# Data for Sehara et al., 2021 eNeuro (the real-time DeepLabCut project)

The data repository for: [Sehara K, Zimmer-Harwood P, Larkum ME, Sachdev RNS (2021) Real-time closed-loop feedback in behavioral time scales using DeepLabCut. ENEURO.0415-20.2021](https://doi.org/10.1523/eneuro.0415-20.2021).

![Acquisition setup](setup.png)

(The image is licensed under CC-BY 4.0, 2020 Keisuke Sehara)

The head-fixed mouse was allowed to whisk freely under infrared illumination.
The behavior of the mouse was captured from the above, using the ImagingSource DMK37BUX287 camera.

The [Pose-Trigger](https://github.com/gwappa/python-posetrigger) program was used
to capture images and to generate output triggers.

## Datasets

You can find more details about each dataset in the README file of the corresponding subfolder.

1. [Raw videos](01_RawVideos): the raw videos acquired using [Pose-Trigger](https://github.com/gwappa/python-posetrigger). The files are converted to HDF5 files (instead of the original NumPy files).
2. [Spike2 recordings](02_Spike2Recordings): recordings of the frame and trigger signals during Pose-Trigger acquisition, using Spike2. The files are converted to HDF5 files (instead of the original `.smrx` files).
3. [DeepLabCut projects](03_DLCProjects): the [DeepLabCut (v2.1)](https://github.com/DeepLabCut/DeepLabCut/releases/tag/v2.0.8) projects used in the study.
4. [_Post hoc_ pose estimation](04_PostHocEstimation): the _post-hoc_ pose-estimation data to be compared with the real-time data. The files are in the HDF5 format (in the structure different from the "original" PyTables format that DeepLabCut generates).
5. [Performance profiling](05_PerformanceProfiling): the data and analytical procedures (and some figures) used to profile the speed and accuracy of Pose-Trigger.

## License

Copyright (c) 2020 Keisuke Sehara, Paul Zimmer-Harwood, Matthew E. Larkum, and Robert N.S. Sachdev, [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/).