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Michael Denker d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.		7 years ago
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doc	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
images	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
odml	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
test	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
INSTALL	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
LICENSE	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
README.md	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
setup.py	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago

odML libraries

The Python-odML library is available on GitHub <https://github.com/G-Node/python-odml>. If you are not familiar with the version control system git, but still want to use it, have a look at the documentaion available on the git-scm website <https://git-scm.com/>.

Dependencies

The Python-odML library runs under Python 2.7.
The Python-odML library depends on Enum (version 0.4.4).

Installation

To download the Python-odML library please either use git and clone the repository from GitHub:

$ cd /home/usr/toolbox/
$ git clone https://github.com/G-Node/python-odml.git

... or if you don't want to use git download the ZIP file also provided on GitHub to your computer (e.g. as above on your home directory under a "toolbox" folder).

To install the Python-odML library, enter the corresponding directory and run::

$ cd /home/usr/toolbox/python-odml/
$ python setup.py install

Documentation

Bugs & Questions

Should you find a behaviour that is likely a bug, please file a bug report at the github bug tracker.

If you have questions regarding the use of the library or the editor, ask the question on Stack Overflow, be sure to tag it with odml and we'll do our best to quickly solve the problem.

datacite.yml
Title	Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task
Authors	Brochier,Thomas;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0001-6948-1234 Zehl,Lyuba;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0002-5947-9939 Hao,Yaoyao;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0002-9390-4660 Duret,Margaux;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0002-6557-748X Sprenger,Julia;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0002-9986-7477 Denker,Michael;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0003-1255-7300 Grün,Sonja;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0003-2829-2220 Riehle,Alexa;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France
Description	We provide two electrophysiological datasets recorded via a 10-by-10 multi-electrode array chronically implanted in the motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task. The datasets contain the continuous measure of extracellular potentials at each electrode sampled at 30 kHz, the local field potentials sampled at 1 kHz and the timing of the online and offline extracted spike times. It also includes the timing of several task and behavioral events recorded along the electrophysiological data. Finally, the datasets provide a complete set of metadata structured in a standardized format. These metadata allow easy access to detailed information about the datasets such as the settings of the recording hardware, the array specifications, the location of the implant in the motor cortex, information about the monkeys, or the offline spike sorting.
License	CC-BY (http://creativecommons.org/licenses/by/4.0/)
References	Zehl, L., Jaillet, F., Stoewer, A., Grewe, J., Sobolev, A., Wachtler, T., … Grün, S. (2016). Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics, 10, 26. [] (HasMetadata) Riehle, A., Wirtssohn, S., Grün, S., & Brochier, T. (2013). Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements. Frontiers in Neural Circuits, 7, 48 [] (HasMetadata)
Funding	Helmholtz Association, Supercomputing and Modeling for the Human Brain EU, EU.604102 EU, EU.720270 DFG, DFG.GR 1753/4-2 DFG, DFG.DE 2175/2-1 RIKEN-CNRS, Collaborative Research Agreement ANR, GRASP CNRS, PEPS CNRS, Neuro_IC2010 DAAD LIA Vision for Action
Keywords	Neuroscience Electrophysiology Utah Array Spikes Local Field Potential Macaque Motor Cortex
Resource Type	Dataset