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  • Keeping Archives Online Learning Series

    1 July 2016 - 31 December 2025

    Keeping Archives Online Learning Series https://dresa.org.au/events/keeping-archives-online-learning-series Our pioneering online learning program, based on our respected publication Keeping Archives, was launched in 2016. It provides a new level of learning in the archives and records profession, filling a gap between a tertiary course and on-the-job experience. These courses are ideal for: - People who are new to archives and need a grounding in archival principles; - Students who wish to enhance the archival component of their training; - Professional archivists who may require a refresher in new archival methods and theory – e.g. emergent web technologies and social media platforms; - Statutory organisations whose staff need records and archives knowledge as part of their responsibilities. - Organisations with volunteers who engage in archival work and need basic knowledge. 2016-07-01 09:00:00 UTC 2025-12-31 17:00:00 UTC Australian Society of Archivists Australia Australia Australian Society of Archivists office@archivists.org.au [] [] [] open_to_all ArchivesRecordsArvchivingRecordkeeping

  • Introductory Statistics Online

    2 October - 10 November 2023

    Introductory Statistics Online https://dresa.org.au/events/introductory-statistics-online This six week online course introduces students to the basics of applied statistics used in disciplines including psychology, biology, medicine, health, engineering, business, sociology and the arts. The three main areas of study are descriptive statistics, probability distributions and statistical inference. The SPSS statistical computing package is an integral part of the course. 2023-10-02 09:00:00 UTC 2023-11-10 17:00:00 UTC La Trobe University Statistics Consultancy Platform La Trobe University, Kingsbury Drive, Bundoora, Australia La Trobe University, Kingsbury Drive Bundoora Australia 3086 La Trobe University Statistics.Consultancy@latrobe.edu.au [] researchersresearch students 30 workshop open_to_all StatisticsFundamentals of Statistics SPSS

  • Longitudinal and Mixed Model Analysis with R

    4 October 2023

    Longitudinal and Mixed Model Analysis with R https://dresa.org.au/events/longitudinal-and-mixed-model-analysis-with-r-f0938187-3ce9-48ba-bc25-da0237cb83c9 This workshop will develop participants’ understanding of the principles, methods, and interpretation of statistical models for longitudinal data (i.e. repeated measures over time) using R. The course will cover the principles of Linear Mixed Models from simple models to more complex ones and includes practical sessions getting hands-on experience of longitudinal analysis in R. 2023-10-04 09:00:00 UTC 2023-10-04 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using Python: Classification at UOA Online

    4 - 5 October 2023

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online-d4093c47-e816-465d-86c2-114d85d12d88 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2023-10-04 09:30:00 UTC 2023-10-05 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • WEBINAR: BioSamples: supporting multi-omics data integration with FAIR sample records

    4 October 2023

    WEBINAR: BioSamples: supporting multi-omics data integration with FAIR sample records https://dresa.org.au/events/webinar-biosamples-supporting-multi-omics-data-integration-with-fair-sample-records The [BioSamples database](https://www.ebi.ac.uk/biosamples/) at EMBL-EBI is the ELIXIR deposition database and EMBL-EBI's central institutional repository for information about biological samples (metadata). BioSamples can be used to search, submit and curate sample metadata across multiple projects and contexts. BioSamples records are the key point of connection between EMBL-EBI archives (e.g ENA, ArrayExpress) and other resources. This webinar will highlight how BioSamples can be used to enable multi-omic data sharing and integration including how to submit to the database in combination with other major public repositories. We will look at how BioSamples supports Findable, Accessible, Interoperable and Reusable (FAIR) principles for sample metadata management, and examine case studies where this has been beneficial, for example for integrating data to support the COVID-19 pandemic response. **Speaker:** Tony Burdett, Technical Team Leader - Archival Infrastructure and Technology, EMBL-EBI **Date/Time:** Wednesday 4 October 2023, 4pm AEDT/ 3pm AEST/ 3:30pm ACDT/ 1pm AWST ([Check in your timezone](https://www.timeanddate.com/worldclock/fixedtime.html?ah=1&iso=20231004T16&msg=BioSamples%3A%20Supporting%20multi-omics%20data%20integration%20with%20FAIR%20sample%20records&p1=152)) **Who the webinar is for:** This webinar is for biologists and bioinformaticians who search, submit and/or curate samples across multiple projects and contexts. It’s particularly relevant to anyone interested in linking datasets to support the FAIR principles. **How to join:** This webinar is free to join but you must register for a place in advance. **[Register here](https://unimelb.zoom.us/webinar/register/WN_ndRqEPcWSvqGnbkSl5EqUg)** 2023-10-04 16:00:00 UTC 2023-10-04 17:00:00 UTC Australian BioCommons Australian BioCommons training@biocommons.org.au [] [] [] open_to_all BioSamplesData IntegrationMetadataMultiomicsData submissionData curation

