Handling of manual refresh for publications list

.github/workflows/build.yml

@@ -8,15 +8,46 @@ on:
     branches:
       - master
   workflow_dispatch:
+    inputs:
+      force:
+        description: 'Set to "true" to mark this run as forced when manually triggered'
+        required: false
+        default: 'false'
 
 jobs:
   build:
+    # Skip this job on push events when the head commit message contains [skip ci]
+    if: ${{ github.event_name != 'push' || !contains(github.event.head_commit.message, '[skip ci]') }}
+    permissions:
+      contents: write
     runs-on: ubuntu-latest
     steps:
     - name: Checkout code
       uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        persist-credentials: true
     - name: Setup Quarto
       uses: quarto-dev/quarto-actions/setup@v2
+    - name: refresh publications and commit changes
+      if: ${{ github.event_name == 'workflow_dispatch' || github.event.inputs.force == 'true' }}
+      env:
+        API_GITHUB_TOKEN: ${{ secrets.API_GITHUB_TOKEN }}
+      run: |
+        dotnet fsi getcomputo-pub.fsx
+        git config user.name "github-actions[bot]"
+        git config user.email "github-actions[bot]@users.noreply.github.com"
+        # Stage the generated files (ignore errors if files missing)
+        git add site/published.yml site/pipeline.yml site/mock-papers.yml || true
+        # Only commit if there are staged changes
+        if git diff --staged --quiet; then
+          echo "No publication changes to commit"
+        else
+          git commit -m "Update publications from getcomputo-pub.fsx [skip ci]"
+          # push to the branch that triggered the workflow
+          git push origin HEAD:${{ github.ref_name }}
+        fi
+
     - name: Build site
       uses: quarto-dev/quarto-actions/render@v2
     - name: Upload artifact

README.md

@@ -9,11 +9,9 @@ This repository stores the source of Computorg.
 
 Our website has been built with [Quarto](https://quarto.org), an open-source scientific and technical publishing system. The first thing you need to compile the website is therefore to install Quarto, which can be done by downloading the corresponing installer here: <https://quarto.org/docs/get-started/>.
 
-::: {.callout-note}
 ## Positron
 
 If you are using the new [Positron IDE](https://positron.posit.co), quarto is already bundled with it. You can simply type `which quarto` within the built-in terminal in Positron and add the returned path to your `PATH`.
-:::
 
 ### Microsoft DotNet SDK
 

_quarto.yml

@@ -1,7 +1,6 @@
 project:
   type: website
   output-dir: _site
-#  pre-render: dotnet fsi getcomputo-pub.fsx
 website:
   title: COMPUTO
   site-url: https://computo.sfds.asso.fr/

getcomputo-pub.fsx

@@ -15,16 +15,16 @@ open DrBiber
 open System.Threading.Tasks
 
 // exit if QUARTO_PROJECT_RENDER_ALL is set in the environment
-if System.Environment.GetEnvironmentVariable("QUARTO_PROJECT_RENDER_ALL") = null then
-    printfn "QUARTO_PROJECT_RENDER_ALL is not set, exiting."
-    exit 0
+// if System.Environment.GetEnvironmentVariable("QUARTO_PROJECT_RENDER_ALL") = null then
+//     printfn "QUARTO_PROJECT_RENDER_ALL is not set, exiting."
+//     exit 0
 // Load environment variables from .env file
 Env.Load(".env-secret")
 
 let client =
     let client = new GitHubClient(new ProductHeaderValue("computo"))
     // Using environment variable for token is a good security practice
-    match System.Environment.GetEnvironmentVariable("GITHUB_TOKEN") with
+    match System.Environment.GetEnvironmentVariable("API_GITHUB_TOKEN") with
     | null
     | "" -> client // No authentication
     | token ->

site/mock-papers.yml

@@ -1,4 +1,69 @@
-- abstract': >-
+- 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.
+  authors@: Laurens van der Maaten and Geoffrey Hinton
+  bibtex@: >+
+    @article{van_der_maaten2008,
+    	author = {van der Maaten, Laurens and Hinton, Geoffrey},
+    	publisher = {French Statistical Society},
+    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
+    	journal = {Computo},
+    	date = {2008-08-11},
+    	doi = {10.57750/xxxxxx},
+    	issn = {2824-7795},
+    	langid = {en},
+    	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.}
+    }
+
+  date@: 2008-08-11
+  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.
+  doi@: 10.57750/xxxxxx
+  draft@: false
+  journal@: Computo
+  pdf@: ''
+  repo@: published-paper-tsne
+  title@: Visualizing Data using t-SNE (mock contributon)
+  url@: ''
+  year@: 2008
+  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
@@ -23,12 +88,11 @@
   bibtex: >+
     @article{van_der_maaten2008,
     	author = {van der Maaten, Laurens and Hinton, Geoffrey},
-    	publisher = {Société Française de Statistique},
-    	title = {Visualizing {Data} Using {t-SNE:} A Practical Computo Example
-        (Mock)},
+    	publisher = {French Statistical Society},
+    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
     	journal = {Computo},
     	date = {2008-08-11},
-    	url = {https://computo.sfds.asso.fr/published-paper-tsne},
+    	doi = {10.57750/xxxxxx},
     	issn = {2824-7795},
     	langid = {en},
     	abstract = {We present a new technique called “t-SNE” that visualizes
@@ -56,15 +120,80 @@
   date: 2008-08-11
   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.
-  doi: ''
+  doi: 10.57750/xxxxxx
   draft: false
   journal: Computo
   pdf: ''
   repo: published-paper-tsne
   title: Visualizing Data using t-SNE (mock contributon)
-  url: https://computo-journal.org/published-paper-tsne
+  url: ''
   year: 2008
-- abstract': >-
+- 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 Embeddi{[}@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.
+  authors@: Laurens van der Maaten and Geoffrey Hinton
+  bibtex@: >+
+    @article{van_der_maaten2008,
+    	author = {van der Maaten, Laurens and Hinton, Geoffrey},
+    	publisher = {French Statistical Society},
+    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
+    	journal = {Computo},
+    	date = {2008-08-11},
+    	doi = {10.57750/xxxxxx},
+    	issn = {2824-7795},
+    	langid = {en},
+    	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 Embeddi{[}@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.}
+    }
+
+  date@: 2008-08-11
+  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.
+  doi@: 10.57750/xxxxxx
+  draft@: false
+  journal@: Computo
+  pdf@: ''
+  repo@: published-paper-tsne-R
+  title@: Visualizing Data using t-SNE (mock contributon)
+  url@: ''
+  year@: 2008
+  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
@@ -90,11 +219,10 @@
     @article{van_der_maaten2008,
     	author = {van der Maaten, Laurens and Hinton, Geoffrey},
     	publisher = {French Statistical Society},
-    	title = {Visualizing {Data} Using {t-SNE:} A Practical {Computo}
-        Example (Mock)},
+    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
     	journal = {Computo},
     	date = {2008-08-11},
-    	url = {https://computo-journal.org/published-paper-tsne-R},
+    	doi = {10.57750/xxxxxx},
     	issn = {2824-7795},
     	langid = {en},
     	abstract = {We present a new technique called “t-SNE” that visualizes
@@ -122,11 +250,11 @@
   date: 2008-08-11
   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.
-  doi: ''
+  doi: 10.57750/xxxxxx
   draft: false
   journal: Computo
   pdf: ''
   repo: published-paper-tsne-R
   title: Visualizing Data using t-SNE (mock contributon)
-  url: https://computo-journal.org/published-paper-tsne-R
+  url: ''
   year: 2008