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166 material found

WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia

This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022.

Event description 

Have you discovered a brilliant...

Keywords: Bioinformatics, Workflows, FAIR, Galaxy Australia

WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia https://dresa.org.au/materials/webinar-here-s-one-we-prepared-earlier-re-creating-bioinformatics-methods-and-workflows-with-galaxy-australia This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022. Event description  Have you discovered a brilliant bioinformatics workflow but you’re not quite sure how to use it? In this webinar we will introduce the power of Galaxy for construction and (re)use of reproducible workflows, whether building workflows from scratch, recreating them from published descriptions and/or extracting from Galaxy histories. Using an established bioinformatics method, we’ll show you how to: Use the workflows creator in Galaxy Australia  Build a workflow based on a published method Annotate workflows so that you (and others) can understand them  Make workflows finable and citable (important and very easy to do!) Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. GalaxyWorkflows_Slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/IMkl6p7hkho Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, FAIR, Galaxy Australia
Introduction to Unix

A hands-on workshop covering the basics of the Unix command line interface.

Knowledge of the Unix operating system is fundamental to the use of many popular bioinformatics command-line tools. Whether you choose to run your analyses locally or on a high-performance computing system, knowing...

Keywords: Unix, Command line, Command-line, CLI

Resource type: tutorial

Introduction to Unix https://dresa.org.au/materials/introduction-to-unix A hands-on workshop covering the basics of the Unix command line interface. Knowledge of the Unix operating system is fundamental to the use of many popular bioinformatics command-line tools. Whether you choose to run your analyses locally or on a high-performance computing system, knowing your way around a command-line interface is highly valuable. This workshop will introduce you to Unix concepts by way of a series of hands-on exercises. This workshop is designed for participants with little or no command-line knowledge. Tools: Standard Unix commands, FileZilla Topic overview: Section 1: Getting started Section 2: Exploring your current directory Section 3: Making and changing directories Section 4: Viewing and manipulating files Section 5: Removing files and directories Section 6: Searching files Section 7: Putting it all together Section 8: Transferring files Tutorial instructions available here: https://www.melbournebioinformatics.org.au/tutorials/tutorials/unix/unix/ For queries relating to this workshop, contact Melbourne Bioinformatics (bioinformatics-training@unimelb.edu.au). Find out when we are next running this training as an in-person workshop, by visiting the Melbourne Bioinformaitcs Eventbrite page: https://www.eventbrite.com.au/o/melbourne-bioinformatics-13058846490 For queries relating to this workshop, contact Melbourne Bioinformatics (bioinformatics-training@unimelb.edu.au). Unix, Command line, Command-line, CLI ugrad masters mbr phd ecr researcher support professional
WEBINAR: Effective, inclusive, and scalable training in the life sciences, clinical education and beyond

This record includes training materials associated with the Australian BioCommons/Melbourne Genomics webinar ‘Effective, inclusive, and scalable training in the life sciences, clinical education and beyond’. This webinar took place on 4 November 2022.

Event description 

Scientists and educators...

Keywords: Short-format training, Clinical education, Continuing education, Professional development, Training, Lifelong learning, Pedagogy

WEBINAR: Effective, inclusive, and scalable training in the life sciences, clinical education and beyond https://dresa.org.au/materials/webinar-effective-inclusive-and-scalable-training-in-the-life-sciences-clinical-education-and-beyond This record includes training materials associated with the Australian BioCommons/Melbourne Genomics webinar ‘Effective, inclusive, and scalable training in the life sciences, clinical education and beyond’. This webinar took place on 4 November 2022. Event description  Scientists and educators working in the life sciences must continuously acquire new knowledge and skills to stay up-to-date with the latest methods, technologies and research. Short-format training, such as webinars, workshops and bootcamps, are popular ways of quickly learning about new topics and gaining new skills. As trainers and educators, how can we ensure that short-format training is effective and inclusive for all? How can we ensure that our learners are equipped to continue learning and applying their new skills once they return to their day jobs? And how can we do this in a way that is scalable and sustainable? The Bicycle Principles assemble education theory and community experience into a framework for improving short-format training so that it is effective, inclusive and scalable. Over 30 international experts, including colleagues from the Australian BioCommons, Melbourne Genomics and other Australian and New Zealand organisations, helped develop the principles and an associated set of recommendations. Jason Williams, Assistant Director, DNA Learning Center, Cold Spring Harbor Laboratory - a leading genomics and bioinformatics educator and project lead, joins us to discuss the Principles and how they can be applied to achieve scalable and sustainable training in a range of Australian settings. This webinar is co-hosted by Australian BioCommons and Melbourne Genomics Training Materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. WILLIAMS-Jason_aus-biocommons_nov-2022 (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere:   A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/18dub7jGeQ8 Melissa Burke (melissa@biocommons.org.au) Short-format training, Clinical education, Continuing education, Professional development, Training, Lifelong learning, Pedagogy
Managing Active Research Data

In this train-the-trainer workshop, we will be exploring and discussing methods for active data management.