  • Getting started with NVivo for Windows at Western Sydney: Online

    5 October 2023

    Getting started with NVivo for Windows at Western Sydney: Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-western-sydney-online-dfc95a87-9afe-4e05-869d-4af448c2b4a5 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2023-10-05 09:30:00 UTC 2023-10-05 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []

  • Statistical Comparisons for HASS

    9 October 2023

    Statistical Comparisons for HASS https://dresa.org.au/events/statistical-comparisons-for-hass This practical workshop will help participants to choose and use the appropriate standard statistical test for their data by introducing key concepts of inferential statistics in SPSS. Participants will learn how to compute and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA. 2023-10-09 09:00:00 UTC 2023-10-09 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 101: Getting Started with REDCap

    9 October 2023

    REDCap 101: Getting Started with REDCap https://dresa.org.au/events/redcap-101-getting-started-with-redcap-4245f757-ab20-4428-9062-ad78f60d596c REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-09 14:00:00 UTC 2023-10-09 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Programming: R for Reproducible Scientific Analysis

    10 - 13 October 2023

    Introduction to Programming: R for Reproducible Scientific Analysis https://dresa.org.au/events/introduction-to-programming-r-for-reproducible-scientific-analysis-5f88b611-c157-447f-b2e9-b538137a1837 This Software Carpentry workshop will introduce novice programmers to the R software environment, a powerful, popular and free statistical and graphical programming language. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. The emphasis of this workshop is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. 2023-10-10 09:00:00 UTC 2023-10-13 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using R: Introduction & Linear Regression at UNSW Online

    11 - 13 October 2023

    Introduction to Machine Learning using R: Introduction & Linear Regression at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use R and and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r205).** 2023-10-11 09:30:00 UTC 2023-10-13 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Surveying with Qualtrics at Deakin Online

    11 October 2023

    Surveying with Qualtrics at Deakin Online https://dresa.org.au/events/surveying-with-qualtrics-at-deakin-online-54ed13e0-a160-492f-b675-6ab63464dc07 Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics? Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow from building a survey to analysing the results using Qualtrics. We will discover the numerous design elements available in order to get the most useful results and make life as easy as can be for your respondents. If your institution has a licence to Qualtrics, then this course is right for you. #### You'll learn: - Format a sample survey using the Qualtrics online platform - Configure the survey using a range of design features to improve user experience - Decide which distribution channel is right for your needs - Understand the available data analysis and export options in Qualtrics #### Prerequisites: You must have access to a Qualtrics instance, such as through your university license. Speak to your local university IT or Research Office for assistance in accessing the Qualtrics instance. **For more information, please click [here](https://intersect.org.au/training/course/qltrics101).** 2023-10-11 09:30:00 UTC 2023-10-11 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []

  • Data Capture and Surveys with REDCap at Western Sydney: Online

    11 October 2023

    Data Capture and Surveys with REDCap at Western Sydney: Online https://dresa.org.au/events/data-capture-and-surveys-with-redcap-at-western-sydney-online-dc42896e-164b-4a24-a35f-85cc3395a041 Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: - Get started with REDCap - Create and set up projects - Design forms and surveys using the online designer - Learn how to use branching logic, piping, and calculations - Enter data via forms and distribute surveys - Create, view and export data reports - Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/redcap101).** 2023-10-11 09:30:00 UTC 2023-10-11 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using Python: Classification at UOA Online

    11 - 12 October 2023

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online-220e8b2d-98f6-4cd5-b649-bd59e78b3195 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2023-10-11 09:30:00 UTC 2023-10-12 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Longitudinal Trials with REDCap at La Trobe Online

    11 October 2023

    Longitudinal Trials with REDCap at La Trobe Online https://dresa.org.au/events/longitudinal-trials-with-redcap-at-la-trobe-online-1ad78bea-39b8-40b6-a9c5-31696c223413 REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with very little configuration. This course will introduce some of REDCap's more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 - Managing Data Capture and Surveys with REDCap. #### You'll learn: - Build a longitudinal project - Manage participants throughout multiple events - Configure and use Automated Survey Invitations - Use Smart Variables to add powerful features to your logic - Take advantage of high-granularity permissions for your collaborators - Understand the data structure of a longitudinal project #### Prerequisites: This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our [Data Capture and Surveys with REDCap](https://intersect.org.au/training/course/redcap101/) workshop. **For more information, please click [here](https://intersect.org.au/training/course/redcap201).** 2023-10-11 10:00:00 UTC 2023-10-11 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []

