From 7bb13efba5d8416c0e879505005d9033a38a40de Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Wed, 3 Dec 2025 17:08:18 -0700 Subject: [PATCH 1/9] add abbreviations and central glossary along with some minor editorial changes to why-python.md --- appendix/glossary.md | 47 +++++++++++++++++++++++ foundations/conda.md | 14 ------- foundations/getting-started-jupyter.ipynb | 31 +-------------- foundations/why-python.md | 14 +++---- myst.yml | 20 +++++++--- 5 files changed, 70 insertions(+), 56 deletions(-) create mode 100644 appendix/glossary.md diff --git a/appendix/glossary.md b/appendix/glossary.md new file mode 100644 index 0000000000..ba3fc0c419 --- /dev/null +++ b/appendix/glossary.md @@ -0,0 +1,47 @@ +--- +title: Glossary +description: Glossary of terms used in Pythia Foundations. +--- + +:::{glossary} + +[Binder](https://jupyter.org/binder) +: An open-source service that allows users to create sharable, interactive computing environments from {term}`Jupyter Notebooks` and other repositories. Binder can reproduce a computational environment directly from a GitHub repository, providing a seamless way to share and interact with code and data. + +: The public service to run Binder is on , which is running {term}`BinderHub`. ProjectPythia + +BinderHub +: The underlying technology and infrastructure that powers {term}`Binder`. BinderHub deploys and manages the interactive computing environments for {term}`Jupyter Notebooks`, ensuring that users can access and share reproducible computational work. + +Conda +: Conda is an open-source, cross-platform, language-agnostic package manager and environment management system that allows you to quickly install, run, and update packages within your work environment(s). To install `conda`, we recommend {term}`miniconda`. +: See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of ProjectPythia. + +Jupyter Notebooks +: The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (*.ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`. + +Jupyter Kernels +: Software engines and their environments (e.g., conda environments) that execute the code contained in {term}`Jupyter Notebooks`. + +Jupyter Lab +: A popular web application on which users can create and write their {term}`Jupyter Notebooks`, as well as explore data, install software, etc. You can find more information on running Jupyter Lab [here](https://jupyter.org/install). + +: See @installing-python-in-jupyter for more. + +Jupyter Hub +: A web-based platform that authenticates users and launches {term}`Jupyter Lab` applications for users on remote systems. + +Miniconda +: [Miniconda](https://docs.conda.io/en/latest/miniconda.html) is a free minimal installer for {term}`Conda`. Miniconda only comes with the {term}`Conda` package management system; it is a pared-down version of the full Anaconda Python distribution. +: See @installing-conda. + +Miniforge +: Miniforge is the community driven and more permissively licensed minimal installer. + +[NCL](https://www.ncl.ucar.edu/) +: The NCAR Command Language (NCL) is an interpreted language designed specifically for scientific data analysis and visualization. + +Python package +: A Python package is a collection of modules, which, in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules. You can install Python packages with {term}`conda`. +::: + diff --git a/foundations/conda.md b/foundations/conda.md index 910613da18..b3cc938abd 100644 --- a/foundations/conda.md +++ b/foundations/conda.md @@ -138,17 +138,3 @@ Conda is a package and environment management system that allows you to quickly - [Anaconda](https://docs.anaconda.com/anaconda/install/) - [Miniconda](https://docs.conda.io/en/latest/miniconda.html) -## Glossary - -:::{glossary} -Conda -: Conda is an open-source, cross-platform, language-agnostic package manager and environment management system that allows you to quickly install, run, and update packages within your work environment(s). To install `conda`, we recommend {term}`miniconda`. -: See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of ProjectPythia. - -Miniconda -: [Miniconda](https://docs.conda.io/en/latest/miniconda.html) is a free minimal installer for {term}`conda`. Miniconda only comes with the {term}`conda` package management system; it is a pared-down version of the full Anaconda Python distribution. -: See @installing-conda. - -Python Package -: A Python package is a collection of modules, which, in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules. You can install Python packages with {term}`conda`. -::: diff --git a/foundations/getting-started-jupyter.ipynb b/foundations/getting-started-jupyter.ipynb index cbae2729b1..0810041890 100644 --- a/foundations/getting-started-jupyter.ipynb +++ b/foundations/getting-started-jupyter.ipynb @@ -187,35 +187,6 @@ "- [Jupyter Documentation](https://jupyter.org/)\n", "- [Xdev Python Tutorial Seminar Series - Jupyter Notebooks](https://youtu.be/xSzXvwzFsDU)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Glossary\n", - "\n", - ":::{glossary}\n", - "Jupyter Notebooks\n", - ": The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (*.ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`.\n", - "\n", - "Jupyter Kernels\n", - ": Software engines and their environments (e.g., conda environments) that execute the code contained in {term}`Jupyter Notebooks`.\n", - "\n", - "Jupyter Lab\n", - ": A popular web application on which users can create and write their {term}`Jupyter Notebooks`, as well as explore data, install software, etc. You can find more information on running Jupyter Lab [here](https://jupyter.org/install).\n", - ": See @installing-python-in-jupyter for more.\n", - "\n", - "Jupyter Hub\n", - ": A web-based platform that authenticates users and launches {term}`Jupyter Lab` applications for users on remote systems.\n", - "\n", - "Binder\n", - ": An open-source service that allows users to create sharable, interactive computing environments from {term}`Jupyter Notebooks` and other repositories. Binder can reproduce a computational environment directly from a GitHub repository, providing a seamless way to share and interact with code and data. \n", - ": The public service to run Binder is on , which is running {term}`BinderHub`. ProjectPythia \n", - "\n", - "BinderHub\n", - ": The underlying technology and infrastructure that powers {term}`Binder`. BinderHub deploys and manages the interactive computing environments for {term}`Jupyter Notebooks`, ensuring that users can access and share reproducible computational work.\n", - ":::" - ] } ], "metadata": { @@ -239,7 +210,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.14.0" }, "nbdime-conflicts": { "local_diff": [ diff --git a/foundations/why-python.md b/foundations/why-python.md index cd31cf975b..cb7f94a1d5 100644 --- a/foundations/why-python.md +++ b/foundations/why-python.md @@ -2,26 +2,26 @@ You're already here because you want to learn to use Python for your data analysis and visualizations. -**Perhaps the #1 reason to use Python is because it is so widely used in the scientific community!** +**Perhaps the #1 reason to use Python is that it is so widely used in the scientific community!** Python can be compared to other high-level, interpreted, object-oriented languages, but is especially great because it is free and open source! Want to know what these terms mean for you and your work? Read on! -## High level languages +## High-level languages -Other high level languages include MatLab, IDL, and NCL. The advantage of high level languages is that they provide built-in functions, data structures, and other utilities that are commonly used, which means it takes less code to get real work done. The disadvantage of high level languages is that they tend to obscure the low level aspects of the machine such as memory use, how many floating point operations are happening, and other information related to performance. C, C++, and Fortran are all examples of lower level languages. The "higher" the level of language, the more computing fundamentals are abstracted. +Other high-level languages include [MATLAB](wiki:MATLAB), [IDL](wiki:IDL_(programming_language)), and {term}`NCL`. The advantage of high-level languages is that they provide built-in functions, data structures, and other utilities that are commonly used, which means it takes less code to get real work done. The disadvantage of high-level languages is that they tend to obscure the low-level considerations such as memory use, how many floating-point operations are happening, and other information related to performance. [C](wiki:C_(programming_language)), [C++](wiki:C%2B%2B), and [Fortran](wiki:Fortran) are all examples of lower-level languages. The "higher" the level of language, the more computing fundamentals are abstracted. ## Interpreted languages -Most of your work is probably already in interpreted languages if you've ever used IDL, NCL, or MatLab (interpreted languages are typically also high level). So you are already familiar with the advantages of this: you don't have to worry about compiling or machine compatibility (it is portable). And you are probably familiar with their deficiencies: sometimes they can be slower than compiled languages and potentially