Training List
Handles creating, reading and updating training events.
GET /api/training/?format=api&offset=140&ordering=-prerequisites
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L'appropriation par les biologistes des méthodes et des outils de biostatistique et bioinformatique intégrative est un enjeu majeur pour la montée en compétence des équipes de recherche et des plateformes de service.\r\n\r\nL'université de Paris propose en partenariat avec l'Institut Français de Bioinformatique (IFB) la troisième édition du Diplôme Universitaire en Bioinformatique intégrative (DUBii). Cette formation s’adresse en priorité à des biologistes ou à des médecins souhaitant évoluer en compétences ou envisager une reconversion professionnelle et ayant déjà acquis des compétences (formation courte, autoapprentissage, expérience de terrain) en informatique ou bioinformatique / biostatistique (environnement Unix, Python ou R ou autre langage de programmation). \r\n\r\nLe DUBii fournira une formation théorique et pratique, complétée par une période d'immersion de 20 jours sur l'une des plateformes régionales de l'IFB, qui mobilisera, dans le cadre d'un projet tutoré, l'ensemble des méthodes et outils appris durant les cours pour réaliser un projet personnel de bioinformatique intégrative. 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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://ifb-elixirfr.github.io/IFB-FAIR-data-training/", "is_draft": false, "costs": [ "Free" ], "topics": [ "http://edamontology.org/topic_3420", "http://edamontology.org/topic_3571", "http://edamontology.org/topic_0219" ], "keywords": [ "Données" ], "prerequisites": [], "openTo": "Everyone", "accessConditions": "public", "maxParticipants": null, "contacts": [], "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": 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exemples de jeux de données omiques ;\r\n- Le choix des métadonnées : panorama des ressources existantes pour choisir des métadonnées et mise en pratique pour annoter des jeux de données omiques en vue de la publication des données dans une banque internationale ou un dataverse institutionnel.", "hoursPresentations": null, "hoursHandsOn": null, "hoursTotal": null, "personalised": false, "event_set": [ "https://catalogue.france-bioinformatique.fr/api/event/418/?format=api", "https://catalogue.france-bioinformatique.fr/api/event/404/?format=api", "https://catalogue.france-bioinformatique.fr/api/event/465/?format=api" ] }, { "id": 210, "name": "Python avance", "shortName": "", "description": "", "homepage": "", "is_draft": false, "costs": [ "Priced" ], "topics": [], "keywords": [], "prerequisites": [], "openTo": "Internal personnel", "accessConditions": "", "maxParticipants": null, "contacts": [], "elixirPlatforms": [], "communities": [], "sponsoredBy": [], "organisedByOrganisations": [], 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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": "", "is_draft": false, "costs": [], "topics": [], "keywords": [], "prerequisites": [], "openTo": "Everyone", "accessConditions": "", "maxParticipants": 30, "contacts": [], "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 Core", "url": "https://catalogue.france-bioinformatique.fr/api/team/IFB%20Core/?format=api" } ], "logo_url": "https://www.dissco.eu/wp-content/uploads/Elixir-Europe-logo-1.png", "updated_at": "2022-06-02T11:50:50.812642Z", "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "trainingMaterials": [], "learningOutcomes": "", "hoursPresentations": null, "hoursHandsOn": null, "hoursTotal": null, "personalised": null, "event_set": [ "https://catalogue.france-bioinformatique.fr/api/event/415/?format=api" ] }, { "id": 64, "name": "Read alignment and SNP calling", "shortName": "", "description": "This training session, organized jointly with the Sigenae platform, is designed to help you deal with NGS data, in particular Roche 454 and Illumina Solexa technologies. 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