  • WORKSHOP: RNASeq: reads to differential genes and pathways

    11 - 12 October 2023

    WORKSHOP: RNASeq: reads to differential genes and pathways https://dresa.org.au/events/workshop-rnaseq-reads-to-differential-genes-and-pathways RNA 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](https://nf-co.re/rnaseq/usage) 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 is being delivered as a part of the Australian BioCommons[ Bring Your Own Data Platforms Project](https://www.biocommons.org.au/byo-data-platform-expansion) 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](https://pawsey.org.au/systems/nimbus-cloud-service/) platform and be able to process your own RNAseq datasets and perform differential expression analysis on the command-line. **Lead Trainers:** Dr Nandan Deshpande, Senior Research Bioinformatician, Sydney Informatics Hub Dr Georgina Samaha, Bioinformatics Group Lead, Sydney Informatics Hub **Date/Time:** 11 & 12 October 2023, 2 - 5pm AEST/1:30 - 4:30pm ACST/12 - 3pm AWST **Location:** Online **Format:** This online workshop will take place over two three-hour sessions. You must attend both sessions to get the most out of the workshop. Expert trainers will introduce new topics and guide you through hands-on activities to help you put your new skills into action. **Learning outcomes:** By the end of the workshop you should be able to: - List the steps involved in RNAseq data processing and differential expression analysis - Understand key concepts and considerations for RNAseq experiments - Describe the benefits of using nf-core workflows - Run the nf-core/rnaseq workflow to perform: - Quality control - Read alignment - Read quantification to generate raw counts - Use R/RStudio on to perform: - Quality control - Identify differentially expressed genes - Perform functional enrichment/pathway analysis **Who the workshop is for:** This workshop is for Australian researchers or bioinformaticians who are new to working with RNAseq datasets on the command-line interface and have or will be using bulk RNAseq datasets to identify differentially expressed genes as part of their projects. You must be associated with an Australian organisation for your application to be considered. The workshop will be conducted in a Unix environment and will use R/RStudio. Basic command line knowledge is required. You must know how to navigate the directory structure and copy files between the computers. If you need a refresher on Unix/Linux try[ this online tutorial](https://linuxjourney.com/lesson/the-shell). Basic knowledge of R/RStudio is required. You must know how to set up directories, run commands, reading in and outputting files. If you need a refresher on R/RStudio try the[ Introduction to R and RStudio section](https://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro.html) of this online tutorial. It’s recommended that you watch the following webinars before joining the workshop: - [Getting started with RNAseq: Transforming raw reads into biological insights](https://www.biocommons.org.au/events/rnaseqwebinar) - [Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud](https://youtu.be/VnLX63yXbJU). **How to apply:** **[Apply here](https://www.eventbrite.com.au/e/rna-seq-reads-to-differential-genes-and-pathways-tickets-677905422367)** This workshop is free but participation is subject to application with selection. **_Applications close at 11:59pm AEST, Monday 25 September 2023._** Applications will be reviewed by the organising committee and all applicants will be informed of the status of their application (successful, waiting list, unsuccessful). Successful applicants will be provided with a Zoom meeting link closer to the date. More information on the selection process is provided in our[ Advice on applying for Australian BioCommons workshops.](https://www.biocommons.org.au/workshop-applications) **[Apply here](https://www.eventbrite.com.au/e/rna-seq-reads-to-differential-genes-and-pathways-tickets-677905422367)** _This workshop is presented by the[ Australian BioCommons](https://www.biocommons.org.au/),[ Sydney Informatics Hub](https://www.sydney.edu.au/research/facilities/sydney-informatics-hub.html) and[ Pawsey Supercomputing Research Centre](https://pawsey.org.au/) with the assistance of a network of facilitators from the national[ Bioinformatics Training Cooperative](https://www.biocommons.org.au/training-cooperative)._ _This event is part of a series of[ bioinformatics training events](https://www.biocommons.org.au/events). If you'd like to hear when registrations open for other events, please[ subscribe](https://www.biocommons.org.au/subscribe) to Australian BioCommons._ 2023-10-11 14:00:00 UTC 2023-10-12 17:00:00 UTC Australian BioCommons Australian BioCommons training@biocommons.org.au [] [] 50 [] expression_of_interest RNASeqRNA-SeqTranscriptomics

  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    12 October 2023

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-3a9e5ef0-1a71-4e77-afbf-3748f915a7cf Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2023-10-12 09:30:00 UTC 2023-10-12 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Unix

    16 October 2023

    Introduction to Unix https://dresa.org.au/events/introduction-to-unix-bc01596a-861f-43f8-ae53-4b31eec8246d The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a power tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including 'high-performance computing' supercomputers). This Software Carpentry workshop will start you on a path towards using these resources effectively. 2023-10-16 09:00:00 UTC 2023-10-16 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 102: Creating Projects

    16 October 2023

    REDCap 102: Creating Projects https://dresa.org.au/events/redcap-102-creating-projects-8e01d54b-38ab-4ea1-be86-2ba97687b93f REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-16 14:00:00 UTC 2023-10-16 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Learn to Program: Python at ACU Online

