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Python code to simulate formose reaction in osmotically coupled droplets, and associated data, related to the article "Emergent fitness and ecological dynamics of compartmentalised autocatalytic chemical reactions" by Heng Lu, Alex Blokhuis, Rebecca Turk-MacLeod, Jayaprakash Karuppusamy, Andrea Franconi, Gabrielle Woronoff, Cyrille Jeancolas, Afshin Abrishamkar, Estelle Loire, Fabien Ferrage, Philippe Pelupessy, Ludovic Jullien, Eörs Szathmary, Philippe Nghe and Andrew. D. Griffiths This software is distributed under the Creative Commons International 4.0 License.

The purpose of this webapp tool is to identify a Klebsiella isolate from its MALDI-TOF mass spectrometry spectrum. This is applicable to members of the Klebsiella pneumoniae complex (K. pneumoniae, K. variicola, and phylogenetically related species) and of the Klebsiella oxytoca species complex (Klebsiella oxytoca, K. pasteurii, K. spallanzanii and phylogenetically related species).

The Human Brain Project Model Validation Framework provides a REST API and a web app for searching, browsing and viewing the results of model validation experiments. This is version 1.7.

Django/Angular software for graphing neural activity data (analog signals, spike trains etc.), read from Neo files, in a web browser.This is version 1.4.0

Python package that implements Tractograms As Linear Operators in Neuroimaging (TALON).

Code for the front-end of the Human Brain Project Neuromorphic Computing Platform. Release 2021A.

PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models.In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian) and on a number of neuromorphic hardware systems.Home page: http://neuralensemble.org/PyNN/Documentation: http://neuralensemble.org/docs/PyNN/Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensembleBug reports: https://github.com/NeuralEnsemble/PyNN/issuesThis is version 0.9.6. of PyNN

Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Kwik and HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization). OpenElectrophy (data analysis and visualization) used an older version of Neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo’s data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. This is version 0.9.0 of Neo. Release notes are available at https://neo.readthedocs.io/en/stable/releases/0.9.0.html

fairgraph is an experimental Python library for working with metadata in the HBP/EBRAINS Knowledge Graph, with a particular focus on data reuse, although it is also useful in metadata registration/curation. This is v0.5.1 of fairgraph.

Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Kwik and HDF5.The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.Neo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization). OpenElectrophy (data analysis and visualization) used an older version of Neo.Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo’s data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion.This is version 0.8.0 of Neo. Release notes are available at https://neo.readthedocs.io/en/stable/releases/0.8.0.html