210 materials found
Learn to Program: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Programming, Julia
Learn to Program: Julia
https://intersect.org.au/training/course/julia101
https://dresa.org.au/materials/learn-to-program-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop expects that you are coming to Julia with some experience in the basic concepts of programming in another language. It is designed to help you migrate the basic concepts of programming that you already know to the Julia context.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Julia
- How to load external data into Julia
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data using the Plots library in Julia
#### Prerequisites:
Some experience with the basic concepts of programming in another language needed to attend this course. It is an intensive course that is designed to help you migrate the basic concepts of programming that you already know to the Julia context in half a day instead of a full day. If you don't have any prior experience in programming, please consider attending one of the [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) prior to this course.
We also strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/julia101).**
training@intersect.org.au
Programming, Julia
Beyond the Basics: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Programming, Julia
Beyond the Basics: Julia
https://intersect.org.au/training/course/julia201
https://dresa.org.au/materials/beyond-the-basics-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop explores the more advanced features of functions in Julia, introduces widely used tools within Julia, as well as demonstrates the speed of Julia by benchmarking functions and different styles of scripting within Julia.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
#### You'll learn:
- Understand the role of Types within Julia
- Create functions with complex arguments
- Demonstrate programming patterns of list comprehension, pipes, and anonymous functions.
- Benchmark Julia code and understand how to make it fast
#### Prerequisites:
If you already have experience with programming, please check the topics covered in the [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/) 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/julia201).**
training@intersect.org.au
Programming, Julia
Learn to Program: MATLAB
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you're just getting started - with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!
So...
Keywords: Programming, MATLAB
Learn to Program: MATLAB
https://intersect.org.au/training/course/matlab101
https://dresa.org.au/materials/learn-to-program-matlab
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you're just getting started - with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!
So join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the MATLAB interface for programming
- Basic syntax and data types in MATLAB
- How to load external data into MATLAB
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS – CONDITIONALS)
- Ways to visualise data in MATLAB
#### Prerequisites:
In order to participate, attendees must have a licensed copy of MATLAB installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/matlab101).**
training@intersect.org.au
Programming, MATLAB
Getting started with NVivo for Windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply...
Keywords: Data Analysis, NVivo
Getting started with NVivo for Windows
https://intersect.org.au/training/course/nvivo101
https://dresa.org.au/materials/getting-started-with-nvivo-for-windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
#### You'll learn:
- Create and organise a qualitative research project in NVivo
- Import a range of data sources using NVivo's integrated tools
- Code and classify your data
- Format your data to take advantage of NVivo’s auto-coding ability
- Use NVivo to discover new themes and trends in research
- Visualise relationships and trends in your data
#### Prerequisites:
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users.
**For more information, please click [here](https://intersect.org.au/training/course/nvivo101).**
training@intersect.org.au
Data Analysis, NVivo
Getting Started with NVivo for Mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply...
Keywords: Data Management, NVivo
Getting Started with NVivo for Mac
https://intersect.org.au/training/course/nvivo102
https://dresa.org.au/materials/getting-started-with-nvivo-for-mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
#### You'll learn:
- Create and organise a qualitative research project in NVivo
- Import a range of data sources using NVivo's integrated tools
- Code and classify your data
- Format your data to take advantage of NVivo’s auto-coding ability
- Use NVivo to discover new themes and trends in research
- Visualise relationships and trends in your data
#### Prerequisites:
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users.
**For more information, please click [here](https://intersect.org.au/training/course/nvivo102).**
training@intersect.org.au
Data Management, NVivo
Learn to Program: Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results,...
Keywords: Programming, Python
Learn to Program: Python
https://intersect.org.au/training/course/python101
https://dresa.org.au/materials/learn-to-program-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Python
- How to load external data into Python
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in Python
#### Prerequisites:
No prior experience with programming is needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/python101).**
training@intersect.org.au
Programming, Python
Python for Research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames using...
Keywords: Programming, Python
Python for Research
https://intersect.org.au/training/course/python110
https://dresa.org.au/materials/python-for-research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames using the pandas library) and visualisation (using the matplotlib library) in Python. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to Libraries and Built-in Functions in Python
- Introduction to DataFrames using the pandas library
- Reading and writing data in DataFrames
- Selecting values in DataFrames
- Quick introduction to Plotting using the matplotlib library
#### Prerequisites:
[Learn to Program: Python](https://intersect.org.au/training/course/python101/) or any of the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) or [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) course 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/python110).**
training@intersect.org.au
Programming, Python
Data Manipulation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the...