Participants will become familiar with cloud storage and associated tools and services for managing active research data. Learn how to organise, maintain, store and analyse active data,...

Keywords: RDM Training, CloudStor, cloud

Resource type: lesson

Managing Active Research Data https://dresa.org.au/materials/managing-active-research-data In this train-the-trainer workshop, we will be exploring and discussing methods for active data management. Participants will become familiar with cloud storage and associated tools and services for managing active research data. Learn how to organise, maintain, store and analyse active data, and understand safe and secure ways of sharing and storing data. Topics such as cloud storage, collaborative editing, versioning and data sharing will be discussed and demonstrated. Sara King RDM Training, CloudStor, cloud phd support masters ecr researcher
Learn R or Python, generate Species Distribution Models (SDM) or SDM Climate Projections

EcoCommons has a variety of videos, R scripts, and support articles that introduce users to learning how to code in R or Python, how to generate Species Distribution Models (SDMs) or generate SDM climate projections.

We also have a growing number of use cases where users can see and work...

Keywords: Species Distribution Modelling, Beginner R coding, Beginer ecological modelling, Climate projections

Learn R or Python, generate Species Distribution Models (SDM) or SDM Climate Projections https://dresa.org.au/materials/learn-r-or-python-generate-species-distribution-models-sdm-or-sdm-climate-projections EcoCommons has a variety of videos, R scripts, and support articles that introduce users to learning how to code in R or Python, how to generate Species Distribution Models (SDMs) or generate SDM climate projections. We also have a growing number of use cases where users can see and work through examples that highlight the power of bringing data together. support@ecocommons.org.au EcoCommons Species Distribution Modelling, Beginner R coding, Beginer ecological modelling, Climate projections ugrad mbr phd ecr professional
Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training

This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems.

The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM...

Keywords: PAI-C, Training, Data Management

Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training https://dresa.org.au/materials/principles-aligned-institutionally-contextualised-pai-c-rdm-training This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems. The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM across institutions, which in turn will facilitate cross institutional management of data (e.g. when researchers move between institutions, and collaborate across institutions). Dr Adrian W. Chew (w.l.chew@unsw.edu.au) PAI-C, Training, Data Management
VOSON Lab Code Blog

The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.

Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics

Resource type: tutorial, other

VOSON Lab Code Blog https://dresa.org.au/materials/voson-lab-code-blog The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages. robert.ackland@anu.edu.au visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics researcher support phd masters
WEBINAR: Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud

This record includes training materials associated with the Australian BioCommons webinar ‘Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud’. This webinar took place on 20 September 2022.

Event description 

Bioinformatics workflows can support...

Keywords: Bioinformatics, Workflows, Nextflow, Containerisation

WEBINAR: Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud https://dresa.org.au/materials/webinar-portable-reproducible-and-scalable-bioinformatics-workflows-using-nextflow-and-pawsey-nimbus-cloud This record includes training materials associated with the Australian BioCommons webinar ‘Portable, reproducible and scalable bioinformatics workflows using Nextflow and Pawsey Nimbus Cloud’. This webinar took place on 20 September 2022. Event description  Bioinformatics workflows can support portable, reproducible and scalable analysis of omics datasets but using workflows can be challenging for both beginners and experienced bioinformaticians. Beginners face a steep learning curve to be able to build and deploy their own bioinformatics workflows while those with more experience face challenges productionising and scaling code for custom workflows and big data.  Bioinformaticians across the world are using Nextflow to build and manage workflows. Many of these workflows are shared for others to use and supported by the community via nf-co.re. So far, 39 workflows for omics data are available with another 23 under development. These workflows cover common analyses such as RNAseq, mapping, variant calling, single cell transcriptomics and more and can be easily deployed by anyone, regardless of skill level. In this webinar, Nandan Deshpande from the Sydney Informatics Hub, University of Sydney, will discuss how you can deploy freely available Nextflow (nf.co-re) bioinformatics workflows with a single command. We describe how you can quickly get started deploying these workflows using Pawsey Nimbus Cloud. For advanced users, we introduce you to Nextflow concepts to get you started with building your own workflows that will save you time and support reproducible, portable and scalable analysis. In the latter half of the webinar, Sarah Beecroft from the Pawsey Supercomputing Research Centre will talk about their Nimbus Cloud systems. While Nextflow supports portability and can run on many computing infrastructures, we describe why we specifically love using Nimbus with Nextflow for many bioinformatics projects. We will describe some of the nf.co-re workflows that we have used on Nimbus and the research outcomes. We will also cover when not to use Nimbus and the alternatives we recommend.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Nextflow_Nimbus_slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/VnLX63yXbJU Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, Nextflow, Containerisation
WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.

