Event List
Handles creating, reading and updating events.
GET /api/event/?format=api&offset=200&ordering=registration_opening
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The Workflow4Metabolomics.org (W4M) online infrastructure provides a user-friendly and high-performance environment with advanced computational modules for building, running, and sharing complete workflows for LC-MS, GC-MS, FIA and NMR analysis. 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La formation abordera les différents points fondamentaux (théoriques, pratiques, juridiques) en lien avec la politique nationale d’ouverture des données de la recherche et présentera sous forme de séances pratiques les ressources nationales accessibles à la communauté scientifique ainsi que les solutions proposées par l’IFB pour gérer les données d’un projet de recherche.", "homepage": "https://moodle.france-bioinformatique.fr/course/view.php?id=8&username=guest", "is_draft": false, "costs": [ "Free" ], "topics": [ "http://edamontology.org/topic_3420", "http://edamontology.org/topic_0219", "http://edamontology.org/topic_3571" ], "keywords": [ "Données" ], "prerequisites": [], "openTo": "Internal personnel", "accessConditions": "public", "maxParticipants": 20, "contacts": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/116/?format=api" ], "elixirPlatforms": [], "communities": [], "sponsoredBy": [ { "id": 3, "name": "IFB", "url": "https://catalogue.france-bioinformatique.fr/api/eventsponsor/IFB/?format=api" } ], "organisedByOrganisations": [ { "id": 43, "name": "IFB-core", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/IFB-core/?format=api" } ], "organisedByTeams": [ { "id": 29, "name": "IFB Core", "url": "https://catalogue.france-bioinformatique.fr/api/team/IFB%20Core/?format=api" } ], "logo_url": "https://www.france-bioinformatique.fr/wp-content/uploads/logo-ifb-couleur.svg", "updated_at": "2023-10-16T08:30:29.394936Z", "type": "Training course", "start_date": "2022-03-22", "end_date": "2022-03-23", "venue": "", "city": "Paris", "country": "France", "geographical_range": "Local", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": null, "registration_closing": null, "registration_status": "unknown", "courseMode": "Onsite" }, { "id": 97, "name": "Formation CeSGO", "shortName": "", "description": "", "homepage": "", "is_draft": false, "costs": [], "topics": [], "keywords": [], "prerequisites": [], "openTo": "Internal personnel", "accessConditions": "", "maxParticipants": null, "contacts": [], "elixirPlatforms": [], "communities": [], "sponsoredBy": [], "organisedByOrganisations": [], "organisedByTeams": [], "logo_url": "https://ressources.france-bioinformatique.fr/sites/default/files/150x150.png", "updated_at": "2022-06-02T11:50:50.627601Z", "type": "Training course", "start_date": "2017-07-02", "end_date": null, "venue": "", "city": "Rennes", "country": "", "geographical_range": "", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": null, "registration_closing": null, "registration_status": "unknown", "courseMode": null }, { "id": 279, "name": "Cycle « Analyse de données de séquençage à haut-débit » - Module 4/5 : Analyses RNA-seq - partie 2 (biostatistique)", "shortName": "", "description": "Bilille propose chaque année un cycle de formation d'introduction à l'analyse des données de séquençage à haut débit.\r\nCe cycle est composé de 5 modules, à la carte : \r\n- Module 1: Analyses ADN\r\n- Module 2: Analyses de variants\r\n- Module 3: Analyses RNA-seq, bioinformatique\r\n- Module 4: Analyses RNA-seq, biostatistique\r\n- Module 5: Métagénomique\r\nLes fiches descriptives sont accessibles sur le site de Bilille. Chaque module comprend des présentations générales et des séances pratiques sur ordinateur, avec Galaxy.\r\nLes objectifs du module 4 sont :\r\n- Savoir réaliser une analyse différentielle de données RNA-seq à partir d’une table de comptage (quantifiant les lectures alignées) à l’aide du portail Galaxy\r\n- Avoir un regard critique sur les résultats d’une analyse différentielle\r\n- Comprendre différentes méthodes de normalisation et les contextes d’utilisation correspondants", "homepage": "https://bilille.univ-lille.fr/training/training-offer", "is_draft": false, "costs": [ "Free" ], "topics": [], "keywords": [ "NGS Data Analysis", "Transcriptomics (RNA-seq)", "NGS Sequencing Data Analysis" ], "prerequisites": [ "Galaxy - Basic usage" ], "openTo": "Internal personnel", "accessConditions": "- Etre familier avec la plate-forme web Galaxy (idéalement avoir suivi la formation bilille « Initiation à Galaxy »)\r\n- Avoir suivi le module 2/5 «Analyses RNA-seq–partie 1 (bioinformatique)» de ce cycle ou toute autre formation permettant de justifier de connaissances sur les données de séquençage haut débit et la façon d’obtenir une table de comptage", "maxParticipants": null, "contacts": [], "elixirPlatforms": [], "communities": [], "sponsoredBy": [], "organisedByOrganisations": [ { "id": 66, "name": "University of Lille", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/University%20of%20Lille/?format=api" }, { "id": 56, "name": "INSERM", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/INSERM/?format=api" }, { "id": 52, "name": "CNRS", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/CNRS/?format=api" } ], "organisedByTeams": [ { "id": 3, "name": "Bilille", "url": "https://catalogue.france-bioinformatique.fr/api/team/Bilille/?format=api" } ], "logo_url": "https://bilille.univ-lille.fr/fileadmin/_processed_/9/2/csm_logo_bilille_complet_65be9bda8b.png", "updated_at": "2024-12-09T17:38:26.986303Z", "type": "Training course", "start_date": "2019-09-15", "end_date": "2019-09-16", "venue": "", "city": "Villeneuve d'Ascq", "country": "", "geographical_range": "", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": null, "registration_closing": "2019-02-15", "registration_status": "closed", "courseMode": "Onsite" }, { "id": 415, "name": "Introduction to Machine Learning Using R - 2021", "shortName": "", "description": "With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics.\r\n\r\nThis 2-day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.", "homepage": "https://fpsom.github.io/2021-06-ml-elixir-fr/", "is_draft": false, "costs": [ "Free" ], "topics": [], "keywords": [], "prerequisites": [], "openTo": "Everyone", "accessConditions": "", "maxParticipants": 30, "contacts": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/556/?format=api" ], "elixirPlatforms": [], "communities": [], "sponsoredBy": [], "organisedByOrganisations": [ { "id": 8, "name": "Elixir", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/Elixir/?format=api" }, { "id": 4, "name": "IFB - ELIXIR-FR", "url": "https://catalogue.france-bioinformatique.fr/api/organisation/IFB%20-%20ELIXIR-FR/?format=api" } ], "organisedByTeams": [ { "id": 29, "name": "IFB 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