Keywords: Programming, Python
Data Manipulation in Python
https://intersect.org.au/training/course/python201
https://dresa.org.au/materials/data-manipulation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) 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/python201).**
training@intersect.org.au
Programming, Python
Data Visualisation in Python
Course Materials
You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
Prerequisites:
Either [Learn to...
Keywords: Programming, Python
Data Visualisation in Python
https://intersect.org.au/training/course/python202
https://dresa.org.au/materials/data-visualisation-in-python
Course Materials
#### You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
We also strongly recommend attending the [Data Manipulation in Python](https://intersect.org.au/training/course/python201/).
**For more information, please click [here](https://intersect.org.au/training/course/python202).**
training@intersect.org.au
Programming, Python
Data Manipulation and Visualisation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the...
Keywords: Programming, Python
Data Manipulation and Visualisation in Python
https://intersect.org.au/training/course/python203
https://dresa.org.au/materials/data-manipulation-and-visualisation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation).
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) 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/python203).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: 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, Python
Introduction to Machine Learning using Python: Introduction & Linear Regression
https://intersect.org.au/training/course/python205
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-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 Python programming language and its scientific computing libraries.
#### 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 Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python205).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: 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, Python
Introduction to Machine Learning using Python: Classification
https://intersect.org.au/training/course/python206
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-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 Python programming language and its scientific computing libraries.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python206).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: 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, Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
https://intersect.org.au/training/course/python207
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-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 Python programming language and its scientific computing libraries.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python207).**
training@intersect.org.au
Programming, Python
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...
Keywords: Programming, Python, R
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
https://intersect.org.au/training/course/coding002
https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python.
#### You'll learn:
- Cleaning up a dataset for analysis
- Using Jupyter lab for interactive analysis
- Making the most of the tidyverse (R) and pandas (python)
- Simple data visualisation using ggplot (R) and seaborn (python)
- Best practices for readable code
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/coding002).**
training@intersect.org.au
Programming, Python, R
Start Coding without Hesitation: Programming Languages Showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...
Keywords: Programming, Python, R, MATLAB, Julia
Start Coding without Hesitation: Programming Languages Showdown
https://intersect.org.au/training/course/coding001
https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia.
#### You'll learn:
- Why use Programming
- An overview of Python, R, MATLAB, and Julia
- Code comparison of the four programming languages
- Popularity and job opportunities
- Intersect's comparison
- General guidelines on how to choose the best programming language for your research
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/coding001).**
training@intersect.org.au
Programming, Python, R, MATLAB, Julia
Surveying with Qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow...
Keywords: Data Management, Qualtrics
Surveying with Qualtrics
https://intersect.org.au/training/course/qltrics101
https://dresa.org.au/materials/surveying-with-qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow from building a survey to analysing the results using Qualtrics. We will discover the numerous design elements available in order to get the most useful results and make life as easy as can be for your respondents.
If your institution has a licence to Qualtrics, then this course is right for you.
#### You'll learn:
- Format a sample survey using the Qualtrics online platform
- Configure the survey using a range of design features to improve user experience
- Decide which distribution channel is right for your needs
- Understand the available data analysis and export options in Qualtrics
#### Prerequisites:
You must have access to a Qualtrics instance, such as through your university license. Speak to your local university IT or Research Office for assistance in accessing the Qualtrics instance.
**For more information, please click [here](https://intersect.org.au/training/course/qltrics101).**
training@intersect.org.au
Data Management, Qualtrics
Learn to Program: R
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be challenging,...
Keywords: Programming, R
Learn to Program: R
https://intersect.org.au/training/course/r101
https://dresa.org.au/materials/learn-to-program-r
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the RStudio interface for programming
- Basic syntax and data types in R
- How to load external data into R
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in R
#### Prerequisites:
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/r101).**
training@intersect.org.au
Programming, R
R for Social Scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory...
Keywords: Programming, R
R for Social Scientists
https://intersect.org.au/training/course/r103
https://dresa.org.au/materials/r-for-social-scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Data Carpentry.
#### You'll learn:
- Basic syntax and data types in R
- RStudio interface
- How to import CSV files into R
- The structure of data frames
- A brief introduction to data wrangling and data transformation
- How to calculate summary statistics
- A brief introduction to visualise data
#### Prerequisites:
No prior experience with programming needed to attend this course.
