 26.6 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-05-06__19-06-00(1).JPG |
| Station | utm_y | utm_x | Setup_date | Retrieval_date | Problem1_from | Problem1_to |
|---|---|---|---|---|---|---|
| StationA | 604000 | 526000 | 2009-04-02 | 2009-05-14 | NA | NA |
| StationB | 606000 | 523000 | 2009-04-03 | 2009-05-16 | NA | NA |
| StationC | 607050 | 525000 | 2009-04-04 | 2009-05-17 | 12/05/2009 | 17/05/2009 |
In case cameras in records and in the cameras file do not match,
clean_data has an option allowing to keep only shared
cameras. We create a new dataset where the observations table has
Stations A, B and C and the deployments table has stations B, C and
D:
# Initialize new data
dat_diffcam <- dat
# Replace a camera in deployments
newcam <- dat_diffcam$data$deployments[1, ]
newcam$Station <- "StationD"
dat_diffcam$data$deployments <- dat_diffcam$data$deployments |>
filter(Station != "StationA") |>
bind_rows(newcam)
unique(dat_diffcam$data$observations$Station)
#> [1] "StationA" "StationB" "StationC"
unique(dat_diffcam$data$deployments$Station)
#> [1] "StationB" "StationC" "StationD"Cleaning the data will keep only data with cameras that are common between the two datasets (B and C);
dat_diffcam_clean <- clean_data(dat_diffcam,
rec_type = rec_type,
cam_type = cam_type,
cam_col_dfrec = "Station",
cam_col_dfcam = "Station",
only_shared_cam = TRUE)
unique(dat_diffcam_clean$data$observations$Station)
#> [1] "StationB" "StationC"
unique(dat_diffcam_clean$data$deployments$Station)
#> [1] "StationB" "StationC"Records and cameras in the same csv (1 csv file)
Then, we see how data import and cleaning is performed with a unique csv file containing records and cameras information:
Read data
dat <- read_data(path_rec = recordcamfile,
sep_rec = ",")| Station | Species | DateTimeOriginal | Date | Time | delta.time.secs | delta.time.mins | delta.time.hours | delta.time.days | Directory | FileName | utm_y | utm_x | Setup_date | Retrieval_date | Problem1_from | Problem1_to |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| StationA | PBE | 2009-04-21 00:40:00 | 2009-04-21 | 00:40:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-21__00-40-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationA | PBE | 2009-04-22 20:19:00 | 2009-04-22 | 20:19:00 | 157140 | 2619 | 43.6 | 1.8 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-22__20-19-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationA | PBE | 2009-04-23 00:07:00 | 2009-04-23 | 00:07:00 | 13560 | 226 | 3.8 | 0.2 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-23__00-07-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationA | PBE | 2009-05-07 17:11:00 | 2009-05-07 | 17:11:00 | 1270920 | 21182 | 353.0 | 14.7 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-05-07__17-11-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationA | VTA | 2009-04-10 05:07:00 | 2009-04-10 | 05:07:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-04-10__05-07-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
| StationA | VTA | 2009-05-06 19:06:00 | 2009-05-06 | 19:06:00 | 2296740 | 38279 | 638.0 | 26.6 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-05-06__19-06-00(1).JPG | 604000 | 526000 | 02/04/2009 | 14/05/2009 | NA | NA |
Again the imported file is a list with one component
$data:
-
$data$observationscontains the cameras and records information -
$data$deploymentsisNULL(because only one file was imported)
Clean data
In this step, will split the information from the observations table
between observations and deployments. To do this,
clean_data will move all columns listed in
cam_cols in the deployments table. The column containing
cameras IDs must be specified in the cam_col_dfrec argument
(so that this column is kept in the observations table).
Since at the beginning, all columns are in the observations
dataframe, the casting specifications should be in the
rec_type argument.
