PyBpod DatasetTypes
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alyx-UUIDGroup
namespace
namePhysical filesDimensiondtypeDescription
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Spike detectionstandardspikes.timesnpy[ns]Times of spikes (seconds, relative to experiment onset). Note this includes spikes from all probes, merged together
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standardspikes.clustersnpy[ns]Cluster assignments for each spike (integers counting from 0).
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spikes._ks2_contamination[ns]Peorcentage from kilosort 2
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standardspikes.depthsnpy[ns]Depth along probe of each spike (µm; computed from waveform center of mass). 0 means deepest site, positive means above this
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standardspikes.ampsnpy[ns]Amplitude of each spike (µV)
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standardclusters.brainLocationtsv[nc, 4]4-column .tsv file with columns ccf_ap, ccf_dv, ccf_lr, ccf_acronym. nChannels rows: one for every channel recorded
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standardclusters.meanWaveforms[nc, nsw, nch]
Mean unfiltered waveform of spikes in this cluster (but for neuropixels data will have been hardware filtered): nClusters*nSamples*nChannels
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standardclusters.templateWaveforms[ncks2, nsw, nch]
Ideal Waveform that was used to detect those spikes in Kilosort, in whitened space (or the most representative such waveform if multiple templates were merged): nClusters*nSamples*nChannels
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standardclusters.depths[nc]Depth of mean cluster waveform on probe (µm). 0 means deepest site, positive means above this.
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standardclusters._phy_annotationtsv[nc]int0 = noise, 1 = MUA, 2 = Good, 3 = Unsorted, other number indicates manual quality score (from 4 to 100)
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standardclusters.waveformDuration[nc]trough to peak time, ms
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standardclusters.ampstsv[nc]Mean amplitude of each cluster (µV)
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standardclusters.peakChannel[nc]
Channel which has the largest amplitude for this cluster. Note this counts all channels in the whole recording (starting from zero), rather than just the channels on this cluster's home probe - so if you want to find this cluster's brain location, it would be channels.brainLocation[clusters.peakChannel[i],:]
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standardclusters.probes[nc]Which probe this cluster came from (counting from zero)
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Probe locations and geometrystandardprobes.trajectorytsv[np, 7]apdvlr_insertion, apdvlr_tip, axial_angle. All relative to Bregma
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standardprobes.descriptiontsv[np]String naming probe model (E.g. "Neuropixels phase 3a")
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standardprobes.sitePositionstsv[np, nch]
json file: one entry per probe, each containing nSites by 2 array of site positions in local coordinates. Probe tip is at the origin. Note that there is an entry for all sites, even if they were not recorded in this experiment. This allows you to use the same probes.sitePositions file for all recordings with a single probe model, even if different channel subsets are recorded on different experiments
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standardprobes.rawFilenameName of the raw data file this probe was recorded in
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Channel mappingstandardchannels.probe[nch]nChannels*1 array of integers saying which probe each channel in the recording came from (counting from zero)
NOTE "channel" refers ONLY to those channels that made it through to spike sorting (probably not all of them). "rawRow" means a row in the raw recording file, of which there may be more
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standardchannels.sitenpy[nch]
nChannels*1 array of integers saying which site on that probe the channel corresponds to (counting from zero). Note that not all sites need to be recorded - so there can be "gaps" in this file
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standardchannels.brainLocation[nch, 4]4-column .tsv file with columns ccf_ap, ccf_dv, ccf_lr, ccf_acronym. nChannels rows: one for every channel recorded
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standardchannels.rawRow[nch]
Each channel's row in its home file (look up via probes.rawFileName), counting from zero. Note some rows don't have a channel, for example if they were sync pulses
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standardchannels.sitePositions[nch, 2]nChannels by 2 array of site positions of all channels in local coordinates. Probe tip is at the origin.