**For more information, please click [here](https://intersect.org.au/training/course/r103).**
training@intersect.org.au
Programming, R
R for Research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data...
Keywords: Programming, R
R for Research
https://intersect.org.au/training/course/r110
https://dresa.org.au/materials/r-for-research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data structures (DataFrames) and visualisation (using the ggplot2 package) in R. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Project Management with RStudio
- Introduction to Data Structures in R
- Introduction to DataFrames in R
- Selecting values in DataFrames
- Quick introduction to Plotting using the ggplot2 package
#### Prerequisites:
[Learn to Program: R](https://intersect.org.au/training/course/r101/) or any of the [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/), [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) course 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/r110).**
training@intersect.org.au
Programming, R
Data Manipulation 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 in R
https://intersect.org.au/training/course/r201
https://dresa.org.au/materials/data-manipulation-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).
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
#### 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/r201).**
training@intersect.org.au
Programming, R
Survey Tools in Research: REDCap and Qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your...
Keywords: Data Management, REDCap, Qualtrics
Survey Tools in Research: REDCap and Qualtrics
https://intersect.org.au/training/course/surveys001
https://dresa.org.au/materials/survey-tools-in-research-redcap-and-qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your research up for success through intuitive data collection and validation, scheduling and reporting.
This webinar will introduce and compare two of the most popular research tools for the collection of survey data and patient records: REDCap and Qualtrics.
#### You'll learn:
- Electronic Data Capture: Surveys vs Forms
- Confidential vs Anonymous data collection
- Strengths and weaknesses of Qualtrics and REDCap
- Real-life use cases for each tool
- Using survey tools for longitudinal studies
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/surveys001).**
training@intersect.org.au
Data Management, REDCap, Qualtrics
Heurist Tutorials
A set of video tutorials with accompanying walkthroughs for building your first Heurist database and website. The first three tutorials show you how to get started in Heurist. The five subsequent tutorials introduce you to the five main menus in the Heurist interface.
Keywords: Heurist, Data management, Data visualisation, Digital Humanities, Databasing, website
Resource type: tutorial
Heurist Tutorials
https://heuristnetwork.org/tutorials
https://dresa.org.au/materials/heurist-tutorials
A set of video tutorials with accompanying walkthroughs for building your first Heurist database and website. The first three tutorials show you how to get started in Heurist. The five subsequent tutorials introduce you to the five main menus in the Heurist interface.
michael.falk@sydney.edu.au
Falk, Michael
Johnson, Ian
Osmakov, Artem
Heurist, Data management, Data visualisation, Digital Humanities, Databasing, website
mbr
phd
ecr
researcher
support
ARDC digital research capabilities and skills framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role...
Keywords: training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
https://zenodo.org/record/6558642
https://dresa.org.au/materials/ardc-digital-research-capabilities-and-skills-framework-e0acf524-0666-466c-ac93-f13c133b03cf
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role profiles
Learning paths
Skills/Data roles matrix
contact@ardc.edu.au
ARDC
Savill, Jo (type: Editor)
Duncan, Ian (type: Editor)
Unsworth, Kathryn (type: Editor)
Murphy, Paul (type: Editor)
training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role...
Keywords: training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
https://zenodo.org/record/6540798
https://dresa.org.au/materials/ardc-digital-research-capabilities-and-skills-framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role profiles
Learning paths
Skills/Data roles matrix
Kathryn Unsworth (kathryn.unsworth@ardc.edu.au)
ARDC
Savill, Jo (type: Editor)
Duncan, Ian (type: Editor)
Unsworth, Kathryn (type: Editor)
Murphy, Paul (type: Editor)
training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
WEBINAR: Protection of genomic data and the Australian Privacy Act: when is genomic data 'personal information'?
This record includes training materials associated with the Australian BioCommons webinar ‘Protection of genomic data and the Australian Privacy Act: when is genomic data ‘personal information’?’. This webinar took place on 6 April 2022.
Event description
It is easy to assume that...
Keywords: Bioinformatics, Genomics, Genetic data, Personal information, Health information, Privacy
WEBINAR: Protection of genomic data and the Australian Privacy Act: when is genomic data 'personal information'?
https://zenodo.org/record/6423621
https://dresa.org.au/materials/webinar-protection-of-genomic-data-and-the-australian-privacy-act-when-is-genomic-data-personal-information
This record includes training materials associated with the Australian BioCommons webinar ‘Protection of genomic data and the Australian Privacy Act: when is genomic data ‘personal information’?’. This webinar took place on 6 April 2022.
