YAML simplification

simtools/ANNarchy.yaml

@@ -1,18 +1,16 @@
-- name: ANNarchy
-- features: frontend, simulator
-- operating_system: Linux, MacOS
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model
-- processing_support: Single Machine, GPU
-- interface_language: Python, C++
-- summary: >
-    ANNarchy (Artificial Neural Networks architect) is a neural simulator designed for distributed rate-coded or spiking
-    neural networks. The core of the library is written in C++ and distributed using openMP or CUDA. It provides an
-    interface in Python for the definition of the networks.
-- urls:
-    documentation: https://annarchy.readthedocs.io
-    installation: https://annarchy.readthedocs.io/en/latest/Installation.html
-    examples: https://annarchy.readthedocs.io/en/latest/example/List.html
-    source: https://github.com/ANNarchy/ANNarchy
-    issue tracker: https://github.com/ANNarchy/ANNarchy/issues
-    download: https://pypi.org/project/ANNarchy/
-    forum: https://groups.google.com/forum/#!forum/annarchy
+name: ANNarchy
+features: frontend, simulator
+operating_system: Linux, MacOS
+biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model
+processing_support: Single Machine, GPU
+interface_language: Python, C++
+summary: |
+  ANNarchy (Artificial Neural Networks architect) is a neural simulator designed for distributed rate-coded or spiking neural networks. The core of the library is written in C++ and distributed using openMP or CUDA. It provides an interface in Python for the definition of the networks.
+urls:
+  documentation: https://annarchy.readthedocs.io
+  installation: https://annarchy.readthedocs.io/en/latest/Installation.html
+  examples: https://annarchy.readthedocs.io/en/latest/example/List.html
+  source: https://github.com/ANNarchy/ANNarchy
+  issue tracker: https://github.com/ANNarchy/ANNarchy/issues
+  download: https://pypi.org/project/ANNarchy/
+  forum: https://groups.google.com/forum/#!forum/annarchy

simtools/Arbor-GUI.yaml

@@ -1,29 +1,28 @@
-- name: Arbor GUI
-- features: frontend
-- operating_system: Linux, MacOS, Windows
-- interface_language: GUI
-- summary: >
-    Arbor GUI is a comprehensive tool for building single cell models using Arbor.
-    It strives to be self-contained, fast, and easy to use.
+name: Arbor GUI
+features: frontend
+operating_system: Linux, MacOS, Windows
+interface_language: GUI
+summary: |
+  Arbor GUI is a comprehensive tool for building single cell models using Arbor.
+  It strives to be self-contained, fast, and easy to use.
 
-
-     - Design morphologically detailled cells for simulation in Arbor.
-     - Load morphologies from SWC .swc, NeuroML .nml, NeuroLucida .asc.
-     - Define and highlight Arbor regions and locsets.
-     - Paint ion dynamics and bio-physical properties onto morphologies.
-     - Place spike detectors and probes.
-     - Export cable cells to Arbor’s internal format (ACC) for direct simulation.
-     - Import cable cells in ACC format
-- urls:
-    homepage: https://github.com/arbor-sim/gui
-    tutorial: https://docs.arbor-sim.org/en/latest/tutorial/single_cell_gui.html
-    source: https://github.com/arbor-sim/gui
-    email: contact@arbor-sim.org
-    issue tracker: https://github.com/arbor-sim/gui/issues
-    installation: https://docs.arbor-sim.org/en/latest/install/gui.html
-    download: https://github.com/arbor-sim/gui/releases/
-    forum: https://github.com/arbor-sim/arbor/discussions
-    chat: https://gitter.im/arbor-sim/gui
-- relations:
-  - name: Arbor
-    description: GUI for
+   - Design morphologically detailled cells for simulation in Arbor.
+   - Load morphologies from SWC .swc, NeuroML .nml, NeuroLucida .asc.
+   - Define and highlight Arbor regions and locsets.
+   - Paint ion dynamics and bio-physical properties onto morphologies.
+   - Place spike detectors and probes.
+   - Export cable cells to Arbor’s internal format (ACC) for direct simulation.
+   - Import cable cells in ACC format
+urls:
+  homepage: https://github.com/arbor-sim/gui
+  tutorial: https://docs.arbor-sim.org/en/latest/tutorial/single_cell_gui.html
+  source: https://github.com/arbor-sim/gui
+  email: contact@arbor-sim.org
+  issue tracker: https://github.com/arbor-sim/gui/issues
+  installation: https://docs.arbor-sim.org/en/latest/install/gui.html
+  download: https://github.com/arbor-sim/gui/releases/
+  forum: https://github.com/arbor-sim/arbor/discussions
+  chat: https://gitter.im/arbor-sim/gui
+relations:
+- name: Arbor
+  description: GUI for

