Getting Started with Deep Learning
This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning...
Keywords: Deep learning, Machine learning
Resource type: presentation
Getting Started with Deep Learning
https://doi.org/10.26180/15032688
https://dresa.org.au/materials/getting-started-with-deep-learning
This lecture provides a high level overview of how you could get started with developing deep learning applications. It introduces deep learning in a nutshell and then provides advice relating to the concepts and skill sets you would need to know and have in order to build a deep learning application. The lecture also provides pointers to various resources you could use to gain a stronger foothold in deep learning.
This lecture is targeted at researchers who may be complete beginners in machine learning, deep learning, or even with programming, but who would like to get into the space to build AI systems hands-on.
datascienceplatform@monash.edu
Titus Tang
Deep learning, Machine learning
Visualisation and Storytelling
This workshop explores how data visualisation techniques could be utilised to better understand data and to communicate research efforts and outcomes. The workshop covers a broad range of techniques from simple and static 2D graphics to advanced 3D visualisations in order to provide a broad...
Keywords: data visualisation, storytelling
Resource type: presentation, tutorial
Visualisation and Storytelling
https://doi.org/10.26180/13100510
https://dresa.org.au/materials/visualisation-and-storytelling
This workshop explores how data visualisation techniques could be utilised to better understand data and to communicate research efforts and outcomes. The workshop covers a broad range of techniques from simple and static 2D graphics to advanced 3D visualisations in order to provide a broad overview of the tools available for data analysis, presentation and storytelling. We explore, among others, animated charts and graphs, web visualisation tools such as scrollytellers, and the possibilities of 3D, interactive, and even immersive visualisations. We use real world, concrete examples along the way in order to tangibly illustrate how these visualisations can be created and how viewers perceive and interact with them. We also introduce the various tools and skill sets you would need to be proficient at presenting your data to the world.
By the conclusion of this workshop, you would gain familiarity with the various possibilities for presenting your own research data and outcomes. You would have a more intuitive understanding of the strengths and weaknesses of various modes of data visualisation and storytelling, and would have a starting point to obtain the right skill sets relevant to developing your visualisations of choice.
datascienceplatform@monash.edu
Daniel Waghorn
Nora Hamacher
Owen Kaluza
data visualisation, storytelling
Semi-Supervised Deep Learning
Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled...
Keywords: Deep learning, Machine learning, semi-supervised
Resource type: presentation, tutorial
Semi-Supervised Deep Learning
https://doi.org/10.26180/14176805
https://dresa.org.au/materials/semi-supervised-deep-learning
Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled data. If successful, it allows better training of a model despite the limited amount of labelled data available.
This workshop is designed to be instructor led and covers various semi-supervised learning techniques available in the literature. The workshop consists of a lecture introducing at a high level a selection of techniques that are suitable for semi-supervised deep learning. We discuss how these techniques can be implemented and the underlying assumptions they require. The lecture is followed by a hands-on session where attendees implement a semi-supervised learning technique to train a neural network. We observe and discuss the changing performance and behaviour of the network as varying degrees of labelled and unlabelled data is provided to the network during training.
datascienceplatform@monash.edu
Titus Tang
Deep learning, Machine learning, semi-supervised
Introduction to Deep Learning and TensorFlow
This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise.
Part 1 - Introduction to Deep Learning and TensorFlow
Part 2 - Introduction to Convolutional Neural Networks
The Powerpoints contain...
Keywords: Deep learning, convolutional neural network, tensorflow, Machine learning
Resource type: presentation, tutorial
Introduction to Deep Learning and TensorFlow
https://doi.org/10.26180/13100519
https://dresa.org.au/materials/introduction-to-deep-learning-and-tensorflow
This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise.
Part 1 - Introduction to Deep Learning and TensorFlow
Part 2 - Introduction to Convolutional Neural Networks
The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises.
This workshop is an introduction to how deep learning works and how you could create a neural network using TensorFlow v2. We start by learning the basics of deep learning including what a neural network is, how information passes through the network, and how the network learns from data through the automated process of gradient descent. Workshop attendees would build, train and evaluate a neural network using a cloud GPU (Google Colab).
In part 2, we look at image data and how we could train a convolution neural network to classify images. Workshop attendees will extend their knowledge from the first part to design, train and evaluate this convolutional neural network.
datascienceplatform@monash.edu
Titus Tang
Deep learning, convolutional neural network, tensorflow, Machine learning
Use QueryPic to visualise searches in Trove's digitised newspapers (part 2)
This video shows how you can construct and visualise more complex searches for digitised newspaper articles in Trove using QueryPic (see part 1 for the basics). This includes limiting the date range of your query, and changing the time...