**Event description**
It is easy to assume that genomic data will be captured by legal definitions of ‘health information’ and ‘genetic information’, but the legal meaning of ‘genetic information’ need not align with scientific categories.
There are many different types of genomic data, with varied characteristics, uses and applications. Clarifying when genomic data is covered by the Privacy Act 1988 (Cth) is an ongoing evaluative exercise but is important for at least 3 reasons:
1. those subject to the Privacy Act need to be able to confidently navigate their responsibilities
2. understanding current controls is a prerequisite for meaningful external critique (and this is particularly important at a time when the Privacy Act is under review), and
3. while legislation that applies to state public sector agencies is generally distinct from the Privacy Act there are similarities that extend the relevance of the question when is genomic data ‘personal information’ under the Privacy Act?
In this presentation, Mark will explore the relationship between the legal concept of genetic information and the concept of genomic data relevant to health and medical research, reflect on the characteristics of each, and the possibility
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.
- Taylor_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/Iaei-9Gu-AI
Melissa Burke (melissa@biocommons.org.au)
Taylor, Mark (orcid: 0000-0003-2009-6284)
Bioinformatics, Genomics, Genetic data, Personal information, Health information, Privacy
Network Know-how and Data Handling Workshop
This workshop is a ‘train-the-trainer’ session that covers topics such as jargon busting, network literacy and data movement solutions. The workshop will also provide a peek at some collaborative research tools such as Jupyter Notebooks and CloudStor. You will learn about networks, integrated...
Keywords: Networks, data handling
Resource type: lesson, presentation
Network Know-how and Data Handling Workshop
https://zenodo.org/record/6403757#.Yk-Gl8gza70
https://dresa.org.au/materials/network-know-how-and-data-handling-workshop
This workshop is a ‘train-the-trainer’ session that covers topics such as jargon busting, network literacy and data movement solutions. The workshop will also provide a peek at some collaborative research tools such as Jupyter Notebooks and CloudStor. You will learn about networks, integrated tools, data and storage and where all these things fit in the researcher’s toolkit.
This workshop is targeted at staff who would like to be more confident in giving advice to researchers about the options available to them. It is especially tailored for those with little to no technical knowledge and includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming.
Sara King - sara.king@aarnet.edu.au
King, Sara (orcid: 0000-0003-3199-5592)
Mason, Ingrid (orcid: 0000-0002-0658-6095)
Burke, Melissa (orcid: 0000-0002-5571-8664)
Networks, data handling
WORKSHOP: Introduction to Metabarcoding using QIIME2
This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Metabarcoding using QIIME2’. This workshop took place on 22 February 2022.
Event description
Metabarcoding has revolutionised the study of biodiversity science. By combining DNA...
Keywords: Bioinformatics, Analysis, Workflows, Microbial ecology, Metabarcoding, Microbiome
WORKSHOP: Introduction to Metabarcoding using QIIME2
https://zenodo.org/record/6350808
https://dresa.org.au/materials/workshop-introduction-to-metabarcoding-using-qiime2
This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Metabarcoding using QIIME2’. This workshop took place on 22 February 2022.
**Event description**
Metabarcoding has revolutionised the study of biodiversity science. By combining DNA taxonomy with high-throughput DNA sequencing, it offers the potential to observe a larger diversity in the taxa within a single sample, rapidly expanding the scope of microbial analysis and generating high-quality biodiversity data.
This workshop will introduce the topic of metabarcoding and how you can use Qiime2 to analyse 16S data and gain simultaneous identification of all taxa within a sample. Qiime2 is a popular tool used to perform powerful microbiome analysis that can transform your raw data into publication quality visuals and statistics. In this workshop, using example 16S data from the shallow-water marine anemone E. diaphana, you will learn how to use this pipeline to run essential steps in microbial analysis including generating taxonomic assignments and phylogenic trees, and performing both alpha- and beta- diversity analysis.