simtools/Arbor-Playground.yaml

@@ -1,13 +1,13 @@
-- name: Arbor Playground
-- features: frontend
-- operating_system: Linux, MacOS, Windows
-- interface_language: GUI, Python
-- summary: Arbor Playground is an Emscripten + Pyodide port of Arbor and is meant to be a simple showcase of neural modelling in Arbor.
-- urls:
-    homepage: https://arbor-sim.org/playground
-    documentation: https://docs.arbor-sim.org
-    source: https://github.com/arbor-sim/playground
-    email: contact@arbor-sim.org
-    issue tracker: https://github.com/arbor-sim/playground/issues
-    forum: https://github.com/arbor-sim/arbor/discussions
-    chat: https://gitter.im/arbor-sim/community
+name: Arbor Playground
+features: frontend
+operating_system: Linux, MacOS, Windows
+interface_language: GUI, Python
+summary: Arbor Playground is an Emscripten + Pyodide port of Arbor and is meant to be a simple showcase of neural modelling in Arbor.
+urls:
+  homepage: https://arbor-sim.org/playground
+  documentation: https://docs.arbor-sim.org
+  source: https://github.com/arbor-sim/playground
+  email: contact@arbor-sim.org
+  issue tracker: https://github.com/arbor-sim/playground/issues
+  forum: https://github.com/arbor-sim/arbor/discussions
+  chat: https://gitter.im/arbor-sim/community

simtools/Arbor.yaml

@@ -1,28 +1,28 @@
-- name: Arbor
-- features: frontend, simulator
-- operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- processing_support: Single Machine, Cluster, Supercomputer, GPU
-- interface_language: Python, C++
-- summary: >
-    Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks.
-    Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs.
+name: Arbor
+features: frontend, simulator
+operating_system: Linux, MacOS, Windows
+biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
+processing_support: Single Machine, Cluster, Supercomputer, GPU
+interface_language: Python, C++
+summary: |
+  Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks.
+  Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs.
 
 
-    Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE).
-    When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software.
+  Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE).
+  When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software.
 
 
-    Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.
-- urls:
-    homepage: https://arbor-sim.org
-    documentation: https://docs.arbor-sim.org
-    tutorial: https://docs.arbor-sim.org/en/stable/tutorial
-    examples: https://github.com/arbor-sim/arbor/tree/master/python/example
-    source: https://github.com/arbor-sim/arbor
-    email: contact@arbor-sim.org
-    issue tracker: https://github.com/arbor-sim/arbor/issues
-    installation: https://docs.arbor-sim.org/en/stable/install
-    download: https://pypi.org/project/arbor/
-    forum: https://github.com/arbor-sim/arbor/discussions
-    chat: https://gitter.im/arbor-sim/community
+  Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.
+urls:
+  homepage: https://arbor-sim.org
+  documentation: https://docs.arbor-sim.org
+  tutorial: https://docs.arbor-sim.org/en/stable/tutorial
+  examples: https://github.com/arbor-sim/arbor/tree/master/python/example
+  source: https://github.com/arbor-sim/arbor
+  email: contact@arbor-sim.org
+  issue tracker: https://github.com/arbor-sim/arbor/issues
+  installation: https://docs.arbor-sim.org/en/stable/install
+  download: https://pypi.org/project/arbor/
+  forum: https://github.com/arbor-sim/arbor/discussions
+  chat: https://gitter.im/arbor-sim/community

simtools/BMTK.yaml

@@ -1,27 +1,24 @@
-- name: Brain Modelling Toolkit (BMTK)
-- short_name: BMTK
-- features: frontend
-- operating_system: Linux, MacOS, Windows
-- interface_language: Python
-- summary: >
-    The Brain Modeling Toolkit (BMTK) is a python-based software package for building, simulating and analyzing large-scale neural network models.
-    It supports the building and simulation of models of varying levels-of-resolution; from multi-compartment biophysically detailed networks, to point-neuron models, to filter-based models, and even population-level firing rate models.
+name: Brain Modelling Toolkit (BMTK)
+short_name: BMTK
+features: frontend
+operating_system: Linux, MacOS, Windows
+interface_language: Python
+summary: |
+  The Brain Modeling Toolkit (BMTK) is a python-based software package for building, simulating and analyzing large-scale neural network models.
+  It supports the building and simulation of models of varying levels-of-resolution; from multi-compartment biophysically detailed networks, to point-neuron models, to filter-based models, and even population-level firing rate models.
 
+  The BMTK is not itself a simulator and will utilize existing simulators, like NEURON and NEST, to run different types of models.
+  What BMTK does provide:
 
-    The BMTK is not itself a simulator and will utilize existing simulators, like NEURON and NEST, to run different types of models.
-    What BMTK does provide:
+    - A unified interface across different simulators, so that modelers can work with and study their own network models across different simulators without having to learn how to use each tool.
+    - An easy way to setup and initialize network simulations with little-to-no programming necessary
+    - Automatic integration of parallelization when running on HPC.
+    - Extra built-in features which the native simulators may not support out-of-the-box.
 
