Handles creating, reading and updating training materials.

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            "name": "IFB Cloud tutorial: Gene regulation",
            "description": "1. Using the Gene-regulation appliance\n1.1 Requirements\n1.2 Virtual disk creation\n1.3 Creation of an instance\n1.4 Connection to the device\n1.5 Download source data\n1.6 Execute workflow\n\n2. Visualizing results\n2.1 Install and run the X2Go client on your host computer\n2.2 Visualize results\nFastQC\nIGV\n\n\n3. Create your own Gene-regulation appliance\nCreation of an instance\nInstalling programs and dependencies\nGet the gene-regulation repository\nRun makefile to install the dependencies\n\n",
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            "id": 29,
            "name": "Adding bioschemas markup to data repository",
            "description": "\n \n\nAdding bioschemas markup to data repository\n \n",
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            "id": 59,
            "name": "Variant annotation",
            "description": "Add meta-information on variant to facilitate interpretation\n",
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            "id": 125,
            "name": "ETBII 2023",
            "description": "All the training materials dedicated to the IFB's Integrative Bioinformatics Thematic School, which took place in January 2023.",
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            "doi": null,
            "fileLocation": "https://moodle.france-bioinformatique.fr/course/view.php?id=13",
            "fileName": "ETBII 2023 Training materials",
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                "http://edamontology.org/topic_3316",
                "http://edamontology.org/topic_3474",
                "http://edamontology.org/topic_3391",
                "http://edamontology.org/topic_3365",
                "http://edamontology.org/topic_2269",
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                "Multivariate analyses",
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                "Data Integration"
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            "id": 28,
            "name": "Alternative episodes for the 4 Open Source Software",
            "description": "\n \n\nAlternative episodes for the 4 Open Source Software (4OSS) lesson focused on different Open Source technologies: Github, Docker, Jupyter Notebook and so on\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": 26,
            "name": "Assessing the FAIRness of Training Materials",
            "description": "\n \n\nAssessing the FAIRness of Training Materials\n \n",
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            "id": 109,
            "name": "RNA-Seq: Differential Expression Analysis",
            "description": "\n \n\nBe careful about experimental design : avoid putting all the\nreplicates in the same lane, using the same barcode for the\nreplicates, putting different number of samples in lanes etc...\nNon- uniformity of the per base read distribution (Illumina Random\nHexamer Priming bias visible on the 13 first bases)\nBias hierarchy : biological condition >> concentration > run/flowcell> lane\nAt equivalent expression level, a long gene will have more reads than a short one.\nNon random coverage along the transcript.\nMultiple hit for some reads alignments.\n \n",
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            "id": 46,
            "name": "BioBlend API",
            "description": "BioBlend module, a python library to use Galaxy API\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": 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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                "Metagenomics"
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        },
        {
            "id": 24,
            "name": "bio-tools - EDAM drop-in hackathon - discussions",
            "description": "bio.tools, EDAM drop-in hackathon and discussions\n",
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            "doi": null,
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        {
            "id": 23,
            "name": "Building a semantic search engine for biology publications using event stream processing",
            "description": "\n \n\nBuilding a semantic search engine for biology publications using event stream processing\n \n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/c-sparql-powered_d842/scormcontent/index.html",
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            "id": 86,
            "name": "Gut metagenomics in cardiometabolic diseases",