cam_cols <- c("Station", "Setup_date", "Retrieval_date",
"utm_y", "utm_x", "Problem1_from", "Problem1_to")
rec_type2 <- list(Station = "as.character",
Date = list("as_date",
format = "%Y-%m-%d"),
Time = "times",
DateTimeOriginal = list("as.POSIXct",
tz = "Etc/GMT-8"),
Setup_date = list("as.Date",
format = "%d/%m/%Y"),
Retrieval_date = list("as.Date",
format = "%d/%m/%Y"),
Problem1_from = list("as.Date",
format = "%d/%m/%Y"),
Problem1_to = list("as.Date",
format = "%d/%m/%Y"))
dat_clean <- clean_data(dat,
rec_type = rec_type2,
cam_col_dfrec = "Station",
cam_cols = cam_cols,
split = TRUE)| Station | Species | DateTimeOriginal | Date | Time | delta.time.secs | delta.time.mins | delta.time.hours | delta.time.days | Directory | FileName |
|---|---|---|---|---|---|---|---|---|---|---|
| StationA | PBE | 2009-04-21 00:40:00 | 2009-04-21 | 00:40:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-21__00-40-00(1).JPG |
| StationA | PBE | 2009-04-22 20:19:00 | 2009-04-22 | 20:19:00 | 157140 | 2619 | 43.6 | 1.8 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-22__20-19-00(1).JPG |
| StationA | PBE | 2009-04-23 00:07:00 | 2009-04-23 | 00:07:00 | 13560 | 226 | 3.8 | 0.2 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-04-23__00-07-00(1).JPG |
| StationA | PBE | 2009-05-07 17:11:00 | 2009-05-07 | 17:11:00 | 1270920 | 21182 | 353.0 | 14.7 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/PBE | StationA__2009-05-07__17-11-00(1).JPG |
| StationA | VTA | 2009-04-10 05:07:00 | 2009-04-10 | 05:07:00 | 0 | 0 | 0.0 | 0.0 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-04-10__05-07-00(1).JPG |
| StationA | VTA | 2009-05-06 19:06:00 | 2009-05-06 | 19:06:00 | 2296740 | 38279 | 638.0 | 26.6 | C:/Users/niedballa/Documents/R/win-library/3.1/camtrapR/pictures/sample_images/StationA/VTA | StationA__2009-05-06__19-06-00(1).JPG |
| Station | Setup_date | Retrieval_date | utm_y | utm_x | Problem1_from | Problem1_to |
|---|---|---|---|---|---|---|
| StationA | 2009-04-02 | 2009-05-14 | 604000 | 526000 | NA | NA |
| StationB | 2009-04-03 | 2009-05-16 | 606000 | 523000 | NA | NA |
| StationC | 2009-04-04 | 2009-05-17 | 607050 | 525000 | 2009-05-12 | 2009-05-17 |
CamtrapDP format (json file)
Then, we see how data import and cleaning is performed with a dataset in camtrapDP format.
Read data
The read_data function can also read json files
corresponding to the camtrapDP datapackage.
camtrap_dp_file <- system.file(
"extdata", "mica", "datapackage.json",
package = "camtraptor"
)
dat <- read_data(path_rec = camtrap_dp_file)
# dat <- read_data(path_rec = "https://raw.githubusercontent.com/tdwg/camtrap-dp/main/example/datapackage.json")Internally, we use the function read_camtrap_dp from the
camtraptor package (here, it would give the same result to
use use directly this function).
The imported object is a list with several slots, and
the observations and deployments info are in the $data
slot.
class(dat)
#> [1] "datapackage" "list"
names(dat)
#> [1] "name" "id" "profile" "created"
#> [5] "sources" "contributors" "organizations" "project"
#> [9] "spatial" "temporal" "taxonomic" "platform"
#> [13] "resources" "directory" "data"| observationID | deploymentID | sequenceID | mediaID | timestamp | observationType | cameraSetup | taxonID | taxonIDReference | scientificName | count | countNew | lifeStage | sex | behaviour | individualID | classificationMethod | classifiedBy | classificationTimestamp | classificationConfidence | comments | _id | vernacularNames.en | vernacularNames.nl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ef2f7140-ae97-4b44-8309-ab1ffbc02879 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 8f5ffbf2-52c4-4dd2-b502-d93a8aa64640 | NA | 2020-07-29 05:29:41 | unknown | FALSE | NA | NA | NA | NA | NA | NA | NA | NA | NA | human | NA | NA | NA | NA | NA | NA | NA |
