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You know your research and what you need. Maybe it’s text analysis. Maybe it’s ecological data analysis. Maybe you’re writing code explicitly to be deployed in data gathering. Maybe you just need prose that’s not wrapped inside of Microsoft Word. The following is a list of resources for learning and working with open software tools that hit some of these aspects.
This is just a small example of what is out there. From texts, to blogs, forums, MOOCs, YouTube channels, and other online courses, you will likely encounter a number of other learning opportunities. The hope is that you’ll find at least one tool that you can spend a bit of time with, explore how it best fits in with your research life cycle. Pick a tool that is at your level, and expand out as you feel comfortable.
Word Processing
LibreOffice LibreOffice is a full suite alternative to Microsoft Office, available for Linux, MacOS, and Windows. Installation and use is very straight forward.
- Download the full suite here: https://www.libreoffice.org/
- Support Page: https://www.libreoffice.org/get-help/community-support/
Markdown In a plain text editor or dedicated markdown editor, you can mark up your text to indicate what should be a header, bold, italic etc. You can then send your plain text document to pdf, html and other document formats. You can even create presentation slides.
- Read a bit more about Markdown: https://www.markdownguide.org/getting-started/
- Take a tutorial: https://www.markdowntutorial.com/
- Keep a cheat sheet: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet
- Dig deeper and read about Pandoc, the engine that fuels the conversion of document types https://pandoc.org/index.html
R Markdown Do you work in R or want to work in R? R Markdown allows you to engage in literate programming, interweaving formatted text with R code that is processed on the fly and can be sent to html, pdf etc. You won’t look back.
- Read and tutorials: R Markdown: The Definitive Guide: https://bookdown.org/yihui/rmarkdown/
- Get an overview and tutorial of R Markdown in R Studio: https://rmarkdown.rstudio.com/lesson-1.html
LaTex Need some serious document formatting? A high quality typesetting application: work in plain text, output beautiful documents.
- Start at the project page: https://www.latex-project.org/
- Get everything you need to get started installed: https://www.latex-tutorial.com/installation/
- Get started on your first document: https://www.latex-tutorial.com/tutorials/first-document/
- Dig deeper and read about Pandoc, the engine that fuels the conversion of document types https://pandoc.org/index.html
Data
LibreOffice Calc Need a spreadsheet? LibreOffice Calc is the perfect open source alternative to Excel.
- Download the Libre Office suite here: https://www.libreoffice.org/
- Official support pages: https://www.libreoffice.org/get-help/community-support/
- An overview of basic functions in Calc https://libreofficehelp.com/libreoffice-calc-tutorials/
- A quick introduction to the interface: https://www.youtube.com/watch?v=nl-nXjJurhQ
Open Refine Have dirty data? Want to keep a forensic track record of your changes for reproducibility? Still want a graphical user interface, but also the ability to explore introducing customized elements? Check out Open Refine.
- Get Open Refine: https://openrefine.org/download.html
- Get an introduction: https://openrefine.org/
- Find additional online learning opportunities: https://openrefine.org/documentation.html
R R has made a name for itself in the statistical world. It is usually paired with the integrated development environment RStudio. So, likely you’ll want both R and RStudio. But RStudio isn’t strictly necessary.
- Get R: https://www.r-project.org/
- Get RStudio: https://rstudio.com/products/rstudio/
- Get an introduction to R and RStudio https://people.ok.ubc.ca/jpither/modules/Intro_to_R.html
- Start Learning to use R: https://education.rstudio.com/learn/beginner/
- Learn about how R can support reproducible work https://people.ok.ubc.ca/jpither/modules/ReproducibleR.html
- Get more in depth: R for Data Science https://r4ds.had.co.nz/
Python When paired with Jupyter Notebooks, you and Python can join the world of literate programming. Like with R, you’ll need both Python and Jupyter Notebooks installed.
- Get Python: https://www.python.org/
- Get Jupyter Notebooks: https://jupyter.org/
- Start with a tutorial, like this one from Dataquest (https://www.dataquest.io/blog/jupyter-notebook-tutorial/) or this one from Data Camp (https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook)
- Learn about reproducible research with Jupyter Notebooks: Ten Simple Rules for Reproducible Research in Jupyter Notebooks https://arxiv.org/ftp/arxiv/papers/1810/1810.08055.pdf
- Get more in depth: Python for data analysis: data wrangling with pandas, NumPy, and IPython http://tinyurl.com/ydaj46ft
Text Analysis
Voyant tools An open source, web based graphical text analysis application. Upload a text, or find one online and start your analysis with things like word frequency lists, frequency distribution plots, and keywords in context. Built for the digital humanities, it’s applications can definitely reach further.
- Find a text to play with if you don’t already have one at Project Gutenberg Canada http://gutenberg.ca/index.html
- Head over to Voyant Tools and plug in your text https://voyant-tools.org/
- Get in depth guidance through the Voyant Tools help pages https://voyant-tools.org/docs/#!/guide/start
Terminal Not where we would normally turn for text analysis, but the command line is designed for handling text and has some powerful tools for interacting with text. A great introduction to both text analysis and the Linux command line.
- Get the bash terminal. On a Mac of Linux? You’re already set, just pull up your terminal. On Windows? Follow these instructions https://stackoverflow.com/questions/36352627/how-to-enable-bash-in-windows-10-developer-preview/36465000#36465000
- Take a tutorial: Basic Text Analysis with Command Line Tools in Linux https://williamjturkel.net/2013/06/15/basic-text-analysis-with-command-line-tools-in-linux/
R Ostensibly not designed for text analysis, some great tools have been developed to facilitate this work. See above for basic installation.
- Read and tutorial: Text Mining with R: A Tidy Approach https://www.tidytextmining.com/index.html
Python Python is great for natural language processing especially with NLTK. See above for basic installation.
- Read and tutorial: Text Analytics for Beginners using NLTK https://www.datacamp.com/community/tutorials/text-analytics-beginners-nltk
Workflow Management
OSF Everything you need to keep your project organized and connected.
- Check out the module on OSF if you haven’t already Sandbox:Sandbox:Open UBC/POSE/Open Research/OSF
- Install the R package osfr and get R and OSF connected https://github.com/ropensci/osfr
GitHub Version control and so much more. GitHub lives in the cloud and on your local machine.
- Create an account https://github.com/
- Install Git on your computer https://git-scm.com/book/en/v2/Getting-Started-Installing-Git
- Take your first tutorial https://towardsdatascience.com/getting-started-with-git-and-github-6fcd0f2d4ac6
- Read way more in depth https://git-scm.com/book/en/v2/
Explore Linux
Virtual Box Not ready to commit to a full Linux install? Don’t want to mess with a dual boot machine? Virtual Box will allow you to install a virtual operating environment on your PC.
- Get Virtual Box https://www.virtualbox.org/wiki/Downloads
- Pick a Linux distribution https://itsfoss.com/best-linux-beginners/
- Download your chosen distribution. No links here, it will depend on what you learn above.
- Install your new operating system https://www.virtualbox.org/manual/UserManual.html#gui-createvm
- Launch your new operating system https://www.virtualbox.org/manual/UserManual.html#intro-running
- Learn all about open source alternatives and how to’s with it’s FOSS (Free and Open Source Software)