update network

client/src/pages/networks/locales/en.json

@@ -47,7 +47,7 @@
     "networks.tab.of.software": "of software",
     "networks.tab.of.projects": "of projects",
     "networks.tab.of.countries": "of countries",
-    "networks.get-started.home.text-1": "Welcome to our scientometric tool for exploring and analyzing bibliographic documents from research and innovation.",
+    "networks.get-started.home.text-1": "Welcome to our tool for exploring and analyzing bibliographic documents from research and innovation.",
     "networks.get-started.home.text-2": "It takes the form of a network visualization, highlighting co-occurrences and communities present in a corpus of documents.",
     "networks.get-started.search-bar.title": "What are you looking for?",
     "networks.get-started.search-bar.description": "Define the scope of the corpus to be analyzed by searching our databases.",

client/src/pages/networks/locales/fr.json

@@ -45,7 +45,7 @@
     "networks.tab.of.software": "de logiciels",
     "networks.tab.of.projects": "de projets",
     "networks.tab.of.countries": "de pays",
-    "networks.get-started.home.text-1": "Bienvenue sur notre outil scientométrique d'exploration et d'analyse de documents bibliographiques issus de la recherche et de l'innovation.",
+    "networks.get-started.home.text-1": "Bienvenue sur notre outil d'exploration et d'analyse de documents bibliographiques issus de la recherche et de l'innovation.",
     "networks.get-started.home.text-2": "Il prend la forme d'une visualisation de réseaux, mettant en évidence les co-occurrences et les communautés présentes dans un corpus de documents.",
     "networks.get-started.search-bar.title": "Que recherchez vous ?",
     "networks.get-started.search-bar.description": "Définissez le périmètre du corpus à analyser grâce à une recherche dans nos bases de données.",
@@ -140,4 +140,4 @@
     "networks.section.clusters.see-more": "En voir plus ({count})",
     "networks.section.clusters.see-less": "Réduire",
     "networks.contribute.button": "Signaler une erreur"
-}
+}

doc_network/bso.md

@@ -30,7 +30,7 @@ geometry: "left=3cm, right=3cm, top=3cm, bottom=3cm"
 # Abstract
 
 
-This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). All tools and methodologies are open-source on the repo [https://github.com/dataesr/scanr-ui](https://github.com/dataesr/scanr-ui).
+This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). The scanR community analysis tool is available online [https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started](https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started). All tools and methodologies are open-source on the repo [https://github.com/dataesr/scanr-ui](https://github.com/dataesr/scanr-ui).
 
 # 1. Motivation
 

doc_network/mapping_at_scale.tex

@@ -187,8 +187,10 @@
 strategic decision-making in science policy and funding. These web tools
 are effective at the global (national) scale but are also available (and
 can be integrated via iframes) on the perimeter of any French research
-institution (from large research organisms to any laboratory). All tools
-and methodologies are open-source on the repo
+institution (from large research organisms to any laboratory). The scanR
+community analysis tool is available online
+\url{https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started}.
+All tools and methodologies are open-source on the repo
 \url{https://github.com/dataesr/scanr-ui}.
 \end{abstract}
 

doc_network/out.html

@@ -503,7 +503,7 @@ <h1 property="headline">Mapping scientific communities at scale</h1>
 </div>
 <div class="author-info">
         </div>
-<p class="abstract" property="description"><p>This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). All tools and methodologies are open-source on the repo <a href="https://github.com/dataesr/scanr-ui">https://github.com/dataesr/scanr-ui</a>.</p></p>
+<p class="abstract" property="description"><p>This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). The scanR community analysis tool is available online <a href="https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started">https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started</a>. All tools and methodologies are open-source on the repo <a href="https://github.com/dataesr/scanr-ui">https://github.com/dataesr/scanr-ui</a>.</p></p>
 
 
 

doc_network/out.latex

@@ -187,8 +187,10 @@ collaborations and thematic structures, with potential applications for
 strategic decision-making in science policy and funding. These web tools
 are effective at the global (national) scale but are also available (and
 can be integrated via iframes) on the perimeter of any French research
-institution (from large research organisms to any laboratory). All tools
-and methodologies are open-source on the repo
+institution (from large research organisms to any laboratory). The scanR
+community analysis tool is available online
+\url{https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started}.
+All tools and methodologies are open-source on the repo
 \url{https://github.com/dataesr/scanr-ui}.
 \end{abstract}