    17 - 18 October 2023

    Learn to Program: Python at ACU Online https://dresa.org.au/events/learn-to-program-python-at-acu-online-347d2cb5-a609-4d51-9c05-534c9253090f Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** 2023-10-17 09:30:00 UTC 2023-10-18 12:30:00 UTC Intersect Australia Australia Australia ACU training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using R: Classification at UNSW Online

    18 - 20 October 2023

    Introduction to Machine Learning using R: Classification at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r206).** 2023-10-18 09:30:00 UTC 2023-10-20 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Getting started with NVivo for Windows at La Trobe Online

    18 October 2023

    Getting started with NVivo for Windows at La Trobe Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-la-trobe-online-a3dd8958-ebcc-45e5-8332-af973d8883b1 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2023-10-18 10:00:00 UTC 2023-10-18 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []

  • Linear Regression using SPSS

    19 October 2023

    Linear Regression using SPSS https://dresa.org.au/events/linear-regression-using-spss-2a64ca34-1c35-4950-a19f-8e5cc8c3e17b This workshop is designed to increase participants understanding of statistical relationships between data. It introduces principles and methods of regression models using SPSS, and how to interpret relationships between variables. The course covers basic principles of regression methods through to interpreting the output of statistical analyses, and also includes practical sessions giving hands-on experience with regression analysis in SPSS. 2023-10-19 09:00:00 UTC 2023-10-19 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    19 October 2023

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-ce2ed889-d55b-4cd9-a8d2-3ff96c96744f Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2023-10-19 09:30:00 UTC 2023-10-19 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Programming: Plotting and Programming with Python

    23 - 26 October 2023

    Introduction to Programming: Plotting and Programming with Python https://dresa.org.au/events/introduction-to-programming-plotting-and-programming-with-python-5d29aca3-e30a-4588-84a7-16ddd16ed6aa This Software Carpentry workshop will introduce the building blocks of the Python scripting environment. Participants will start by exploring the command-line interface and basic programming concepts using Unix, before moving on to learn about simple and complex data types, conditionals and looping in Python. This workshop will prepare participants to carry out batch analysis and equip them with the knowledge to start creating automated pipelines to increase data processing power and productivity. 2023-10-23 09:00:00 UTC 2023-10-26 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 201: Advanced Project Management

    23 October 2023

    REDCap 201: Advanced Project Management https://dresa.org.au/events/redcap-201-advanced-project-management-c8faa9d2-2521-4f93-842a-35cd11622791 REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-23 14:00:00 UTC 2023-10-23 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Learn to Program: R at UniSA

    24 - 25 October 2023

    Magill, Australia

    Learn to Program: R at UniSA https://dresa.org.au/events/learn-to-program-r-at-unisa R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2023-10-24 09:00:00 UTC 2023-10-25 12:00:00 UTC Intersect Australia UniSA, Magill, Australia UniSA Magill Australia UniSA training@intersect.org.au [] [] [] host_institution []

  • Learn to Program: R at UniSA Online

    24 - 25 October 2023

    Learn to Program: R at UniSA Online https://dresa.org.au/events/learn-to-program-r-at-unisa-online-5a191b52-9d3e-4fda-a892-6efecfb725f9 R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2023-10-24 09:00:00 UTC 2023-10-25 12:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []

  • Data Capture and Surveys with REDCap at UOA Online

    24 October 2023

    Data Capture and Surveys with REDCap at UOA Online https://dresa.org.au/events/data-capture-and-surveys-with-redcap-at-uoa-online-47c1ad90-65ea-42ee-988f-bb1b82913394 Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: - Get started with REDCap - Create and set up projects - Design forms and surveys using the online designer - Learn how to use branching logic, piping, and calculations - Enter data via forms and distribute surveys - Create, view and export data reports - Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/redcap101).** 2023-10-24 09:30:00 UTC 2023-10-24 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using R: SVM & Unsupervised Learning at UNSW Online

    25 October 2023

    Introduction to Machine Learning using R: SVM & Unsupervised Learning at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-svm-unsupervised-learning-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r207).** 2023-10-25 09:30:00 UTC 2023-10-25 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Getting Started with NVivo for Mac at UNSW Online

    25 October 2023

    Getting Started with NVivo for Mac at UNSW Online https://dresa.org.au/events/getting-started-with-nvivo-for-mac-at-unsw-online-190a561d-cb6f-40ba-872c-ae529dfc558e Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo102).** 2023-10-25 09:30:00 UTC 2023-10-25 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Keeping Archives Online Learning Series