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
**Materials shared elsewhere:**
This workshop follows the tutorial ‘Introduction to metabarcoding with QIIME2’ which has been made publicly available by Melbourne Bioinformatics.
https://www.melbournebioinformatics.org.au/tutorials/tutorials/qiime2/qiime2/
Melissa Burke (melissa@biocommons.org.au)
Dungan, Ashley (orcid: 0000-0003-0958-2177)
Philip, Gayle (orcid: 0000-0002-2671-5093)
Perry, Andrew (orcid: 0000-0001-9256-6068)
Ismail, Rania
Geissler, Laura
Tandon, Kshitij (orcid: 0000-0003-3022-0808)
Makunin, Igor
Bioinformatics, Analysis, Workflows, Microbial ecology, Metabarcoding, Microbiome
WEBINAR: Conservation genomics in the age of extinction
This record includes training materials associated with the Australian BioCommons webinar ‘Conservation genomics in the age of extinction’. This webinar took place on 8 March 2022.
Event description
Biodiversity is crashing and millions of plant and animal species are at the edge of...
Keywords: Conservation genomics, Genomics, Bioinformatics, Sequencing, Threatened Species Initiative, Galaxy Australia
WEBINAR: Conservation genomics in the age of extinction
https://zenodo.org/record/6350785
https://dresa.org.au/materials/webinar-conservation-genomics-in-the-age-of-extinction
This record includes training materials associated with the Australian BioCommons webinar ‘Conservation genomics in the age of extinction’. This webinar took place on 8 March 2022.
**Event description**
Biodiversity is crashing and millions of plant and animal species are at the edge of extinction. Understanding the genetic diversity of these species is an important tool for conservation biology but obtaining high quality genomes for threatened species is not always straightforward.
In this webinar Dr Carolyn Hogg speaks about the work she has been doing with the Threatened Species Initiative to build genomic resources to understand and protect Australia’s threatened species. Using examples such as the Kroombit Tinker Frog and the Greater Bilby, Carolyn describes some of the complexities and challenges of generating genomes from short reads and HiFi reads for critically endangered species. She outlines the technologies and resources being used and how these are bridging the gap between genomicists, bioinformaticians and conservation experts to help save Australian species.
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.
**Materials shared elsewhere:**
A recording of this webinar is available on the Australian BioCommons YouTube Channel:
https://youtu.be/Bl7CaiGQ91s
Melissa Burke (melissa@biocommons.org.au)
Hogg, Carolyn (orcid: 0000-0002-6328-398X)
Conservation genomics, Genomics, Bioinformatics, Sequencing, Threatened Species Initiative, Galaxy Australia
WEBINAR: Establishing Gen3 to enable better human genome data sharing in Australia
This record includes training materials associated with the Australian BioCommons webinar ‘Establishing Gen3 to enable better human genome data sharing in Australia’. This webinar took place on 16 February 2022.
Event description
Australian human genome initiatives are generating vast...
Keywords: Bioinformatics, Genomics, Human genomics, Digital infrastructure, Gen3, Data sharing, Data management
WEBINAR: Establishing Gen3 to enable better human genome data sharing in Australia
https://zenodo.org/record/6233075
https://dresa.org.au/materials/webinar-establishing-gen3-to-enable-better-human-genome-data-sharing-in-australia
This record includes training materials associated with the Australian BioCommons webinar ‘Establishing Gen3 to enable better human genome data sharing in Australia’. This webinar took place on 16 February 2022.
**Event description**
Australian human genome initiatives are generating vast amounts of human genome data. There is a desire and need to share data with collaborators but researchers face significant infrastructural, technical and administrative barriers in achieving this. To efficiently share and distribute their genome data they need scalable services and infrastructure that: is easily administered; allows for the efficient data management; enables sharing and interoperability; and is aligned with global standards for human genome data sharing.
Australian BioCommons has brought together a team from Zero Childhood Cancer (Zero), the University of Melbourne Centre for Cancer Research (UMCCR) and Australian Access Federation to explore the use of Gen3 technology. Establishing systems for easier management and sharing of their human genome data holdings is no simple task, and the group wants to ensure that other Australian providers and Institutions can benefit from their experience and easily deploy the same solution in the future.
Gen3 is an open source software suite that makes use of private and public clouds to tackle the challenges of data management, interoperability, data sharing and analysis. It has been used in several very large NIH-funded projects that collectively house and describe data derived from hundreds of thousands of human samples (e.g. NCI Genomic Data Commons, BioData Catalyst, BloodPAC, BrainCommons, Kids First Data Commons).
