add README rendered by quarto with shortcode

jchiquet · jchiquet · commit 359cc5e7f667 · 2025-07-30T12:10:08.000+02:00
diff --git a/.gitignore b/.gitignore
@@ -1,176 +1,47 @@
-/.quarto/
-/.joblib
-/_extensions/
-*.html
-*.ipynb
-*.tex
-*_files/
-.jupyter_cache/
-renv/
+# R and co
+.Rhistory
 .Rprofile
-/.luarc.json
-/.micromamba
-_site/
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
+.Rapp.history
+.RData
+.Ruserdata
+*.Rproj
+.Rproj.user
+.Rproj.user/
+/*.Rcheck/
+renv/
 
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
+# Python and co
+_environment
+/.micromamba/
+/.joblib
+/.pytest_cache/
+scripts/__pycache__/
 
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
+# Quarto tmp files
+/.quarto/
+/*_files
+/*_cache
+/_freeze
+_site/
+/cache/
 
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
+# Quarto extension files
+_extensions/
+logo_text_white.pdf
+logo_notext_white.png
 
-# Translations
-*.mo
-*.pot
+# Quarto output files
+published-paper-tsne.pdf
+published-paper-tsne.html
+published-paper-tsne.ipynb
+published-paper-tsne.tex
 
-# Django stuff:
+# Latex compilation files
+*.aux
 *.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
+*.toc
 
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
+# Various system files
+DS_Store
+/.luarc.json
 
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-/*.pdf
diff --git a/README.md b/README.md
@@ -1,17 +1,62 @@
-# Visualizing Data using t-SNE: a mock Computo contribution
+---
+format:
+  gfm:
+    variant: +yaml_metadata_block
+---
 
-[![build status](https://github.com/computorg/published-paper-tsne/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-paper-tsne)
-[![](https://img.shields.io/github/last-commit/computorg/published-paper-tsne.svg)](https://github.com/computorg/published-paper-tsne/commits/main)
-[![DOI:xxx/xxx-xxx](https://img.shields.io/badge/DOI-xxx/xxx--xxx-034E79.svg)](https://doi.org/xxx/xxx-xxx)
-[![review](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-paper-tsne/issues?q=is%3Aopen+is%3Aissue+label%3Areview)
-[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-paper-tsne)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-paper-tsne)
-[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
+
+# Visualizing Data using t-SNE (mock contributon)
+
+*This page is a reworking of the original t-SNE article using the
+Computo template. It aims to help authors submitting to the journal by
+using some advanced formatting features. We warmly thank the authors of
+t-SNE and the editor of JMLR for allowing us to use their work to
+illustrate the Computo spirit.*
+
+[![build
+status](https://github.com/computorg/published-paper-tsne/workflows/build/badge.svg)](https://github.com/computorg/published-paper-tsne/)
+[![DOI:10.57750/xx-xxx](https://img.shields.io/badge/DOI-10.57750/xx-xxx-034E79.svg)](https://doi.org/10.57750/xx-xxx)
+[![reviews](https://img.shields.io/badge/review-report%201-blue.png)](https://github.com/computorg/published-paper-tsne/issues?q=is:open is:issue label:review)
+[![Creative Commons
+License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
 
 Authors:
 
-- Laurens van der Maaten (TiCC, Tilburg University)
-- Geoffrey Hinton (Department of Computer Science, University of Toronto)
-- (Remake/formatting by the Computo team)
+- [Laurens van der Maaten](https://lvdmaaten.github.io/) (TiCC, Tilburg
+  University)
+- [Geoffrey Hinton](https://www.cs.toronto.edu/~hinton/) (Department of
+  Computer Science, University of Toronto)
+
+We present a new technique called “t-SNE” that visualizes
+high-dimensional data by giving each datapoint a location in a two or
+three-dimensional map. The technique is a variation of Stochastic
+Neighbor Embedding (Hinton and Roweis 2003) that is much easier to
+optimize, and produces significantly better visualizations by reducing
+the tendency to crowd points together in the center of the map. t-SNE is
+better than existing techniques at creating a single map that reveals
+structure at many different scales. This is particularly important for
+high-dimensional data that lie on several different, but related,
+low-dimensional manifolds, such as images of objects from multiple
+classes seen from multiple viewpoints. For visualizing the structure of
+very large data sets, we show how t-SNE can use random walks on
+neighborhood graphs to allow the implicit structure of all the data to
+influence the way in which a subset of the data is displayed. We
+illustrate the performance of t-SNE on a wide variety of data sets and
+compare it with many other non-parametric visualization techniques,
+including Sammon mapping, Isomap, and Locally Linear Embedding. The
+visualization produced by t-SNE are significantly better than those
+produced by other techniques on almost all of the data sets.
+
+<div id="refs" class="references csl-bib-body hanging-indent"
+entry-spacing="0">
+
+<div id="ref-hinton:stochastic" class="csl-entry">
+
+Hinton, Geoffrey E, and Sam Roweis. 2003. “Stochastic Neighbor
+Embedding.” In *Advances in Neural Information Processing Systems*,
+edited by S. Becker, S. Thrun, and K. Obermayer. Vol. 15. MIT Press.
+<https://proceedings.neurips.cc/paper/2002/file/6150ccc6069bea6b5716254057a194ef-Paper.pdf>.
 