Keywords: Trove, GLAM Workbench, visualisation, newspapers, HASS
Resource type: video
Use QueryPic to visualise searches in Trove's digitised newspapers (part 2)
https://youtu.be/J_LgNL2EM4M
https://dresa.org.au/materials/use-querypic-to-visualise-searches-in-trove-s-digitised-newspapers-part-2
This video shows how you can construct and visualise more complex searches for digitised newspaper articles in Trove using [QueryPic](https://glam-workbench.net/trove-newspapers/#querypic) (see part 1 for the basics). This includes limiting the date range of your query, and changing the time scale to zoom in and out of your search results.
The GLAM Workbench is a collection of tools, examples, tutorials, and apps that help you make use of collection data from GLAM organisations (Galleries, Libraries, Archives, and Museums). See: https://glam-workbench.net/
Tim Sherratt (tim@timsherratt.org and @wragge on Twitter)
Trove, GLAM Workbench, visualisation, newspapers, HASS
ugrad
masters
phd
ecr
researcher
Use QueryPic to visualise searches in Trove's digitised newspapers (part 1)
This video demonstrates how to use the GLAM Workbench to visualise searches for digitised newspaper articles in Trove. Using the latest version of QueryPic, we can explore the complete result set, showing how the number of matching articles...
Keywords: Trove, GLAM Workbench, visualisation, newspapers, HASS
Resource type: video
Use QueryPic to visualise searches in Trove's digitised newspapers (part 1)
https://youtu.be/vdyKNowv9gw
https://dresa.org.au/materials/use-querypic-to-visualise-searches-in-trove-s-digitised-newspapers-part-1
This video demonstrates how to use the GLAM Workbench to visualise searches for digitised newspaper articles in Trove. Using the latest version of [QueryPic](https://glam-workbench.net/trove-newspapers/#querypic), we can explore the complete result set, showing how the number of matching articles changes over time. We can even compare queries to visualise changes in language or technology. It's a great way to start exploring the possibilities of GLAM data.
The GLAM Workbench is a collection of tools, examples, tutorials, and apps that help you make use of collection data from GLAM organisations (Galleries, Libraries, Archives, and Museums). See: https://glam-workbench.net/
Tim Sherratt (tim@timsherratt.org & @wragge on Twitter)
Trove, GLAM Workbench, visualisation, newspapers, HASS
ugrad
masters
ecr
researcher
Galaxy Training
Galaxy is a hosted web-accessible platform that lets you conduct accessible, reproducible, and transparent computational biological research. It is an international, community driven effort to make it easy for life scientists to analyse their data for free and without the need for programmatic...
Keywords: Galaxy Australia, Galaxy Project, Bioinformatics, Data analysis
Galaxy Training
https://training.galaxyproject.org/training-material/
https://dresa.org.au/materials/galaxy-training
Galaxy is a hosted web-accessible platform that lets you conduct accessible, reproducible, and transparent computational biological research. It is an international, community driven effort to make it easy for life scientists to analyse their data for free and without the need for programmatic skills.
This is a collection of tutorials developed and maintained by the worldwide Galaxy community that show you how to analyse a variety of biological data using Galaxy.
Melissa (melissa@biocommons.org.au)
Galaxy Australia, Galaxy Project, Bioinformatics, Data analysis
Research Data Governance
This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders.
**Cite...
Keywords: data governance, research data
Resource type: video
Research Data Governance
https://youtu.be/K_xVQRdgCIc
https://dresa.org.au/materials/research-data-governance
This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders.
**Cite as**
Australian Research Data Commons. (2021, June 30). Research Data Governance. Zenodo. https://doi.org/10.5281/zenodo.5044585
ARDC contact: https://ardc.edu.au/contact-us
Australian Research Data Commons
Max Wilkinson
Shannon Callaghan
Jo Savill
Kristan Kang
Kerry Levett
Keith Russell
Natasha Simons
data governance, research data
ecr
researcher
support
How can Software Containers help your Research?
This video explains software containers to a research audience. It is an introduction to why containers are beneficial for research. These benefits are standardisation, portability, reliability and reproducibility.
Software Containers in research are a solution that addresses the challenge of...