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LFPstandardlfp.rawLFP: array of size nSamples * nChannels. Channels from all probes are included
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standardlfp.timestampsTimestamps for LFP timeseries: 2 column array giving sample number and time in seconds
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Raw ephysstandardephys.rawmultiple subdirectories, one for each probe
Raw ephys: array of size nSamples * nChannels. Channels from all probes are included. NOTE: this is huge, and hardly even used. To allow people to load it, we need to add slice capabilities to ONE
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standardephys.timestampsTimestamps for raw ephys timeseries: 2 column array giving sample number and time in seconds
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Eye camera pupil trackingstandardeye.timestampsTimestamps for pupil tracking timeseries: 2 column array giving sample number and time in seconds
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standardeye.rawRaw movie data for pupil tracking
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standardeye.areaArea of pupil (pixels^2)
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standardeye.xyPosmatrix with 2 columns giving x and y position of pupil (in pixels)
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standardeye.blinkBoolean array saying whether eye was blinking in each frame
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Detected licksstandardlicks.timesTimes of licks
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Spontaneous timesstandardspontaneous.intervalsTimes when no other protocol was going on for at least 30 sec or so
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Raw Data Folders (naming convention - ALFlike)folderraw_behavior_datacontains all data from behavioral system
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folderraw_ephys_datarecording system
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folderraw_imaging_dataimaging system
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folderraw_video_dataraw video files and possible online processing outputs
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b9898fac-8106-4db4-99a4-f81f6a53fc57raw_behavior_data folder_iblrig__iblrig_taskData.raw_iblrig_taskData.raw.jsonable
Data file saved by PyBpod in json serializable lines (file itself is not a json object, each line is a json object corresponding to a trial) - Training task w/ automated contrast
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26e731af-67a7-4be0-9dbc-c25eb153da31_iblrig__iblrig_taskSettings.raw_iblrig_taskSettings.raw.json
Metadata/Settings json (only one line). All information about the task, user, subject, contrasts, gain etc are included. (We should have a link with the structure of it somewhere)
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91aec5d6-a10a-41e8-abcd-fbd7bacf69e3_iblrig__iblrig_codeFiles.raw_iblrig_codeFiles.raw.zipZip of Gabor2D and basicChoiceWorld code folders that generated the data
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62998630-0d69-45c1-9c5e-c74a1a509fb1_iblrig__iblrig_encoderEvents.raw_iblrig_encoderEvents.raw.ssv
Data file saved from Bonsai (Event, RE timestamp, Source, data, Bonsai Timestamp) - Each row is an event sent from the state machine (1, 2, 3 - stim off, on close loop)
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e0c77fba-1e8d-435a-b57e-b33c867ede83_iblrig__iblrig_encoderPositions.raw_iblrig_encoderPositions.raw.ssvData file saved from Bonsai (Position, RE timestamp, Position, Bonsai Timestamp) - Each row is a position change of the RE
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a5ef4d78-e667-4e85-9fd7-141ae5ad3ae9_iblrig__iblrig_encoderTrialInfo.raw_iblrig_encoderTrialInfo.raw.ssv
Raw trial data sent at beginning of trial from Bpod to Bonsai (TrialNum, init position of stim, stim_contrast, stim_freq, stim_angle, stim_gain, stim_sigma/size, Bonsai timestamp),
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8d9e3083-9cb5-4f21-b945-cd445772d7a7_iblrig__iblrig_ambientSensorData.raw_iblrig_ambientSensorData.raw.jsonableTemperature and humidity from sensor in rig
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_iblrig__iblrig_micData.raw_iblrig_micData.raw.wav
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df68dbb7-8d0e-4a5b-b829-e2a832a89b62raw_video_data folder_iblrig__iblrig_bodyCamera.raw_iblrig_bodyCamera.raw.avi
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e40899d0-a883-40ac-8214-344bcf249d09_iblrig__iblrig_leftCamera.raw_iblrig_leftCamera.raw.avi
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b3e9dded-027e-4cf0-8392-2baeb3bfcabd_iblrig__iblrig_rightCamera.raw_iblrig_rightCamera.raw.avi
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93b79e71-c8f6-4d81-b298-2c8f6aefe192_iblrig__iblrig_bodyCamera.timestamps_iblrig_bodyCamera.timestamps.ssv
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b5ec79de-9c9e-4009-8892-10aa2ddb9638_iblrig__iblrig_leftCamera.timestamps_iblrig_leftCamera.timestamps.ssv
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169323bd-7f91-4b75-a3e5-69530db33d21_iblrig__iblrig_rightCamera.timestamps_iblrig_rightCamera.timestamps.ssv
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02492ac1-d2a5-4ca9-b545-a9a18fbbc31b_iblrig__iblrig_VideoCodeFiles.raw_iblrig_VideoCodeFiles.raw.zip
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8e896348-e852-49ec-b195-be56af9c0180matlab_rig_raw_for_legacy_data_iblrig__rigbox_jsonParameters.raw_rigbox_jsonParameters.raw.jsonParameter structure used for the task in Json format.
