Merge branch 'master' into add_smp_case

_data/live_deployments.json

@@ -1728,6 +1728,26 @@
           "highlight": "Over 1,500 ontologies, controlled vocabularies, and resources including their preferred prefix, name, description, homepage, mappings to other registries, and more."
         }
       ]
+    },
+    {
+      "name": "Metabolic Atlas",
+      "description": "A web platform integrating open-source genome scale metabolic models (GEMs) for easy browsing and analysis. ",
+      "url": "https://metabolicatlas.org",
+      "nodes": ["SE"],
+      "profiles": [
+        {
+          "profileName": "MolecularEntity",
+          "conformsTo": "0.5-RELEASE",
+          "exampleURL": "https://metabolicatlas.org/explore/Human-GEM/gem-browser/metabolite/MAM00217c",
+          "highlight": "Over 3'000 metabolites."
+        },
+        {
+          "profileName": "Gene",
+          "conformsTo": "1.0-RELEASE",
+          "exampleURL": "https://metabolicatlas.org/explore/Human-GEM/gem-browser/gene/ENSG00000078237",
+          "highlight": "Over 15'000 genes from multiple organisms."
+        }
+      ]
     }
   ]
 }

_data/type_versions.yaml

@@ -124,13 +124,13 @@ Study:
 Taxon:
     name: "Taxon"
     latest_publication: "0.4-DRAFT"
-    latest_release: "0.3-RELEASE-2019_11_18"
+    latest_release: "1.0-RELEASE"
     status: "active"
 
 TaxonName:
     name: "TaxonName"
     latest_publication: "0.1-DRAFT"
-    latest_release:
+    latest_release: "1.0-RELEASE"
     status: "active"
 
 ---

pages/_groups/MachineLearning.md

@@ -33,10 +33,12 @@ specifications:
 members:
 - AlbanGaignard
 - DietrichRebholzSchuhmann
+- IvanMicetic
+
 ---
 
 <h2>Further Details</h2>
 
-<p>Machine Learning (ML) are nowadays a common path in data-driven research due to the amount of available data and the resources needed to process it and make sense out of it. In addition to data, software also plays and important role in ML. Models produced by an ML training process also become a thing on their own, a thing that could be seen as similar to software (e.g., prediction model that can be executed with some input and produce a prediction as output) or to data (e.g., clusters emerged from a clustering approach). Furthermore, the training software has to be tuned and optimized while the model has to be evaluted, either intrinsic or extrinsic. Ideally, all of this information should be reported and represented as metadata of the ML process. However, this is not always the case. This group, a joint effort across <a href="https://www.rd-alliance.org/groups/fair-machine-learning-fair4ml-ig" target="_blank">Research Data Alliance FAIR4ML Interest Group</a>, <a href="https://elixir-europe.org/focus-groups/machine-learning" target="_blank">ELIXIR Machine Learning Focus Group</a> and <a href="https://www.nfdi4datascience.de/" target="_blank">NFDI4DataScience</a>, aims at providing a common ground for the metadata necessary to describe ML approaches. </p>
+<p>Machine Learning (ML) is nowadays a common path in data-driven research due to the amount of available data and the resources needed to process it and make sense out of it. In addition to data, software also plays and important role in ML. Models produced by an ML training process also become a thing on their own, a thing that could be seen as similar to software (e.g., prediction model that can be executed with some input and produce a prediction as output) or to data (e.g., clusters emerged from a clustering approach). Furthermore, the training software has to be tuned and optimized while the model has to be evaluted, either intrinsic or extrinsically. Ideally, all of this information should be reported and represented as metadata of the ML process. However, this is not always the case. This group, a joint effort across <a href="https://www.rd-alliance.org/groups/fair-machine-learning-fair4ml-ig" target="_blank">Research Data Alliance FAIR4ML Interest Group</a>, <a href="https://elixir-europe.org/focus-groups/machine-learning" target="_blank">ELIXIR Machine Learning Focus Group</a> and <a href="https://www.nfdi4datascience.de/" target="_blank">NFDI4DataScience</a>, aims at providing a common ground for the metadata necessary to describe ML approaches. </p>
 
-<p>To achieve its objectives, this group is using as a starting point Machine Learning Cards for models and datasets. Other efforst will also be taken into account, e.g., <a href="https://www.nature.com/articles/s41592-021-01205-4" target="_blank">Data, Optimization, Model and Evaluation (DOME) recommendations</a> and <a href="https://doi.org/10.1038/s41592-021-01241-0" target="_blank">AIMe registry for artificial intelligence in biomedical research</a>.</p>
+<p>To achieve its objectives, this group is using as a starting point <a href="https://research.google/pubs/pub48120/" target="_blank">Machine Learning Cards for models and datasets</a>. Other efforst will also be taken into account, e.g., <a href="https://www.nature.com/articles/s41592-021-01205-4" target="_blank">Data, Optimization, Model and Evaluation (DOME) recommendations</a>, <a href="https://doi.org/10.1038/s41592-021-01241-0" target="_blank">AIMe registry for artificial intelligence in biomedical research</a> and <a href="https://huggingface.co/" target="_blank">HuggingFace</a>.</p>

pages/_news/2023-06-24-BioHackathon-Europe-2023.md

@@ -0,0 +1,19 @@
+
+---
+layout: post
+title: "Projects Accepted at BioHackathon-Europe 2023"
+tags:
+- Community
+- Event
+---
+
+We are pleased to announce that the Bioschemas community have had three projects accepted at this year's [BioHackathon-Europe event](https://biohackathon-europe.org/) beginning 30th October.
+
+The projects are:
+- [Bioschemas resource index for chem and plants](https://github.com/elixir-europe/biohackathon-projects-2023/tree/main/7)
+- [Enabling FAIR Digital Objects with RO-Crates, Signposting and Bioschemas](https://github.com/elixir-europe/biohackathon-projects-2023/tree/main/15)
+- [Improving Bioschemas creation and community adoption through process improvements and tool development, and advancing compliance to FAIR standards](https://github.com/elixir-europe/biohackathon-projects-2023/tree/main/23)
+
+These projects should give us the opportunity to engage with the wider community, target specific domains, as well as improving our tooling for users.
+
+Please come and join - virtual participation is highly encouraged!