Keywords: Containers, software containers, reproducibility, replicable computational environment, software, research, reusable, cloud, standardisation
Resource type: video
How can Software Containers help your Research?
https://www.youtube.com/watch?v=HelrQnm3v4g
https://dresa.org.au/materials/how-can-software-containers-help-your-research
This video explains software containers to a research audience. It is an introduction to why containers are beneficial for research. These benefits are standardisation, portability, reliability and reproducibility.
Software Containers in research are a solution that addresses the challenge of a replicable computational environment and supports reproducibility of research results. Understanding the concept of software containers enables researchers to better communicate their research needs with their colleagues and other researchers using and developing containers.
**Cite as**
Australian Research Data Commons. (2021, July 26). How can software containers help your research?. Zenodo. https://doi.org/10.5281/zenodo.5091260
Contact us: https://ardc.edu.au/contact-us/
Australian Research Data Commons
Containers, software containers, reproducibility, replicable computational environment, software, research, reusable, cloud, standardisation
phd
ecr
researcher
support
Merit Allocation Training for 2022
This merit allocation training session provides critical information for researchers considering to apply for time on Pawsey’s new Setonix supercomputer in 2022.
Keywords: supercomputer, supercomputing, merit allocation, allocation
Resource type: video
Merit Allocation Training for 2022
https://www.youtube.com/watch?v=XpAg5zsNu3g&t=1110s
https://dresa.org.au/materials/merit-allocation-training-for-2022
This merit allocation training session provides critical information for researchers considering to apply for time on Pawsey’s new Setonix supercomputer in 2022.
training@pawsey.org.au
supercomputer, supercomputing, merit allocation, allocation
ARDC FAIR Data 101 self-guided
FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles
The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.
Keywords: training material, FAIR data, research data, data management, FAIR
Resource type: presentation, quiz, activity
ARDC FAIR Data 101 self-guided
https://zenodo.org/record/5094034#.YQyLbY4zaUk
https://dresa.org.au/materials/ardc-fair-data-101-self-guided
FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles
The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.
ARDC Contact us: https://ardc.edu.au/contact-us/
Liz Stokes
Matthias Liffers
Nichola Burton
Paula A. Martinez
Natasha Simons
Keith Russell
Siobhann McCafferty
Richard Ferrers
Steve McEachern
Melanie Barlow
Tom Honeyman
Maria del Mar Quiroga
training material, FAIR data, research data, data management, FAIR
phd
ecr
researcher
support
Software publishing, licensing, and citation
A short presentation for reuse includes speaker notes.
Making software citable using a code repository, an ORCID and a licence.
Cite as
Liffers, Matthias. (2021, July 12). Software publishing, licensing, and citation. Zenodo. https://doi.org/10.5281/zenodo.5091717
Keywords: software citation, software publishing, software registry, software repository, research software
Resource type: presentation
Software publishing, licensing, and citation
https://zenodo.org/record/5091717#.YQyPtY4zaUk
https://dresa.org.au/materials/software-publishing-licensing-and-citation
A short presentation for reuse includes speaker notes.
Making software citable using a code repository, an ORCID and a licence.
**Cite as**
Liffers, Matthias. (2021, July 12). Software publishing, licensing, and citation. Zenodo. https://doi.org/10.5281/zenodo.5091717
ARDC Contact us: https://ardc.edu.au/contact-us/
Matthias Liffers
software citation, software publishing, software registry, software repository, research software
phd
ecr
researcher
support
WEBINAR: Getting started with deep learning
This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021.
Are you wondering what deep learning is and how it might be useful in your research? This high level overview introduces...
Keywords: Deep learning, Bioinformatics, Machine learning
Resource type: video, presentation
WEBINAR: Getting started with deep learning
https://zenodo.org/record/5121004#.YQN_QlMzY3Q
https://dresa.org.au/materials/webinar-getting-started-with-deep-learning
This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021.
Are you wondering what deep learning is and how it might be useful in your research? This high level overview introduces deep learning ‘in a nutshell’ and provides tips on which concepts and skills you will need to know to build a deep learning application. The presentation also provides pointers to various resources you can use to get started in deep learning.
The webinar is followed by a short Q&A session.
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.
Getting Started with Deep Learning - Slides (PDF): Slides used in the presentation
Materials shared elsewhere:
A recording of the webinar is available on the Australian BioCommons YouTube Channel:
https://youtu.be/I1TmpnZUuiQ
Melissa Burke (melissa@biocommons.org.au)
Titus Tang
Deep learning, Bioinformatics, Machine learning
ARDC Guide to making Software Citable
A short guide to making software citable using a code repository, an ORCID and a licence.