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156b266c-abc8-416d-96d4-db85fd232909_iblrig__rigbox_matParameters.raw_rigbox_matParameters.raw.matParameter structure used for the task in Matlab format.
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b7fe7846-731e-407c-8187-d2cfa3978587_iblrig__rigbox_block.raw_rigbox_block.raw.matFile containing the session data, unparsed in Matlab format.
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fd0654a6-9d26-4e04-99cc-6608e9621684_iblrig__rigbox_code.raw_rigbox_code.raw.matCopy of the expDef.m code file used for the session
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389b2a23-d032-4909-9ad1-c59903718daf_iblrig__rigbox_timeLine.raw_rigbox_timeLine.raw.mat
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cecb16be-781f-4859-bfb9-47406bdb1724_iblrig__rigbox_hardwareInfo.raw_rigbox_hardwareInfo.raw.jsonHardware configuration used for the task, json format
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33a5fc59-83ce-4671-a4f0-a1ad0ef53f28Output of lick pizeo (if that's what we end up using to detect licks...)_ibl__ibl_lickPiezo.rawUNSURE WE ARE GOING TO USE THESE ONESRaw lick trace (1 column array; volts)
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076dffde-7c7e-4636-99f1-3ae6e9d0fd4f_ibl__ibl_lickPiezo.timestampsTimestamps for lick trace timeseries: 2 column array giving sample number and time in seconds
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54c9a39c-665c-4cf6-b06c-d7f18dae1e78Raw wheel position trace_ibl__ibl_wheel.position_ibl_wheel.position.npyfloat64Absolute position of wheel (cm)
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b396747f-0c67-4de4-9610-c7e210a5b86a_ibl__ibl_wheel.times_ibl_wheel.times.npyfloat64times of position in absolute seconds from session start
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74c0120c-7515-478f-9725-53d587d86c49_ibl__ibl_wheel.timestamps_ibl_wheel.timestamps.npyfloat64times of position in absolute seconds from session start, continuous (evenly spaced)
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e1542d34-9618-4369-aab7-0489484f6a12_ibl__ibl_wheel.velocity_ibl_wheel.velocity.npyfloat64Signed velocity of wheel (cm/s) positive = CW
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dea53510-d3e0-4b94-9739-2fe548a6f898Detected wheel movements_ibl__ibl_wheelMoves.intervals2 column array with onset and offset times of detected wheel movements in secondsask how
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81ea14a9-1512-4c1c-94e3-ccb7b42f6755_ibl__ibl_wheelMoves.typestring array containing classified type of movement ('CW', 'CCW', 'Flinch', 'Other')ask how
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678a7e65-1dd1-4bff-b8c9-508e1f3a1cdcInformation about trials_ibl__ibl_trials.intervals_ibl_trials.intervals.npyfloat642 column array giving each trials start (i.e. beginning of quiescent period) and stop (i.e. end of iti) times of trials in universal seconds
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d6584a34-f9dd-4870-ac02-0feb50fdf5f6_ibl__ibl_trials.included
boolean suggesting which trials to include in analysis, chosen at experimenter discretion, e.g. by excluding the block of incorrect trials at the end of the session when the mouse has stopped
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4cd7dcc9-62c1-4471-afa7-60544fc7e1ff_ibl__ibl_trials.repNum_ibl_trials.repNum.npyint64the repetition number of the trial, i.e. how many trials have been repeated on this side (counting from 1)
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f7dbfad8-1cfc-459e-874f-4065cf9def86_ibl__ibl_trials.goCue_times_ibl_trials.goCue_times.npyfloat64Time of go cues in choiceworld - in absolute seconds, rather than relative to trial onset (from souncard sync on BNC2)
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63ea5aaa-1b51-4378-b515-2b6ec0502e05_ibl_trials.goCueTrigger_times_ibl_trials.goCueTrigger_times.npyfloat64
Time of go cues in choiceworld - in absolute seconds, rather than relative to trial onset NOTE: this is the time the trigger command is sent, we need the data from the mic to get exact presentation time OR the new system josh developed dispenses from this if properly characterized...
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5df90bef-df4f-4850-92b4-f0e43d619a0a_ibl__ibl_trials.response_times_ibl_trials.response_times.npyfloat64
Time of "response" in choiceworld- in absolute seconds, rather than relative to trial onset. This is when one of the three possible choices is registered in software, will not be the same as when the mouse's movement to generate that response begins.
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378aa050-4924-4a9b-9fe7-b7cf86dff93f_ibl__ibl_trials.choice_ibl_trials.choice.npyint64which choice was made in choiceworld: -1 (turn CCW), +1 (turn CW), or 0 (nogo)
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72344745-c248-45e5-bf73-a9329c1720d2_ibl__ibl_trials.stimOn_times_ibl_trials.stimOn_times.npyfloat64Times of stimuli in choiceworld - in absolute seconds, rather than relative to trial onset
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979f9f7c-7d67-48d5-9042-a9000a8e66a2_ibl__ibl_trials.contrastLeft_ibl_trials.contrastLeft.npyfloat64contrast of left-side stimulus (0...1) nan if trial is on other side
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9d44dc73-67cd-4de7-b115-7d25723bc0da_ibl__ibl_trials.contrastRight_ibl_trials.contrastRight.npyfloat64contrast of right-side stimulus (0...1) nan if trial is on other side
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a60425e9-c5ab-4827-88a3-79b4eb68f989_ibl__ibl_trials.feedback_times_ibl_trials.feedback_timesfloat64Time of feedback delivery (reward or not) in choiceworld - in absolute seconds, rather than relative to trial onset
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2a9092b0-c9fc-4740-bb43-28b254e3386e_ibl__ibl_trials.feedbackType_ibl_trials.feedbackType.npyint64Whether feedback is positive or negative in choiceworld (-1 for negative, +1 for positive, 0 for no_go feedback==negative)
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30717938-d036-40de-942f-b3dfe0c39c3b_ibl__ibl_trials.rewardVolume_ibl_trials.rewardVolume.npyfloat64volume of reward given each trial in µl
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f010bc2d-0211-41e7-930f-08c927a78d82
_ibl__ibl_trials.itiDuration_ibl_trials.itiDurationfloat64Intertrial interval duration for each trial
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_ibl__ibl_trials.deadTime_ibl_trials.deadTime.npyfloat64
time between state machine trial end and restart for every trial trial_length + iti_duration (using camelBack notation to not confuse with the _times keywork of ALF)
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9295f7a4-dd1b-440e-8ad0-53223aebab81_ibl__ibl_trials.probabilityLeft_ibl_trials.probabilityLeft.npyfloat64Probability that the stimulus will be on the left hand side for the current block. The probability of right is 1 minus this
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07aea4a4-189c-46f7-867d-8179561caf94Information about passive mock trials_ibl__ibl_passiveTrials.included
boolean suggesting which trials to include in analysis, chosen at experimenter discretion, e.g. by excluding the block of incorrect trials at the end of the session when the mouse has stopped
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0c635fca-650e-45c9-8e23-b22fe1f6e60e_ibl__ibl_passiveTrials.stimOn_timesTimes of stimuli in choiceworld - in absolute seconds, rather than relative to trial onset
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9b929d17-bd42-459d-a604-b4251d847da8_ibl__ibl_passiveTrials.contrastLeftcontrast of left-side stimulus (0...1)
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b73caa77-3e26-44a4-9809-b0885b47e77e_ibl__ibl_passiveTrials.contrastRightcontrast of right-side stimulus (0...1)
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72d9e4b1-295a-4698-bca7-650e7a62a330_ibl__ibl_passiveValveClicks.timesTimes of valve opening during passive trial presentation
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361ca040-16e5-45d8-8486-ba8d19538452_ibl__ibl_passiveBeeps.timesTimes of the beep, equivilent to the go cue during the choice world task
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cbf4b6b1-9202-4468-bd65-22df1713ae60_ibl__ibl_passiveWhiteNoise.timesTimes of white noise bursts, equivilent to the negative feedback sound during the choice world task
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83c45a9b-3e78-460f-a035-cb00cf4f9709_ibl__ibl_passiveNoise.intervals
2 column array giving each passive noise trial's start (i.e. beginning of quiescent period) and stop (i.e. end of iti) times of trials in universal seconds
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2a80ad9c-0d66-47b5-9fe4-2624e3dc439eSparse Noise_ibl__ibl_sparseNoise.xyPos2 column array giving x and y coordiates on screen of sparse noise stimulus squares (WHAT UNIT?)
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6e2913bc-6591-457c-a4e1-df86d96298b4_ibl__ibl_sparseNoise.timestimes of those stimulus squares appeared in universal seconds
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9d9f66f2-a783-46a7-ad03-1cad8aa7b4abInformation about extra rewards given manually by experimenter._ibl__ibl_extraRewards.timesTimes of extra rewards
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