[{"id":120,"title":"Data Manipulation and Visualisation in Python","url":"https://dresa.org.au/materials/data-manipulation-and-visualisation-in-python.json","description":"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.  \n  \n 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).  \n  \n 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.  \n  \n 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.\n\n- Working with pandas DataFrames\n- Indexing, slicing and subsetting in pandas DataFrames\n- Missing data values\n- Combine multiple pandas DataFrames\n- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries\n- Configuring plot elements within seaborn and matplotlib\n- Exploring different types of plots using seaborn\n\nThe fundamental programming concepts taught in our [Learn to Program: Python](https://intersect.org.au/training/course/python101/) course is assumed knowledge for participating in 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/) course to ensure that you are familiar with the knowledge needed for this course.","doi":"10.5281/zenodo.6423718","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":122,"title":"Introduction to Machine Learning using Python: Classification","url":"https://dresa.org.au/materials/introduction-to-machine-learning-using-python-classification.json","description":"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.\n\n- Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.\n- Know the differences between various core Machine Learning models.\n- Understand the Machine Learning modelling workflows.\n- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.\n\nEither \\Learn to Program: Python\\, \\Data Manipulation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ or \\Learn to Program: Python\\, \\Data Manipulation and Visualisation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ needed to attend this course.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423726","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":108,"title":"Getting started with HPC using PBS Pro","url":"https://dresa.org.au/materials/getting-started-with-hpc-using-pbs-pro.json","description":"Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?  \n  \n High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using PBS Pro.\n\nConnect to an HPC cluster  \n Use the Unix command line to operate a remote computer and create job scripts  \n Submit and manage jobs on a cluster using a scheduler  \n Transfer files to and from a remote computer  \n Use software through environment modules  \n Use parallelisation to speed up data analysis  \n Access the facilities available to you as a researcher  \n  \n This is the PBS Pro version of the Getting Started with HPC course.\n\nThis course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our \\Unix Shell and Command Line Basics\\ course.","doi":"10.5281/zenodo.6423641","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":123,"title":"Introduction to Machine Learning using Python: SVM \u0026 Unsupervised Learning","url":"https://dresa.org.au/materials/introduction-to-machine-learning-using-python-svm-unsupervised-learning.json","description":"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.\n\nComprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.  \n Know the differences between various core Machine Learning models.  \n Understand the Machine Learning modelling workflows.  \n Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.\n\nEither \\Learn to Program: Python\\, \\Data Manipulation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ or \\Learn to Program: Python\\, \\Data Manipulation and Visualisation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ needed to attend this course.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423728","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":109,"title":"Getting started with HPC using Slurm","url":"https://dresa.org.au/materials/getting-started-with-hpc-using-slurm.json","description":"Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?  \n  \n High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using Slurm.\n\nConnect to an HPC cluster  \n Use the Unix command line to operate a remote computer and create job scripts  \n Submit and manage jobs on a cluster using a scheduler  \n Transfer files to and from a remote computer  \n Use software through environment modules  \n Use parallelisation to speed up data analysis  \n Access the facilities available to you as a researcher  \n  \n This is the Slurm version of the Getting Started with HPC course.\n\nThis course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our \\Unix Shell and Command Line Basics\\ course.","doi":"10.5281/zenodo.6423645","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":119,"title":"Data Visualisation in Python","url":"https://dresa.org.au/materials/data-visualisation-in-python.json","description":"[Course Materials](https://intersectaustralia.github.io/training/PYTHON203/sources/Data-Adv_Python.zip)\n\nUsing the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries  \n Configuring plot elements within seaborn and matplotlib  \n Exploring different types of plots using seaborn\n\nEither \\Learn to Program: Python\\ or \\Learn to Program: Python\\ and \\Python for Research\\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \\Learn to Program: Python\\ and \\Python for Research\\ courses to ensure that you are familiar with the knowledge needed for this course.  \n  \n We also strongly recommend attending the \\Data Manipulation in Python\\.","doi":"10.5281/zenodo.6423716","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":117,"title":"Python for Research","url":"https://dresa.org.au/materials/python-for-research.json","description":"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.  \n  \n 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.  \n  \n 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.  \n  \n 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.