| 68686a75-ad44-4676-b45e-2b85f60e4d11 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 1d98da96-c3ce-4479-9b97-8883cd33724f | NA | 2020-07-29 05:38:55 | blank | FALSE | NA | NA | NA | NA | NA | NA | NA | NA | NA | human | NA | NA | NA | NA | NA | NA | NA |
| 3d065f23-426a-449b-8693-f3c6b2ac9c7a | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 89b807ca-697f-4452-8349-b235622c94fa | NA | 2020-07-29 05:46:48 | animal | FALSE | DGPL | https://www.catalogueoflife.org | Anas strepera | 4 | NA | subadult | unknown | NA | NA | human | Danny Van der beeck | 2020-08-17 06:57:28 | NA | NA | NA | gadwall | krakeend |
| 1fa8b00a-8f0e-485e-b98e-eb9bb1ee5a9f | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 710eac2a-e261-478d-84e8-de99f418138c | NA | 2020-07-30 04:29:31 | animal | FALSE | DGPL | https://www.catalogueoflife.org | Anas strepera | 1 | NA | adult | female | NA | NA | human | Danny Van der beeck | 2020-08-17 06:57:50 | NA | NA | NA | gadwall | krakeend |
| e57d97b3-f15b-48f5-a86c-477952bf8b7a | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 149f42ec-37bb-499b-9dac-24a90979e92c | NA | 2020-07-31 04:43:33 | animal | FALSE | DGP6 | https://www.catalogueoflife.org | Anas platyrhynchos | 2 | NA | unknown | unknown | NA | NA | human | Danny Van der beeck | 2020-08-17 07:03:19 | NA | NA | NA | mallard | wilde eend |
| f5707f70-c264-4f81-9e2f-06d55ad23d37 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 45ee3031-93c2-4684-a917-d33d5274780f | NA | 2020-08-02 05:00:14 | animal | FALSE | DGP6 | https://www.catalogueoflife.org | Anas platyrhynchos | 5 | NA | subadult | unknown | NA | NA | human | Danny Van der beeck | 2020-08-18 21:09:30 | NA | NA | NA | mallard | wilde eend |
| deploymentID | locationID | locationName | longitude | latitude | coordinateUncertainty | start | end | setupBy | cameraID | cameraModel | cameraInterval | cameraHeight | cameraTilt | cameraHeading | timestampIssues | baitUse | session | array | featureType | habitat | tags | comments | _id |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 2df5259b-b4b4-4f43-8cf7-effcced06d6f | B_DL_val 5_beek kleine vijver | 5.655 | 51.181 | NA | 2020-07-29 05:29:41 | 2020-08-08 04:20:40 | Danny Van der beeck | NA | NA | 0 | 0.7 | NA | NA | NA | NA | NA | NA | NA | NA | boven de stroom | van 29/07/2020 tot 08/08/2020 120 foto’s | NA |
| 577b543a-2cf1-4b23-b6d2-cda7e2eac372 | ff1535c0-6b5d-44be-b3ef-c4d4204dad74 | B_DL_val 3_dikke boom | 5.659 | 51.184 | NA | 2020-06-19 21:00:00 | 2020-06-28 23:33:22 | Danny Van der beeck | NA | NA | 0 | 0.8 | NA | NA | NA | NA | NA | NA | NA | NA | linkeroever | van 19/06/2020 tot 29/06/2020 63 foto’s | NA |
| 62c200a9-0e03-4495-bcd8-032944f6f5a1 | ce943ced-1bcf-4140-9a2e-e8ee5e8c10e6 | B_DM_val 4_’t WAD | 4.013 | 50.699 | NA | 2021-03-27 20:38:18 | 2021-04-18 21:25:00 | Davy | NA | NA | 0 | 1.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 7ca633fa-64f8-4cfc-a628-6b0c419056d7 | 3232bcfd-5dfa-496e-b7ab-14593bb1b7f1 | Mica Viane | 3.898 | 50.742 | NA | 2019-10-09 11:18:07 | 2019-10-23 10:00:16 | Axel Neukermans | NA | NA | 0 | 2.0 | NA | NA | NA | NA | NA | NA | NA | NA | boven de stroom | CAM_244 HC600 Boven de Stroom | NA |
Clean data
Here, the data follows the camtrapDP standard and does not need cleaning. However, for this demonstration we change the time stamp type to character:
dat$data$observations$timestamp <- as.character(dat$data$observations$timestamp)
class(dat$data$observations$timestamp)
#> [1] "character"
rec_type <- list(timestamp = list("as.POSIXct",
tz = "UTC"))
dat_clean <- clean_data(dat,
rec_type = rec_type)In the cleaned data, timestamp is converted back to
POSIX:
class(dat_clean$data$observations$timestamp)
#> [1] "POSIXct" "POSIXt"The timezone is UTC, as we specified in the casting function:
attr(dat_clean$data$observations$timestamp, "tzone")
#> [1] "UTC"Else, the data is unchanged.
