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 | align="center" | phys
 | align="center" | 1.2.5
-| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Boltzmann Transport Properties (BoltzTraP) is a program for calculating the semi-classic transport coefficients. Homepage: http://www.icams.de/content/departments/cmat/boltztrap/ Keyword:phys<br /><br /><br /></div>
+| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Boltzmann Transport Properties (BoltzTraP) is a program for calculating the semi-classic transport coefficients. Homepage: http://www.icams.de/content/departments/cmat/boltztrap/ URL: http://www.icams.de/content/departments/cmat/boltztrap/ Keyword:phys<br /><br /><br /></div>
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 | align="center" | [https://www.boost.org/ boost]
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 | align="center" | -
 | align="center" | 3.3.2, 3.3.8
-| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: GLFW is an Open Source, multi-platform library for OpenGL, OpenGL ES and Vulkan development on the desktop Homepage: https://www.glfw.org URL: https://www.glfw.org<br /><br /><br /></div>
+| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: GLFW is an Open Source, multi-platform library for OpenGL, OpenGL ES and Vulkan development on the desktop Homepage: https://www.glfw.org URL: https://www.glfw.org Compatible modules: python/3.10, python/3.10, python/3.10, python/3.10, python/3.11, python/3.11, python/3.11, python/3.11 Extensions: glfw-2.6.2<br /><br /><br /></div>
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 | align="center" | [https://ccb.jhu.edu/software/glimmerhmm glimmerhmm]
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 | align="center" | chem
 | align="center" | 2021.7-0.5.1
-| Documentation: [[GROMACS]]<div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Modified GROMACS for small-angle scattering calculations (SAXS/WAXS/SANS). This is a CPU only build, containing both MPI and threadMPI builds. - CC-Wiki: GROMACS Homepage: https://cbjh.gitlab.io/gromacs-swaxs-docs/ URL: https://cbjh.gitlab.io/gromacs-swaxs-docs/ Keyword:chem<br /><br /><br /></div>
+| Documentation: [[GROMACS]]<div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Modified GROMACS for small-angle scattering calculations (SAXS/WAXS/SANS). This is a GPU enabled build, containing both MPI and threadMPI builds. - CC-Wiki: GROMACS Homepage: https://cbjh.gitlab.io/gromacs-swaxs-docs/ URL: https://cbjh.gitlab.io/gromacs-swaxs-docs/ Keyword:chem<br /><br /><br /></div>
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 | align="center" | [https://www.gnu.org/software/gsl/ gsl]
@@ Line 2,978: / Line 2,978: @@
 | align="center" | math
 | align="center" | 4.0.3, 5.1.0
-| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes. Homepage: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview Keyword:math<br /><br /><br /></div>
+| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes. Homepage: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview URL: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview Keyword:math<br /><br /><br /></div>
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 | align="center" | [http://glaros.dtc.umn.edu/gkhome/metis/metis/overview metis-64idx]
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 | align="center" | bio
 | align="center" | 2.0.1
-| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Minimac3 is a lower memory and more computationally efficient implementation of the genotype imputation algorithms in minimac and minimac2. Minimac3 is designed to handle very large reference panels in a more computationally efficient way with no loss of accuracy. Homepage: http://genome.sph.umich.edu/wiki/Minimac3 Keyword:bio<br /><br /><br /></div>
+| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Minimac3 is a lower memory and more computationally efficient implementation of the genotype imputation algorithms in minimac and minimac2. Minimac3 is designed to handle very large reference panels in a more computationally efficient way with no loss of accuracy. Homepage: http://genome.sph.umich.edu/wiki/Minimac3 URL: http://genome.sph.umich.edu/wiki/Minimac3 Keyword:bio<br /><br /><br /></div>
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 | align="center" | [https://genome.sph.umich.edu/wiki/Minimac4 minimac4]
@@ Line 4,487: / Line 4,487: @@
 | align="center" | [https://repast.github.io/ repasthpc]
 | align="center" | bio
-| align="center" | 2.0, 2.2.0, 2.3.0
+| align="center" | 2.2.0, 2.3.0
 | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: The Repast Suite is a family of advanced, free, and open source agent-based modeling and simulation platforms that have collectively been under continuous development for over 15 years: Repast for High Performance Computing 2.2.0, released on 30 September 2016, is a lean and expert-focused C++-based modeling system that is designed for use on large computing clusters and supercomputers. Homepage: https://repast.github.io/ URL: https://repast.github.io/ Keyword:bio<br /><br /><br /></div>
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 | align="center" | -
 | align="center" | 6.0.1.5, 8.6.1.6
-| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: NVIDIA TensorRT is a platform for high-performance deep learning inference Homepage: https://developer.nvidia.com/tensorrt URL: https://developer.nvidia.com/tensorrt Compatible modules: python/3.10, python/3.11<br /><br /><br /></div>
+| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: NVIDIA TensorRT is a platform for high-performance deep learning inference Homepage: https://developer.nvidia.com/tensorrt URL: https://developer.nvidia.com/tensorrt Compatible modules: python/3.8, python/3.9, python/3.10, python/3.11<br /><br /><br /></div>
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 | align="center" | [https://github.com/tesseract-ocr/tesseract tesseract]