type: map mapping:
layout: type: str enum: - learning-pathway title: type: str required: true description: | Title of the pathway _examples: - Clustering in Machine Learning - Breve introducción a Galaxy - en español - Pangeo ecosystem 101 for everyone - Introduction to Xarray Galaxy Tools description: type: str required: true description: | Description of the pathway type: type: str enum: - use - admin-dev - instructors cover-image: type: str description: cover image cover-image-alt: type: str description: cover image alt text priority: type: int description: priority within learning path list editorial_board: type: seq required: true sequence: - type: str required: true description: A contributor's ID in the CONTRIBUTORS.yaml file. enum: - CONTRIBUTORS funding: type: seq description: These entities provided funding support for the development of this resource sequence: - type: str enum: - ORGANISATIONS - GRANTS tags: type: seq description: Any relevant tags that would help a user discover this LP sequence: - type: str required: true type: type: str description: | The type of topic, some have subtly different behaviours. `admin-dev` : should be used for admin and developer topics that are not scientifically focused. `basics` : Only used for galaxy-interface type topics `data-science` : Topics which are not necessarily Galaxy focused but expand into broader communities `use` : These topics use galaxy for some analysis `instructors` : Specific to topics related to instruction of Galaxy required: true enum: - admin-dev - basics - data-science - use - instructors draft: type: bool description: | `true` to hide your LP from the LP list (optional). This is useful if you need an LP for a workshop, but have not finished making it up to GTN standards. pathway: type: seq required: true sequence: - type: map mapping: section: type: str required: true description: type: str learning_objectives: type: seq sequence: - type: str required: true description: | List of Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) learning objectives for the tutorial A learning objective is a single sentence describing what a learner will be able to do once they have done the tutorial. Generally it is best to follow a 2C or 3C learning objective such as: - Compute (Skill) - multiple whole genome assemblies (Objective) - in such a way to develop big data processing skills (Result) _examples: - Understand the basic concepts behind phylogenetic trees, as applied to *Mycobacterium tuberculosis* - Explore Biodiversity data with taxonomic, temporal and geographical informations - Generate a DotPlot emulating the original paper using a different analysis tool tutorials: type: seq sequence: - type: map mapping: name: type: str required: true topic: type: str enum: - admin - covid19 - ai4life - assembly - climate - community - computational-chemistry - contributing - data-science - dev - ecology - epigenetics - evolution - fair - galaxy-interface - genome-annotation - imaging - introduction - metabolomics - microbiome - proteomics - sequence-analysis - single-cell - statistics - synthetic-biology - teaching - transcriptomics - variant-analysis - visualisation link: type: str external: type: bool type: type: str enum: - hands_on - slides