more memory intensive. +Most of your work is probably already in interpreted languages if you've ever used [IDL](wiki:IDL_(programming_language)), {term}`NCL`, or [MATLAB](wiki:MATLAB) (interpreted languages are typically also high-level). So you are already familiar with the advantages of this: you don't have to worry about compiling or machine compatibility (it is portable). And you are probably familiar with their deficiencies: sometimes they can be slower than compiled languages and potentially more memory-intensive. -## Object Oriented languages +## Object-oriented languages -Objects are custom datatypes. For every custom datatype, you usually have a set of operations you might want to conduct. For example, if you have an object that is a list of numbers, you might want to apply a mathematical operation, such as sum, onto this list object in bulk. Not every function can be applied to every datatype; it wouldn't make sense to apply a logarithm to a string of letters or to capitalize a list of numbers. Data and the operations applied to them are grouped together into one object. +Objects are custom datatypes. For every custom datatype, you usually have a set of operations you might want to conduct. For example, if you have an object that is a list of numbers, you might want to apply a mathematical operation, such as sum, to this list object in bulk. Not every function can be applied to every datatype; it wouldn't make sense to apply a logarithm to a string of letters or to capitalize a list of numbers. Data and the operations applied to them are grouped together into one object. ## Open source -Python as a language is open source, which means that there is a community of developers behind its codebase. Anyone can join the developer community and contribute to deciding the future of the language. When someone identifies gaps in Python's abilities, they can write up the code to fill these gaps. The open source nature of Python means that Python as a language is very adaptable to the shifting needs of the user community. This harkens back to the idea that the widespread use of Python within the scientific community is a benefit to you! The large Python user base within your field has established high level community Python packages that are available to you in your workflow. +Python, as a language, is open source, which means that there is a community of developers behind its codebase. Anyone can join the developer community and contribute to deciding the future of the language. When someone identifies gaps in Python's abilities, they can write up the code to fill these gaps. The open source nature of Python means that Python as a language is very adaptable to the shifting needs of the user community. This harkens back to the idea that the widespread use of Python within the scientific community is a benefit to you! The large Python user base within your field has established high-level community Python packages that are available to you in your workflow. Python is a language designed for rapid prototyping and efficient programming. It is easy to write new code quickly with less typing. diff --git a/myst.yml b/myst.yml index 523792929f..3947b89ac8 100644 --- a/myst.yml +++ b/myst.yml @@ -15,17 +15,26 @@ project: url: https://binder.projectpythia.org repo: projectpythia/pythia-foundations abbreviations: - FOSS: Free and open-source software - VCS: Version control system - FTP: File transfer protocol + API: application programming interface + FOSS: free and open-source software + FTP: File Transfer Protocol + HTTP: Hypertext Transfer Protocol + ID: identifier + IDL: Interactive Data Language + NCAR: NSF National Center for Atmospheric Research + NCL: NCAR Command Language + NSF: U.S. National Science Foundation + PR: pull request + SSH: Secure Shell Protocol TCP: Transmission Control Protocol + VCS: version control system toc: - file: landing-page.md - title: Preamble children: - file: preamble/how-to-use.md - file: preamble/how-to-cite.md - - title: Foundational skills + - title: Foundational Skills children: - file: foundations/overview.md - file: foundations/why-python.md @@ -54,7 +63,7 @@ project: - file: foundations/github/review-pr.md - file: foundations/github/github-workflows.md - file: foundations/github/contribute-to-pythia.md - - title: Core Scientific Python packages + - title: Core Scientific Python Packages children: - file: core/overview.md - file: core/numpy.md @@ -87,6 +96,7 @@ project: - file: core/xarray/dask-arrays-xarray.ipynb - title: Appendix children: + - file: appendix/glossary.md - file: appendix/how-to-contribute.md children: - file: appendix/template.ipynb From 094156352c1761c414dbd451fcdbcfe29c4fc8db Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 4 Dec 2025 02:20:35 +0000 Subject: [PATCH 2/9] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- appendix/glossary.md | 3 +-- foundations/conda.md | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/appendix/glossary.md b/appendix/glossary.md index ba3fc0c419..85ef458cbd 100644 --- a/appendix/glossary.md +++ b/appendix/glossary.md @@ -8,7 +8,7 @@ description: Glossary of terms used in Pythia Foundations. [Binder](https://jupyter.org/binder) : An open-source service that allows users to create sharable, interactive computing environments from {term}`Jupyter Notebooks` and other repositories. Binder can reproduce a computational environment directly from a GitHub repository, providing a seamless way to share and interact with code and data. -: The public service to run Binder is on , which is running {term}`BinderHub`. ProjectPythia +: The public service to run Binder is on , which is running {term}`BinderHub`. ProjectPythia BinderHub : The underlying technology and infrastructure that powers {term}`Binder`. BinderHub deploys and manages the interactive computing environments for {term}`Jupyter Notebooks`, ensuring that users can access and share reproducible computational work. @@ -44,4 +44,3 @@ Miniforge Python package : A Python package is a collection of modules, which, in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules. You can install Python packages with {term}`conda`. ::: - diff --git a/foundations/conda.md b/foundations/conda.md index b3cc938abd..58c33c1b66 100644 --- a/foundations/conda.md +++ b/foundations/conda.md @@ -137,4 +137,3 @@ Conda is a package and environment management system that allows you to quickly - [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf) - [Anaconda](https://docs.anaconda.com/anaconda/install/) - [Miniconda](https://docs.conda.io/en/latest/miniconda.html) - From fbdc69dfac1c7c46f6b63b05180c56def40a6948 Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 19:53:03 -0700 Subject: [PATCH 3/9] fix typo Co-authored-by: Brian Rose --- appendix/glossary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appendix/glossary.md b/appendix/glossary.md index 85ef458cbd..5608233066 100644 --- a/appendix/glossary.md +++ b/appendix/glossary.md @@ -15,7 +15,7 @@ BinderHub Conda : Conda is an open-source, cross-platform, language-agnostic package manager and environment management system that allows you to quickly install, run, and update packages within your work environment(s). To install `conda`, we recommend {term}`miniconda`. -: See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of ProjectPythia. +: See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of Project Pythia. Jupyter Notebooks : The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (*.ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`. From e3d15d3fa9782b45c29cd5a8df5c39b2a7f625fa Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 19:53:30 -0700 Subject: [PATCH 4/9] formatting Co-authored-by: Brian Rose --- appendix/glossary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appendix/glossary.md b/appendix/glossary.md index 5608233066..c5d7ca1242 100644 --- a/appendix/glossary.md +++ b/appendix/glossary.md @@ -18,7 +18,7 @@ Conda : See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of Project Pythia. Jupyter Notebooks -: The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (*.ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`. +: The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (`*.ipynb` files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`. Jupyter Kernels : Software engines and their environments (e.g., conda environments) that execute the code contained in {term}`Jupyter Notebooks`. From 803e588aed260b72f2d4c5cb81e8e127fc5be0b3 Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 20:02:02 -0700 Subject: [PATCH 5/9] update BinderHub definition Co-authored-by: Brian Rose --- appendix/glossary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appendix/glossary.md b/appendix/glossary.md index c5d7ca1242..7aab4b0c88 100644 --- a/appendix/glossary.md +++ b/appendix/glossary.md @@ -8,7 +8,7 @@ description: Glossary of terms used in Pythia Foundations. [Binder](https://jupyter.org/binder) : An open-source service that allows users to create sharable, interactive computing environments from {term}`Jupyter Notebooks` and other repositories. Binder can reproduce a computational environment directly from a GitHub repository, providing a seamless way to share and interact