Cite as
Liffers, Matthias, & Honeyman, Tom. (2021). ARDC Guide to making software citable. Zenodo. https://doi.org/10.5281/zenodo.5003989
Keywords: software citation, software publishing, software registry, software repository, research software
Resource type: guide
ARDC Guide to making Software Citable
https://zenodo.org/record/5003989#.YQyRI44zaUk
https://dresa.org.au/materials/ardc-guide-to-making-software-citable
A short guide to making software citable using a code repository, an ORCID and a licence.
**Cite as**
Liffers, Matthias, & Honeyman, Tom. (2021). ARDC Guide to making software citable. Zenodo. https://doi.org/10.5281/zenodo.5003989
ARDC Contact us: https://ardc.edu.au/contact-us/
Matthias Liffers
Tom Honeyman
software citation, software publishing, software registry, software repository, research software
phd
ecr
researcher
support
WEBINAR: Getting started with command line bioinformatics
This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with command line bioinformatics’. This webinar took place on 22 June 2021.
Bioinformatics skills are in demand like never before and biologists are stepping up to the challenge of...
Keywords: Command line, Bioinformatics
Resource type: video, presentation
WEBINAR: Getting started with command line bioinformatics
https://zenodo.org/record/5068997#.YQN4mlMzY3Q
https://dresa.org.au/materials/webinar-getting-started-with-command-line-bioinformatics
This record includes training materials associated with the Australian BioCommons webinar ‘Getting started with command line bioinformatics’. This webinar took place on 22 June 2021.
Bioinformatics skills are in demand like never before and biologists are stepping up to the challenge of learning to analyse large and ever growing datasets. Learning how to use the command line can open up many options for data analysis but getting started can be a little daunting for those without a background in computer science.
Parice Brandies and Carolyn Hogg have recently put together ten simple rules for getting started with command-line bioinformatics to help biologists begin their computational journeys. In this webinar Parice walks you through their hints and tips for getting started with the command line. She covers topics like learning tech speak, evaluating your data and workflows, assessing computational requirements, computing options, the basics of software installation, curating and testing scripts, a bit of bash and keeping good records. The webinar will be followed by a short Q&A session.
The slides were created by Parice Brandies and are based on the publication ‘Ten simple rules for getting started with command-line bioinformatics’ (https://doi.org/10.1371/journal.pcbi.1008645). The slides are shared under a Creative Commons Attribution 4.0 International unless otherwise specified and were current at the time of the webinar.
**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.
Getting started with command line bioinformatics - slides (PDF): Slides presented during the webinar
**Materials shared elsewhere:**
A recording of the webinar is available on the Australian BioCommons YouTube Channel
https://youtu.be/p7pA4OLB2X4
Melissa (melissa@biocommons.org.au)
Parice Brandies
Command line, Bioinformatics
ARDC Research Data Rights Management Guide
A practical guide for people and organisations working with data, about rights information and licences, and to raise awareness of the implications of not having licences on data.
Who is this for? This guide is primarily directed toward members of the research sector, particularly data rights...
Keywords: research data
Resource type: guide
ARDC Research Data Rights Management Guide
https://zenodo.org/record/5091580#.YS26Co4zaUk
https://dresa.org.au/materials/ardc-research-data-rights-management-guide
A practical guide for people and organisations working with data, about rights information and licences, and to raise awareness of the implications of not having licences on data.
Who is this for? This guide is primarily directed toward members of the research sector, particularly data rights holders users and suppliers. Some general reference is made to characteristics and management of government data, acknowledging that this kind of data can be input to the research process. Government readers should consult their agency’s data management policies, in addition to reading this guide.
**Cite as**
Australian Research Data Commons. (2019). ARDC Research Data Rights Management Guide. Zenodo. https://doi.org/10.5281/zenodo.5091580
ARDC Contact us: contact@ardc.edu.au
Greg Laughlin
Baden Appleyard
research data
mbr
phd
ecr
researcher
support
professional
Australian BioCommons YouTube Channel
The Australian BioCommons YouTube channel hosts a collection of recorded webinars on a variety of bioinformatics topics from genomics, to metabolomics, containers, machine learning and more.
Keywords: Bioinformatics
Resource type: video
Australian BioCommons YouTube Channel
https://www.youtube.com/c/AustralianBioCommons
https://dresa.org.au/materials/australian-biocommons-youtube-channel
The Australian BioCommons YouTube channel hosts a collection of recorded webinars on a variety of bioinformatics topics from genomics, to metabolomics, containers, machine learning and more.
Melissa (melissa@biocommons.org.au)
Bioinformatics