In this webinar you’ll hear from UMCCR and Zero about their experiences and progress towards establishing Gen3 instances to better enable better human genome data sharing in Australia. They will outline the challenges and opportunities that have arisen through this Australian BioCommons project and demonstrate the capabilities of Gen3 for human genome research.
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.
- Gen3_Webinar_Slides (PDF): Slides presented during the webinar
**Materials shared elsewhere:**
A recording of this webinar is available on the Australian BioCommons YouTube Channel:
https://youtu.be/1F6B03Byigk
Melissa Burke (melissa@biocommons.org.au)
Pope, Bernie (orcid: 0000-0002-4840-1095)
Hofmann, Oliver (orcid: 0000-0002-7738-1513)
Wong-Erasmus, Marie (orcid: 0000-0003-0066-6606)
Taouk, Kamile (orcid: 0000-0001-8389-510X)
Bioinformatics, Genomics, Human genomics, Digital infrastructure, Gen3, Data sharing, Data management
WORKSHOP: Refining genome annotations with Apollo
This record includes training materials associated with the Australian BioCommons workshop ‘Refining genome annotations with Apollo’. This workshop took place on 17 November 2021.
Workshop description
Genome annotation is crucial to defining the function of genomic sequences. This...
Keywords: Apollo Software, Bioinformatics, Analysis, Workflows, Genomics, Genome annotation
WORKSHOP: Refining genome annotations with Apollo
https://zenodo.org/record/5781812
https://dresa.org.au/materials/workshop-refining-genome-annotations-with-apollo
This record includes training materials associated with the Australian BioCommons workshop ‘Refining genome annotations with Apollo’. This workshop took place on 17 November 2021.
**Workshop description**
Genome annotation is crucial to defining the function of genomic sequences. This process typically involves a round of automated annotation followed by manual curation. Manual curation allows you to visualise your annotations so you can understand what your organism looks like, and then to manually refine these annotations along with any additional data you might have. This process is typically performed collaboratively as part of a team effort.
Apollo is a popular tool for facilitating real-time collaborative, manual curation and genome annotation editing. In this workshop we will learn how to use Apollo to refine genome annotations using example data from an E. coli strain. We’ll focus on the basics like getting data into Apollo, viewing evidence tracks, editing and adding structural and functional annotation, visualising the results and collaborating on genome annotations.
This workshop made use of a training instance of the new Australian Apollo Service. This service enables Australian-based research groups and consortia to access Apollo and host genome assembly and supporting evidence files for free. This service has been made possible by The Australian BioCommons and partners at QCIF and Pawsey. To learn more about the Australian Apollo Service you can watch the Australian Apollo Launch Webinar.
This workshop was presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) .
The Australian Apollo Service is operated by QCIF and underpinned by computational resources provided by the Pawsey Supercomputing Research Centre and receives NCRIS funding through Bioplatforms Australia and the Australian Research Data Commons as well as Queensland Government RICF funding.
The training materials presented in this workshop were developed by Anthony Bretaudeau, Helena Rasche, Nathan Dunn, Mateo Boudet for the Galaxy Training Network. Helena and Anthony are part of the Gallantries project which is supported by Erasmus Programme of the European Union.
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
- 2021 Apollo Training Intro (PPTX and PDF): Slides used to introduce the Australian Apollo Service
- Augustus.gff3 (gff3): E.coli derived data file used in the tutorial. Data was obtained from the Galaxy Training Network and pre-processed using Galaxy Australia.
- Blastp_vs_swissprot.gff3: E.coli derived data file used in the tutorial. Data was obtained from the Galaxy Training Network and pre-processed using Galaxy Australia.
**Materials shared elsewhere:**
This workshop is based on the tutorial ‘Refining genome annotations with Apollo’ which was developed for the Galaxy Training Network.
Anthony Bretaudeau, Helena Rasche, Nathan Dunn, Mateo Boudet, Erasmus Programme, 2021 Refining Genome Annotations with Apollo (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/apollo/tutorial.html Online; accessed Wed Dec 15 2021
See also:
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
Melissa Burke (melissa@biocommons.org.au)
Bretaudeau, Anthony (orcid: 0000-0003-0914-2470)
Rasche, Helena (orcid: 0000-0001-9760-8992)
Williams, Sarah
Nelson, Tiffanie (orcid: 0000-0002-5341-312X)
Thang, Mike
Lee, Justin
Apollo Software, Bioinformatics, Analysis, Workflows, Genomics, Genome annotation