1. Home
  2. Materials
Register training material
9 materials found

Authors: Barlow, Melanie (orcid: 000...  or Cooke, Steele  or Titus Tang 

WORKSHOP: Unlocking nf-core - customising workflows for your research

This record includes training materials associated with the Australian BioCommons workshop Unlocking nf-core - customising workflows for your research’. This workshop took place over two, 3 hour sessions on 18-19 May 2023.

Event description

Processing and analysing omics datasets poses many...

Keywords: Bioinformatics, Workflows, Nextflow, nf-core

WORKSHOP: Unlocking nf-core - customising workflows for your research https://dresa.org.au/materials/workshop-unlocking-nf-core-customising-workflows-for-your-research-1584ff39-e007-4422-9fd5-4e407df6b6c5 This record includes training materials associated with the Australian BioCommons workshop Unlocking nf-core - customising workflows for your research’. This workshop took place over two, 3 hour sessions on 18-19 May 2023. Event description Processing and analysing omics datasets poses many challenges to life scientists, particularly when we need to share our methods with other researchers and scale up our research. Public and reproducible bioinformatics workflows, like those developed by nf-core, are invaluable resources for the life science community. nf-core is a community-driven effort to provide high-quality bioinformatics workflows for common analyses including, RNAseq, mapping, variant calling, and single cell transcriptomics. A big advantage of using nf-core workflows is the ability to customise and optimise them for different computational environments, types and sizes of data and research goals.  This workshop will set you up with the foundational knowledge required to run and customise nf-core workflows in a reproducible manner. On day 1 you will learn about the nf-core tools utility, and step through the code structure of nf-core workflows. Then on day 2, using the nf-core/rnaseq workflow as an example, you will explore the various ways to adjust the workflow parameters, customise processes, and configure the workflow for your computational environment. This workshop event and accompanying materials were developed by the Sydney Informatics Hub, University of Sydney in partnership with Seqera Labs, Pawsey Supercomputing Research Centre, and Australia’s National Research Education Network (AARNet). The workshop was enabled through the Australian BioCommons - Bring Your Own Data Platforms project (Australian Research Data Commons and NCRIS via Bioplatforms Australia).  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. nfcore_Schedule: Schedule for the workshop providing a breakdown of topics and timings nfcore_Q_and_A: Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere: This workshop follows the accompanying training materials that were developed by the Sydney Informatics Hub, University of Sydney in partnership with Seqera Labs, Pawsey Supercomputing Research Centre, and Australia’s National Research Education Network (AARNet).  https://sydney-informatics-hub.github.io/customising-nfcore-workshop Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, Nextflow, nf-core
ARDC FAIR Data 101 self-guided

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

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

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

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

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097 FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
WORKSHOP: Unlocking nf-core - customising workflows for your research

This record includes training materials associated with the Australian BioCommons workshop Unlocking nf-core - customising workflows for your research’. This workshop took place over two, 3 hour sessions on 18-19 May 2023.

Event description

Processing and analysing omics datasets poses many...

Keywords: Bioinformatics, Workflows, Nextflow, nf-core

WORKSHOP: Unlocking nf-core - customising workflows for your research https://dresa.org.au/materials/workshop-unlocking-nf-core-customising-workflows-for-your-research This record includes training materials associated with the Australian BioCommons workshop Unlocking nf-core - customising workflows for your research’. This workshop took place over two, 3 hour sessions on 18-19 May 2023. Event description Processing and analysing omics datasets poses many challenges to life scientists, particularly when we need to share our methods with other researchers and scale up our research. Public and reproducible bioinformatics workflows, like those developed by nf-core, are invaluable resources for the life science community. nf-core is a community-driven effort to provide high-quality bioinformatics workflows for common analyses including, RNAseq, mapping, variant calling, and single cell transcriptomics. A big advantage of using nf-core workflows is the ability to customise and optimise them for different computational environments, types and sizes of data and research goals.  This workshop will set you up with the foundational knowledge required to run and customise nf-core workflows in a reproducible manner. On day 1 you will learn about the nf-core tools utility, and step through the code structure of nf-core workflows. Then on day 2, using the nf-core/rnaseq workflow as an example, you will explore the various ways to adjust the workflow parameters, customise processes, and configure the workflow for your computational environment. This workshop event and accompanying materials were developed by the Sydney Informatics Hub, University of Sydney in partnership with Seqera Labs, Pawsey Supercomputing Research Centre, and Australia’s National Research Education Network (AARNet). The workshop was enabled through the Australian BioCommons - Bring Your Own Data Platforms project (Australian Research Data Commons and NCRIS via Bioplatforms Australia).  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. nfcore_Schedule: Schedule for the workshop providing a breakdown of topics and timings nfcore_Q_and_A: Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere: This workshop follows the accompanying training materials that were developed by the Sydney Informatics Hub, University of Sydney in partnership with Seqera Labs, Pawsey Supercomputing Research Centre, and Australia’s National Research Education Network (AARNet).  https://sydney-informatics-hub.github.io/customising-nfcore-workshop Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, Nextflow, nf-core
ARDC FAIR Data 101 self-guided

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

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

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

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

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-bba41a59-8479-4f4f-b9ee-337b9eb294bf FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
Deep Learning for Natural Language Processing

This workshop is designed to be instructor led and consists of two parts.
Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.
Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for...

Keywords: Deep learning, NLP, Machine learning

Resource type: presentation, tutorial

Deep Learning for Natural Language Processing https://dresa.org.au/materials/deep-learning-for-natural-language-processing This workshop is designed to be instructor led and consists of two parts. Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset. Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN. The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop introduces natural language as data for deep learning. We discuss various techniques and software packages (e.g. python strings, RegEx, NLTK, Word2Vec) that help us convert, clean, and formalise text data “in the wild” for use in a deep learning model. We then explore the training and testing of a Recurrent Neural Network on the data to complete a real world task. We will be using TensorFlow v2 for this purpose. datascienceplatform@monash.edu Deep learning, NLP, Machine learning
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://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 Deep learning, Machine learning
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://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 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://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 Deep learning, convolutional neural network, tensorflow, Machine learning
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://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) Deep learning, Bioinformatics, Machine learning