-
-      - A unified interface across different simulators, so that modelers can work with and study their own network models across different simulators without having to learn how to use each tool.
-      - An easy way to setup and initialize network simulations with little-to-no programming necessary
-      - Automatic integration of parallelization when running on HPC.
-      - Extra built-in features which the native simulators may not support out-of-the-box.
-
-
-    The BMTK was developed and is supported at the Allen Institute for Brain Science and released under a BSD 3-clause license.
-    We encourage others to use the BMTK for their own research, and suggestions and contributions to the BMTK are welcome.
-- urls:
-      homepage: https://alleninstitute.github.io/bmtk/
-- relations:
-  - name: SONATA
-    description: exports to
+  The BMTK was developed and is supported at the Allen Institute for Brain Science and released under a BSD 3-clause license.
+  We encourage others to use the BMTK for their own research, and suggestions and contributions to the BMTK are welcome.
+urls:
+    homepage: https://alleninstitute.github.io/bmtk/
+relations:
+- name: SONATA
+  description: exports to

simtools/BluePyOpt.yaml

@@ -1,19 +1,16 @@
-- name: BluePyOpt
-- features: tool
-- operating_system: Linux, MacOS, Windows
-- interface_language: Python
-- summary: >
-    The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.
+name: BluePyOpt
+features: tool
+operating_system: Linux, MacOS, Windows
+interface_language: Python
+summary: |
+  The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.
 
+  It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge.
+  This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.
 
-    It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge.
-    This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.
-
-
-    Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.
-
-- urls:
-    homepage: https://bluepyopt.readthedocs.io
-- relations:
-    - name: NeuroML
-      description: exports to
+  Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.
+urls:
+  homepage: https://bluepyopt.readthedocs.io
+relations:
+  - name: NeuroML
+    description: exports to

simtools/Brain-Scaffold-Builder.yaml

@@ -1,24 +1,21 @@
-- name: Brain Scaffold Builder
-- features: frontend
-- operating_system: Linux, MacOS, Windows
-- interface_language: Python
-- summary: >
-    The Brain Scaffold Builder (BSB) is a black box component framework for multiparadigm neural modelling: we provide structure, architecture and organization, and you provide the use-case specific parts of your model.
-    In our framework, your model is described in a code-free configuration of components with parameters.
+name: Brain Scaffold Builder
+features: frontend
+operating_system: Linux, MacOS, Windows
+interface_language: Python
+summary: |
+  The Brain Scaffold Builder (BSB) is a black box component framework for multiparadigm neural modelling: we provide structure, architecture and organization, and you provide the use-case specific parts of your model.
+  In our framework, your model is described in a code-free configuration of components with parameters.
 
+  For the framework to reliably use components, and make them work together in a complex workflow, it asks a fixed set of questions per component type: e.g. a connection component will be asked how to connect cells.
+  These contracts of cooperation between you and the framework are called interfaces. The framework executes a transparently parallelized workflow, and calls your components to fulfill their role.
 
-    For the framework to reliably use components, and make them work together in a complex workflow, it asks a fixed set of questions per component type: e.g. a connection component will be asked how to connect cells.
-    These contracts of cooperation between you and the framework are called interfaces. The framework executes a transparently parallelized workflow, and calls your components to fulfill their role.
-
-
-    This way, by implementing our component interfaces and declaring them in a configuration file, most models end up being code-free, well-parametrized, self-contained, human-readable, multi-scale models!
-
-- urls:
-    homepage: https://bsb.readthedocs.io
-- relations:
-    - name: Neuron
-      description: simulates with
-    - name: NEST
-      description: simulates with
-    - name: Arbor
-      description: simulates with
+  This way, by implementing our component interfaces and declaring them in a configuration file, most models end up being code-free, well-parametrized, self-contained, human-readable, multi-scale models!
+urls:
+  homepage: https://bsb.readthedocs.io
+relations:
+  - name: Neuron
+    description: simulates with
+  - name: NEST
+    description: simulates with
+  - name: Arbor
+    description: simulates with

simtools/Brain-dynamics-toolbox.yaml

@@ -1,12 +1,12 @@
-- name: Brain dynamics toolbox
-- features: frontend, simulator
-- operating_system: Linux, MacOS, Windows
-- biological_level: Population Model
-- processing_support: Single Machine
-- interface_language: MATLAB
-- summary: >
-    The Brain Dynamics Toolbox is open-source Matlab software for simulating bespoke dynamical systems in neuroscience and beyond.
-    Users define their system of equations as a custom matlab function.
-    Interchangeable solvers and plotting tools can then be applied to that system with no additional programming effort.
-- urls:
-      homepage: http://bdtoolbox.org/
+name: Brain dynamics toolbox
+features: frontend, simulator
+operating_system: Linux, MacOS, Windows
+biological_level: Population Model
+processing_support: Single Machine
+interface_language: MATLAB
+summary: |
+  The Brain Dynamics Toolbox is open-source Matlab software for simulating bespoke dynamical systems in neuroscience and beyond.
+  Users define their system of equations as a custom matlab function.
+  Interchangeable solvers and plotting tools can then be applied to that system with no additional programming effort.
+urls:
+    homepage: http://bdtoolbox.org/