    1 July 2016 - 31 December 2025

    Keeping Archives Online Learning Series https://dresa.org.au/events/keeping-archives-online-learning-series Our pioneering online learning program, based on our respected publication Keeping Archives, was launched in 2016. It provides a new level of learning in the archives and records profession, filling a gap between a tertiary course and on-the-job experience. These courses are ideal for: - People who are new to archives and need a grounding in archival principles; - Students who wish to enhance the archival component of their training; - Professional archivists who may require a refresher in new archival methods and theory – e.g. emergent web technologies and social media platforms; - Statutory organisations whose staff need records and archives knowledge as part of their responsibilities. - Organisations with volunteers who engage in archival work and need basic knowledge. 2016-07-01 09:00:00 UTC 2025-12-31 17:00:00 UTC Australian Society of Archivists Australia Australia Australian Society of Archivists office@archivists.org.au [] [] [] open_to_all ArchivesRecordsArvchivingRecordkeeping

  • Introductory Statistics Online

    2 October - 10 November 2023

    Introductory Statistics Online https://dresa.org.au/events/introductory-statistics-online This six week online course introduces students to the basics of applied statistics used in disciplines including psychology, biology, medicine, health, engineering, business, sociology and the arts. The three main areas of study are descriptive statistics, probability distributions and statistical inference. The SPSS statistical computing package is an integral part of the course. 2023-10-02 09:00:00 UTC 2023-11-10 17:00:00 UTC La Trobe University Statistics Consultancy Platform La Trobe University, Kingsbury Drive, Bundoora, Australia La Trobe University, Kingsbury Drive Bundoora Australia 3086 La Trobe University Statistics.Consultancy@latrobe.edu.au [] researchersresearch students 30 workshop open_to_all StatisticsFundamentals of Statistics SPSS

  • Longitudinal and Mixed Model Analysis with R

    4 October 2023

    Longitudinal and Mixed Model Analysis with R https://dresa.org.au/events/longitudinal-and-mixed-model-analysis-with-r-f0938187-3ce9-48ba-bc25-da0237cb83c9 This workshop will develop participants’ understanding of the principles, methods, and interpretation of statistical models for longitudinal data (i.e. repeated measures over time) using R. The course will cover the principles of Linear Mixed Models from simple models to more complex ones and includes practical sessions getting hands-on experience of longitudinal analysis in R. 2023-10-04 09:00:00 UTC 2023-10-04 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using Python: Classification at UOA Online

    4 - 5 October 2023

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online-d4093c47-e816-465d-86c2-114d85d12d88 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2023-10-04 09:30:00 UTC 2023-10-05 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • WEBINAR: BioSamples: supporting multi-omics data integration with FAIR sample records

    4 October 2023

    WEBINAR: BioSamples: supporting multi-omics data integration with FAIR sample records https://dresa.org.au/events/webinar-biosamples-supporting-multi-omics-data-integration-with-fair-sample-records The [BioSamples database](https://www.ebi.ac.uk/biosamples/) at EMBL-EBI is the ELIXIR deposition database and EMBL-EBI's central institutional repository for information about biological samples (metadata). BioSamples can be used to search, submit and curate sample metadata across multiple projects and contexts. BioSamples records are the key point of connection between EMBL-EBI archives (e.g ENA, ArrayExpress) and other resources. This webinar will highlight how BioSamples can be used to enable multi-omic data sharing and integration including how to submit to the database in combination with other major public repositories. We will look at how BioSamples supports Findable, Accessible, Interoperable and Reusable (FAIR) principles for sample metadata management, and examine case studies where this has been beneficial, for example for integrating data to support the COVID-19 pandemic response. **Speaker:** Tony Burdett, Technical Team Leader - Archival Infrastructure and Technology, EMBL-EBI **Date/Time:** Wednesday 4 October 2023, 4pm AEDT/ 3pm AEST/ 3:30pm ACDT/ 1pm AWST ([Check in your timezone](https://www.timeanddate.com/worldclock/fixedtime.html?ah=1&iso=20231004T16&msg=BioSamples%3A%20Supporting%20multi-omics%20data%20integration%20with%20FAIR%20sample%20records&p1=152)) **Who the webinar is for:** This webinar is for biologists and bioinformaticians who search, submit and/or curate samples across multiple projects and contexts. It’s particularly relevant to anyone interested in linking datasets to support the FAIR principles. **How to join:** This webinar is free to join but you must register for a place in advance. **[Register here](https://unimelb.zoom.us/webinar/register/WN_ndRqEPcWSvqGnbkSl5EqUg)** 2023-10-04 16:00:00 UTC 2023-10-04 17:00:00 UTC Australian BioCommons Australian BioCommons training@biocommons.org.au [] [] [] open_to_all BioSamplesData IntegrationMetadataMultiomicsData submissionData curation

