nwb-datajoint 0.2.5

Neuroscience data pipeline for reproducible research used by Loren Frank Lab, UCSF

nwb_datajoint

The Frank lab Datajoint pipeline facilitates the storage, analysis, and sharing of neuroscience data to support reproducible research. It integrates existing open-source projects into a coherent framework so that they can be easily used.

Setup

Installing packages

1. Clone this repository:

   git clone https://github.com/LorenFrankLab/nwb_datajoint.git
   

2. Set up and activate a conda environment from environment.yml:

   cd nwb_datajoint
   conda env create -f environment.yml
   conda activate nwb_datajoint
   

3. Install this repository:

   # to use the package
   pip install nwb_datajoint
   # if you're a developer:
   pip install -e .
   

Setting up database access

1. Ask Loren or Eric to set up an account for you on the Frank lab database (lmf-db.cin.ucsf.edu). Note that you have to be connected to UCSF LAN to access this server.

2. Add the following environment variables (e.g. in ~/.bashrc). This example assumes that you are interacting with the database on a computer that has mounted stelmo at /stelmo.

   export NWB_DATAJOINT_BASE_DIR="/stelmo/nwb/" 
   export SPIKE_SORTING_STORAGE_DIR="/stelmo/nwb/spikesorting" # where output of spike sorting will be sorted
   export DJ_SUPPORT_FILEPATH_MANAGEMENT="TRUE"
   export KACHERY_P2P_API_HOST="typhoon"
   export KACHERY_P2P_API_PORT="14747"
   export KACHERY_TEMP_DIR="/stelmo/nwb/tmp"
   

3. Configure DataJoint. When your account is created, you will be given a temporary password. You can change your password and set up external stores. You should need to run these only once.

Finally, open up a python console and import nwb_datajoint to check that the setup has worked.

Tutorials

The tutorials are in the form of Jupyter Notebooks and can be found in the notebooks directory. We strongly recommend opening them in the context of jupyterlab. Start with these tutorials:

• 0_intro.ipynb: general introduction to the database
• 1_spikesorting.ipynb: how to run spike sorting