One of the first things I did after passing my PhD confirmation exercise (like a qualifying exam in the USA) was to research the best way to write my thesis. As a side note, I use the word 'thesis' to refer to any large written work, including a PhD, while other English speakers might use the word 'dissertation' to refer specifically to the work that a PhD student produces. In any case, the relevant information here is the term "large", since I knew I was going to be writing a lot. I now consider the tools I'm writing about here to be essential for a productive workflow, and so this post continues the theme of an earlier post on linguistic tools.
In researching how to write my thesis, I asked friends and fellow linguists who had written grammatical descriptions. Most of them had used MS Word, and told me horror stories of lost work, un-editable documents due to the sheer size of their files, difficulties formatting and printing the thing, etc.. So that was out of the question for me, at least at the time (2011; I think more recent versions of MS Office may have fixed some of these issues). But one of them mentioned a program called LaTeX (the funny capitalization is actually part of the name), and that it made typesetting and organization a breeze. And it's free! Which is pretty important to students (if not everyone).
So I checked it out, and ended up spending the next few months learning how to set it up on my computer and how to use it (I use MacTeX as the backend). I am fortunate that I have a little background in coding, because LaTeX is essentially a markup language. You write the text of what you want, formatting parts of it by using special combinations of characters and commands (or 'tags') that tell a program how to format them. Then you run a 'compiler' that outputs everything in the correct layout in a PDF. This is pretty brilliant, because it lets you (the writer) worry mostly about the content rather than the format. But learning how to fiddle with the code is rather time-consuming, so if you're not a hardcore programmer (and I still don't really consider myself one of the hardcore types) there is quite a learning curve. Worth it, but steep.
This is where a visual editor like LyX comes in. LyX is, pretty much out of the box, a simple way of interacting with your LaTeX code. It hides most of the code and offers formatting options, similar to MS Word or other word processors. Unlike them, however, you choose the general formatting parameters and let the backend handle the layout. You can also fiddle directly with the code if you need to, or add code to the front of the document for particular use cases, like a PhD cover page, interlinearized glossed text (IGT) examples, and more. Basically anything you need to add has probably been coded or figured out by someone, and if you're a troubleshooter like me you can run a Google search and find forums (and contribute to some yourself) that deal with your particular problem or at least something similar. And the assistance you get can be pretty phenomenal.
LyX does take a bit of configuration, and I might write another post that explains how I set it up for my use case(s). But for now, I’ll just say that using LaTeX/LyX was one of the best decisions I made as a PhD student. It really simplified my writing process and allowed me to do so much more. Rather than spending the final month on formatting my thesis, I was writing and making final changes all the way up to the deadline. I probably wrote more, and re-organized the structure more, in the last month than I had in the previous three. And the text file that contains my 700+ pages of analysis, examples, and appendices is only ~6 MB. Possibly the greatest benefit was that LyX kept track of all my linked example sentences, and formatted them all properly. Once I got it set up this saved me days and weeks of man-hours. The learning curve was totally worth it.
In closing, if you are seriously considering using LaTeX/LyX, there’s lots of good articles about this online. Here’s one, and here’s a discussion on the topic, to get you started.
Recently, I started to explore using Git to maintain and organize my data. This includes both the primary data I work from as a linguist and the various kinds of data I produce in the form of written material (books, papers, etc..). As a field linguist, I primarily work with text that is transcribed from audio and video recordings. Early on I developed an archiving system to preserve the original files in their raw form as much as possible, but I didn't develop a similar system for maintaining my working files, and ended up with lots of duplicates of files that I had copied various places for various purposes.
In this fifth post on Linguistic tools (the overview of which is here), I plan to describe the way I am trying to overcome this issue and become more organized by adopting a version control system. Mostly, though, I'll just try to explain what Git is and what I use it for. This is by no means a complete tutorial, since other people have done that better, but hopefully it will provide some direction for others who are interested in streamlining and organizing their workflow.
When I started learning to program in Python and was introduced to Git, I saw the benefits for coding, but began to wonder if it was possible/useful to use it for other organizational tasks in relation to other kinds of writing and data structures. There are a few blog posts about using git for paper writing here and here and here, but they are written primarily for coders who also write papers (I'm looking at you, academic comp-sci folks!) and don't translate super well for my use case.
What is Git?
First, what is Git? Git is a version control system, which means it tracks changes in a repository (or 'repo'), basically serving as a series of backups for your work. It also has tools that allow you to make a copy (or 'branch'), work on it separately, and then when you're ready, you can see the changes made to individual files and 'merge' them together into a new version. For a simple tutorial, see here, and here is a great visual walkthrough.
Most explanations of Git use the example of a tree or a river, which do help to understand the process, but for total newbies (and possibly to address the difficulty of finding a good metaphor) I came up with the following metaphor, and find it to be a bit more useful.
The Git Metaphor
Imagine you have a drawing project. You have an empty piece of paper - this is your initial blank Git repository. Now you draw a picture on the piece of paper and decide it's an ok version - this is a snapshot that is your first 'commit'. You then decide you want to make some adjustments to the drawing, but you want to preserve the initial drawing, so you take out another piece of paper, lay it on top of the first, trace out the shape in pencil, and then begin to modify the edges of the drawing or the background by erasing or adding new bits. When you're happy with that you 'commit' it, and then every time you modify it, you repeat the process of first tracing it out on a piece of paper. Each of these pieces of paper stack, so if you want to go back to an earlier drawing, you can, without losing any of the other drawings.
