Handles creating, reading and updating training events.

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            "name": "Initiation à Git / Git Initiation",
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            "name": "Utilisation du cluster - SLURM / Cluster usage - SLURM",
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            "description": "Objectifs\r\n- Disposer des concepts et de bonnes pratiques d’utilisation des ressources de calcul.\r\n- Être capable d’utiliser les ressources de calcul de la plateforme en toute autonomie.\r\nProgramme\r\n- Introduction : les équipements (calcul et stockage), espaces de travail, les outils et les données.\r\n- Calcul parallèle : concepts, ressources\r\n- Soumission de jobs (srun, sbatch)\r\n- Monitorer, vérifier, controler les jobs (squeue, scontrol, scancel, sacct).\r\n- Base de l’optimisation d’un job\r\n- Solutions de parallélisation des jobs : (--array)",
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            "name": "WheatIS data discovery",
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            "name": "Principes FAIR  & Git Initiation",
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            "description": "Objectifs\r\n- Principes FAIR :\r\n    Connaître les principes FAIR\r\n    Être capable de prendre en compte les principes FAIR dans l'ensemble des étapes d'un projet impliquant la \r\n    collecte et/ou l'analyse de données\r\n- Initiation à Git :\r\n    Savoir définir ce qu’est un outil de gestion de version\r\n    Être capable d’initialiser un entrepôt Git pour un projet\r\n    Être capable de définir quels fichiers inclure/exclure d’un projet\r\n    Savoir enregistrer localement une nouvelle version pour un projet\r\n    Savoir partager des modifications locales avec tous les contributeurs d’un projet\r\n    Savoir gérer des modifications en parallèle en utilisant les branches\r\n   Connaître les bonnes pratiques pour contribuer à projet tiers\r\n\r\nProgramme : \r\n- Principes FAIR\r\n    Présentation des principes FAIR\r\n    Exemples de bonnes pratiques dans la gestion des données : description, organisation du stockage, \r\n    traitements et analyses, mise en accès\r\n- Initiation à Git\r\n    Présentation des avantages de la gestion de versions (projets individuels & projets collaboratifs)\r\n    Présentation des principes de fonctionnement de Git\r\n    Présentation et mise en œuvre des commandes principales de Git (clone, checkout, add, rm, commit, merge,\r\n    push, pull) ; en ligne de commande ou en utilisant une interface graphique (GitHub et GitLab)",
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            "name": "Manipulating  & Visualizing Data with R",
            "shortName": "R - DataViz",
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            "name": "Galaxy Beyond Basics: Mastering Workflows, Automation, and Scalability",
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            "description": "Join us for an intensive, week-long, in-person training designed to elevate your Galaxy expertise to new heights. This workshop is tailored for data scientists, advanced Galaxy users, and team leaders who need to scale, automate, and publish their data analysis workflows for batch processing and production-level applications.\r\n\r\nOver five days, you’ll embark on a comprehensive journey through Galaxy’s advanced capabilities:\r\n\r\nMonday: Introduction & Workflow Development\r\n\r\nStart with a welcome and icebreaker to foster collaboration, followed by a brief overview of Galaxy and its workflow features. 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Learn about managing databases in Galaxy and the IDC (Intergalactic Data Commission) effort for efficient data organization. The workshop concludes with a general recap, supplementary exercises, and feedback and closing remarks, ensuring you leave with a comprehensive understanding and resources for continued success.\r\n\r\nThis training will be conducted in French, while the materials (slides) will be in English.\r\n\r\nLearning Objectives\r\nAt the end of the workshop, you will be able to:\r\n\r\nWorkflow development\r\n    Understand the key aspects of workflows by identifying their core components and purpose.\r\n    Create clean, non-repetitive workflows by applying best practices for process design.\r\n    Use workflow parameters to customize and optimize workflows for specific tasks.\r\n    Generate user-friendly workflow reports to display workflow results in a structured way.\r\nWorkflow FAIRyfication\r\n    Annotate a Galaxy workflow with essential metadata to ensure it is findable and reusable.\r\n    Apply best practices to data analysis workflows to improve consistency and interoperability.\r\n    Implement robust tests to validate workflow reliability and accuracy.\r\n    Publish a Galaxy workflow on WorkflowHub and Dockstore via its integration into the IWC, demonstrating enhanced findability,accessibility, interroperability and usability for the scientific community.\r\nWorkflow Documentation\r\n    Design a high-resolution workflow image optimized for documentation and presentations.\r\n    Develop a hands-on tutorial with a “Choose Your Own Tutorial” approach, including:\r\n        A step-by-step tutorial with skeleton generation from the workflow.\r\n        A real-time tutorial that runs and explains the workflow interactively.\r\n    Produce a final documentation package that includes both tutorial formats and high-resolution visuals.\r\nWorkflow Export\r\n    Apply the process of creating a Galaxy Workflow Run RO-Crate by packaging a workflow with its metadata, inputs, and outputs, ensuring it is reproducible and FAIR-compliant.\r\n    Evaluate the completeness and accuracy of a Galaxy Workflow Run RO-Crate by reviewing its structure, metadata, and included files for adherence to best practices.