Dataset Open Access
Giulia LB Spampinato;
Elric Esposito;
Pierre Yger;
Jens Duebel;
Serge Picaud;
Olivier Marre
Ground-truth recordings for validation of spike sorting algorithms
This datasets is composed of simultaneous loose patch recordings of Ganglion Cells in mice retina, combined with dense extra-cellular recordings (252 channels). The details of the dataset can be found here https://elifesciences.org/articles/34518
Probe layout
The probe layout can be found as mea_256.prb. This is a 16x16 Multi Electrode Array with 30um spacing. Only 252 channels are extra-cellular signals, and the 4 corners are devoted to triggers/sync/juxta.
Struture of the data
In this dataset, you will find several individual recordings, at max 5min long each (but please do not hesitate to contact us if interested by longer recordings). The extra-cellular data are saved as 16bits unsigned integer, with a variable offset at the beginning of the file. The value of this offset is given, for every datafile, in the additional text file (padding value (see following for more details)). The files have already been filtered with a Butterworth filter of order 3 with a cut-off frequency at 100Hz
Structure of a given dataset
Please read carefully the following to understand how to load and perform spike sorting with the data. In every .tar.gz file, you will find:
How to load the raw data in numpy
#Using the offset value from the txt file, we can load the data with memmap arrays
data=numpy.memmap('mydata.raw', dtype='uint16', offset=offset, mode='r')
data=data.reshape(len(data)//256, 256)
#Then for example, to display the first second of channel 0
one_channel = data[:20000, 0].astype('float32')
#If we want to center data around 0
one_channel -= 2**15 - 1
#And if we want to display data in micro volt, we must use the gain factor of 0.1042 provided in the header
one_channel *= 0.1042
Name | Size | |
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20160415_patch2.tar.gz
md5:196d2a1afb2e55488bda0cc9baf47883 |
2.0 GB | Download |
20160426_patch2.tar.gz
md5:06f5fe2ad6c9d02bc9a08c2a29723b98 |
1.5 GB | Download |
20160426_patch3.tar.gz
md5:dfb6045c114b459b63373324963c82ba |
1.3 GB | Download |
20170621_patch1.tar.gz
md5:c4a1073d2a2f3445bdb9adc1916e2f5a |
1.9 GB | Download |
20170622_patch1.tar.gz
md5:ecf4367f385d5b376a3cac8925af6489 |
1.8 GB | Download |
20170622_patch2.tar.gz
md5:ab2c5267aef675b7b988021eeaf28fb3 |
1.9 GB | Download |
20170623_patch1.tar.gz
md5:efe9026921a731cabde2befccc858f61 |
1.9 GB | Download |
20170627_patch1.tar.gz
md5:7919caafb6eea4e7c37b2e6680329b10 |
1.9 GB | Download |
20170629_patch2.tar.gz
md5:48580bb2849335a4d767b00e7901ed9a |
1.9 GB | Download |
20170629_patch3.tar.gz
md5:6409f3f2c7b802fb70f4009e1ac49701 |
1.9 GB | Download |
20170630_patch1.tar.gz
md5:0432815b86e613339f7b70d7ccf1c727 |
1.9 GB | Download |
20170706_patch1.tar.gz
md5:550bf521cce4f5020c935422d8dd42fe |
2.0 GB | Download |
20170706_patch2.tar.gz
md5:0a34eac1b81dbdb1644ad9a432595d38 |
2.0 GB | Download |
20170706_patch3.tar.gz
md5:ac9f6ed96e897e2be3b798dc09d772c6 |
2.0 GB | Download |
20170713_patch1.tar.gz
md5:70838dcf458498ca6f29b74a92352928 |
2.0 GB | Download |
20170725_patch1.tar.gz
md5:1bc59535d4edfd9427e6fd6d69164fd0 |
1.9 GB | Download |
20170726_patch1.tar.gz
md5:f97ae5e7f528c67f15914e15d0d2ceaa |
1.9 GB | Download |
20170728_patch2.tar.gz
md5:1639defeeccf5ea82c156dac7d8413e3 |
1.9 GB | Download |
20170803_patch1.tar.gz
md5:6ec775991a39b4d687cf239b1e503fd5 |
2.0 GB | Download |
mea_256.prb
md5:ba8889165d01b059f7ad090a2bb79a2d |
6.8 kB | Download |
README.rst
md5:62f23cdccfe7be85ecbf87e0d8c74ec0 |
2.8 kB | Download |
https://elifesciences.org/articles/34518
All versions | This version | |
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Views | 3,865 | 3,877 |
Downloads | 14,539 | 14,539 |
Data volume | 25.4 TB | 25.4 TB |
Unique views | 3,371 | 3,383 |
Unique downloads | 2,098 | 2,098 |