Now let's say your friend wants to work on the same drawing project. You can give him your latest drawing, he can copy it and work on it in the same way. Then when he's done he can give it back to you and you can choose which bits of his version you want to incorporate into your current version. This is super useful for projects with lots of collaborators, especially if you're writing code, which are basically text files and relatively easy to merge based on differences in lines.
[Note that this isn't exactly what the Git system does - it doesn't actually create a NEW version of the repository, like the tracing paper, but instead uses the most recently committed version as the new base/foundation for future modifications.]
The most important thing I've had to keep in mind is to write myself detailed commit messages, and to figure out a good protocol or format for these messages. This is because once you've committed something, you can go back and search through your commits (save states), but this is only easy IF YOU KNOW WHAT THE COMMIT CONTAINS!!! Using the full filename of the committed file, for example, is much better than writing out 'New version of Paper'. Giving more information, such as about what you changed in the file, is even better.
My Use Case
My use case, however (as opposed to collaborating on code with a large team), is for a single repository of data in which I will occasionally share a single paper with collaborators. What I want is a bit more like a backup system (think Apple's Time Machine or the versioning system in Mac OS) but with a lot more control. Git, for example, works on the command line and requires you to provide messages for each commit you make, which also requires you, the user, to be clear about what changes are and why they were made. This (ideally) means less clutter and assists with organization.
In thinking about my needs and doing research, I discovered a drawback of version control systems like Git. If I have a single repository but am working on multiple papers at once (like I often do), guess what happens when I save changes on document A and then checkout a previous version of paper B? Yup, everything reverts to the state it was before, and I 'lose' all the changes I just made to A. I put 'lose' in quotes here because I don't actually lose anything, as long as I had committed it properly, but I'd then have to checkout the commit, copy the file, and then revert... anyway it's annoying.
What I need
So what I REALLY need is a single Git repository with the ability to track individual folders/files separately with their own version histories (like embedding repositories). Git can do this in several ways, none of which are particularly intuitive: submodules, subtrees, subrepo, and simple embedding. The first two are designed for somewhat different use cases than mine, namely using (part of) someone else's code in your own project. They're not really meant to create sub-repositories in your local repo to track separate histories. The third, subrepo, looks quite promising but requires that you run everything from the root of your main repo, and since I have lots of organizational subfolders, typing the names every time just gets tedious. So, I use the strategy of simple embedding.
By simple embedding, I refer to navigating to a subfolder within my main repo that I want to track and running the command 'git init'. This does two things:
This is probably a hack, and not the way Git was intended to be used, but it works for me. The only problem I foresee happening is making too many Git repositories, and not being able to keep track of the multiplicity. So I plan to use this sparingly. For the most part, I'm ok with committing changes across the whole repository, since one of the real benefits of using a VCS is the freedom to throw things away.
Some other great posts on version control systems include this one on the benefits of version control, this one on several different version control systems for writers, this post on the limitations of Github for writers - the last of a 6-part series worth reading if you are debating whether to start on the Git/version control journey. And finally, this one is about one person's suggestions for academic writing and Git.
In any case, Git or another VCS is definitely worth checking out. I also recently discovered that Git repos are supported 'out of the box' by LyX, my go-to LaTeX editor! More on that to come... I also recently discovered SRC (Simple Revision Control), which focuses on tracking revisions on single files. This might actually be what I need. I'm still exploring this, and it might not be necessary since my LaTeX editor supports Git. We'll see.
When I started my PhD program in Linguistics (language documentation and description), I had some experience with linguistic analysis, but not to the degree that I had to learn in order to complete my PhD. I had tuned my ear to be able to hear the sounds of the IPA, and had practice transcribing and learning a range of languages, but I had never analyzed an unwritten language completely by myself. During the course of my PhD I learned much more about how to analyze languages 'from the ground up', so to speak.
Along the way, I discovered that there were some excellent tools that made me much more effective and efficient at the task of documenting and describing an unwritten language. I was fortunate that I already had a good foundation in recording and processing audio from my experiences recording, mixing, and releasing my music, so the fact that the audio data I recorded would form the basis of my analysis didn't phase me. However, there were another whole set of tools that would allow me to investigate the details of the language I planned to work on.
Each of these programs is open source or free, though some are developed for Windows and others are developed for MacOS, which might be a problem for some people. Since I grew up with DOS and Windows but then later switched to a Mac, I'm comfortable with both systems. The Apple/Mac laptop build quality was my first choice for travel and portability combined with power. I say 'was' since some of Apple's recent design choices mean I might be switching back to Windows on my next laptop. But for now I run an old Windows version on my Mac via Virtualbox or bundle Windows software in a Wine port so I can run it as a native app in MacOS.
I'll plan to describe each of these tools in more detail in future posts, but for now here's
A list of the tools I currently use for my linguistic work:
Tools other linguists use, but that I don't use much:
I'm a linguist and singer-songwriter. I write about life, travel, language and technology.