Handles creating, reading and updating training events.

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            "description": "Les UE de bioinformatique de la spécialité AMD sont ouvertes aux doctorants de l'école doctorale SVSAE dans le cadre de leur formation doctorale. 3 UE sont particulièrement suivies : UE « Programmation en perl », UE « Bioistatistiques et programmation sous R » et UE « Génomique et bioinformatique ». Les formations continues proposées dans le domaine de la bioinformatique sont également ouvertes aux étudiants de l'Ecole Doctorale, et permettent de valider un module de biologie. Forme 2 à 3 doctorants par an.\n \n",
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            "description": "URGI organizes a BYOD-­style (Bring Your Own Data) training course on manual curation of transposable elements reference sequences obtained with REPET pipelines.\n",
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            "name": "Cycle « Initiation à la bioinformatique » - Module 4/4 : Initiation à la reconstruction phylogénétique en biologie moléculaire",
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            "description": "Bilille propose un cycle de découverte de la bioinformatique à destination des chercheur·euses, enseignant·es-chercheur·euses, ingénieur·es, technicien·nes et doctorant·es en biologie. Aucun pré-requis en informatique n'est attendu.\r\nLe cycle est constitué de quatre modules de deux jours:\r\n- Banques de données et BLAST\r\n- Alignement de séquences\r\n- Prédiction de gènes et annotation de protéines\r\n- Initiation à la reconstruction phylogénétique en biologie moléculaire\r\nCes modules peuvent être suivis indépendamment, mais ont une cohérence. Suivre chaque module peut aider à une meilleure compréhension des modules suivants.\r\nLes fiches descriptives des différents modules sont accessibles sur le site web de Bilille.\r\nLes objectifs du module 4 sont :\r\n- Comprendre les grands principes de l’évolution moléculaire et de la reconstruction phylogénétique\r\n- Savoir construire des alignements informatifs pour une analyse phylogénétique\r\n- Comprendre les modèles phylogénétiques probabilistes, les méthodes d'inférence et savoir les appliquer\r\n- Savoir reconstruire des arbres phylogénétiques en Maximum de vraisemblance (ML) et par Inférence Bayésienne (BI)\r\n- Etre capable d’analyser avec un regard critique les résultats obtenus",
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            "name": "Single-Cell : Transcriptomics, Spatial and Long reads",
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            "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. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nA 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. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nRequirements : Participants must have prior experience on NGS data analysis  with everyday use of R and good knowledge of Unix command line. 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.",
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            "id": 375,
            "name": "RNASeq Analysis",
            "shortName": "RNASeq Analysis",
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            "name": "Formation interne pipeline RNASeq",
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                "Analysis of RNAseq data"
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            "id": 15,
            "name": "IFSBM",
            "shortName": "",
            "description": "\nUFR médecine Paris-Sud (mai 2015). Big data en médecine. Acquisition et analyse des données de haut débit dans le but de détecter des variants génomiques (CGH, exome, génome), étudier les profils d’expression ou le statut épigénétique des cellules (expression array, RNA-seq, ChiP-seq, medip-seq).\n \nUFR médecine Paris-Sud (janvier 2015) Méthodologie en biologie moléculaire et cellulaire et analyse d’article\n\n",
            "homepage": "",
            "is_draft": false,
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                "Autre (Diplôme universitaire, école d'ingénieur ...)"
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            "id": 285,
            "name": "Linux Avancé / Advanced Linux",
            "shortName": "Advanced Linux",
            "description": "Objectifs\r\n- Savoir utiliser des commandes linux pour traiter de grosses quantités de données : fichiers\r\nvolumineux et/ou en grands nombres : recherche, comptage, tri, fusion, …\r\nProgramme\r\n- Introduction\r\n- Décrire (wc, grep)\r\n- Manipuler des fichiers tabulés (cut, sort)\r\n- Rechercher (grep)\r\n- Redirection / Pipeline (stdin, stdout, stderr, >, 2>, &&, |)\r\n- Recherche avancée : notion d’expression régulière (egrep)\r\n- Rechercher/Remplacer haut débit (tr, sed)\r\n- Manipulation de fichier tabulé – mode avancé (awk)\r\n- Traitement séquentiel de nombreux fichiers (for)",
            "homepage": "https://abims.sb-roscoff.fr/module/linux_advanced",
            "is_draft": false,
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                "Free"
            ],
            "topics": [
                "http://edamontology.org/topic_3316"
            ],
            "keywords": [],
            "prerequisites": [
                "Linux - Basic Knowledge"
            ],
            "openTo": "Everyone",
            "accessConditions": "Preregistration required using: https://abims.sb-roscoff.fr/ateliers/preinscription",
            "maxParticipants": 18,
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            "organisedByOrganisations": [
                {
                    "id": 65,
                    "name": "SBR - Roscoff Marine Station",
                    "url": "https://catalogue.france-bioinformatique.fr/api/organisation/SBR%20-%20Roscoff%20Marine%20Station/?format=api"
                }
            ],
            "organisedByTeams": [
                {
                    "id": 4,
                    "name": "ABiMS",
                    "url": "https://catalogue.france-bioinformatique.fr/api/team/ABiMS/?format=api"
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            ],
            "logo_url": "https://abims.sb-roscoff.fr/sites/default/files/abims.png",
            "updated_at": "2025-02-21T08:42:13.294211Z",
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                "Graduate",
                "Professional (initial)",
                "Professional (continued)"
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        {
            "id": 134,
            "name": "Initiation à l'analyse de données avec R",
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            "description": "Le cours s’adresse à des personnes qui veulent apprendre ou ré-apprendre à utiliser les statistiques à bon escient pour leurs propres projets. L’objectif est de présenter et expliquer les principales notions de statistiques utiles pour décrire un jeu de données, en explorer les propriétés afin d’en tirer des conclusions robustes, utiliser à bon escient les méthodes les plus courantes (tests d’hypothèse, ACP, …) et savoir lire, interpréter (et éventuellement aborder d’un œil critique) les résultats présentés dans les publications. Nous utiliserons le moins possible le formalisme mathématique mais insisterons sur les propriétés des méthodes, leurs pré-requis, l’interprétation des résultats. Nous aborderons les notions d’analyse exploratoire, ACP, clustering, estimation, échantillonnage, régression, tests d’hypothèse, planification d’expérience\nCe cours est une initiation à l’analyse de données. Il est préférable d’avoir une connaissance minimale de R. Dans le cas contraire, un tutoriel d’initiation est disponible sur la page web du cours et les notions de base de R seront rappelées pendant la pratique. Pour les personnes n’ayant jamais utilisé R, il peut être utile d’avoir des connaissances de base en programmation, quel que soit le langage.\nLes cours seront donnés en Français et alterneront théorie et pratique avec RStudio. Il est demandé à chaque participant de venir avec un ordinateur portable chargé sur lequel il aura préalablement installé les éléments nécessaires (R, Rstudio et les fichiers de données sur lesquels nous travaillerons). La liste complète des fichiers et logiciels nécessaires sera disponible sur la page web du cours une dizaine de jours avant le début de la session.\nderniere session: Mar 2016\n \n",
            "homepage": "https://c3bi.pasteur.fr",
            "is_draft": false,
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            "keywords": [
                "Biostatistics",
                "Programming Languages & Computer Sciences",
                "Statistical Tests",
                "R Language",
                "Descriptive statistics"
            ],
            "prerequisites": [],
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