\n\nIntroduction to Libraries and Built-in Functions in Python  \n Introduction to DataFrames using the pandas library  \n Reading and writing data in DataFrames  \n Selecting values in DataFrames  \n Quick introduction to Plotting using the matplotlib library\n\n\\Learn to Program: Python\\ or any of the \\Learn to Program: R\\, \\Learn to Program: MATLAB\\ or \\Learn to Program: Julia\\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \\Learn to Program: Python\\ course to ensure that you are familiar with the knowledge needed for this course.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":118,"title":"Data Manipulation in Python","url":"https://dresa.org.au/materials/data-manipulation-in-python.json","description":"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.  \n  \n 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.  \n  \n 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.  \n  \n 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.\n\nWorking with pandas DataFrames  \n Indexing, slicing and subsetting in pandas DataFrames  \n Missing data values  \n Combine multiple pandas DataFrames\n\nEither \\Learn to Program: Python\\ or \\Learn to Program: Python\\ and \\Python for Research\\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \\Learn to Program: Python\\ and \\Python for Research\\ courses to ensure that you are familiar with the knowledge needed for this course.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":121,"title":"Introduction to Machine Learning using Python: Introduction \u0026 Linear Regression","url":"https://dresa.org.au/materials/introduction-to-machine-learning-using-python-introduction-linear-regression.json","description":"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.\n\nUnderstand the difference between supervised and unsupervised Machine Learning.  \n Understand the fundamentals of Machine Learning.  \n Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.  \n Understand the Machine Learning modelling workflows.  \n Use Python and scikit-learn to process real datasets, train and apply Machine Learning models\n\nEither \\Learn to Program: Python\\ and \\Data Manipulation in Python\\ or \\Learn to Program: Python\\ and \\Data Manipulation and Visualisation in Python\\ needed to attend this course.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423722","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":107,"title":"Version Control with Git","url":"https://dresa.org.au/materials/version-control-with-git.json","description":"Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available.  \n  \n Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.\n\nkeep versions of data, scripts, and other files  \n examine commit logs to find which files were changed when  \n restore earlier versions of files  \n compare changes between versions of a file  \n push your versioned files to a remote location, for backup and to facilitate collaboration\n\nThe course has no prerequisites.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":102,"title":"Thinking like a computer: The Fundamentals of Programming","url":"https://dresa.org.au/materials/thinking-like-a-computer-the-fundamentals-of-programming.json","description":"Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a time, and can keep the results in a large bank of short-term memory for quick recall. These two approaches are fundamentally different.  \n  \n Humans can only reasonably retain seven plus or minus two pieces of information in short-term memory, and new items push older items out, whereas a computer is hopeless when given multiple pieces of information simultaneously.  \n  \n Understanding this fact is key to being able to write instructions for computers – also known as programs – in a way that takes advantage of their strengths, and overcomes their drawbacks.  \n  \n Suitable for the programming novice, this webinar is good preparation for researchers wanting to learn how to program.\n\nHow a human solves tasks  \n How a computer solves tasks  \n Overview of programming concepts:  \n Variables  \n Loops  \n Conditionals  \n Functions  \n Data types\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423528","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":110,"title":"Parallel Programming for HPC","url":"https://dresa.org.au/materials/parallel-programming-for-hpc.json","description":"You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you’ve submitted batch jobs.  \n  \n Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance Computing.\n\nOpenMP (Open Multi-Processing): a widespread method for shared memory programming  \n MPI (Message Passing Interface): a leading distributed memory programming model\n\nTo do this course you need to have:  \n  \n A good working knowledge of HPC. Consider taking our  \n Getting Started with HPC using PBS Pro course to come up to speed beforehand.  \n Prior experience of writing programs in either C or Fortran.","doi":"10.5281/zenodo.6423649","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":100,"title":"Start Coding without Hesitation: Programming Languages Showdown","url":"https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown.json","description":"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.\n\nWhy use Programming  \n An overview of Python, R, MATLAB, and Julia  \n Code comparison of the four programming languages  \n Popularity and job opportunities  \n Intersect’s comparison  \n General guidelines on how to choose the best programming language for your research\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423516","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":126,"title":"R for Social Scientists","url":"https://dresa.org.au/materials/r-for-social-scientists.json","description":"R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.  \n  \n But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.  \n  \n 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.\n\nBasic syntax and data types in R  \n RStudio interface  \n How to import CSV files into R  \n The structure of data frames  \n A brief introduction to data wrangling and data transformation  \n How to calculate summary statistics  \n A brief introduction to visualise data\n\nNo prior experience with programming needed to attend this course.