Detections of the Common Pauraque (Nyctidromus albicollis) by two automated classifiers in one-minute recordings made at seven sites in the Pantanal wetlands and adjacent Cerrado, Mato Grosso do Sul, Brazil, between November 2023 and February 2025, with an independent set of recordings annotated by ear. The occupancy unit is the site-night, the occasion is the one-minute recording, and the annotated recordings are the verified method of the multiple-methods design.
Format
A list with three data frames:
minutesOne row per one-minute recording (97,806 rows):
site_night(site-night identifier, the occupancy unit),site(recorder site, one of seven),time(start of the recording),conf_customandconf_birdnet(confidence scores of a custom-trained classifier and of the BirdNET global model v2.4, 0 when the classifier returned nothing), and the acoustic indicesbio(Bioacoustic Index) andaci(Acoustic Complexity Index).nightsOne row per site-night (1,753 rows):
site_night,site,night(date of the evening), and the nightly means of temperature (temp, °C) and relative humidity (rh, %), and the fraction of the lunar disc illuminated (moon).verifiedOne row per annotated recording (1,199 rows):
site_night,site,time, andy, 1 if any 3-s segment of the recording held the species.
Source
Piatti, L., de Sousa, D. L. H., Oliveira, B. S., de Melo, A. V., Sugai, L. S. M., & Provete, D. B. (2026). Data and code for: Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar. Zenodo. doi:10.5281/zenodo.21795898 . Licence CC BY 4.0.
Details
The annotated recordings are an independent ground-truth test set of about
1% of the recordings, chosen in proportion to the number of recordings at
each site, and annotated without reference to the classifiers (Piatti et
al., 2026). They can therefore serve as the verified method of
fp_data(). A detection by a classifier is a confidence score at or above a
threshold; Piatti et al. (2026) used 0.40 (custom) and 0.10 (BirdNET) to
maximize F1, and 0.95 for both to maximize precision.
The source table has one to 20 rows (detection windows) per recording,
and a classifier's score is non-zero only in the windows where it detected
the species. This dataset keeps one row per recording, with the maximum
score over its windows, so a recording is a detection when any of its
windows reaches the threshold. The script
that builds it from the public deposit is data-raw/curiango.R in the
package repository.
References
Piatti, L., de Sousa, D. L. H., Oliveira, B. S., de Melo, A. V., Sugai, L. S. M., & Provete, D. B. (2026). Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar. Journal of Avian Biology, in press.
Examples
str(curiango, max.level = 1)
#> List of 3
#> $ minutes :'data.frame': 97806 obs. of 7 variables:
#> $ nights :'data.frame': 1753 obs. of 6 variables:
#> $ verified:'data.frame': 1199 obs. of 4 variables:
# detections by the custom classifier at the MaxF1 threshold
mean(curiango$minutes$conf_custom >= 0.40)
#> [1] 0.01795391