At the heart of Observable is the community. The people who make up our community create, collaborate, connect, and influence with data. The purpose of the Observable Ambassador program is to partner with community members who are actively using Observable.
Learn more about the Observable Ambassador program here.
Previous Ambassador Stories: David Kirkby
In June 2022, Maxene Graze was kind enough to share her story with the Observable team. We learned a bit more about her background and interests, as well as how she's used Observable with her unique fermentation and linguistic data sets.
First, some quick facts.
Ambassador since: March, 2022
Uses Observable for: Personal research, fun
Why Observable?: "Learning D3.js and finding dataviz inspiration are the main reasons why I started using Observable. It continues to be a central part of my toolset for quick prototyping, data exploration with Plot, and the Observable community who are always there to help."
Maxene, thanks for taking the time to share your story! I'm a big fan of your notebooks, especially the Fermentation Jar one. (I'll tell you how the carrots turn out.) Can you talk a bit about your background and your interests? My background is actually in research-based academia. As an undergraduate, I studied biology and foreign languages (French, German & Japanese), and I later pursued a master's in linguistics, which was also research-based. I was first exposed to data visualization when I was getting my master's in linguistics when I took a linguistic cartography course. We poured over dining room table-size dialect charts that were 200 years old - who wouldn’t find that impressive and somewhat magical?! Although I dreamed about pursuing it as a career, I didn’t think linguistic cartography was a real-life job, so I integrated it into my dissertation and then dropped it. To be honest, I didn't even know that cartography was still a living profession.
I decided to quit academia for a few reasons. First, because it wasn't very collaborative. Also, I wanted to make more of an immediate impact, which isn’t possible in academia, where your work is only seen by your professors and just a few people who are interested in it. After I left academia, I decided to do some freelance writing and realized all of the “serious” writers had their own portfolios. I discovered data visualization could be a job while learning tools to develop my own portfolio ↓ .
How did you get started in dataviz? My transition to data visualization was a natural progression since I already had a research background in scientific and linguistic data collection. Despite my short intro to dataviz earlier while studying linguistics, I didn’t realize you could actually get paid to do dataviz until a few years later when I was creating an online portfolio for my writing. Since I didn’t have the coding or design chops needed to create my own, or the money to use a paid service, I started learning basic CSS/HTML and taking a UX class. As part of that class, there was a 20-minute module on data visualization.
It sounds cheesy, but that was the moment when I knew it was my calling. After that, my goal was to make interactive data visualization. I took a coding boot camp with Codeworks to remedy my rudimentary coding skills, and later some design courses so that if anything, I could align and choose fonts properly.
Dataviz was essentially everything I wanted to get out of academia but didn't: research, creativity, design, a collaborative, supportive environment, a more immediate impact, and remote working (I have a say in where I want to live!).
What about your data? Where do you find it and what kind of data are you working with? I started working with and collecting data in academic research, as I mentioned early. I prefer collecting the data myself as it fosters more intimacy with the data - I believe this is crucial in honestly representing and fundamentally understanding it. Then there’s the creativity in deciding, whether for good or for bad, what data you're going to collect, how to collect it, and describe it. Sure, there’s an aspect that feels powerful, but it’s also humbling since I am forced to grapple with the imperfections and human qualities of the data.
The data I collect tends to come from other researchers, professors, research papers, and more and I end up assembling fermentation-related datasets. I also work with linguistic data. When I'm engaging in side projects (which is constant), I work mostly with datasets that I've collected myself.
Now, I'd like to learn a bit about how you use Observable. How did you first come across Observable? I was learning D3.js and google searching for code snippets brought me to bl.ocks.org, which directed me to Observable.
Why did you choose to use Observable? I started to use Observable to learn D3.js - there are some bite-sized, interactive tutorials that make it easier for a coding n00b to dive right in since you don’t need to set up a coding environment. When you're learning, the best way (for me, at least) is definitely by looking at other people's code and either trying to recreate it or fiddling around with it and seeing what happens. The fact that I can do that in Observable is really helpful, and it made me learn D3.js much faster.
Do you use Observable on your own, with a team, or both? I've collaborated on Observable a couple of times, but mostly use it on my own. It is handy when you want feedback or help trouble-shooting a notebook, since others can comment, or fork and suggest improvements more quickly than is possible by using Github.
What are the top reasons you continue to use Observable? In a nutshell, the reactive notebooks, Plot, and open-source code are the main reasons why I continue to use Observable.
It's really easy to prototype interactivity in Observable due to the built-in inputs. For example, if I’m building a scrollytelling piece, I might start prototyping in Observable, then expand and weave my code into a coding editor to build a web app.
Lastly, the Observable community is very supportive, helpful, and passionate about what they do!
What would make your experience better? A better way to organize "liked" notebooks and create collections out of them.
Before you used Observable, what challenges were you having? To be honest, I didn't have "challenges" because I started doing dataviz engineering in conjunction with Observable...so it's been with me from the start.
Has Observable led to any interesting discoveries that may not have been possible otherwise? The discoveries made with Observable are honestly innumerable. If Observable and all its notebooks didn't exist, I wouldn't be half as good in D3.js. I can't even imagine if neither Observable nor Blockbuilder existed because it's the home of a huge library of open-source D3 code.
If you need an example of how something is done, it's really easy to find a variety of ways that people have implemented it. That helps while learning D3.js, or improving your own coding style. If there’s a specific D3.js concept I want to grasp or chart I’m looking to develop, I go to Observable before Google.
How does Observable’s repository of reusable code components help you? Rapid prototyping!
What other data tools do you use in conjunction with Observable? Adobe Illustrator, as well as Excel / Airtable.
What are some of your favorite notebooks that you've created, or that you're currently working on? Why? Definitely, the ones related to fermentation. One is the fermentation jar notebook.
Also, my historical map day notebook. I like this one for a few reasons. First, because I spent so much time on it. Second, I collected that data and it's data I've kind of been obsessed with for a while. Third, I was really happy with how it turned out and I learned a lot due to the kind Observable Ambassadors and employees who helped me troubleshoot and clean up my code!
What are some of your favorite notebooks by other users? Who inspires you in data visualization? While there are many Observable users who inspire me, my top 2 are easy: Olivia Vane and her waterlines collection and, of course, Fil. I'm just flabbergasted by his competency and code.
The list of dataviz inspiration is long. I've been more and more interested in multi-sensory data representation, so Jer Thorp and Duncan Geere come to mind.