Open Source Alternatives

I don’t know if it is possible at this point in time to avoid working in Microsoft applications. Given how many organizations now have some version of Office 365, it seems like all outputs are eventually a Word document, a PowerPoint slide deck, or an Excel spreadsheet. But those are outputs, not throughputs, and I don’t think any of those applications is terribly good to think with. Communicate to/with others who are used to things being packaged in a particular way? Okay, fine, here’s a Word document.

The process involved in getting to an outcome should be more flexible, more tuned to how individuals and teams work. For the record, Teams works well enough, but OneNote is a disaster. Personally, I use Devonthink and Scrivener for the iterative process of bigger projects, but neither of them is team-friendly. And neither is really all that iPhone-friendly, really. They have iPhone apps, which are really quite functional, but the true power of both apps is really best experienced on the Mac. (I only use two devices: a Mac and an iPhone, so the iPad version of both may very well be an incredible experience, but I decided a while ago that my version of techno-minimalism meant I only ever worked on, and worried about, a computer and a phone.)

The problem, for me, is that collaboration needs to occur across platforms, so it looks like I am stuck with web apps. That noted, there appear to be a range of open source alternatives that look pretty good. BTW has collected over 200 open source alternatives to a range of software tools that businesses use: from note-code databases to note-taking to kanban.

Clarkson’s Farm

Let’s get one thing out of the way upfront: Jeremy Clarkson is a prig. I found his persona on Top Gear virtually unbearable. That noted, his presence in his eponymous series about becoming a gentleman farmer is entirely redeemed by the fact that he, or the production crew, are perfectly happy for his priggishness to lead to his downfall time and time again. With all the gusto of a man who has made too much money too easily and now finding himself essentially a lord of the manor, which here is 1000 acres in the English Cotswalds, Clarkson lurches from one under-considered venture to another. Not once in the first few episodes does anything go right — so much so that I have to believe that the show is built somewhat around him showcasing his own foibles.

Having blown up his house in a pre-series moment, Clarkson’s Farm begins with the man himself buying an over-sized and over-powered tractor, which is something you would entirely expect of the long-time host of a show about cars. And you certainly expect to see him in the tractor, puttering about. You do not expect him to buy sheep, and then get a bit weepy when it turns out that three need to be euthanized. This is Clarkson as you have never seen him before, and, damn it, he is quite likable.

I am still at the beginning of the series, so I cannot offer anything in the way of spoilers, except to say that if you haven’t watched the series because it has Clarkson at the help, have no fear, he is far from being in charge of, well, almost anything. As the show unfolds, other characters become more and more compelling in their heroic efforts to nudge Clarkson into reality. In fact, one of the interesting dimensions of the series is how it carefully peels the onion on the alternate reality in which many celebrities live. Clarkson really has spent considerable time with reality always bending to his will, and, here, with a 1000 acres filled with plants, animals, and people who have their own ideas about how things go, he meets his match. Or, rather, we get to see a grown man come to terms with the fact that reality really does exist “out there.”

And if you don’t give a damn about the likes of Clarkson — and, honestly, the last four years have really been rather over-filled with privileged white guys going on and on about how they know better, it’s okay. The English Cotswalds are quite lovely, and you can always turn the sound down and just enjoy the scenery.

Why I Use a Reference Manager

The process I am going to describe here is drawn from my experience with Bookends, but I am sure the functionality is available in other reference management apps as well. I chose Bookends because it’s focused on Mac users and thus its GUI is native to the platform. I am fairly certain that Zotero has similar functionality, and I may end up using it when I am on Windows (and also because on Windows I am part of a team). The process I have in mind is adding a new reference and then adding its concomitant PDF.

First, an establishing shot drawn from work I am doing now for an essay about COVIDlore. This collection is built on top of some previous work on the flu. (Somewhere I also have Zika and Ebola bibliographies, and one day I will migrate them here as well — for those curious about the library just above entitled Legends/Virality it is in fact related but more focused on the notion of informational “virality.”)

Bookends Main Window

To add a new reference, I usually use the Quick Add function, which is handily called with CMD + CTRL + N:

Screen Shot 2021 08 04 at 09 57

I can paste the DOI from the website where I found the reference, which may or may not be the originating site — it could be a reference from another paper, for example, and Bookends does all the lifting. (This works 80-90% of the time, and so it is frustrating when it doesn’t, but there is a built-in browser that allows you collect metadata for a reference quickly.)

Once the reference is in the collection, I then CMD + OPT + R to fetch the PDF to the reference. (If you have already downloaded the PDF, you can use CMD + OPT + P to attach it from a local source.)

Screen Shot 2021 08 04 at 09 57

That’s it. The PDF is now in that particular collection as well as in the main library. Since the PDF is sitting in a particular folder which I also have indexed by DevonThink, I can take notes in that app, which will create an annotation file just for that purpose.

Automating Text Cleaning

I am fundamentally ambivalent about the automation of text-cleaning: spending time with the data, by getting unexpected results from your attempts at normalization strikes me as one way to get to know the data and to be in a position to do better analysis. That noted, there have been a number of interesting text-cleaning libraries, or text-cleaning functionality built into analytic libraries, that have caught my attention over the past year or so. The most recent of these is clean-text. Installation is simple:

pip install clean-text

And then:

from clean-text import clean

The clean(the_string, *parameters*) takes a number of interesting parameters that focus on a particular array of difficulties: