# A standardized and reproducible method to measure decision-making in mice: Data *Beta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org* The data can be downloaded at: https://figshare.com/articles/A_standardized_and_reproducible_method_to_measure_decision-making_in_mice_Data/11636748 The data is contained in NumPy `.npy` files organized in a hierarchy of subfolders following this structure: `/{lab}/Subjects/{subject}/{date}/{number}/alf/` You can use the ONE interface to load the data in Python. The script `one_example.py` shows an example of searching and retrieving the data locally. You need Python 3.6+ and ibllib 1.4.7+. ibllib depends on a number of packages, including NumPy, SciPy, pandas, matplotlib. At the moment we recommend that you install ibllib in a fresh Python environment, as follows: `virtualenv ibl`, `source ibl/bin/activate`, `pip install ibllib`. To launch the script, go into the data directory named `ibl-behavior-data-Dec2019`, and type `python one_example.py`. This script will only work if you launch this command from within that directory, as the ONE loader takes the current directory as data source by default. ## Dataset types description _ibl_trials.intervals shape : (nTrials, 2) 2 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 _ibl_trials.included shape : (nTrials) 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 _ibl_trials.repNum shape : (nTrials) the repetition number of the trial, i.e. how many trials have been repeated on this side (counting from 1) _ibl_trials.goCue_times shape : (nTrials) Time of go cues in choiceworld - in absolute seconds from session start, rather than relative to trial onset. NOTE: this is the time the sound is actually played. _ibl_trials.goCueTrigger_times shape : (nTrials) Time of go cues in choiceworld - in absolute seconds from session start, rather than relative to trial onset. NOTE: this is the time the trigger command is sent. _ibl_trials.response_times shape : (nTrials) Time (in absolute seconds from session start) when a response was recorded (end of the closed loop state in bpod) _ibl_trials.choice shape : (nTrials) which choice was made in choiceworld: -1 (turn CCW), +1 (turn CW), or 0 (nogo) _ibl_trials.stimOn_times shape : (nTrials) Times of visual stimulus onset in absolute seconds. _ibl_trials.stimOnTrigger_times shape : (nTrials) Times of visual stimulus onset trigger command, from session start in seconds _ibl_trials.contrastLeft shape : (nTrials) contrast of left-side stimulus (0...1) nan if trial is on other side _ibl_trials.contrastRight shape : (nTrials) contrast of right-side stimulus (0...1) nan if trial is on other side _ibl_trials.feedback_times shape : (nTrials) Time of feedback delivery (reward or not) in choiceworld - in absolute seconds, rather than relative to trial onset. _ibl_trials.feedbackType shape : (nTrials) Whether feedback is positive or negative in choiceworld (-1 for negative, +1 for positive) _ibl_trials.rewardVolume shape : (nTrials) volume of reward given each trial in µl _ibl_trials.itiDuration shape : (nTrials) Intertrial interval duration for each trial, from response time to end of trial (end of trial is beginning of quiescence period) this includes the feedback delivery and the 1or 2 seconds delay and the 0.5 sec iti at the end of each trial. _ibl_trials.deadTime shape : (nTrials) time between state machine trial end and restart for every trial trial_length + iti_duration _ibl_trials.probabilityLeft shape : (nTrials) Probability that the stimulus will be on the left hand side for the current block. The probability of right is 1 minus this