| observationID | deploymentID | sequenceID | mediaID | timestamp | observationType | cameraSetup | taxonID | taxonIDReference | scientificName | count | countNew | lifeStage | sex | behaviour | individualID | classificationMethod | classifiedBy | classificationTimestamp | classificationConfidence | comments | _id | vernacularNames.en | vernacularNames.nl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ef2f7140-ae97-4b44-8309-ab1ffbc02879 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 8f5ffbf2-52c4-4dd2-b502-d93a8aa64640 | NA | 2020-07-29 05:29:41 | unknown | FALSE | NA | NA | NA | NA | NA | NA | NA | NA | NA | human | NA | NA | NA | NA | NA | NA | NA |
| 68686a75-ad44-4676-b45e-2b85f60e4d11 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 1d98da96-c3ce-4479-9b97-8883cd33724f | NA | 2020-07-29 05:38:55 | blank | FALSE | NA | NA | NA | NA | NA | NA | NA | NA | NA | human | NA | NA | NA | NA | NA | NA | NA |
| 3d065f23-426a-449b-8693-f3c6b2ac9c7a | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 89b807ca-697f-4452-8349-b235622c94fa | NA | 2020-07-29 05:46:48 | animal | FALSE | DGPL | https://www.catalogueoflife.org | Anas strepera | 4 | NA | subadult | unknown | NA | NA | human | Danny Van der beeck | 2020-08-17 06:57:28 | NA | NA | NA | gadwall | krakeend |
| 1fa8b00a-8f0e-485e-b98e-eb9bb1ee5a9f | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 710eac2a-e261-478d-84e8-de99f418138c | NA | 2020-07-30 04:29:31 | animal | FALSE | DGPL | https://www.catalogueoflife.org | Anas strepera | 1 | NA | adult | female | NA | NA | human | Danny Van der beeck | 2020-08-17 06:57:50 | NA | NA | NA | gadwall | krakeend |
| e57d97b3-f15b-48f5-a86c-477952bf8b7a | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 149f42ec-37bb-499b-9dac-24a90979e92c | NA | 2020-07-31 04:43:33 | animal | FALSE | DGP6 | https://www.catalogueoflife.org | Anas platyrhynchos | 2 | NA | unknown | unknown | NA | NA | human | Danny Van der beeck | 2020-08-17 07:03:19 | NA | NA | NA | mallard | wilde eend |
| f5707f70-c264-4f81-9e2f-06d55ad23d37 | 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 45ee3031-93c2-4684-a917-d33d5274780f | NA | 2020-08-02 05:00:14 | animal | FALSE | DGP6 | https://www.catalogueoflife.org | Anas platyrhynchos | 5 | NA | subadult | unknown | NA | NA | human | Danny Van der beeck | 2020-08-18 21:09:30 | NA | NA | NA | mallard | wilde eend |
| deploymentID | locationID | locationName | longitude | latitude | coordinateUncertainty | start | end | setupBy | cameraID | cameraModel | cameraInterval | cameraHeight | cameraTilt | cameraHeading | timestampIssues | baitUse | session | array | featureType | habitat | tags | comments | _id |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 | 2df5259b-b4b4-4f43-8cf7-effcced06d6f | B_DL_val 5_beek kleine vijver | 5.655 | 51.181 | NA | 2020-07-29 05:29:41 | 2020-08-08 04:20:40 | Danny Van der beeck | NA | NA | 0 | 0.7 | NA | NA | NA | NA | NA | NA | NA | NA | boven de stroom | van 29/07/2020 tot 08/08/2020 120 foto’s | NA |
| 577b543a-2cf1-4b23-b6d2-cda7e2eac372 | ff1535c0-6b5d-44be-b3ef-c4d4204dad74 | B_DL_val 3_dikke boom | 5.659 | 51.184 | NA | 2020-06-19 21:00:00 | 2020-06-28 23:33:22 | Danny Van der beeck | NA | NA | 0 | 0.8 | NA | NA | NA | NA | NA | NA | NA | NA | linkeroever | van 19/06/2020 tot 29/06/2020 63 foto’s | NA |
| 62c200a9-0e03-4495-bcd8-032944f6f5a1 | ce943ced-1bcf-4140-9a2e-e8ee5e8c10e6 | B_DM_val 4_’t WAD | 4.013 | 50.699 | NA | 2021-03-27 20:38:18 | 2021-04-18 21:25:00 | Davy | NA | NA | 0 | 1.0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 7ca633fa-64f8-4cfc-a628-6b0c419056d7 | 3232bcfd-5dfa-496e-b7ab-14593bb1b7f1 | Mica Viane | 3.898 | 50.742 | NA | 2019-10-09 11:18:07 | 2019-10-23 10:00:16 | Axel Neukermans | NA | NA | 0 | 2.0 | NA | NA | NA | NA | NA | NA | NA | NA | boven de stroom | CAM_244 HC600 Boven de Stroom | NA |