Is your data digital or just pseudodigital?

A rite of passage for a geologist is the making of an original geological map, starting from scratch. In the UK, this is known as the ‘independent mapping project’ and is usually done at the end of the second year of an undergrad degree. I did mine on the eastern shore of the Embalse de Santa Ana, just north of Alfarras in Catalunya, Spain. (I wrote all about it back in 2012.)

The map I drew was about as analog as you can get. I drew it with Rotring Rapidograph pens on drafting film. Mistakes had to be painstakingly scraped away with a razor blade. Colour had to be added in pencil after the map had been transferred onto paper. There is only one map in existence. The data is gone. It is absolutely unreproducible.

Digitize!

In order to show you the map, I had to digitize it. This word makes it sound like the map is now ‘digital data’, but it’s really not useful for anything scientific. In other words, while it is ‘digital’ in the loosest sense — it’s a bunch of binary bits in the cloud — it is not digital in the sense of organized data elements with semantic meaning. Let’s call this non-useful format palaeodigital. The lowest rung on the digital ladder.

You can get palaeodigital files from many state and national data repositories. For example, it’s how the Government of Nova Scotia stores its offshore seismic ‘data’ files — as TIFF files representing scans of paper sections submitted by operators. Wiggle trace, obviously, making them almost completely useless.

Protodigital

Nobody draws map by hand anymore, that would be crazy. Adobe Illustrator and (better) Inkscape mean we can produce beautifully rendered maps with about the same amount of effort as the hand-drawn version. But… this still isn’t digital. This is nothing more than a computerized rip-off of the analog workflow. The result is almost as static and difficult to edit as it was on film. (Wish you’d used a thicker line for your fault traces on those 20 maps? Have fun editing those files!)

Let’s call the computerization of analog workflows or artifacts protodigital. I’m thinking of Word and Powerpoint. Email. SeisWorks. Techlog. We can think of data in the same way… LAS files are really just a text-file manifestation of a composite log (plus their headers are often garbage). SEG-Y is nothing more than a bunch of traces with a sidelabel.

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Together, palaeodigital and protodigital data might be called pseudodigital. They look digital, but they’re not quite there.

(Just to be clear, I made all these words up. They are definitely silly… but the point is that there’s a lot of room between analog and useful, machine-learning-ready digital.)

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Digital data

So what’s at the top of the digital ladder? In the case of maps, it’s shapefiles or, better yet, GeoJSON. In these files, objects are described in terms of real geographic parameters, such at latitiude and longitude. The file contains the CRS (you know you need that, right?) and other things you might need like units, data provenance, attributes, and so on.

What makes these things truly digital? I think the following things are important:

• They can all be self-documenting…

• …and can carry more or less arbitrary amounts of metadata.

• They depend on open formats, some text and some binary, that are widely used.

• There is free, open-source tooling for reading and writing these formats, usually with reference implementations in major languages (e.g. C/C++, Python, Java).

• They are composable. Without too much trouble, you could write a script to process batches of these files, adapting to their content and context.

Here’s how non-digital versions of a document, e.g. a scholoarly article, compare to digital data:

And pseudodigital well logs:

Some more examples:

• Photographs with EXIF data and geolocation.

• GIS tools like QGIS let us make beautiful maps with data.

• Drawing striplogs with a data-driven tool like Python striplog.

• A fully-labeled HDF5 file containing QC’d, machine-learning-ready well logs.

• Structured, metadata-rich documents, perhaps in JSON format.

Watch out for pseudodigital

Why does all this matter? It matters because we need digital data before we can do any analysis, or any machine learning. If you give me pseudodigital data for a project, I’m going to spend at least 50% of my time, probably more, making it digital before I can even get started. So before embarking on a machine learning project, you really, really need to know what you’re dealing with: digital or just pseudodigital?

Training digital scientists

Gulp. My first post in… a while. Life, work, chaos, ideas — it all caught up with me recently. I’ve missed the blog greatly, and felt a regular pang of guilt at letting it gather dust. But I’m back! The 200+ draft posts in my backlog ain’t gonna write themselves. Thank you for returning and reading this one.

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Recently I wrote about our continuing adventures in training; since I wrote that post in April, we’ve taught another 166 people. It occurred to me that while teaching scientists to code, we’ve also learned a bit about how to teach, and I wanted to share that too. Perhaps you will be inspired to share your skills, and together we can have exponential impact.

