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README.rst

.. image:: https://travis-ci.org/G-Node/python-odml.svg?branch=master
:target: https://travis-ci.org/G-Node/python-odml
.. image:: https://ci.appveyor.com/api/projects/status/2wfvsu7boe18kwjy?svg=true
:target: https://ci.appveyor.com/project/mpsonntag/python-odml
.. image:: https://coveralls.io/repos/github/G-Node/python-odml/badge.svg?branch=master
:target: https://coveralls.io/github/G-Node/python-odml?branch=master

odML (Open metaData Markup Language) core library
=================================================

The open metadata Markup Language is a file based format (XML, JSON, YAML) for storing
metadata in an organised human- and machine-readable way. odML is an initiative to define
and establish an open, flexible, and easy-to-use format to transport metadata.

The Python-odML library can be easily installed via :code:`pip`. The source code is freely
available on `GitHub `_. If you are not familiar
with the version control system **git**, but still want to use it, have a look at the
documentation available on the `git-scm website `_.


Breaking changes
----------------

odML Version 1.4 introduced breaking format and API changes compared to the previous
versions of odML. Files saved in the previous format versions can be converted to a 1.4
compatible format using the version converter from the odml/tools package.

Be aware that the value dtype :code:`binary` has been removed. Incorporating actual binary
data into odML files is discouraged, provide references to the original files using the
:code:`URL` dtype instead.

For details regarding the introduced changes please check the `github release notes
`_.


Dependencies
------------

* Python 2.7 or 3.5
* Python packages:

* enum (version 0.4.4)
* lxml (version 3.7.2)
* yaml (version 3.12)
* rdflib (version >=4.2.2)

* These packages will be downloaded and installed automatically if the :code:`pip`
method is used to install odML. Alternatively, they can be installed from the OS
package manager. On Ubuntu, they are available as:

* python-enum
* python-lxml
* python-yaml
* python-rdflib

* If you prefer installing using the Python package manager, the following packages are
required to build the lxml Python package on Ubuntu 14.04:

* libxml2-dev
* libxslt1-dev
* lib32z1-dev


Installation
------------

The simplest way to install Python-odML is from PyPI using the pip tool::

$ pip install odml

On Ubuntu, the pip package manager is available in the repositories as :code:`python-pip`.

If this method is used, the appropriate Python dependencies are downloaded and installed
automatically.


Building from source
--------------------

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

$ git clone https://github.com/G-Node/python-odml.git

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 python-odml
$ python setup.py install

**Note** The master branch is our current development branch, not all features might be
working as expected. Use the release tags instead.

odML Project page
-----------------

More information about the project including related projects as well as tutorials and
examples can be found at our odML `project page `_.

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, feel free to join the
`#gnode `_ IRC channel on freenode.
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 Brochier, T., Zehl, L., Hao, Y., Duret, M., Sprenger, J., Denker, M., Grün, S. & Riehle, A. (2018). Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task, Scientific Data, 5, 180055. [] (IsPartOf)
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