Working with IBL data

Many projects make use of the large dataset collected by the International Brain Laboratory.

General info

• Preprint describing the experiments and the data.
• Video description of the data by Eric deWitt, for NeuroMatchAcademy.
• Explore the data through the interactive DataJoint portal.

Getting started with IBL data

• See data.internationalbrainlab.org for a landing page and overview.
• Request access credentials to the database.
• Explore the collab notebooks created by DataJoint: NMA-IBL on GitHub.

To set up your local configuration

This assumes that you’ve worked through the general Python setup, and that you understand GitHub, conda and some basic command line tools.

1. Create and activate your conda environment (e.g. called iblenv)
2. Use conda install to install seaborn and statsmodels.
   • You can also install the useful pingouin package for basic stats: conda install -c conda-forge pingouin.
3. Run pip install nma-ibl to get access to the IBL data via DataJoint.
4. Now create a new GitHub repo that will host your analysis files (if you’d rather not share it with the world, set it to private) and clone it to a convenient location on your local machine. Launch Python, either in the Terminal or in your Python IDE.
5. From the NMA-IBL DataJoint GitHub, launch the first Colab notebook and follow the instructions to enter your newly obtained credentials (but do this locally, rather than inside the Colab). After entering your username and password, check with dj.conn() that everything works. Then run dj.config.save_local() to create a local config file: you won’t have to enter your DataJoint credentials anymore in the future! Note that this local file dj_local_config.json will only live inside the current folder, so it won’t be recognized if you launch Python from somewhere else.
   • See more here and here.
   • Important: make sure you add dj_local_conf.json to the .gitignore file in your repo, so that your credentials don’t end up on your public-facing repository on GitHub. You can also copy this .gitignore file.

Play with the data

Now create a script that will load and save some data.

Import things

# datajoint-specific stuff
import datajoint as dj
from nma_ibl import reference, subject, acquisition, behavior, behavior_analyses

Then, query some basic information about all sessions that were run.

# which subjects (i.e. mice) are in the database?
subjects = (subject.Subject * subject.SubjectLab * reference.Lab)
# this contains a lot of information that we don't really need 
# (and will increase the size of the data we want to download). 
# so let's get only the columns that we're interested in
subjects = subjects.proj('subject_nickname', 'sex', 'subject_birth_date', 'time_zone')
# note that this is not yet data - it's only a query to the database. fetch will actually get those data
df_subjects = subjects.fetch(format='frame').sort_values(by=['lab_name', 'subject_nickname']).reset_index()

# same for sessions - only take training sessions here
sessions =  behavior.TrialSet * behavior_analyses.PsychResults * behavior_analyses.ReactionTime \
            * behavior_analyses.SessionTrainingStatus \
            * (acquisition.Session & 'task_protocol LIKE "%training%"') * acquisition.SessionUser \
            & subjects # 
# only save some fields that we really care about for now (otherwise, the dataframe will explode)
sessions = sessions.proj('n_trials', 'performance_easy', 'threshold', 'bias', 'lapse_low', 'lapse_high',
                        'training_status', 'user_name', 
                        session_duration='TIMEDIFF(session_end_time,session_start_time)')
df_sessions = sessions.fetch(format='frame').reset_index()
# note: the two dataframes containing subject info and sessions info share 
# the column subject_uuid, which is called the 'primary key' that uniquely 
# identifies each mouse. use pandas' join to combine the two dataframes - 
# but beware the size of the data you're working with.

Now explore the DataFrame, for instance in ‘scientific mode’ in PyCharm or simply by printing different parts and groups to your command line.

Exercise 1: write some code to save this newly created Pandas DataFrame as a csv file. Make sure to avoid that this (large) datafile gets pushed to GitHub, for instance by creating a /data folder that is listed in your .gitignore. Then, for any analysis you want to run, load in this local file - you’ll now be able to get data without connecting to the DataJoint database. Of course, you may need different datafiles for different purposes (at the level of animals, sessions, or trials).

Exercise 2: plot some basic information about all the sessions. When (at what time of day) where they collected? How many sessions were collected per lab, user, and animal? How does performance change as a function of each animal’s progression in training?

Exercise 3: get more detailed info not at the session level (overall accuracy on easy stimuli), but at the individual trial level. You can use sessions * behavior.TrialSet.Trial to get this, but be warned that this will become huge/slow quickly. Better to first restrict to a subset of sessions (e.g. from one mouse), or to use .proj to select only those attributes of the TrialSet that you really need. See here for an example.

Exercise 4: recreate a figure from the preprint, for instance figure 2a. Then compare your solution against the code here.

Extra: light cycles

# for each lab, see if the ligth cycle is inverted (1) or non-inverted (0)
housing = subject.Housing * subject.SubjectHousing * subject.SubjectLab
housing = housing.proj('lab_name', 'light_cycle')
hs = housing.fetch(format='frame').reset_index()
hs.groupby(['lab_name'])['light_cycle'].unique()