Data related to Lillvis JL et al., 2023: Nested neural circuits generate distinct acoustic signals during Drosophila courtship

Data related to Lillvis JL et al. 2023, Nested neural circuits generate distinct acoustic signals during Drosophila courtship.This dataset includes the raw and analyzed audio files for all optogenetic experiments and the ground truth song annotations used to train the model used to segment song in SongExplorer. Details of each can be found below.RAW AND ANALYZED AUDIO FILES:This dataset includes courting male D. melanogaster optogenetic silencing experiments (GtACR_MaleFemalePairs.zip), courting male optogenetic activation experiments (CsChrimson_MaleFemalePairs.zip), and isolated male optogenetic activation experiments (CsChrimson_isolatedMales.zip).The file naming scheme is as follows:date_recordingID_chamber_genotypegenotype e.g., SS46542CHRIM = SS46542&gt;CsChrimson = CsChrimson expressed in pIP10 or SS46542GTACR = SS46542&gt;GtACR1 = GtACR expressed in pIP10.WAV files are the raw audio files-ambient, -mel-ipi, -mel-pulse, -mel-sine, -other, -poly .wav files are the probabilities of each characteristic as classified in SongExplorer-predicted-1.0pr.csv are the ethograms of each recording after thresholding the probabilities for each wordLED2.WAV is the timing and relative amplitude of the 525 nm light (GtACR experiments)the intensities used for GtACR_MaleFemalePairs.zip were (in uW/mm²) 3, 5, 8, 10, 18, 24, 30, 36LED1.WAV is the timing and relative amplitude of the 625 nm light (CsChrimson experiments)the intensities used for CsChrimson_MaleFemalePairs.zip were (in uW/mm²) 2, 4, 6, 8, 17, 25, 34, 42the intensities used for CsChrimson_isolatedMales.zip were (in uW/mm²) 0.3, 0.7, 1, 1.5, 2, 2.4, 2.8, 3.2, 3.7, 4.1, 4.5, 4.9, 5.4, 5.8, 6.2, 8.4, 10.5, 12.7, 14.8, 17, 19, 21, 25, 29, 34, 38, 42GROUND TRUTH DATA FOR SONGEXPLORER MODELThis dataset includes audio .wav files and annotated .csv files used to train the classifier model that was used to segment all audio recordings. See https://github.com/JaneliaSciComp/SongExplorer for detailed instructions on training a classifier and analyzing data using SongExplorer. In brief, the files shared here can be inserted into the groundtruth directory used for training. The model used in Lillvis JL et al., 2023 used this ground truth data to train for 4,000,000 steps at a learning rate of 0.000001. 10% of the data was used for validation during training. The model used in Lillvis JL et al., 2023 was generated and run in a previous version of SongExplorer and will not work in the latest version of SongExplorer. However, training a new model using this data will generate a model with similar ability to segment D. melanogaster song.<br>