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":105,"title":"Getting Started with Excel","url":"https://dresa.org.au/materials/getting-started-with-excel.json","description":"We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors.   \n  \n This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data.\n\nIntroduction to Microsoft Excel user interface  \n Interpret data using sorting, filtering, and conditional formatting  \n Summarise data using functions  \n Analyse data using pivot tables  \n Manipulate and visualise data  \n Handy tips to speed up your work\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423545","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":104,"title":"Survey Tools in Research: REDCap and Qualtrics","url":"https://dresa.org.au/materials/survey-tools-in-research-redcap-and-qualtrics.json","description":"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.  \n  \n 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.\n\nElectronic Data Capture: Surveys vs Forms  \n Confidential vs Anonymous data collection  \n Strengths and weaknesses of Qualtrics and REDCap  \n Real-life use cases for each tool  \n Using survey tools for longitudinal studies\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423562","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":127,"title":"R for Research","url":"https://dresa.org.au/materials/r-for-research.json","description":"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.  \n  \n 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.  \n  \n We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.  \n  \n 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.\n\nProject Management with RStudio  \n Introduction to Data Structures in R  \n Introduction to DataFrames in R  \n Selecting values in DataFrames  \n Quick introduction to Plotting using the ggplot2 package\n\n\\Learn to Program: R\\ or any of the \\Learn to Program: Python\\, \\Learn to Program: MATLAB\\, \\Learn to Program: Julia\\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \\Learn to Program: R\\ course to ensure that you are familiar with the knowledge needed for this course.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":101,"title":"A showcase of Data Analysis in Python and R: A case study using COVID-19 data","url":"https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data.json","description":"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.\n\nCleaning up a dataset for analysis  \n Using Jupyter lab for interactive analysis  \n Making the most of the tidyverse (R) and pandas (python)  \n Simple data visualisation using ggplot (R) and seaborn (python)  \n Best practices for readable code\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423522","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":124,"title":"Surveying with Qualtrics","url":"https://dresa.org.au/materials/surveying-with-qualtrics.json","description":"Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?  \n  \n 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.  \n  \n If your institution has a licence to Qualtrics, then this course is right for you.\n\nFormat a sample survey using the Qualtrics online platform  \n Configure the survey using a range of design features to improve user experience  \n Decide which distribution channel is right for your needs  \n Understand the available data analysis and export options in Qualtrics\n\nYou 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.","doi":"10.5281/zenodo.6423732","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":113,"title":"Learn to Program: MATLAB","url":"https://dresa.org.au/materials/learn-to-program-matlab.json","description":"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?  \n  \n Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!  \n  \n 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.\n\nIntroduction to the MATLAB interface for programming  \n Basic syntax and data types in MATLAB  \n How to load external data into MATLAB  \n Creating functions (FUNCTIONS)  \n Repeating actions and analysing multiple data sets (LOOPS)  \n Making choices (IF STATEMENTS – CONDITIONALS)  \n Ways to visualise data in MATLAB\n\nIn 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.  \n  \n No prior experience with programming needed to attend this course.  \n  \n 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\\.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":114,"title":"Getting started with NVivo for Windows","url":"https://dresa.org.au/materials/getting-started-with-nvivo-for-windows.json","description":"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.  \n  \n 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.\n\nCreate and organise a qualitative research project in NVivo  \n Import a range of data sources using NVivo’s integrated tools  \n Code and classify your data  \n Format your data to take advantage of NVivo’s auto-coding ability  \n Use NVivo to discover new themes and trends in research  \n Visualise relationships and trends in your data\n\nIn 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.  \n  \n This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users.","doi":"10.5281/zenodo.6423687","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":115,"title":"Getting Started with NVivo for Mac","url":"https://dresa.org.au/materials/getting-started-with-nvivo-for-mac.json","description":"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.  \n  \n 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.\n\nCreate and organise a qualitative research project in NVivo  \n Import a range of data sources using NVivo’s integrated tools  \n Code and classify your data  \n Format your data to take advantage of NVivo’s auto-coding ability  \n Use NVivo to discover new themes and trends in research  \n Visualise relationships and trends in your data\n\nIn 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.  \n  \n This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users.","doi":"10.5281/zenodo.6423700","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":111,"title":"Learn to Program: Julia","url":"https://dresa.org.au/materials/learn-to-program-julia.json","description":"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!  \n  \n 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.  \n  \n 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.\n\nIntroduction to the JupyterLab interface for programming  \n Basic syntax and data types in Julia  \n How to load external data into Julia  \n Creating functions (FUNCTIONS)  \n Repeating actions and analysing multiple data sets (LOOPS)  \n Making choices (IF STATEMENTS – CONDITIONALS)  \n Ways to visualise data using the Plots library in Julia\n\nSome 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\\, \\Learn to Program: R\\ or \\Learn to Program: MATLAB\\ prior to this course.   \n  \n 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\\.","doi":"10.5281/zenodo.6423662","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":112,"title":"Beyond the Basics: Julia","url":"https://dresa.org.au/materials/beyond-the-basics-julia.json","description":"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!  \n  \n 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.  \n  \n 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.\n\nUnderstand the role of Types within Julia  \n Create functions with complex arguments  \n Demonstrate programming patterns of list comprehension, pipes, and anonymous functions.  \n Benchmark Julia code and understand how to make it fast\n\nIf you already have experience with programming, please check the topics covered in the \\Learn to Program: Julia\\ to ensure that you are familiar with the knowledge needed for this course.","doi":"10.5281/zenodo.6423664","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":99,"title":"Heurist Tutorials","url":"https://dresa.org.au/materials/heurist-tutorials.json","description":"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.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":95,"title":"Network Know-how and Data Handling Workshop","url":"https://dresa.org.au/materials/network-know-how-and-data-handling-workshop.json","description":"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.\r\n\r\nThis 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.","doi":"10.5281/zenodo.6403757\t","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":76,"title":"ARDC Datacite API Jupyter notebook","url":"https://dresa.org.au/materials/ardc-datacite-api-jupyter-notebook.json","description":"This Jupyter notebook presents a low-barrier entry to using the DataCite REST API to mint, update, publish, and deleted DOIs and their associated metadata.\n\nIt was designed specifically to not use any third-party libraries so that it can be reused in almost any Jupyter notebook environment\n\nCode is presented alongside human readable comments that explain the use of each component of the notebook.","doi":"10.5281/zenodo.5574653","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":30,"title":"The Living Book of Digital Skills","url":"https://dresa.org.au/materials/the-living-book-of-digital-skills.json","description":"*The Living Book of Digital Skills (You never knew you needed until now)* is a living, open source online guide to 'modern not-quite-technical computer skills' for researchers and the broader academic community.\r\n\r\nA collaboration between Australia's Academic Research Network (AARNet) and the Council of Australian Librarians (CAUL), this book is the creation of the CAUL Digital Dexterity Champions and their communities.\r\n\r\n**Contributing to the Digital Skills GitBook**\r\n\r\nThe Digital Skills GitBook is an open source project and like many projects on GitHub we welcome your contributions.\r\n\r\nIf you have knowledge or expertise on one of our [requested topics](https://aarnet.gitbook.io/digital-skills-gitbook-1/requested-articles), we would love you to write an article for the book. Please let us know what you'd like to write about via our [contributor form](https://github.com/AARNet/Digital-Skills-GitBook/issues/new?assignees=sarasrking\u0026amp;labels=contributors\u0026amp;template=contributor-form.yml\u0026amp;title=Contributor+form%3A+).\r\n\r\nThere are other ways to contribute too. For example, you might:\r\n\r\n* have a great idea for a new topic to be included in one of our chapters (make a new page)\r\n* notice some information that’s out-of-date or that could be explained better (edit a page)\r\n* come across something in the GitBook that’s not working as it should be (submit an issue)","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":29,"title":"Create a website resume","url":"https://dresa.org.au/materials/create-a-website-resume.json","description":"Written for the Qld Research Bazaar conference 2021, this self paced lesson breaks down how to use Github pages to make a resume, with a simple and basic template to start off with. It discusses how to use Markdown and minimum HTML to customize the template, and offers explanations on how the components work together.","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":28,"title":"10 Reproducible Research things - Building Business Continuity","url":"https://dresa.org.au/materials/9-reproducible-research-things-building-business-continuity.json","description":"The idea that you can duplicate an experiment and get the same conclusion is the basis for all scientific discoveries. Reproducible research is data analysis that starts with the raw data and offers a transparent workflow to arrive at the same results and conclusions. However not all studies are replicable due to lack of information on the process. Therefore, reproducibility in research is extremely important.\r\nResearchers genuinely want to make their research more reproducible, but sometimes don’t know where to start and often don’t have the available time to investigate or establish methods on how reproducible research can speed up every day work. We aim for the philosophy “Be better than you were yesterday”. Reproducibility is a process, and we highlight there is no expectation to go from beginner to expert in a single workshop. Instead, we offer some steps you can take towards the reproducibility path following our Steps to Reproducible Research self paced program.\r\n\r\nVideo:\r\nhttps://www.youtube.com/watch?v=bANTr9RvnGg\r\nTutorial:\r\nhttps://guereslib.github.io/ten-reproducible-research-things/","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]}]