  • Getting started with NVivo for Windows at Western Sydney: Online

    5 October 2023

    Getting started with NVivo for Windows at Western Sydney: Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-western-sydney-online-dfc95a87-9afe-4e05-869d-4af448c2b4a5 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2023-10-05 09:30:00 UTC 2023-10-05 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []

  • Statistical Comparisons for HASS

    9 October 2023

    Statistical Comparisons for HASS https://dresa.org.au/events/statistical-comparisons-for-hass This practical workshop will help participants to choose and use the appropriate standard statistical test for their data by introducing key concepts of inferential statistics in SPSS. Participants will learn how to compute and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA. 2023-10-09 09:00:00 UTC 2023-10-09 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 101: Getting Started with REDCap

    9 October 2023

    REDCap 101: Getting Started with REDCap https://dresa.org.au/events/redcap-101-getting-started-with-redcap-4245f757-ab20-4428-9062-ad78f60d596c REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-09 14:00:00 UTC 2023-10-09 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Programming: R for Reproducible Scientific Analysis

    10 - 13 October 2023

    Introduction to Programming: R for Reproducible Scientific Analysis https://dresa.org.au/events/introduction-to-programming-r-for-reproducible-scientific-analysis-5f88b611-c157-447f-b2e9-b538137a1837 This Software Carpentry workshop will introduce novice programmers to the R software environment, a powerful, popular and free statistical and graphical programming language. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. The emphasis of this workshop is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. 2023-10-10 09:00:00 UTC 2023-10-13 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using R: Introduction & Linear Regression at UNSW Online

    11 - 13 October 2023

    Introduction to Machine Learning using R: Introduction & Linear Regression at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use R and and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r205).** 2023-10-11 09:30:00 UTC 2023-10-13 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Surveying with Qualtrics at Deakin Online

    11 October 2023

    Surveying with Qualtrics at Deakin Online https://dresa.org.au/events/surveying-with-qualtrics-at-deakin-online-54ed13e0-a160-492f-b675-6ab63464dc07 Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics? Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow from building a survey to analysing the results using Qualtrics. We will discover the numerous design elements available in order to get the most useful results and make life as easy as can be for your respondents. If your institution has a licence to Qualtrics, then this course is right for you. #### You'll learn: - Format a sample survey using the Qualtrics online platform - Configure the survey using a range of design features to improve user experience - Decide which distribution channel is right for your needs - Understand the available data analysis and export options in Qualtrics #### Prerequisites: You must have access to a Qualtrics instance, such as through your university license. Speak to your local university IT or Research Office for assistance in accessing the Qualtrics instance. **For more information, please click [here](https://intersect.org.au/training/course/qltrics101).** 2023-10-11 09:30:00 UTC 2023-10-11 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []

  • Data Capture and Surveys with REDCap at Western Sydney: Online

    11 October 2023

    Data Capture and Surveys with REDCap at Western Sydney: Online https://dresa.org.au/events/data-capture-and-surveys-with-redcap-at-western-sydney-online-dc42896e-164b-4a24-a35f-85cc3395a041 Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: - Get started with REDCap - Create and set up projects - Design forms and surveys using the online designer - Learn how to use branching logic, piping, and calculations - Enter data via forms and distribute surveys - Create, view and export data reports - Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/redcap101).** 2023-10-11 09:30:00 UTC 2023-10-11 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using Python: Classification at UOA Online

    11 - 12 October 2023

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online-220e8b2d-98f6-4cd5-b649-bd59e78b3195 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2023-10-11 09:30:00 UTC 2023-10-12 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Longitudinal Trials with REDCap at La Trobe Online

    11 October 2023

    Longitudinal Trials with REDCap at La Trobe Online https://dresa.org.au/events/longitudinal-trials-with-redcap-at-la-trobe-online-1ad78bea-39b8-40b6-a9c5-31696c223413 REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with very little configuration. This course will introduce some of REDCap's more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 - Managing Data Capture and Surveys with REDCap. #### You'll learn: - Build a longitudinal project - Manage participants throughout multiple events - Configure and use Automated Survey Invitations - Use Smart Variables to add powerful features to your logic - Take advantage of high-granularity permissions for your collaborators - Understand the data structure of a longitudinal project #### Prerequisites: This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our [Data Capture and Surveys with REDCap](https://intersect.org.au/training/course/redcap101/) workshop. **For more information, please click [here](https://intersect.org.au/training/course/redcap201).** 2023-10-11 10:00:00 UTC 2023-10-11 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []

