Handles creating, reading and updating training events.

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            "name": "Rôles multiples de l’ARN",
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            "description": "Ce cours théorique et pratique de deux semaines est orienté sur les méthodes pour étudier la synthèse, maturation et la dégradation d’une large variété de molécules d’ARN dans les cellules eucaryotes - voir plus\n",
            "homepage": "https://www.pasteur.fr/fr/enseignement/programmes-doctoraux-et-cours/cours-paste…",
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            "name": "INTRODUCTION À L'ANALYSE DE DONNÉES",
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            "description": "\nL'utilisation de plus en plus répandue de techniques d’imagerie et de séquençage à haut-débit en biologie est en train de révolutionner les sciences du vivant et de modifier en profondeur leurs pratiques. 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. Plusieurs séances seront consacrées à une introduction à l’utilisation du langage de programmation R avant d’aborder les notions de statistiques et d’analyse de données.\nLe module d’analyse d’images introduira les principes de base de l’analyse d’image, et portera plus particulièrement sur l’extraction d’information quantitative d’images de microscopie. Ce cours est destiné aux personnes ayant peu ou pas d’expérience en analyse d’image. Il sera très orienté sur la pratique : des cours magistraux de courte durée seront immédiatement suivis de sessions pratiques. Il aidera à la fois les microscopistes débutants et experts qui n’ont jamais eu de formation concrète en analyse d’image.\n \n\n",
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            "name": "COURS PROGRAMMATION SCIENTIFIQUE EN PYTHON",
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            "description": "The ever growing usage of high throughput technologies in Biology is revolutionizing the life sciences and profoundly changing its practices. Scripting languages are used on a daily basis in life science labs in order to mine huge data sets produced by high-throughput devices. This two-week course will give participants basic knowledge in python and state-of-the-art machine learning methods to analyze their own data sets.\nDescription:\nThis course is intended for PhD students, engineers and research scientists willing to acquire knowledge in scientific programming. Throughout the course, we will use Python language to lead participants from the basics of computer programming to more advanced techniques such as practical machine learning techniques.\n",
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                "Workflow development",
                "Parallelization",
                "Développements technologiques de l‘Information et de la Communication"
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            "description": "Researchers often have access to or generate multiple omics data (RNAseq, metabolomics, lipidomics, proteomics…) within a single study. Although each omics data is usually analyzed individually, combining complementary data can yield a better understanding of the mechanisms involved in biological processes. Several integrative approaches are now available to combine such data, coming essentially from two families of methods, namely multivariate statistical analyses and network-based approaches. During this summer school both methodologies will be covered, introducing RGCCA and mixOmics for multivariate analyses and WGCNA and SNF for network-based strategies. To get meaningful biological information, the interpretation of statistical results needs to be done contextualizing them in the available biological knowledge. To address this major step we need to be able to access and interrogate databases. We will harness this subject introducing semantic web and knowledge graphs in the context of metabolic networks.\r\n\r\nDuring the School, significant time will be devoted to hands-on and the program will be divided into three phases / topics:\r\n- Multivariate statistical analyses (Instructors: Arnaud Gloaguen & Jimmy Vandel)\r\n- Network-based approaches (Instructors: Morgane Térézol & Marie-Galadriel Brière)\r\n- Results contextualisation: an introduction to metabolic models, web semantic and knowledge graphs (Instructors: Jean-Clément Gallardo, Maxime Delmas & Marco Pagni)\r\n\r\nThe participants will work in groups and shortly present the application of what they have learned to their own project.",
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            "homepage": "http://www.biosciencesco.fr/formation-continue/bio-informatique/analyse-des-donn…",
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                "Read alignment on genomes",
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                "Gene expression differential analysis",
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        },
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            "id": 46,
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            "is_draft": false,
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                "Statistical Tests",
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                "R Language",
                "Descriptive statistics"
            ],
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            "openTo": "Internal personnel",
            "accessConditions": "Informations et inscriptions:\nhttp://www.biosciencesco.fr/formation-continue/bio-informatique/formation-au-logiciel-r/\n \n",
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        },
        {
            "id": 50,
            "name": "Fc3-Bio",
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            "description": "1 à 4 séances de deux jours par an\n",
            "homepage": "http://www.fc3bio.fr/",
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                "Comparative and de novo structure modeling",
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                "Dynamic and thermodynamic structure properties analysis",
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                "Virtual screening",
                "Structure-based screening",
                "Sequence Algorithm",
                "Ligand-Based Screening (QSAR)",
                "Sequence analysis",
                "2D/3D",
                "ADME/tox",
                "Small chemical compound libraries",
                "Structure analysis",
                "homology and structural pattern matching",
                "Homology/orthology prediction",
                "Structural Bioinformatics",
                "Predictions of structural properties",
                "Sequence annotation",
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                "Multiple sequence alignment"
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            "id": 54,
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            "id": 53,
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