with code and data. -: The public service to run Binder is on , which is running {term}`BinderHub`. ProjectPythia +: The public service to run Binder is on , which is running {term}`BinderHub`. The Binder links on most Project Pythia pages point to , which is a Pythia-specific implementation of {term}`BinderHub` running on the National Science Foundation-supported [Jetstream2](https://jetstream-cloud.org/) cloud computing service. BinderHub : The underlying technology and infrastructure that powers {term}`Binder`. BinderHub deploys and manages the interactive computing environments for {term}`Jupyter Notebooks`, ensuring that users can access and share reproducible computational work. From cd43d7e36109b0ac78053208c6bee641c8e26dd4 Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 20:05:38 -0700 Subject: [PATCH 6/9] Update myst.yml Co-authored-by: Brian Rose --- myst.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/myst.yml b/myst.yml index a8e077a28e..ff0d8a3416 100644 --- a/myst.yml +++ b/myst.yml @@ -27,6 +27,7 @@ project: NCL: NCAR Command Language NSF: U.S. National Science Foundation PR: pull request + SPE: Scientific Python Ecosystem SSH: Secure Shell Protocol TCP: Transmission Control Protocol VCS: version control system From 84d141e305924f464d2b497b5dddb5c307850927 Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 20:12:28 -0700 Subject: [PATCH 7/9] capitalize all abbreviations Co-authored-by: Brian Rose --- myst.yml | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/myst.yml b/myst.yml index ff0d8a3416..4b971665ec 100644 --- a/myst.yml +++ b/myst.yml @@ -18,19 +18,20 @@ project: output_matplotlib_strings: remove abbreviations: API: application programming interface - FOSS: free and open-source software + DOI: Digital Object Identifier + FOSS: Free and Open-Source Software FTP: File Transfer Protocol HTTP: Hypertext Transfer Protocol - ID: identifier + ID: Identifier IDL: Interactive Data Language NCAR: NSF National Center for Atmospheric Research NCL: NCAR Command Language NSF: U.S. National Science Foundation - PR: pull request + PR: Pull Request SPE: Scientific Python Ecosystem SSH: Secure Shell Protocol TCP: Transmission Control Protocol - VCS: version control system + VCS: Version Control System toc: - file: landing-page.md - title: Preamble From 1a565b589ba0730205a6ad68455a51bf3b3362bd Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Tue, 9 Dec 2025 20:13:55 -0700 Subject: [PATCH 8/9] capitalize one more --- myst.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/myst.yml b/myst.yml index 4b971665ec..e149466ccf 100644 --- a/myst.yml +++ b/myst.yml @@ -17,7 +17,7 @@ project: settings: output_matplotlib_strings: remove abbreviations: - API: application programming interface + API: Application Programming Interface DOI: Digital Object Identifier FOSS: Free and Open-Source Software FTP: File Transfer Protocol From afdd0bd353ed27257f6d83171a9e9b1b0c046e19 Mon Sep 17 00:00:00 2001 From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com> Date: Wed, 10 Dec 2025 09:52:23 -0700 Subject: [PATCH 9/9] leverage NSF abbreviation Co-authored-by: Brian Rose --- appendix/glossary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appendix/glossary.md b/appendix/glossary.md index 7aab4b0c88..b2a3d2bc94 100644 --- a/appendix/glossary.md +++ b/appendix/glossary.md @@ -8,7 +8,7 @@ description: Glossary of terms used in Pythia Foundations. [Binder](https://jupyter.org/binder) : An open-source service that allows users to create sharable, interactive computing environments from {term}`Jupyter Notebooks` and other repositories. Binder can reproduce a computational environment directly from a GitHub repository, providing a seamless way to share and interact with code and data. -: The public service to run Binder is on , which is running {term}`BinderHub`. The Binder links on most Project Pythia pages point to , which is a Pythia-specific implementation of {term}`BinderHub` running on the National Science Foundation-supported [Jetstream2](https://jetstream-cloud.org/) cloud computing service. +: The public service to run Binder is on , which is running {term}`BinderHub`. The Binder links on most Project Pythia pages point to , which is a Pythia-specific implementation of {term}`BinderHub` running on the NSF-supported [Jetstream2](https://jetstream-cloud.org/) cloud computing service. BinderHub : The underlying technology and infrastructure that powers {term}`Binder`. BinderHub deploys and manages the interactive computing environments for {term}`Jupyter Notebooks`, ensuring that users can access and share reproducible computational work.