Event List
Handles creating, reading and updating events.
GET /api/event/?format=api&offset=100&ordering=-organisedByTeams
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Cette formation requiert une bonne maîtrise de R.\r\n\r\nKey Highlights:\r\nSmall group sessions for interactive and personalized learning.\r\nTailored feedback on your own data to reinforce the learning process.\r\nLimited spots available, registration is now open.", "homepage": "https://inforbio.github.io/ioc_r_scrnaseq.html", "is_draft": false, "costs": [ "Priced" ], "topics": [], "keywords": [ "Single-Cell Analysis" ], "prerequisites": [ "R programming" ], "openTo": "Everyone", "accessConditions": "Followed R training or equivalent level", "maxParticipants": 6, "contacts": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/809/?format=api" ], "elixirPlatforms": [], "communities": [], "sponsoredBy": [ { "id": 18, "name": "IBiSA", "url": "https://catalogue.france-bioinformatique.fr/api/eventsponsor/IBiSA/?format=api" }, { "id": 19, "name": "Sorbonne Université", "url": "https://catalogue.france-bioinformatique.fr/api/eventsponsor/Sorbonne%20Universit%C3%A9/?format=api" } ], "organisedByOrganisations": [], "organisedByTeams": [], "logo_url": "https://github.com/InforBio/InforBio.github.io/blob/main/images/logoInforBio_fond_blanc.png?raw=true", "updated_at": "2024-12-11T08:35:29.702215Z", "type": "Training course", "start_date": "2025-04-07", "end_date": "2025-06-30", "venue": "", "city": "online", "country": "", "geographical_range": "", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": "2024-12-01", "registration_closing": "2024-12-31", "registration_status": "closed", "courseMode": "Online" }, { "id": 253, "name": "Python avancé", "shortName": "", "description": "\nObjectifs\n\nEtre autonome pour des manipulations simples visant à extraire, reformater des données issues de fichiers texte.\n \n\n \n \n \n \nProgramme\n\n- Expressions régulières\n- Gestion des erreurs\n- Biopython\n- Réalisation de programmes simples\n", "homepage": "http://migale.jouy.inra.fr/", "is_draft": false, "costs": [], "topics": [], "keywords": [ "Programming Languages & Computer Sciences", "Python Language" ], "prerequisites": [], "openTo": "Internal personnel", "accessConditions": "Ce cycle est ouvert à l'ensemble des agents de l'INRA et aux extérieurs.\n", "maxParticipants": null, "contacts": [], "elixirPlatforms": [], "communities": [], "sponsoredBy": [], "organisedByOrganisations": [], "organisedByTeams": [], "logo_url": "", "updated_at": "2022-06-02T11:50:50.627601Z", "type": "Training course", "start_date": "2017-03-09", "end_date": null, "venue": "", "city": "jouy en josas", "country": "", "geographical_range": "", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": null, "registration_closing": null, "registration_status": "unknown", "courseMode": null }, { "id": 151, "name": "MOD2018", "shortName": "", "description": " Axée sur les technologies pour l'assemblage des génomes et sur les applications en Santé et en Environnement.", "homepage": "https://www.montpellier-omics-days.fr/", "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/index.png", "updated_at": "2022-06-02T11:50:50.627601Z", "type": "Conference", "start_date": "2018-02-08", "end_date": "2018-02-09", "venue": "", "city": "campus du Triolet, Université de Montpellier", "country": "", "geographical_range": "", "trainers": [], "trainingMaterials": [], "computingFacilities": [], "realisation_status": "past", "registration_opening": null, "registration_closing": null, "registration_status": "unknown", "courseMode": null }, { "id": 606, "name": "5th workshop Single-Cell : Transcriptomics, Spatial and Long reads", "shortName": "5th SincellTE", "description": "This workshop focuses on the large-scale study of heterogeneity across individual cells from a genomic, transcriptomic and epigenomic point of view. New technological developments enable the characterization of molecular information at a single cell resolution for large numbers of cells. The high dimensional omics data that these technologies produce raise novel methodological challenges for the analysis. In this regard, dedicated bioinformatics and statistical methods have been developed in order to extract robust information.\r\n\r\nThe workshop aims to provide such methods for engineers and researchers directly involved in functional genomics projects making use of single-cell technologies. A wide range of single cell topics will be covered in lectures, demonstrations and practical classes. Among others, the areas and issues to be addressed will include the choice of the most appropriate single-cell sequencing technology, the experimental design and the bioinformatics and statistical methods and pipelines. 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Before the training, participants will be asked to familiarize themselves with the processing and primary analyses steps of scRNA-seq datasets with provided pedagogic material.\r\n\r\nIt is not necessary to have personal single-cell data to analyse.\r\n\r\nAll the classes will be taught in English", "homepage": "https://moodle.france-bioinformatique.fr/course/view.php?id=27", "is_draft": false, "costs": [ "Priced" ], "topics": [], "keywords": [ "Single-Cell Sequencing", "long read sequencing", "spatial transcriptomics" ], "prerequisites": [ "Master", "Autre (Diplôme universitaire, école d'ingénieur ...)" ], "openTo": "Everyone", "accessConditions": "Participants must have prior experience on NGS data analysis with everyday use of R and/or Python and good knowledge of Unix command line. 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Dans ce contexte, des outils statistiques sont développés pour permettre d’analyser ces données de hautes dimensions, et la maîtrise de ces outils devient de plus en plus nécessaire pour produire des résultats de bonne qualité. Ce cours de 4 semaines couvrira les étapes nécessaires pour mettre en place un processus d’analyse de données, depuis la planification de l’expérience jusqu’à la fouille des données en passant par l’échantillonnage, les test d’hypothèses, la modélisation statistique etc.\nCe cours s’adresse en priorité aux étudiants de première année de thèse de l’Institut Pasteur. Tout étudiant en thèse sera automatiquement inscrit à ce cours, mais les élèves de 2e année, de 3e année ou les post-doctorants peuvent également s’inscrire, dans la limite des places disponibles. Il est à noter que le cours est obligatoire pour les étudiants de 1ère année. Des dispenses partielles ou totales sont possibles pour les étudiants qui ont déjà des connaissances en statistique, en mathématique ou en physique. Le cours déroulera sur 4 semaines, 4 jours par semaine, trois heures par jour. Chaque séance de trois heures alternera cours magistral et mise en pratique. Il y aura deux sessions : la première commencera le 22 octobre 2018 et la deuxième le 14 janvier 2019.\nChacune de ces deux sessions sera précédée d’une séance d’introduction à l’informatique. Cette séance proposera des notions d’architecture de l’ordinateur, de système d’organisation des fichiers et de format de fichiers. Chaque session sera également suivie d’un cours optionnel sur l’analyse et le traitement des images.\nPour plus d’information, ainsi que pour les inscriptions au module optionnel et les demandes d’exemption, rendez-vous sur la page du cours : https://c3bi.pasteur.fr/introduction-to-data-analysis-2018-19/\nThèmes abordés\nLe module d’analyse de données couvrira un large champ de notions nécessaires aux étudiants pour planifier leurs expériences, analyser et explorer leurs données, interpréter les résultats et générer des figures à des fins de publication. Il abordera des notions de base en statistique, dont les analyses uni- et multivariées, les analyses descriptives, les distributions statistiques usuelles utilisées en biologie, ainsi que les tests d’hypothèses. Les exercices et travaux pratiques seront réalisés avec R et RStudio. 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