Occupancy data

Dataset used to run the occupancy models described in Lydia K.D. Katsis, Tessa A. Rhinehart, Elizabeth Dorgay, Emma E. Sanchez, C. Patrick Doncaster, Jake L. Snaddon, Justin Kitzes. A comparison of statistical methods for deriving occupancy estimates from acoustic monitoring data. Data consists of Ground-truth 1) ground_truth_data.csv: csv of manually reviewed files for presence or absence of howler monkeys, used as benchmark to compare modelled occupany estimates. Partially annotated datasets 1) top_ten_annotated.csv : files with top-10 machine learning scores per site manually reviewed 2) random_ten_annotated.csv : 10 randomly selected files per site manually reviewed 3) scheduled_listening_annotated.csv: first file after 5am every 3 days from each site manually reviewed. Machine learning predictions on temporally subset dataset 1) dawn_all.csv : full dataset of predictions, with no subsetting 2) dawn_10_max.csv : temporal interval of 10 minutes, with file with maximum machine learning score sampled 3) dawn_10_random.csv: temporal interval of 10 minutes, first file sampled 4) dawn_30_max.csv: temporal interval of 30 minutes, with file with maximum machine learning score sampled 5) dawn_30_random.csv: temporal interval of 30 minutes, first file sampled Columns in data consist of: LocationID/site: unique identifier for recorder location date/timestamp: when the recording was made annotation: manualy review for presence (1) or absence (0) of howler monkey in clip logit_present: machine learning score with logit transformation softmax_present: machine learning score with softmax transformation