Handles creating, reading and updating training materials.

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            "name": "Chip-seq: Pattern Analysis tutorial",
            "description": "Goal\nThe aim is to :\nGet familiar with motif analysis of ChIP-seq data.\nLearn de novo motif discovery methods.\nIn practice :\nMotif discovery with peak-motifs\nDifferential analysis\nRandom controls\n",
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            "name": "Using blockchain in biomedical provenance, the identifiers use case",
            "description": "\n \n\nUsing blockchain in biomedical provenance, the identifiers use case\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/using-blockchain_039d/scormcontent/index.html",
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            "id": 3,
            "name": "Transfer of Research Assets between FAIRDOM SEEKs",
            "description": "\n \n\nTransfer of Research Assets between FAIRDOM SEEKs\n \n",
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            "name": "Support tools for rapid adoption of compact identifiers in the publishing process",
            "description": "\n \n\nSupport tools for rapid adoption of compact identifiers in the publishing process\n \n",
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            "name": "Putting structured data into individual entry pages in biological database",
            "description": "\n \n\nPutting structured data into individual entry pages in biological database\n \n",
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            "name": "ProtVista (protein annotation viewer) extension using Bioschemas data",
            "description": "\n \n\nProtVista (protein annotation viewer) extension using Bioschemas data\n \n",
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            "id": 7,
            "name": "Prototyping the new PSICQUIC 2-0",
            "description": "\n \n\nPrototyping the new PSICQUIC 2.0\n \n",
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            "doi": null,
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            "name": "Pathway effect prediction for protein targets",
            "description": "\n \n\nPathway effect prediction for protein targets\n \n",
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            "name": "OmicsPath: Finding Relevant omics datasets using pathway information",
            "description": "\n \n\nOmicsPath: Finding Relevant omics datasets using pathway information\n \n",
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            "name": "JSON schema validation with ontologies",
            "description": "\n \n\nJSON schema validation with ontologies\n \n",
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            "id": 11,
            "name": "Improve Shiny and RStudio integration within Galaxy using Galaxy Interactive Environment",
            "description": "\n \n\nImprove Shiny and RStudio integration within Galaxy using Galaxy Interactive Environment\n \n",
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            "doi": null,
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            "name": "Improve Orphanet disease description knowledge by phenotypic automated recognition using scrapping toolkits",
            "description": "\n \n\nImprove Orphanet disease description knowledge by phenotypic automated recognition using scrapping toolkits\n \n",
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            "doi": null,
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            "id": 13,
            "name": "Import workflows into TeSS Concept Maps",
            "description": "\n \n\nImport workflows into TeSS Concept Maps\n \n",
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            "id": 131,
            "name": "16S Microbial Analysis with mothur",
            "description": "This tutorial covers the questions:\r\n- What is the effect of normal variation in the gut microbiome on host health?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Analyze of 16S rRNA sequencing data using the mothur toolsuite in Galaxy\r\n- Using a mock community to assess the error rate of your sequencing experiment\r\n- Visualize sample diversity using Krona and Phinch",
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            "doi": null,
            "fileLocation": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/mothur-miseq-sop-short/tutorial.html",
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                "Metabarcoding"
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            "description": "Slides used for the training \"\t\r\nIntroduction to Oxford Nanopore Technology data analyses\"",
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            "dateCreation": "2022-01-24",
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            "id": 148,
            "name": "One line perl",
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            "fileLocation": "https://web-genobioinfo.toulouse.inrae.fr/~ccabau/Perl_One-liner.pdf",
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            "name": "training material sed and awk training",
            "description": "training material sed and awk training (genotoul bioinfo facility)",
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            "description": "This “Sed and AWK to modify large text files” training session is organized by the Genotoul bioinfo platform.\r\n\r\nThe Linux sed command is a powerful and very fast text editor without an interface. Sed can select, substitute, add, delete, and modify text in files and streams. Sed relies heavily on regular expressions for pattern matching and text selection. We’ll manipulate regexes and the sed command to modify and filter several type of file often used in bioinformatics.\r\n\r\nAWK enables to easily process columns in large text files but is also a quite powerfull programming language. This training session aims at introducing you AWK principles. You will learn about variables, operators and functions useful to manipulate very large files. \r\n\r\nFor example you can use AWK to generate your unix command lines to be launched on the cluster. AWK enables to process millions of lines in text files. The course includes short feature presentations between long hands-on sessions in which you will be able to understand the global ideas as well as details.",
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            "id": 127,
            "name": "Quality Control with Galaxy",
            "description": "This tutorial covers the questions:\r\n- How to perform quality control of NGS raw data?\r\n- What are the quality parameters to check for a dataset?\r\n- How to improve the quality of a dataset?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC\r\n- Assess long reads FASTQ quality using Nanoplot and PycoQC\r\n- Perform quality correction with Cutadapt (short reads)\r\n-  Summarise quality metrics MultiQC\r\n- Process single-end and paired-end data",
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            "id": 144,
            "name": "Reference-based RNA-Seq data analysis with Galaxy",
            "description": "This tutorial covers the questions:\r\n- What are the steps to process RNA-Seq data?\r\n- How to identify differentially expressed genes across multiple experimental conditions?\r\n- What are the biological functions impacted by the differential expression of genes?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Check a sequence quality report generated by FastQC for RNA-Seq data\r\n- Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome\r\n- Select and run a state of the art mapping tool for RNA-Seq data\r\n- Evaluate the quality of mapping results\r\n- Describe the process to estimate the library strandness\r\n- Estimate the number of reads per genes\r\n- Explain the count normalization to perform before sample comparison\r\n- Construct and run a differential gene expression analysis\r\n- Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes\r\n- Perform a gene ontology enrichment analysis\r\n- Perform and visualize an enrichment analysis for KEGG pathways",
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            "doi": null,
            "fileLocation": "https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html",
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