[{"id":260,"title":"Cleaning Data with Open Refine","url":"https://staging.dresa.org.au/materials/cleaning-data-with-open-refine.json","description":"Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet?  \n  \n Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising and cleaning datasets, and extending data by accessing the internet through APIs. In this course we’ll work through the various features of Refine, including importing data, faceting, clustering, and calling remote APIs, by working on a fictional but plausible humanities research project.\n\nDownload, install and run Open Refine  \n Import data from csv, text or online sources and create projects  \n Navigate data using the Open Refine interface  \n Explore data by using facets  \n Clean data using clustering  \n Parse data using GREL syntax  \n Extend data using Application Programming Interfaces (APIs)  \n Export project for use in other applications\n\nThe course has no prerequisites.","doi":"10.5281/zenodo.6423840","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":268,"title":"Collecting Web Data","url":"https://staging.dresa.org.au/materials/collecting-web-data.json","description":"Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.  \n  \n Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it’s a skill worth learning.  \n  \n So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.\n\nThe concept of structured data  \n The use of XPath queries on HTML document  \n How to scrape data using browser extensions  \n How to scrape using Python and Scrapy  \n How to automate the scraping of multiple web pages\n\nA good knowledge of the basic concepts and techniques in Python. Consider taking our \\Learn to Program: Python\\ and \\Python for Research\\ courses to come up to speed beforehand.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":261,"title":"Mastering text with Regular Expressions","url":"https://staging.dresa.org.au/materials/mastering-text-with-regular-expressions.json","description":"Have you ever wanted to extract phone numbers out of a block of unstructured text? Or email addresses. Or find all the words that start with “e” and end with “ed”, no matter their length? Or search through DNA sequences for a pattern? Or extract coordinates from GPS data?  \n  \n Regular Expressions (regexes) are a powerful way to handle a multitude of different types of data. They can be used to find patterns in text and make sophisticated replacements. Think of them as find and replace on steroids. Come along to this workshop to learn what they can do and how to apply them to your research.\n\nComprehend and apply the syntax of regular expressions  \n Use the http://regexr.com tool to test a regular expression against some text  \n Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text  \n Craft and test a progressively more complex regular expression  \n Find helpful resources covering regular expressions on the web\n\nComprehend and apply the syntax of regular expressions  \n Use the http://regexr.com tool to test a regular expression against some text  \n Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text  \n Craft and test a progressively more complex regular expression  \n Find helpful resources covering regular expressions on the web","doi":"10.5281/zenodo.6423846","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":246,"title":"R for Social Scientists","url":"https://staging.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":262,"title":"Regular Expressions on the Command Line","url":"https://staging.dresa.org.au/materials/regular-expressions-on-the-command-line.json","description":"Would you like to use regular expressions with the classic command line utilities find, grep, sed and awk? These venerable Unix utilities allow you to search, filter and transform large amounts of text (including many common data formats) efficiently and repeatably.\n\nfind to locate files and directories matching regexes.  \n grep to filter lines in files based on pattern matches.  \n sed to find and replace using regular expressions and captures.  \n awk to work with row- and column-oriented data.\n\nThis course assumes prior knowledge of the basic syntax of regular expressions. If you’re new to regular expressions or would like a refresher, take our Mastering text with Regular Expressions course first.  \n  \n This course also assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. Take our Unix Shell and Command Line Basics course to get up to speed quickly.","doi":"10.5281/zenodo.6423848","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":247,"title":"R for Research","url":"https://staging.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":223,"title":"From PC to Cloud or High Performance Computing","url":"https://staging.dresa.org.au/materials/from-pc-to-cloud-or-high-performance-computing.json","description":"Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.  \n  \n We may refer to both types as “large scale computing” – but what is the difference? Both systems target scalability of computing, but in different ways.  \n  \n This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster.\n\nIntroduction  \n HPC vs Cloud computing  \n When to use HPC  \n When to use the Cloud  \n The Cloud – Pros and Cons  \n HPC – Pros and Cons\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423543","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":222,"title":"Thinking like a computer: The Fundamentals of Programming","url":"https://staging.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":221,"title":"A showcase of Data Analysis in Python and R: A case study using COVID-19 data","url":"https://staging.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":230,"title":"Parallel Programming for HPC","url":"https://staging.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":220,"title":"Start Coding without Hesitation: Programming Languages Showdown","url":"https://staging.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":225,"title":"Getting Started with Excel","url":"https://staging.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":224,"title":"Survey Tools in Research: REDCap and Qualtrics","url":"https://staging.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":231,"title":"Learn to Program: Julia","url":"https://staging.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":232,"title":"Beyond the Basics: Julia","url":"https://staging.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":[]}]