Minor (site-url and feed) (#60)

* made the link on the news page to the actual publication item

* cosmetics + force doi in getcomputo-pub.fsx, fix badge alignment in publications.ejs

* updated publications metadata to include DOI

* Enhance publication display: update DOI handling, improve layout, and add source links

* Refactor BibTeX handling and update mock papers

- Renamed `getAbstract` to `getBibTeX` for clarity and updated its implementation to return the BibTeX entry.
- Introduced a new function `getAbstract` to extract the abstract from a BibTeX entry.
- Added `getBibTeXFromDict` to retrieve BibTeX from a dictionary containing repository objects.
- Updated `getAbstractFromDict` to utilize the new BibTeX handling functions.
- Enhanced the `publications.ejs` template to generate BibTeX entries directly from the item data.

* Add lightbox filter to author guidelines for enhanced image display

* Enhance GitHub Actions workflow with forced run option and update publication handling

* Remove unnecessary callout formatting from README.md [skip ci]

* Comment out QUARTO_PROJECT_RENDER_ALL exit check in getcomputo-pub.fsx

* Update environment variable for GitHub token to API_GITHUB_TOKEN because GITHUB_* is forbidden for repo secrets [skip ci]

* Add API_GITHUB_TOKEN environment variable for publication refresh step

* Update publications from getcomputo-pub.fsx [skip ci]

* remove lightbox extension as it is included in quarto 1.7

* Update site URL and add feed option to publications listing

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: fradav

_quarto.yml

@@ -3,7 +3,7 @@ project:
   output-dir: _site
 website:
   title: COMPUTO
-  site-url: https://computo.sfds.asso.fr/
+  site-url: https://computo-journal.org/
   description: A platform for computational research and reproducibility
   favicon: assets/favicon.ico
   navbar:

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
@@ -63,7 +128,72 @@
   title: Visualizing Data using t-SNE (mock contributon)
   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

site/publications.qmd

@@ -5,6 +5,7 @@ listing:
   - id: published
     template: publications.ejs
     contents: published.yml
+    feed: true
     sort: date desc
   - id: pipeline
     template: publications.ejs