For researchers deploying acoustic tags with incorporated sensors (e.g.V16TPs), it would be great if the qc analysis could return columns which include 1. the calculated sensor value (generated using the transmitter_sensor_raw_value, the transmitter_sensor_slope, the transmitter_sensor_intercept) as well as 2. the transmitter_sensor_unit. The current format exported following the qc includes only the transmitter_sensor_raw_value.
I can do this myself by merging the qc data with the IMOS_detections.csv based on the transmitter_id (with the columns transmitter_id, transmitter_sensor_unit, transmitter_sensor_slope, transmitter_sensor_intercept) but it would be great if the qc step could do this for me.
Load the data
files <- list(det = paste0(detfolder,"IMOS_detections.csv"),
rmeta = paste0(recfolder,"IMOS_receiver_deployment_metadata.csv"),
tmeta = paste0(tagfolder,"IMOS_transmitter_deployment_metadata.csv"),
meas = paste0(tagfolder,"IMOS_animal_measurements.csv"))
run the QC to remove erronious nationwide pings
tag_qc <- runQC(x = files, .parallel = TRUE, .progress = TRUE)
extract all the relevant data as well as only detections that were deemed valid and likely valid
qc_data <- grabQC(tag_qc, what = "dQC", flag = c("valid", "likely valid"))
This shows the data for a sensor tagged deployed in a freshwater sawfish in the Wenlock River array
qc_data %>% filter(transmitter_id=="A69-9007-11256") %>% data.frame() %>% head()
For researchers deploying acoustic tags with incorporated sensors (e.g.V16TPs), it would be great if the qc analysis could return columns which include 1. the calculated sensor value (generated using the transmitter_sensor_raw_value, the transmitter_sensor_slope, the transmitter_sensor_intercept) as well as 2. the transmitter_sensor_unit. The current format exported following the qc includes only the transmitter_sensor_raw_value.
I can do this myself by merging the qc data with the IMOS_detections.csv based on the transmitter_id (with the columns transmitter_id, transmitter_sensor_unit, transmitter_sensor_slope, transmitter_sensor_intercept) but it would be great if the qc step could do this for me.
Load the data
files <- list(det = paste0(detfolder,"IMOS_detections.csv"),
rmeta = paste0(recfolder,"IMOS_receiver_deployment_metadata.csv"),
tmeta = paste0(tagfolder,"IMOS_transmitter_deployment_metadata.csv"),
meas = paste0(tagfolder,"IMOS_animal_measurements.csv"))
run the QC to remove erronious nationwide pings
tag_qc <- runQC(x = files, .parallel = TRUE, .progress = TRUE)
extract all the relevant data as well as only detections that were deemed
validandlikely validqc_data <- grabQC(tag_qc, what = "dQC", flag = c("valid", "likely valid"))
This shows the data for a sensor tagged deployed in a freshwater sawfish in the Wenlock River array
qc_data %>% filter(transmitter_id=="A69-9007-11256") %>% data.frame() %>% head()