  • WORKSHOP: RNASeq: reads to differential genes and pathways

    11 - 12 October 2023

    WORKSHOP: RNASeq: reads to differential genes and pathways https://dresa.org.au/events/workshop-rnaseq-reads-to-differential-genes-and-pathways RNA 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](https://nf-co.re/rnaseq/usage) 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 is being delivered as a part of the Australian BioCommons[ Bring Your Own Data Platforms Project](https://www.biocommons.org.au/byo-data-platform-expansion) 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](https://pawsey.org.au/systems/nimbus-cloud-service/) platform and be able to process your own RNAseq datasets and perform differential expression analysis on the command-line. **Lead Trainers:** Dr Nandan Deshpande, Senior Research Bioinformatician, Sydney Informatics Hub Dr Georgina Samaha, Bioinformatics Group Lead, Sydney Informatics Hub **Date/Time:** 11 & 12 October 2023, 2 - 5pm AEST/1:30 - 4:30pm ACST/12 - 3pm AWST **Location:** Online **Format:** This online workshop will take place over two three-hour sessions. You must attend both sessions to get the most out of the workshop. Expert trainers will introduce new topics and guide you through hands-on activities to help you put your new skills into action. **Learning outcomes:** By the end of the workshop you should be able to: - List the steps involved in RNAseq data processing and differential expression analysis - Understand key concepts and considerations for RNAseq experiments - Describe the benefits of using nf-core workflows - Run the nf-core/rnaseq workflow to perform: - Quality control - Read alignment - Read quantification to generate raw counts - Use R/RStudio on to perform: - Quality control - Identify differentially expressed genes - Perform functional enrichment/pathway analysis **Who the workshop is for:** This workshop is for Australian researchers or bioinformaticians who are new to working with RNAseq datasets on the command-line interface and have or will be using bulk RNAseq datasets to identify differentially expressed genes as part of their projects. You must be associated with an Australian organisation for your application to be considered. The workshop will be conducted in a Unix environment and will use R/RStudio. Basic command line knowledge is required. You must know how to navigate the directory structure and copy files between the computers. If you need a refresher on Unix/Linux try[ this online tutorial](https://linuxjourney.com/lesson/the-shell). Basic knowledge of R/RStudio is required. You must know how to set up directories, run commands, reading in and outputting files. If you need a refresher on R/RStudio try the[ Introduction to R and RStudio section](https://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro.html) of this online tutorial. It’s recommended that you watch the following webinars before joining the workshop: - [Getting started with RNAseq: Transforming raw reads into biological insights](https://www.biocommons.org.au/events/rnaseqwebinar) - [Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud](https://youtu.be/VnLX63yXbJU). **How to apply:** **[Apply here](https://www.eventbrite.com.au/e/rna-seq-reads-to-differential-genes-and-pathways-tickets-677905422367)** This workshop is free but participation is subject to application with selection. **_Applications close at 11:59pm AEST, Monday 25 September 2023._** Applications will be reviewed by the organising committee and all applicants will be informed of the status of their application (successful, waiting list, unsuccessful). Successful applicants will be provided with a Zoom meeting link closer to the date. More information on the selection process is provided in our[ Advice on applying for Australian BioCommons workshops.](https://www.biocommons.org.au/workshop-applications) **[Apply here](https://www.eventbrite.com.au/e/rna-seq-reads-to-differential-genes-and-pathways-tickets-677905422367)** _This workshop is presented by the[ Australian BioCommons](https://www.biocommons.org.au/),[ Sydney Informatics Hub](https://www.sydney.edu.au/research/facilities/sydney-informatics-hub.html) and[ Pawsey Supercomputing Research Centre](https://pawsey.org.au/) with the assistance of a network of facilitators from the national[ Bioinformatics Training Cooperative](https://www.biocommons.org.au/training-cooperative)._ _This event is part of a series of[ bioinformatics training events](https://www.biocommons.org.au/events). If you'd like to hear when registrations open for other events, please[ subscribe](https://www.biocommons.org.au/subscribe) to Australian BioCommons._ 2023-10-11 14:00:00 UTC 2023-10-12 17:00:00 UTC Australian BioCommons Australian BioCommons training@biocommons.org.au [] [] 50 [] expression_of_interest RNASeqRNA-SeqTranscriptomics

  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    12 October 2023

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-3a9e5ef0-1a71-4e77-afbf-3748f915a7cf Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2023-10-12 09:30:00 UTC 2023-10-12 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Unix

    16 October 2023

    Introduction to Unix https://dresa.org.au/events/introduction-to-unix-bc01596a-861f-43f8-ae53-4b31eec8246d The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a power tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including 'high-performance computing' supercomputers). This Software Carpentry workshop will start you on a path towards using these resources effectively. 2023-10-16 09:00:00 UTC 2023-10-16 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 102: Creating Projects

    16 October 2023

    REDCap 102: Creating Projects https://dresa.org.au/events/redcap-102-creating-projects-8e01d54b-38ab-4ea1-be86-2ba97687b93f REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-16 14:00:00 UTC 2023-10-16 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Learn to Program: Python at ACU Online

