Handles creating, reading and updating training events.

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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. Dive into hands-on workflow development, where you’ll learn to design clean, efficient workflows, customize them with parameters, and generate user-friendly workflow reports—combining theory with practical application.\r\n\r\nTuesday: Workflow FAIRification, Documentation, and Export\r\n\r\nBegin with a recap of Day 1, then explore UseGalaxy.fr and its unique features. Learn to annotate workflows with metadata, apply best practices for FAIR compliance, and implement tests to ensure reliability. Publish your workflows to WorkflowHub and Dockstore via the IWC. Develop high-resolution workflow visualizations and create interactive tutorials using a “Choose Your Own Tutorial” approach. Finally, master workflow export by creating RO-Crates for reproducibility and submitting workflows to LifeMonitor for performance tracking.\r\n\r\nWednesday: Scaling Workflows & Galaxy Using Command-Line and API\r\n\r\nStart with a recap and real-world examples of large-scale Galaxy projects. Learn to execute workflows from the command line using Planemo, automate batch processing with shell scripts, and analyze performance for efficiency. Discover how to scale Galaxy use with BioBlend, designing Python scripts for batch workflow execution and evaluating scalability. The day concludes with an introduction to the “Bring Your Own Work” session.\r\n\r\nThursday: Bring Your Own Work (BYOW)\r\n\r\nDedicate the day to applying your new skills to your own projects. With guidance from trainers, refine your workflows, troubleshoot challenges, and implement solutions using your personal data. Collaborate with peers, document your progress, and optimize your workflows to leave with actionable results for your research.\r\n\r\nFriday: Storage, Data Management, Recap, and Closing\r\n\r\nThe final half-day begins with a recap of the week’s progress, followed by a session on “Bring Your Own Storage”, exploring how to integrate personal or institutional storage with Galaxy. 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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            "learningOutcomes": "At the end of the workshop, you will be able to:\r\n\r\nWorkflow development\r\nUnderstand the key aspects of workflows by identifying their core components and purpose.\r\nCreate clean, non-repetitive workflows by applying best practices for process design.\r\nUse workflow parameters to customize and optimize workflows for specific tasks.\r\nGenerate user-friendly workflow reports to display workflow results in a structured way.\r\nWorkflow FAIRyfication\r\nAnnotate a Galaxy workflow with essential metadata to ensure it is findable and reusable.\r\nApply best practices to data analysis workflows to improve consistency and interoperability.\r\nImplement robust tests to validate workflow reliability and accuracy.\r\nPublish 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\nDesign a high-resolution workflow image optimized for documentation and presentations.\r\nDevelop a hands-on tutorial with a “Choose Your Own Tutorial” approach, including:\r\nA step-by-step tutorial with skeleton generation from the workflow.\r\nA real-time tutorial that runs and explains the workflow interactively.\r\nProduce a final documentation package that includes both tutorial formats and high-resolution visuals.\r\nWorkflow Export\r\nApply 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\nEvaluate 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\nSubmit 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.",
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            "description": "Objectives\r\n- Understand the principles and advantages of the Linux system\r\n- Know and use the main bash commands.\r\n- Ability to chain multiple commands using pipes\r\n- Launch programs with arguments\r\n- Gain independence to perform command line analyses\r\n\r\nCourse Content\r\n- Introduction to the Linux system.\r\n- File system: directory structure, paths, home directory, file and directory management.\r\n- Principle of protections: reading file attributes, access rights, management of user groups.\r\n- Useful commands for file manipulation\r\n- Redirection operators (command input/output)\r\n- Creating and running a bash script\r\nIntroduction to environment variables\r\nConnecting to a remote server via a terminal or via WSL",
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            "name": "Practical session REPET",
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            "description": "How to use REPET for de novo TE annotation",
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            "description": "Objectifs pédagogiques\r\nÀ l'issue de la formation, les stagiaires connaîtront les principales commandes Linux et sauront utiliser le système Linux.\r\n\r\nProgramme\r\n* Connexion (ssh) et transferts de fichiers (scp, rsync)\r\n* Interfaces graphiques (Gnome, KDE) / émulateurs\r\n* Aide en ligne\r\n* Utilisation du shell : le rappel des commandes, l’historique, la complétion\r\n* Système de fichiers : arborescence et chemin d’accès, le répertoire d’accueil…\r\n* Gestion des fichiers et des répertoires\r\n* Principe de protection : les attributs sur les fichiers, les droits d’accès",
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            "name": "Introduction to R",
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            "id": 320,
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            "id": 383,
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            "description": "Artificial intelligence (AI) has permeated our lives, transforming how we live and work. Over the past few years, a rapid and disruptive acceleration of progress in AI has occurred, driven by significant advances in widespread data availability, computing power and machine learning. Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive volumes of unlabelled data. Moreover, given the large amounts of omics data that are being generated and made accessible to researchers due to the drop in the cost of high-throughput technologies, analysing these complex high-volume data is not trivial, and the use of classical statistics can not explore their full potential. As such, Machine Learning (ML) and Artificial Intelligence (AI) have been recognized as key opportunity areas, as evidenced by a number of ongoing activities and efforts throughout the community.\r\n\r\nHowever, beyond the technological advances, it is equally important that the individual researchers acquire the necessary knowledge and skills to fully take advantage of Machine Learning. Being aware of the challenges, opportunities and constraints that ML applications entail, is a critical aspect in ensuring high quality research in life sciences.\r\n\r\nRecognizing this need, this week-long training will bring together experts from four ELIXIR Nodes and deliver a hands-on, high-intensity course available for members from all ELIXIR Nodes.\r\n\r\nLearners will be guided across the various steps in Machine Learning, from the foundational concepts, through the deep learning and generative AI techniques, closely complemented by insights into the existing reporting (DOME Recommendations) and regulatory frameworks (EU AI Act).",
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                    "id": 56,
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            "logo_url": "https://mesocentre.uca.fr/medias/photo/logoaubi-2019minus_1553844844490-jpg?ID_FICHE=41175",
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        },
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            "id": 296,
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            "shortName": "Initiation Galaxy",
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            "homepage": "https://documents.migale.inrae.fr/trainings.html",
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            "id": 333,
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