Event description

Analysis and interpretation of single cell RNAseq (scRNAseq) data...

Keywords: Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data.  We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Slides (PDF): Slides used to introduce topics scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop scRNAseq_Resources (PDF): A list of resources recommended by trainers and participants scRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.   Materials shared elsewhere: This workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’ https://swbioinf.github.io/scRNAseqInR_Doco/index.html This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here. Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq
WEBINAR: Getting started with whole genome mapping and variant calling on the command line

This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with whole genome mapping and variant calling on the command line’. This webinar took place on 24 August 2022.

Event description 

Life scientists are increasingly using whole genome...

Keywords: Genome mapping, Variant calling, Bioinformatics, Workflows

WEBINAR: Getting started with whole genome mapping and variant calling on the command line https://dresa.org.au/materials/webinar-getting-started-with-whole-genome-mapping-and-variant-calling-on-the-command-line This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with whole genome mapping and variant calling on the command line’. This webinar took place on 24 August 2022. Event description  Life scientists are increasingly using whole genome sequencing (WGS) to ask and answer research questions across the tree of life. Before any of this work can be done, there is the essential but challenging task of processing raw sequencing data. Processing WGS data is a computationally challenging, multi-step process used to create a map of an individual’s genome and identify genetic variant sites. The tools you use in this process and overall workflow design can look very different for different researchers, it all depends on your dataset and the research questions you’re asking. Luckily, there are lots of existing WGS processing tools and pipelines out there, but knowing where to start and what your specific needs are is hard work, no matter how experienced you are.  In this webinar we will walk through the essential steps and considerations for researchers who are running and building reproducible WGS mapping and variant calling pipelines at the command line interface. We will discuss how to choose and evaluate a pipeline that is right for your dataset and research questions, and how to get access to the compute resources you need Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. WGS mapping and variant calling _slides (PDF): A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/Q2EceFyizio Melissa Burke (melissa@biocommons.org.au) Genome mapping, Variant calling, Bioinformatics, Workflows
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description 

bio.tools provides easy access to essential scientific...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software-9180e32a-f4f5-4993-a90a-a9bfcfafd4f3 This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. Event description  bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. biotools_EDAM_slides (PDF): A PDF copy of the slides presented during the webinar.   Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Programming and tidy data analysis in R

A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps...

Keywords: R, Tidyverse, Programming

Resource type: tutorial

Programming and tidy data analysis in R https://dresa.org.au/materials/programming-and-tidy-data-analysis-in-r A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps needed to load data and convert it into a tidy form. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R, Tidyverse, Programming phd ecr researcher
Linear models in R

A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the...

Keywords: R statistics

Resource type: tutorial

Linear models in R https://dresa.org.au/materials/linear-models-in-r A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the more complicated statistical and machine learning models available in R. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R statistics phd ecr researcher
Introduction to R

An introduction to R, for people with zero coding experience.

To be taught as a hands on workshop, typically as two half-days.

Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and...

Keywords: R

Resource type: tutorial

Introduction to R https://dresa.org.au/materials/introduction-to-r An introduction to R, for people with zero coding experience. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R phd ecr researcher
Introduction to Jupyter Notebooks

This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data.

You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new,...

Keywords: jupyter, Introductory, training material, CloudStor, markdown, Python, R

Resource type: tutorial

Introduction to Jupyter Notebooks https://dresa.org.au/materials/introduction-to-jupyter-notebooks This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data. You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new, have little or no knowledge of coding and computational methods in research. This workshop is targeted at those who are absolute beginners or ‘tech-curious’. It includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming. sara.king@aarnet.edu.au Mason, Ingrid jupyter, Introductory, training material, CloudStor, markdown, Python, R
WORKSHOP: R: fundamental skills for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.

Event description

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

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

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022. **Event description** Biologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets.  R is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes.  Getting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R. Topics covered in this workshop include: - Spreadsheets, organising data and first steps with R - Manipulating and analysing data with dplyr - Data visualisation - Summarized experiments and getting started with Bioconductor This workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. **Files and materials included in this record:** - Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. - Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. - Schedule (PDF): A breakdown of the topics and timings for the workshop - Recommended resources (PDF): A list of resources recommended by trainers and participants - Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. **Materials shared elsewhere:** This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description

bio.tools provides easy access to essential...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. **Event description** bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. **Files and materials included in this record:** - Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. - Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. - biotools_EDAM_slides (PDF): A PDF copy of the slides presented during the webinar. **Materials shared elsewhere:** A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Beyond Basics: Conditionals and Visualisation in Excel

After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested...

