Occupancy data

Dataset used to run the occupancy models described in <i>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.</i><br>Data consists of<b>Ground-truth</b>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.<br><b>Partially annotated datasets</b>1) top_ten_annotated.csv : files with top-10 machine learning scores per site manually reviewed (highest scoring file on every third day)2) random_ten_annotated.csv : 10 randomly selected files per site manually reviewed (randomly selected file on every third day)3) scheduled_listening_annotated.csv: first file after 5am every 3 days from each site manually reviewed.<br><b>Machine learning predictions on</b> <b>temporally subset dataset</b>1) dawn_all.csv : full dataset of predictions, with no subsetting2) dawn_10_max.csv : temporal interval of 10 minutes, with file with maximum machine learning score sampled3) dawn_10_random.csv: temporal interval of 10 minutes, first file sampled4) dawn_30_max.csv: temporal interval of 30 minutes, with file with maximum machine learning score sampled5) dawn_30_random.csv: temporal interval of 30 minutes, first file sampled<br><br>Columns in data consist of:LocationID/site: unique identifier for recorder locationdate/timestamp: when the recording was madeannotation: manualy review for presence (1) or absence (0) of howler monkey in cliplogit_present: machine learning score with logit transformationsoftmax_present: machine learning score with softmax transformation<br>