[{"id":391,"title":"WORKSHOP: Machine learning in the life sciences","url":"https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences.json","description":"This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024.\nEvent description\nMachine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems.\nBut what exactly is machine learning and when should you use it?\nThis hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them.\nLead trainer: \n\n\nDr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health\n\n\nFacilitators:\n\n\nDr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University\n\n\nWilliam Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF)\n\n\nDr Giorgia Mori, Sydney Informatics Hub, University of Sydney \n\n\nJoseph McConnell, University of Adelaide\n\n\nJessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955\n\n\nHost:\n\n\nDr Melissa Burke, Australian BioCommons.\n\n\nTraining materials\nMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.\nFiles and materials included in this record:\n\n\nEvent metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.\n\n\nSchedule (PDF): Schedule describing the timing of sessions for the in person and online events\n\n\nMaterials shared elsewhere:\n \nThis workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR","doi":"10.5281/zenodo.14676360","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":392,"title":"WORKSHOP: Genetic Outlier Analysis","url":"https://dresa.org.au/materials/workshop-genetic-outlier-analysis.json","description":"This record includes training materials associated with the Australian BioCommons workshop ‘Genetic Outlier Analysis’. \nThese workshops took place on:\n\n27 - 28 February 2024: Online via Zoom\n10 - 11 April 2024: In person in Melbourne\n4 - 5 July 2024: In person in Sydney \n\nEvent description\nThere are many interesting patterns that you can extract from genetic variant data. This can include patterns of linkage, balancing selection, or even inbreeding signals. One of the most common approaches is to find sites on the genome that are under selection. \nThis workshop introduces the basics of genetic selection analysis. It will step you through the process of identifying signals of selection using your own data (or an example genomic dataset) using the outlier analysis method. \nMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.\nThis workshop is presented by the Australian BioCommons and the Genetics Society of AustralAsia\nLead trainer: \n\n\nDr Katarina Stuart, Research Fellow, University of Auckland. \n\n\nFacilitators:\n\n\nAdele Barugahare, Monash Genomics and Bioinformatics Platform \n\n\nDr Georgina Samaha, Sydney Informatics Hub, University of Sydney \n\n\nDr Ching-Yu Lu, Sydney Informatics Hub, University of Sydney\n\n\nSoleille Miller, University of NSW\n\n\nDr Nandan Deshpande, Sydney Informatics Hub, University of Sydney\n\n\n \nInfrastructure provision: Audrey Stott, Pawsey Supercomputing Research Centre\nHost: Dr Melissa Burke, Australian BioCommons\nTraining materials\nFiles and materials included in this record:\n\n\nEvent metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.\n\n\nSchedules describing the timing of sessions for the in person and online events\n\n\nMaterials shared elsewhere:\nThese workshops followed the materials developed by Dr Katarina Stuart\n\n\nhttps://github.com/katarinastuart/Ev1_SelectionMetaAnalysis\n\n\n ","doi":"10.5281/zenodo.14676276","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":295,"title":"WORKSHOP: RNASeq: reads to differential genes and pathways","url":"https://dresa.org.au/materials/workshop-rnaseq-reads-to-differential-genes-and-pathways.json","description":"This record includes training materials associated with the Australian BioCommons workshop 'RNASeq: reads to differential genes and pathways'. This workshop took place over two, 3 hour sessions on 11 and 12 October 2023.Event descriptionRNA sequencing (RNAseq) is a popular and powerful technique used to understand the activity of genes. Using differential gene profiling methods, we can use RNAseq data to gain valuable insights into gene activity and identify variability in gene expression between samples to understand the molecular pathways underpinning many different traits.  In this hands-on workshop, you will learn RNAseq fundamentals as you process, analyse, and interpret the results from a real RNAseq experiment on the command-line. In session one, you will convert raw sequence reads to analysis-ready count data with the nf-core/rnaseq workflow. In session two, you'll work interactively in RStudio to identify differentially expressed genes,perform functional enrichment analysis, and visualise and interpret your results using popular and best practice R packages.  This workshop was delivered as a part of the Australian BioCommons Bring Your Own Data Platforms Project and will provide you with an opportunity to explore services and infrastructure built specifically for life scientists working at the command line. By the end of the workshop, you will be familiar with Pawsey's Nimbus cloud platform and be able to process your own RNAseq datasets and perform differential expression analysis on the command-line. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.Lead trainers: Dr Georgina Samaha (Sydney Informatics Hub), Dr Nandan Deshpande (Sydney Informatics Hub)Facilitators: Ching-Yu Lu and Jessica Chung.Infrastructure provision: Audrey Stott (Pawsey Supercomputing Research Centre), Alex Ip (AARNet)Host: Melissa Burke, Australian BioCommons Training materialsFiles and materials included in this record:Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.Materials shared elsewhere:This workshop follows the tutorial 'Introduction to RNAseq workshop: reads to differential gene expression' developed by the Sydney Informatics Hub.https://sydney-informatics-hub.github.io/rnaseq-workshop-2023/Additional supporting materials are available via GitHubRstudio rnaseq container: https://github.com/Sydney-Informatics-Hub/Rstudio-rnaseq-contained/tree/mainRNAseq differential expression R notebook: https://github.com/Sydney-Informatics-Hub/rna-differential-expression-Rnotebook","doi":"10.5281/zenodo.10045628","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":296,"title":"WORKSHOP: Single cell RNAseq analysis in R","url":"https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-6a1126cf-7105-43ec-bf55-7c492f758301.json","description":"This record includes training materials associated with the Australian BioCommons workshop 'Single cell RNAseq analysis in R'. This workshop took place over two, 3.5 hour sessions on 26 and 27 October 2023.Event descriptionAnalysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data. We