Handles creating, reading and updating training materials.

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            "id": 89,
            "name": "Docker Tutorial",
            "description": "Docker is free software that automates the deployment of applications in software containers executant in isolation. A Docker container, away from traditional virtual machines, requires no separate operating system and not providing any but relies instead on the core functionality and uses the isolation of resources and namespaces separated to isolate the operating system as seen by the application.\n",
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            "name": "Visualization of NGS data with IGV",
            "description": "Visualisation of next-gen sequencing data with Integrative Genomics Viewer\n",
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            "name": "Galaxy Docker Training Tutorial",
            "description": "Galaxy docker integration\nEnable Galaxy to use BioContainers (Docker)\nGalaxy with Docker swarm\n",
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            "name": "Development of a catalog of federated SPARQL queries in the field of Rare Diseases",
            "description": "\n \n\nDevelopment of a catalog of federated SPARQL queries in the field of Rare Diseases\n \n",
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            "id": 93,
            "name": "Cross Taxa Tutorial",
            "description": "How query databases according to complex taxonomic critera\nCross-Taxa allows to retrieve gene families that are shared by a given set of taxa, or which are specific to a set of taxa. It is also possible to select genes families which are associated to a certain set of taxa but which are not found in a second set of taxa. Any taxonomic level can be used.\n",
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            "name": "training RNASEQ Bioinfo part",
            "description": "Cette formation a pour but de vous aider à traiter les séquences courtes issues des plates-formes de séquençage Illumina. Vous y découvrirez les formats de séquences et d’alignement les biais connus et mettrez en œuvre des logiciels d'alignement épissé sur génome de référence, la recherche de nouveaux gènes, de nouveaux transcrits et la quantification de l'expression de ces gènes et transcrits.",
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            "name": "REPET: TEdenovo tutorial",
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            "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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            "name": "Users, Groups, and Quotas in Galaxy",
            "description": "How to handle Users, Groups, and Quotas in Galaxy\n",
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            "id": 25,
            "name": "Bioconda packaging of the Regulatory Sequence Analysis Tools (RSAT)",
            "description": "\n \n\nBioconda packaging of the Regulatory Sequence Analysis Tools (RSAT)\n \n",
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            "id": 66,
            "name": "Galaxy III: Visualization",
            "description": "Visualization of Next Generation Sequencing Data using the Integrative Genomics Viewer (IGV)\n",
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            "name": "Cluster Slides",
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            "id": 98,
            "name": "A Quick and focused overview of R data types and ggplot2 syntax",
            "description": "R and RStudio overview.\n",
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            "dateCreation": "2016-04-01",
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            "id": 82,
            "name": "Sequencing 6000 chloroplast genomes : the PhyloAlps project",
            "description": "Biodiversity is now commonly described by DNA based approches. Several actors are currently using DNA to describe biodiversity, and most of the time they use different genetic markers that is hampering an easy sharing of the accumulated knowledges. Taxonomists rely a lot on the DNA Barcoding initiative, phylogeneticists often prefer markers with better phylogenic properties, and ecologists, with the coming of the DNA metabarcoding, look for a third class of markers easiest to amplify from environmental DNA. Nevertheless they have all the same need of the knowledge accumulated by the others. But having different markers means that the sequecences have been got from different individuals in differente lab, following various protocoles. On that base, building a clean reference database, merging for each species all the available markers becomes a challenge. With the phyloAlps project we implement genome skimming at a large  scale and propose it as a new way to set up such universal reference database usable by taxonomists, phylogeneticists, and ecologists. The Phyloalps project is producing for each species of the Alpine flora at least a genome skim composed of six millions of 100bp sequence reads. From such data it is simple to extract all chloroplastic, mitochondrial and nuclear rDNA markers commonely used. Moreover, most of the time we can get access to the complete chloroplast genome sequence and to a shallow sequencing of many nuclear genes. This methodes have already been successfully applied to algeae, insects and others animals. With the new single cell sequencing methods it will be applicable to most of the unicellular organisms. The good question is now : Can we consider the genome skimming as the next-generation DNA barcode ?\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_3/Sequencing_6000_chloroplast_genomes_the_PhyloAlps_project/scormcontent/",
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            "keywords": [
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            "dateCreation": "2016-12-16",
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            "id": 17,
            "name": "Enrichment and propagation of metagenomic experimental metadata",
            "description": "\n \n\nEnrichment and propagation of metagenomic experimental metadata\n \n",
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            "id": 27,
            "name": "Application of RDF-based models and tools for enhancing interoperable",
            "description": "\n \n\nApplication of RDF-based models and tools for enhancing interoperable use of biomedical resources\n \n",
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            "id": 120,
            "name": "Initiation to Galaxy",
            "description": "DNA-sequence analysis: from raw reads to variants calling within the galaxy environement.\n",
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            "id": 106,
            "name": "Genomic copy number Tutorial",
            "description": "We will analyze the copy number variations of a human tumor (parotid gland carcinoma), limited to the chr17, from a WES (whole-exome sequencing) experiment. All genomic coordinates correspond to the 2009 build of the reference human genome (hg19 / GRC37).\n",
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            "id": 73,
            "name": "200 billion sequences and counting: analysis, discovery and exploration of datasets with EBI Metagenomics",
            "description": "EBI metagenomics (EMG, https://www.ebi.ac.uk/metagenomics/) is a freely available hub for the analysis and exploration of metagenomic, metatranscriptomic, amplicon and assembly data. The resource provides rich functional and taxonomic analyses of user-submitted sequences, as well as analysis of publicly available metagenomic datasets held within the European Nucleotide Archive (ENA). EMG has recently undergone rapid expansion, with an over 10-fold increase in data volumes in the first 5 months of 2016. It now houses ~ 50k publicly available data sets, and represents one of the largest collections of analysed metagenomic data. As its data content has grown, EMG has increasingly become a platform for data discovery. To support this process, we have made a series of user-interface improvements, including the classification of projects by biome, presentation of results data for better visualisation and more convenient download, and provision of project level summary files. More recently, we have indexed project metadata for use with the EBI search engine, enabling exploration across different datasets. For example, users are able to search with a particular taxonomic lineage or protein function and discover the projects, samples and sequencing runs in which that lineage or function is found. This functionality allows users to explore associations between biomes, environmental conditions and organisms and functions (e.g., discovering protein coding sequences that correspond to certain enzyme families found in aquatic environments at a given temperature range). Here, we give an overview of the EMG data analysis pipeline and web site, and illustrate the use of the new search facility for data discovery.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_7/200_billions_sequences_and_counting_discovery_and_exploration_of_datasets_with_EBI_metagenomics/scormcontent/index.html",
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            "dateCreation": "2016-12-16",
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