Wanting to get better

As usual, it all started with not knowing how to do something, doing it anyway, then wanting to get better.

We started teaching in 2014 as rank amateurs, both as coders and as teachers. But we soon discovered the ‘teaching tech’ subculture among computational scientists. In particular, we found Greg Wilson and the Software Carpentry movement he started. By that point, it had been around for many, many years. Incredibly, Software Carpentry has helped more than 34,000 researchers ‘go digital’. The impact on science can’t be measured.

Eager as ever, we signed up for the instructor’s course. It was fantastic. The course, taught by Greg Wilson himself, perfectly modeled the thing it was offering to teach you: “Do what I say, and what I do”. This is, of course, critically important in all things, especially teaching. We accepted the content so completely that I’m not even sure we graduated. We just absorbed it and ran with it, no doubt corrupting it on the way. But it works for us.

What to read

I should preface what follows by telling you that I haven’t taken any other courses on the subject of teaching. For all I know, there’s nothing new here. That said, I have never experienced a course like Greg Wilson’s, so either the methods he promotes are not widely known, or they’re widely ignored, or I’ve been really unlucky.

The easiest way to get Greg Wilson’s wisdom is probably to read his book-slash-website, Teaching Tech Together. (It’s free, but you can get a hard copy if you prefer.) It’s really good. You can get the vibe — and much of the most important advice — from the ten Teaching Tech Together rules laid out on the main page of that site (box, right).

As you can probably tell, most of it is about parking your ego, plus most of your knowledge (for now), and orientating everything — every single thing — around the learner.

If you want to go deeper, I also recommend reading the excellent, if rather academic, How Learning Works, by Susan Ambrose (Northeastern University) and others. It’s strongly research-driven, and contains a lot of great advice. In particular, it does a great job of listing the factors that motivate students to learn (and those that demotivate them), and spelling out the various ways in which students acquire mastery of a subject.

How to practice

It goes without saying that you’ll need to teach. A lot. Not surprisingly, we find we get much better if we teach several courses in a short period. If you’re diligent, take a lot of notes and study them before the next class, maybe it’s okay if a few weeks or months go by. But I highly doubt you can teach once or twice a year and get good at it.

Something it took us a while to get comfortable with is what Evan calls ‘mistaking’. If you’re a master coder, you might not make too many mistakes (but your expertise means you will have other problems). If you’re not a master (join the club), you will make a lot of mistakes. Embracing everything as a learning opportunity is less awkward for you, and for the students — dealing with mistakes is a core competency for all programmers.

Reflective practice means asking for, and then acting on, student feedback — every day. We ask students to write it on sticky notes. Reading these back to the class the next morning is a good way to really read it. One of the many benefits of ‘never teach alone’ is always having someone to give you feedback from another teacher’s perspective too. Multi-day courses let us improve in real time, which is good for us and for the students.

Some other advice:

• Keep the student:instructor ratio to no more than ten; seven or eight is better.

• Take a packet of orange and a packet of green Post-It notes. Use them for names, as ‘help me’ flags, and for feedback.

• When teaching programming, the more live coding — from scratch — you can do, the better. While you code, narrate your thought process. This way, students are able to make conections between ideas, code, and mistakes.

• To explain concepts, draw on a whiteboard. Avoid slides whenever possible.

• Our co-teacher John Leeman likes to say, “I just showed you something new, what questions do you have?” This beats “Any questions?” for opening the door to engagement.

• “No-one left behind” is a nice idea, but it’s not always practical. If students can’t devote 100% to the class and then struggle because of it, you owe it to the the others to politely suggest they pick the class up again next time.

• Devote some time to the practical application of the skills you’re teaching, preferably in areas of the participants’ own choosing. In our 5-day class, we devote a whole day to getting students started on their own projects.

• Don’t underestimate the importance of a nice space, natural light, good food, and frequent breaks.

• Recognize everyone’s achievement with a small gift at the end of the class.

• Learning is hard work. Finish early every day.

Give it a try

If you’re interested in help people learn to code, the most obvious way to start is to offer to assist or co-teach in someone else’s class. Or simply start small, offering a half-day session to a few co-workers. Even if you only recently got started yourself, they’ll appreciate the helping hand. If you’re feeling really confident, or have been coding for a year or two at least, try something bolder — maybe offer a one-day class at a meeting or conference. You will find plenty of interest.

There are few better ways to improve your own skills than to teach. And the feeling of helping people develop a valuable skill is addictive. If you give it a try, let us know how you get on!