will discuss the 'why' behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data.This workshop is presented by the Australian BioCommons, Queensland Cyber Infrastructure Foundation (QCIF) and the Monash Genomics and Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.Lead trainers: Sarah Williams, Adele Barugahare, Paul Harrison, Laura Perlaza JimenezFacilitators: Nick Matigan, Valentine Murigneux, Magdalena (Magda) AntczakInfrastructure provision: Uwe WinterCoordinator: Melissa BurkeTraining materialsMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.Files and materials included in this record:Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshopMaterials shared elsewhere:This workshop follows the tutorial 'scRNAseq Analysis in R with Seurat'https://swbioinf.github.io/scRNAseqInR_Doco/index.htmlSlides used to introduce key topics are available via GitHubhttps://github.com/swbioinf/scRNAseqInR_Doco/tree/main/slidesThis material is based on the introductory Guided Clustering Tutorial tutorial from Seurat.It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here.  ","doi":"10.5281/zenodo.10042919","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":313,"title":"WORKSHOP: Single cell RNAseq analysis in R","url":"https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-4f60b82d-2f1e-4021-9569-6955878dd945.json","description":"This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.\n\nEvent description\n\n\n\t\n\tAnalysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data. \n\t\n\t\n\tWe will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data.\n\t\n\n\nThis workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.\n \n\nMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.\n\nFiles and materials included in this record:\n\n\n\t\n\tEvent metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.\n\t\n\t\n\tIndex of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.\n\t\n\t\n\tscRNAseq_Slides (PDF): Slides used to introduce topics\n\t\n\t\n\tscRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop\n\t\n\t\n\tscRNAseq_Resources (PDF): A list of resources recommended by trainers and participants\n\t\n\t\n\tscRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.\n\t\n\n\n \n\nMaterials shared elsewhere:\n\nThis workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’\n\nhttps://swbioinf.github.io/scRNAseqInR_Doco/index.html\n\nThis material is based on the introductory Guided Clustering Tutorial tutorial from Seurat.\nIt is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here.","doi":"10.5281/zenodo.7072910","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":316,"title":"WORKSHOP: R: fundamental skills for biologists","url":"https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists-81aa00db-63ad-4962-a7ac-b885bf9f676b.json","description":"This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.\n\n \n\nEvent description\n\nBiologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets. \n\nR is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes. \n\nGetting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R.\n\nTopics covered in this workshop include:\n\n\n\t\n\tSpreadsheets, organising data and first steps with R\n\t\n\t\n\tManipulating and analysing data with dplyr\n\t\n\t\n\tData visualisation\n\t\n\t\n\tSummarized experiments and getting started with Bioconductor\n\t\n\n\n \n\nThis workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.\n\nMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.\n\nFiles and materials included in this record:\n\n\n\t\n\tEvent metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.\n\t\n\t\n\tIndex of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.\n\t\n\t\n\tSchedule (PDF): A breakdown of the topics and timings for the workshop\n\t\n\t\n\tRecommended resources (PDF): A list of resources recommended by trainers and participants\n\t\n\t\n\tQ_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel.\n\t\n\n\nMaterials shared elsewhere:\n\n \n\nThis workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available.\n\nhttps://saskiafreytag.github.io/biocommons-r-intro/\n\nThis is derived from material produced as part of The Carpentries Incubator project\n\nhttps://carpentries-incubator.github.io/bioc-intro/","doi":"10.5281/zenodo.6766951","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":323,"title":"WORKSHOP: Working with genomics sequences and features in R with Bioconductor","url":"https://dresa.org.au/materials/workshop-working-with-genomics-sequences-and-features-in-r-with-bioconductor-8399bf0d-1e9e-48f3-a840-3f70f23254bb.json","description":"This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021.\n\nWorkshop description\n\nExplore the many useful functions that the Bioconductor environment offers for working with genomic data and other biological sequences. \n\nDNA and proteins are often represented as files containing strings of nucleic acids or amino acids. They are associated with text files that provide additional contextual information such as genome annotations.\n\nThis workshop provides hands-on experience with tools, software and packages available in R via Bioconductor for manipulating, exploring and extracting information from biological sequences and annotation files. We will look at tools for working with some commonly used file formats including FASTA, GFF3, GTF, methods for identifying regions of interest, and easy methods for obtaining data packages such as genome assemblies. \n\nThis workshop is presented by the Australian BioCommons and Monash Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.\n\nMaterials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.\n\nFiles and materials included in this record:\n\n\n\t\n\tEvent metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.\n\t\n\t\n\tIndex of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file.\n\t\n\n\n\n\t\n\tSchedule (PDF): schedule for the workshop providing a breakdown of topics and timings\n\t\n\n\n \n\nMaterials shared elsewhere:\n\nThis workshop follows the tutorial ‘Working with DNA sequences and features in R with Bioconductor - version 2’ developed for Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison.\n\nhttps://monashdatafluency.github.io/r-bioc-2/","doi":"10.5281/zenodo.5781776","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":267,"title":"ARDC FAIR Data 101 self-guided","url":"https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097.json","description":"FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles\n\nThe FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.\n\nThe course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub.","doi":"10.5281/zenodo.5094034","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]}]