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Authors: Barlow, Melanie (orcid: 000...  or Brady, Catherine (orcid: 00...  or Chung, Jessica (orcid: 0000... 

WORKSHOP: Machine learning in the life sciences

This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024.
Event description
Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of...

Keywords: Bioinformatics, Life Science, Machine Learning

WORKSHOP: Machine learning in the life sciences https://dresa.org.au/materials/workshop-machine-learning-in-the-life-sciences This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This 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. Lead trainer:  Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health Facilitators: Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University William Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF) Dr Giorgia Mori, Sydney Informatics Hub, University of Sydney  Joseph McConnell, University of Adelaide Jessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955 Host: Dr Melissa Burke, Australian BioCommons. Training materials Materials 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. Schedule (PDF): Schedule describing the timing of sessions for the in person and online events Materials shared elsewhere:   This workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Life Science, Machine Learning
WORKSHOP: RNASeq: reads to differential genes and pathways

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...

Keywords: bioinformatics, transcriptomics, RNA-seq, RNAseq

WORKSHOP: RNASeq: reads to differential genes and pathways https://dresa.org.au/materials/workshop-rnaseq-reads-to-differential-genes-and-pathways 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 Melissa Burke (melissa@biocommons.org.au) bioinformatics, transcriptomics, RNA-seq, RNAseq
WORKSHOP: R: fundamental skills for biologists

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.

 

Event description

Biologists need data analysis skills to be able to...

Keywords: Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists-81aa00db-63ad-4962-a7ac-b885bf9f676b 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.   Event description Biologists 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.  R 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.  Getting 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. Topics covered in this workshop include: Spreadsheets, organising data and first steps with R Manipulating and analysing data with dplyr Data visualisation Summarized experiments and getting started with Bioconductor   This 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. Materials 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. Schedule (PDF): A breakdown of the topics and timings for the workshop Recommended resources (PDF): A list of resources recommended by trainers and participants Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere:   This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
23 (research data) Things

23 (research data) things is a set of training materials exploring research data management. Each of the 23 things offers a variety of learning opportunities with activities at three levels of complexity:

  • Getting started
  • Learn more
  • Challenge me

All resources used in the program are online...

Keywords: research data management, training material

23 (research data) Things https://dresa.org.au/materials/23-research-data-things-793872d2-c221-4cd6-91be-11a313c74b78 23 (research data) things is a set of training materials exploring research data management. Each of the 23 things offers a variety of learning opportunities with activities at three levels of complexity: * Getting started * Learn more * Challenge me All resources used in the program are online and free to use and reuse under a Creative Commons Attribution 4.0 International licence. You could use all of them as a self-paced course, or choose components to integrate into your own course. The 23 things are designed to build knowledge as the program progresses, so if you’re new to the world of research data management, we suggest you start with things 1-3 and then decide where you want to go from there. These materials supported an international community-based training program delivered in 2016 by the Australian National Data Service. This release migrates these materials to a GitHub repository for continued maintenance. Some updates were made to material that was outdated. We welcome contributions and suggestions via GitHub Issue or Pull Request. contact@ardc.edu.au research data management, training material
ARDC FAIR Data 101 self-guided

FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles

The 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.

The course structure was based on 'FAIR Data in the...

Keywords: training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097 FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The 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. The 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. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management