    17 - 18 October 2023

    Learn to Program: Python at ACU Online https://dresa.org.au/events/learn-to-program-python-at-acu-online-347d2cb5-a609-4d51-9c05-534c9253090f Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** 2023-10-17 09:30:00 UTC 2023-10-18 12:30:00 UTC Intersect Australia Australia Australia ACU training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using R: Classification at UNSW Online

    18 - 20 October 2023

    Introduction to Machine Learning using R: Classification at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r206).** 2023-10-18 09:30:00 UTC 2023-10-20 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Getting started with NVivo for Windows at La Trobe Online

    18 October 2023

    Getting started with NVivo for Windows at La Trobe Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-la-trobe-online-a3dd8958-ebcc-45e5-8332-af973d8883b1 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2023-10-18 10:00:00 UTC 2023-10-18 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []

  • Linear Regression using SPSS

    19 October 2023

    Linear Regression using SPSS https://dresa.org.au/events/linear-regression-using-spss-2a64ca34-1c35-4950-a19f-8e5cc8c3e17b This workshop is designed to increase participants understanding of statistical relationships between data. It introduces principles and methods of regression models using SPSS, and how to interpret relationships between variables. The course covers basic principles of regression methods through to interpreting the output of statistical analyses, and also includes practical sessions giving hands-on experience with regression analysis in SPSS. 2023-10-19 09:00:00 UTC 2023-10-19 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    19 October 2023

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-ce2ed889-d55b-4cd9-a8d2-3ff96c96744f Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2023-10-19 09:30:00 UTC 2023-10-19 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Programming: Plotting and Programming with Python

    23 - 26 October 2023

    Introduction to Programming: Plotting and Programming with Python https://dresa.org.au/events/introduction-to-programming-plotting-and-programming-with-python-5d29aca3-e30a-4588-84a7-16ddd16ed6aa This Software Carpentry workshop will introduce the building blocks of the Python scripting environment. Participants will start by exploring the command-line interface and basic programming concepts using Unix, before moving on to learn about simple and complex data types, conditionals and looping in Python. This workshop will prepare participants to carry out batch analysis and equip them with the knowledge to start creating automated pipelines to increase data processing power and productivity. 2023-10-23 09:00:00 UTC 2023-10-26 12:30:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • REDCap 201: Advanced Project Management

    23 October 2023

    REDCap 201: Advanced Project Management https://dresa.org.au/events/redcap-201-advanced-project-management-c8faa9d2-2521-4f93-842a-35cd11622791 REDCap is a widely used platform for managing and sharing surveys and registries of clinical research data. This series of short training modules covers everything from an introductory guide to REDCap through to running complex clinical trials. The modules are all stand-alone, and attendees can pick and choose which to attend depending on prior experience and research needs. 2023-10-23 14:00:00 UTC 2023-10-23 16:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []

  • Learn to Program: R at UniSA

    24 - 25 October 2023

    Magill, Australia

    Learn to Program: R at UniSA https://dresa.org.au/events/learn-to-program-r-at-unisa R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2023-10-24 09:00:00 UTC 2023-10-25 12:00:00 UTC Intersect Australia UniSA, Magill, Australia UniSA Magill Australia UniSA training@intersect.org.au [] [] [] host_institution []

  • Learn to Program: R at UniSA Online

    24 - 25 October 2023

    Learn to Program: R at UniSA Online https://dresa.org.au/events/learn-to-program-r-at-unisa-online-5a191b52-9d3e-4fda-a892-6efecfb725f9 R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2023-10-24 09:00:00 UTC 2023-10-25 12:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []

  • Data Capture and Surveys with REDCap at UOA Online

    24 October 2023

    Data Capture and Surveys with REDCap at UOA Online https://dresa.org.au/events/data-capture-and-surveys-with-redcap-at-uoa-online-47c1ad90-65ea-42ee-988f-bb1b82913394 Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: - Get started with REDCap - Create and set up projects - Design forms and surveys using the online designer - Learn how to use branching logic, piping, and calculations - Enter data via forms and distribute surveys - Create, view and export data reports - Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/redcap101).** 2023-10-24 09:30:00 UTC 2023-10-24 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []

  • Introduction to Machine Learning using R: SVM & Unsupervised Learning at UNSW Online

    25 October 2023

    Introduction to Machine Learning using R: SVM & Unsupervised Learning at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-svm-unsupervised-learning-at-unsw-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r207).** 2023-10-25 09:30:00 UTC 2023-10-25 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

  • Getting Started with NVivo for Mac at UNSW Online

    25 October 2023

    Getting Started with NVivo for Mac at UNSW Online https://dresa.org.au/events/getting-started-with-nvivo-for-mac-at-unsw-online-190a561d-cb6f-40ba-872c-ae529dfc558e Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo102).** 2023-10-25 09:30:00 UTC 2023-10-25 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []

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