            "description": "Cardio-metabolic and Nutrition-related diseases (CMDs) represent an enormous burden for health care. They are characterized by very heterogeneous phenotypes progressing with time. It is virtually impossible to predict who will or will not develop cardiovascular comorbidities. There is a clear need to intervene earlier in the natural cycle of the disease, before irreversible tissue damages develop. Predictive tools still remain elusive and environmental factors (food, nutrition, physical activity and psychosocial factors) play major roles in the development of these interrelated pathologies. Poor nutritional environment and lifestyle also promote health deterioration resulting in CMD progression. In the last few years, the characterization of the gut microbiome (i.e. collective bacteria genome) and gut-derived molecules (i.e. metabolites, lipids, inflammatory molecules) has opened up new avenues for the generation of fundamental knowledge regarding putative shared pathways in CMD. The gut microbiome is likely to have an even greater impact than genetic factors given its close relationship with environmental factors. In metabolic disorders, the discoveries that low bacterial gene richness associates with cardiovascular risks stimulate encourage these developments. Due to the complexity of the gut microbiome, and its interactions with human (host) metabolism as well as with the immune system, it is only through integrative analyses where metabolic network models are used as scaffold for analysis that it will be possible to identify markers and shared pathways, which will contribute to improve patient stratification and develop new modes of patient care.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_2/Gut_metagenomics_in_cardiometabolic_diseases/scormcontent/index.html",
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            "dateCreation": "2016-12-15",
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            "id": 135,
            "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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            "doi": null,
            "fileLocation": "https://genotoul-bioinfo.pages.mia.inra.fr/training-rnaseq",
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            "audienceTypes": [],
            "audienceRoles": [],
            "difficultyLevel": "Intermediate",
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            "dateCreation": null,
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            "id": 84,
            "name": "Who is doing what on the cheese surface? Overview of the cheese microbial ecosystem functioning by metatranscriptomic analyses",
            "description": "Cheese ripening is a complex biochemical process driven by microbial communities composed of both eukaryotes and prokaryotes. Surface-ripened cheeses are widely consumed all over the world and are appreciated for their characteristic flavor. Microbial community composition has been studied for a long time on surface-ripened cheeses, but only limited knowledge has been acquired about its in situ metabolic activities. We used an iterative sensory procedure to select a simplified microbial consortium, composed of only nine species (three yeasts and six bacteria), producing the odor of Livarot-type cheese when inoculated in a sterile cheese curd. All the genomes were sequenced in order to determine the functional capacities of the different species and facilitate RNA-Seq data analyses. We followed the ripening process of experimental cheeses made using this consortium during four weeks, by metatranscriptomic and biochemical analyses. By combining all of the data, we were able to obtain an overview of the cheese maturation process and to better understand the metabolic activities of the different community members and their possible interactions. We next applied the same approach to investigate the activity of the microorganisms in real cheeses, namely Reblochon-style cheeses. This provided useful insights into the physiological changes that occur during cheese ripening, such as changes in energy substrates, anabolic reactions, or stresses.\n",
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            "id": 71,
            "name": "Holistic metagenomics in marine communities",
            "description": "Complex microscopic communities are composed of species belonging to all life realms, from single-cell prokaryotes to multicellular eukaryotes of small size. Each component of a community needs to be studied for a full understanding of the functions performed by the whole assemblage, however methods to investigate microbiomes are generally restricted to a single kingdom. Using examples from the Tara Oceans project, we will show how size fractionation and use of varied metabarcoding, metagenomics and metatranscriptomics approaches can help studying the marine plankton community as a whole, in a wide geographic space.\n",
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        },
        {
            "id": 22,
            "name": "CWL support in Galaxy",
            "description": "\n \n\nCWL support in Galaxy\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/cwl-support-in-galaxy_f872/scormcontent/index.html",
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        {
            "id": 21,
            "name": "Data clearinghouse, validation and curation of BioSamples - ENA - Breeding API endpoints - MAR databases",
            "description": "\n \n\nData clearinghouse, validation and curation of BioSamples/ENA/Breeding API endpoints/MAR databases\n \n",
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            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/data-clearinghouse_55d1/scormcontent/index.html",
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            "id": 118,
            "name": "Chip-seq: Introduction to the Workshop",
            "description": "Data visualization, quality control, normalization & peak calling\nPeak annotation\nFrom peaks to motifs\n",
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            "doi": null,
            "fileLocation": "http://dputhier.github.io/EBA_2015_ChIP-Seq/slides/chipseq_CarlHerrmann_Roscoff2015.pdf",
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