Keywords: Data Analysis, Excel

Beyond Basics: Conditionals and Visualisation in Excel https://dresa.org.au/materials/beyond-basics-conditionals-and-visualisation-in-excel After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** training@intersect.org.au Data Analysis, Excel
Data Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will explore different types of graphs and learn how to...

Keywords: Programming, R

Data Visualisation in R https://dresa.org.au/materials/data-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). 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 Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) courses to ensure that you are familiar with the knowledge needed for this course. We also strongly recommend attending the [Data Manipulation in R](https://intersect.org.au/training/course/r201/) course. **For more information, please click [here](https://intersect.org.au/training/course/r202).** training@intersect.org.au Programming, R
Data Manipulation and Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will learn how to manipulate, explore and get insights from...

Keywords: Programming, R

Data Manipulation and Visualisation in R https://dresa.org.au/materials/data-manipulation-and-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). 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 Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - DataFrame Manipulation using the dplyr package - DataFrame Transformation using the tidyr package - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/r203).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Introduction & Linear Regression

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...

Keywords: Programming, R

Introduction to Machine Learning using R: Introduction & Linear Regression https://dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression 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).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Classification

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...

Keywords: Programming, R

Introduction to Machine Learning using R: Classification https://dresa.org.au/materials/introduction-to-machine-learning-using-r-classification 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).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: SVM & Unsupervised Learning

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...

Keywords: Programming, R

Introduction to Machine Learning using R: SVM & Unsupervised Learning https://dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning 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).** training@intersect.org.au Programming, R
Exploring Chi-square and correlation in R

This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures...

Keywords: Programming, R

Exploring Chi-square and correlation in R https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-r This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** training@intersect.org.au Programming, R
Traversing t tests in R

R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

The primary goal of this workshop is to familiarise you with basic statistical concepts in R from...

Keywords: Programming, R

Traversing t tests in R https://dresa.org.au/materials/traversing-t-tests-in-r R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** training@intersect.org.au Programming, R
Exploring ANOVAs in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

This half-day course covers one and two-way Analyses of Variance (ANOVA) and their...

Keywords: Programming, R

Exploring ANOVAs in R https://dresa.org.au/materials/exploring-anovas-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. To better understand the tests, assumptions and associated concepts, we will be using a dataset containing the Mathematics scores of secondary students. This dataset also includes information regarding their mother's and father's jobs and education levels, the number of hours dedicated to study, and time spent commuting to and from school. Lifestyle information about alcohol consumption habits, whether the students have quality relationships with their families and whether they have free time after school is included in this dataset. #### You'll learn: - Basic statistical theory behind ANOVAs - How to check that the data meets the assumptions - One-way ANOVA in R and post-hoc analysis - Two-way ANOVA plus interaction effects and post-hoc analysis - Non-parametric alternatives to one and two-way ANOVA #### Prerequisites: This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required. If you’re new to programming in R, we strongly recommend you register for the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) workshops first. **For more information, please click [here](https://intersect.org.au/training/course/r212).** training@intersect.org.au Programming, R
Research Data Management Techniques

Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don't know how?

This workshop is ideal for researchers who want to know how research data management can support project...

Keywords: Data Management, Data Management

Research Data Management Techniques https://dresa.org.au/materials/research-data-management-techniques Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don't know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: - How to manage research data according to legal, statutory, ethical, funding body and university requirements - Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data - Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/rdmt001).** training@intersect.org.au Data Management, Data Management
Data Capture and Surveys with REDCap

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...

Keywords: Data Management, REDCap

Data Capture and Surveys with REDCap https://dresa.org.au/materials/data-capture-and-surveys-with-redcap 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).** training@intersect.org.au Data Management, REDCap
Longitudinal Trials with REDCap

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...

Keywords: Data Management, REDCap

Longitudinal Trials with REDCap https://dresa.org.au/materials/longitudinal-trials-with-redcap 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).** training@intersect.org.au Data Management, REDCap
Cleaning Data with Open Refine

Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet?

Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising...

Keywords: Data Analysis, Open Refine

Cleaning Data with Open Refine https://dresa.org.au/materials/cleaning-data-with-open-refine Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet? Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising and cleaning datasets, and extending data by accessing the internet through APIs. In this course we'll work through the various features of Refine, including importing data, faceting, clustering, and calling remote APIs, by working on a fictional but plausible humanities research project. #### You'll learn: - Download, install and run Open Refine - Import data from csv, text or online sources and create projects - Navigate data using the Open Refine interface - Explore data by using facets - Clean data using clustering - Parse data using GREL syntax - Extend data using Application Programming Interfaces (APIs) - Export project for use in other applications #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/refine101).** training@intersect.org.au Data Analysis, Open Refine