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setup.py | %!s(int64=3) %!d(string=hai) anos |
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 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.
More information about the project including related projects as well as tutorials and examples can be found at our odML project page.
python-odml is most conveniently installed via pip.
pip install odml
To install the latest development version of odml you can use the git installation option of pip:
pip install git+https://github.com/G-Node/python-odml
Please note that this version might not be stable.
The Python installation features multiple convenience commandline scripts.
odmlconvert
: Converts odML files of previous file versions into the current one.odmltordf
: Converts odML files to the supported RDF version of odML.odmlview
: Render and browse local XML odML files in the webbrowser.All scripts provide detailed usage descriptions by adding the help
flag to the command.
odmlconvert -h
odmltordf -h
odmlview -h
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 binary
has been removed. Incorporating actual binary
data into odML files is discouraged, provide references to the original files using the
For details regarding the introduced changes please check the [github
release notes](https://github.com/G-Node/python-odml/releases).
# Dependencies
* Python 3.6+
* Python packages:
* lxml (version 3.7.2)
* yaml (version >= 5.1)
* rdflib (version >=4.2.2)
* These packages will be downloaded and installed automatically if the ```pip```
method is used to install odML. Alternatively, they can be installed from the OS
package manager. On Ubuntu, they are available as:
* 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
## Previous Python versions
Python 2 has reached end of life. We will not keep any future versions of odml Python 2 compatible and will completely drop support for Python 2 with August 2020. We also recommend using a Python version >= 3.6. If a Python version < 3.6 is a requirement, the following dependency needs to be installed as well:
* pip install
* enum34 (version 0.4.4)
* apt install
* python-enum
# 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.
See the CONTRIBUTING document for more information on this.
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 | |
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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 |