-*This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.*
+</div>
 
+</div>
diff --git a/README.qmd b/README.qmd
@@ -0,0 +1,22 @@
+---
+format: 
+    gfm:
+        variant: +yaml_metadata_block
+---
+
+# {{< meta title >}}
+
+_{{< meta description >}}_
+
+[![build status](https://github.com/{{< meta github-user >}}/{{< meta repo >}}/workflows/build/badge.svg)](https://github.com/{{< meta github-user >}}/{{< meta repo >}}/)
+[![DOI:{{< meta citation.doi >}}](https://img.shields.io/badge/DOI-{{< meta citation.doi >}}-034E79.svg)](https://doi.org/{{< meta citation.doi >}})
+[![reviews](https://img.shields.io/badge/review-report%201-blue)](https://github.com/{{< meta github-user >}}/{{< meta repo >}}/issues?q=is%3Aopen+is%3Aissue+label%3Areview)
+[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
+
+Authors:
+
+- [{{< meta by-author.1.name.literal >}}]({{< meta by-author.1.url >}}) ({{< meta by-affiliation.1.name >}})
+- [{{< meta by-author.2.name.literal >}}]({{< meta by-author.2.url >}}) ({{< meta by-affiliation.2.name >}})
+
+{{< meta abstract >}}
+
diff --git a/_quarto.yml b/_quarto.yml
@@ -1,16 +1,19 @@
 project:
-  type: website
+  type: default
   render:
     - published-paper-tsne.qmd
+    - README.qmd
 
 title: "Visualizing Data using t-SNE (mock contributon)"
 subtitle: "A practical computo example"
 date: 2008-08-11
 date-modified: last-modified
 description: |
   This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
+license: "CC BY"
 author: 
   - name: Laurens van der Maaten
+    email: machine@truc.fr
     url: https://lvdmaaten.github.io/
     affiliation: TiCC, Tilburg University
     affiliation-url: https://www.tilburguniversity.edu/
@@ -21,18 +24,19 @@ author:
     affiliation: Department of Computer Science, University of Toronto
     affiliation-url: https://web.cs.toronto.edu/
     orcid: 0000-0002-8063-7209
+abstract: |
+  We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map.  The technique is a variation of Stochastic Neighbor Embedding [@hinton:stochastic] that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualization produced by t-SNE are significantly better than those produced by other techniques on almost all of the data sets.
 keywords: [visualization, dimensionality reduction, manifold learning, embedding algorithms, multidimensional scaling]
 bibliography: references.bib
 github-user: computorg
 repo: "published-paper-tsne"
 draft: false
 published: true
 citation:
-  title: "Visualizing Data using t-SNE: a practical Computo example (mock)"
   type: article-journal
   container-title: "Computo"
   publisher: "French Statistical Society"
-  url: https://computo-journal.org/published-paper-tsne
+  doi: 10.57750/xx-xxx
   issn: "2824-7795"
 format: 
   computo-html: default
diff --git a/logo_text_white.pdf b/logo_text_white.pdf
diff --git a/published-paper-tsne.qmd b/published-paper-tsne.qmd
@@ -1,7 +1,3 @@
----
-abstract: |
-  We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map.  The technique is a variation of Stochastic Neighbor Embedding [@hinton:stochastic] that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualization produced by t-SNE are significantly better than those produced by other techniques on almost all of the data sets.
----
 
 # Introduction
 
diff --git a/requirements-pip.txt b/requirements-pip.txt
