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Merge pull request #154 from SciML/pl/citation_cff
Add citation file
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on:
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push:
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paths:
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- CITATION.cff
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workflow_dispatch:
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name: CITATION.cff
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jobs:
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Validate-CITATION-cff:
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runs-on: ubuntu-latest
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name: Validate CITATION.cff
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Validate CITATION.cff
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uses: dieghernan/cff-validator@v3

.github/workflows/draft-pdf.yml

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CITATION.cff

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# This CITATION.cff file was generated with cffinit.
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# Visit https://bit.ly/cffinit to generate yours today!
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cff-version: 1.2.0
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title: SBMLToolkit.jl
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message: >-
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If you use this software, please cite the preferred-citation in this file.
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preferred-citation:
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type: article
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authors:
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- given-names: Paul
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name-particle: F
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family-names: Lang
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email: plang@deeporigin.com
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affiliation: Deep Origin
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orcid: 'https://orcid.org/0000-0002-6388-2405'
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- given-names: Anand
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family-names: Jain
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- given-names: Christopher
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family-names: Rackauckas
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orcid: 'https://orcid.org/0000-0001-5850-0663'
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affiliation: JuliaHub
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doi: "10.1515/jib-2024-0003"
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journal: "Journal of Integrative Bioinformatics"
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month: 5
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title: "SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem"
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year: 2024
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keywords:
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- systems biology markup language
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- SBML
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- Julia
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- scientific machine learning
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license: CC-BY-4.0
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repository-code: >-
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https://github.com/JuliaSmoothOptimizers/CaNNOLeS.jl
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abstract: >-
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Julia is a general purpose programming language that was
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designed for simplifying and accelerating numerical
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analysis and computational science. In particular the
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Scientific Machine Learning (SciML) ecosystem of Julia
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packages includes frameworks for high-performance
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symbolic-numeric computations. It allows users to
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automatically enhance high-level descriptions of their
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models with symbolic preprocessing and automatic
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sparsification and parallelization of computations. This
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enables performant solution of differential equations,
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efficient parameter estimation and methodologies for
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automated model discovery with neural differential
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equations and sparse identification of nonlinear dynamics.
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To give the systems biology community easy access to
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SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports
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dynamic SBML models into the SciML ecosystem to accelerate
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model simulation and fitting of kinetic parameters. By
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providing computational systems biologists with easy
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access to the open-source Julia ecosystevnm, we hope to
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catalyze the development of further Julia tools in this
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domain and the growth of the Julia bioscience community.
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SBMLToolkit.jl is freely available under the MIT license.
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The source code is available at
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https://github.com/SciML/SBMLToolkit.jl.
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type: software
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authors:
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- given-names: Paul
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name-particle: F
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family-names: Lang
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affiliation: Deep Origin
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orcid: 'https://orcid.org/0000-0002-6388-2405'
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- given-names: Anand
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family-names: Jain
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- given-names: Christopher
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family-names: Rackauckas
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orcid: 'https://orcid.org/0000-0001-5850-0663'
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affiliation: JuliaHub
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- given-names: contributors
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repository-code: 'https://github.com/SciML/SBMLToolkit.jl'
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license: MIT

docs/make.jl

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assets = ["assets/favicon.ico"]),
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pages = [
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"Home" => "index.md",
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"API documentation" => "api.md",
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"API documentation" => "api.md"
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])
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deploydocs(;

paper/figure.png

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paper/paper.bib

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paper/paper.md

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src/SBMLToolkit.jl

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export ReactionSystem, ODESystem
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export readSBML, readSBMLFromString, set_level_and_version, convert_simplify_math,
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convert_promotelocals_expandfuns
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convert_promotelocals_expandfuns
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export DefaultImporter, ReactionSystemImporter, ODESystemImporter
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end

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