\r\n    Submit a workflow to LifeMonitor, analyzing the platform’s feedback to assess workflow performance and improve its reliability for future use.\r\nWorkflow Scaling using command-line\r\nExecute workflows from the command line using the Planemo run subcommand, demonstrating the ability to run and monitor workflows outside the Galaxy interface.\r\nDevelop simple shell scripts to automate the execution of multiple workflows concurrently or sequentially, optimizing efficiency and scalability.\r\nAnalyze the performance and resource usage of workflows run via shell scripts, evaluating the effectiveness of scaling strategies for large-scale data processing.\r\nScaling Galaxy Use with the API and BioBlend\r\nUtilize the BioBlend library to programmatically interact with Galaxy, executing workflows, managing datasets, and automating repetitive tasks.\r\nDesign a Python script using BioBlend to scale Galaxy workflows for batch processing, ensuring efficient resource use and reproducibility.\r\nEvaluate the performance and scalability of workflows executed via BioBlend, comparing results with manual Galaxy interactions to identify improvements.\r\n“Bring Your Own Work”\r\nApply the concepts and tools learned during the training to develop or refine your own workflows using your personal data, with guidance from trainers.\r\nTroubleshoot challenges in your workflow or data analysis, implementing solutions with the support of trainers and peers.\r\nDemonstrate progress in your project by documenting your workflow, results, and any optimizations made during the sessions.\r\n\r\nRequirements\r\nPrior knowledge and experience using Galaxy\r\nPrior knowledge and experience using command line\r\nFluent in French (materials will be in English and discussions will happen in French)\r\nYour own computer\r\nOptional but encouraged: your own workflow and dataset for the Bring Your Own Work (BYOW) session. 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            "difficultyLevel": "Novice",
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                "http://edamontology.org/topic_3168",
                "http://edamontology.org/topic_2269",
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            "audienceRoles": [
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            "difficultyLevel": "Novice",
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            "accessConditions": "- Be comfortable with basic Linux commands or have completed the training course “Introduction to the command-line interface.”\r\n- Be familiar with the use of a computing cluster, conda/mamba et snakemake or have completed the training course “Best practices in bioinformatics.”",
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            "id": 344,
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            "homepage": "https://cnrsformation.cnrs.fr/analyses-single-cell-rna-seq-scrna-seq-avec-r?axe=176",
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            ],
            "openTo": "Everyone",
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            "logo_url": "https://services.cbib.u-bordeaux.fr/utils/logo_cbib.png",
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            ],
            "difficultyLevel": "Intermediate",
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            "learningOutcomes": "- Savoir expertiser et manipuler des données issues d'expériences Single Cell RNA-seq\r\n- Savoir mener une analyse différentielle à de multiples niveaux\r\n- Savoir intégrer des données complémentaires pour l'analyse Single Cell RNA-seq (spatial, trajectoire, cell communication, cell identification...)",
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            "id": 320,
            "name": "Ecole Thématique de Bioinformatique Intégrative / Integrative Bioinformatics Training School",
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            "description": "Dans l’objectif de développer et fédérer des compétences en bioinformatique intégrative au sein de la communauté, l’IFB propose une nouvelle école thématique ayant un double objectif :\r\n- une montée en compétences théoriques et pratiques des bioinformaticiens,\r\n- la constitution de matériel pédagogique partagé sur ce sujet.\r\n\r\nCette école rassemble une équipe pédagogique de 10 personnes et pourra accueillir 30 participants pour sa première édition.\r\nL’ensemble de la formation reposera sur l’utilisation des ressources de calcul et de la plateforme pédagogique de l’Institut Français de Bioinformatique.\r\n\r\nObjectifs pédagogiques \r\n\r\nLa formation a pour but :\r\n- d’introduire les concepts de bases et les différents types d’approches utilisées en bioinformatique intégrative,\r\n- de proposer un approfondissement et une mise en pratique d’une de ces approches sur un/des jeux de données intégrant différents types de données omiques. Cette mise en oeuvre permettra de balayer l’ensemble des points d’attention d’une analyse intégrative,  de la préparation des données jusqu’à l’interprétation des résultats,\r\n- de créer, améliorer et partager les ressources pédagogiques (supports de formation, jeux de données, tutoriels) sur le thème de la bioinformatique intégrative.\r\n\r\nA la fin de cette formation les participants :\r\n- auront acquis un socle de connaissances générales en bioinformatique intégrative, \r\n- auront mis en oeuvre une analyse intégrative depuis la préparation des données jusqu’à l’analyse critique de résultats sur un/des jeux de données proposés lors de la formation,\r\n- auront contribué à constituer du matériel pédagogique partagé sur le sujet.\r\n\r\nPré-requis\r\n- Connaissances de base en Unix/shell, R et/ou Python \r\n- Autonomie dans la gestion de son poste de travail (installation de librairies et maîtrise des environnements de packaging type conda)",
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