Training materials
Status: Active
and Keywords: HPC or Knowledge graph or Populations or Programming or computer-science
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Bioinformatics, Computational Biology, Computer Science, Programming, Coding, Education, Data Science, Transcriptomics, Machine Learning
R for Data Science
•• intermediateBioinformatics Computational biology Machine learning Transcriptomics Computational Biology Coding Programming Data Science Data Analysis Computer Science Machine Learning -
Tutorial, Presentation
Materials from 'Introduction to High Performance Computing for Life Scientists' course
•• intermediateComputational biology HPC GPU parallel computing -
Learning pathway
Linux learning pathways
• beginnerBioinformatics Linux Programming -
Online material, online course
Workshop on Resources for Plant Sciences, 2023
•• intermediatePlant biology service bundle eLearning EeLP plant science Data Visualization Gene Ontology Knowledge graph MIAPPE -
E-Learning, Training materials
Biology meets Programming - Introduction to Bioinformatics using Python
• beginnerBioinformatics Biology Python Python biologists Programming Data Analysis Sequence Analysis -
WEBINAR: Pro tips for scaling bioinformatics workflows to HPC
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course materials, Online material, Training materials
Cloud-SPAN Genomics
• beginnerBioinformatics Software engineering Genomics DNA polymorphism Workflows Data architecture, analysis and design Shell Command line Cloud computing HPC Data analysis High performance computing -
course materials, online course, Training materials
Cloud-SPAN Prenomics
• beginnerBioinformatics Software engineering Genomics Query and retrieval Data handling Cloud computing Shell Command line Amazon Web Services genomics HPC Data analysis bioinformatics -
Video, E-learning
FAIR principles in practice for health data
• beginnerComputer science Data management FAIR data Medical informatics Standardisation and normalisation Design Clinical data RDF Knowledge graph Semantic framework FAIR Findability Accessibility Interoperability Reusability -
WEBINAR: Where to go when your bioinformatics outgrows your compute
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