Welcome to my first forays into "datart". I call these works data ditties. Folks who work with data and those with art share a common trait: curiosity. This curiosity leads us beyond the surface of things, often with social impact. So far I'm experimenting with pretty basic topics as I learn about how to activate data in compelling ways. From these simple experiments, I've already learned that what we choose to focus attention on is as important as how we do it.
There's a lot to learn, so it's a good thing I love learning.
PS:
This project is wholly motivated by personal interest and curiosity. A total passion project. It pulls together so many things I love doing:
And, it taps into something Rob Walker says, pay attention to what you pay attention to, which has really got my curiosity going even more.
So, we'll see where this goes.
PS:
This project is wholly motivated by personal interest and curiosity. A total passion project. It pulls together so many things I love doing:
- making images
- telling stories
- crunching numbers
- connecting dots
- being silly
- being serious
And, it taps into something Rob Walker says, pay attention to what you pay attention to, which has really got my curiosity going even more.
So, we'll see where this goes.
my first data ditty ...
and thoughts on a 1st try playing with numbers
some stuff in my living room
January 2022
I decided to go with a super simple subject for my very first data ditty (my name for it came after this try), and so I started with stuff in my living room--and not even all of it.
Here I simply counted a few select sets of items: cushions, seating, side tables, light sources, plant life (fake and otherwise). And I kept the reading system simple too; no external legend required to help understand what we're looking at. So, a very basic tally in every way. Yet, even at this basic level, I quickly discovered the amount of forethought one (hopefully) brings to this kind of counting. It quickly apparent to me how editorial counting is. Yes. Counting counts. Numbers are not neutral.
That's not a revolutionary statement--counting counts, numbers are not neutral--plenty of political scientists, economists, accountants and activists will tell us this, and show us how. What's a bit revolutionary is to produce one's own numbers, experiencing what goes into choosing what's to be counted (and, by extension, what won't be); then deciding how to represent the numbers; and then returning to those same numbers again, but on the front end, from the public-facing side, to consider what the numbers say; noticing what they highlight, intentionally or not, and perhaps what they don't. You'll see.
When finished making closets count and reflecting on the piece, I began to notice the embedded stories--or rather, the embedded hints at stories. The stuff close looking surfaces. Cues for critical thinking. For example: Why do I have 3 watering cans and only 2 live plants?
Once that peculiarity struck my attention, I understood the reason why, and it was eye-opening to me to see that the question itself stood out after taking a few moments to consider this piece as a whole. The question and its answer were not, at the outset, things on my mind in any explicit way, yet they emerged and spoke to me in very plain terms, revealing meaning and insight to me in this new city.
So, this first exercise, silly as it seems at first blush, leads me further in my belief that making personal data ditties and then reading them is a worthwhile approach to knowledge and self-knowledge. It also furthers my belief that it's a great way, and a very fun and approachable one, to develop an unusual thinking and interpreting muscle. More on that later.
January 2022
I decided to go with a super simple subject for my very first data ditty (my name for it came after this try), and so I started with stuff in my living room--and not even all of it.
Here I simply counted a few select sets of items: cushions, seating, side tables, light sources, plant life (fake and otherwise). And I kept the reading system simple too; no external legend required to help understand what we're looking at. So, a very basic tally in every way. Yet, even at this basic level, I quickly discovered the amount of forethought one (hopefully) brings to this kind of counting. It quickly apparent to me how editorial counting is. Yes. Counting counts. Numbers are not neutral.
That's not a revolutionary statement--counting counts, numbers are not neutral--plenty of political scientists, economists, accountants and activists will tell us this, and show us how. What's a bit revolutionary is to produce one's own numbers, experiencing what goes into choosing what's to be counted (and, by extension, what won't be); then deciding how to represent the numbers; and then returning to those same numbers again, but on the front end, from the public-facing side, to consider what the numbers say; noticing what they highlight, intentionally or not, and perhaps what they don't. You'll see.
When finished making closets count and reflecting on the piece, I began to notice the embedded stories--or rather, the embedded hints at stories. The stuff close looking surfaces. Cues for critical thinking. For example: Why do I have 3 watering cans and only 2 live plants?
Once that peculiarity struck my attention, I understood the reason why, and it was eye-opening to me to see that the question itself stood out after taking a few moments to consider this piece as a whole. The question and its answer were not, at the outset, things on my mind in any explicit way, yet they emerged and spoke to me in very plain terms, revealing meaning and insight to me in this new city.
So, this first exercise, silly as it seems at first blush, leads me further in my belief that making personal data ditties and then reading them is a worthwhile approach to knowledge and self-knowledge. It also furthers my belief that it's a great way, and a very fun and approachable one, to develop an unusual thinking and interpreting muscle. More on that later.
data ditty 2 ...
a bit more consequential in content
and more revealing about the potential for interpretation and impact
closets count
February 2022
Since it's still frightful weather outside, I continue to look to what I can count indoors, at home. So I turned to my closets.
There's an important caveat to fess up to at the outset: I'm a clothing designer and pattern-maker (now as a hobby, albeit a serious one) and clothing is a serious subject to me. Relatedly (maybe), I have a lot of clothes. Nonetheless, the closet count yielded some pretty interesting results, and again I found myself surprised by some of the information revealed. For example: I thought I wore a lot more solids than patterns. (I was also a bit shocked by how much is in my closets.)
As for close-looking and the deeper stories a bit of critical thinking can surface, I noticed this: How come so little clothing activity--purchases and production--in YUL compared to YVR and MIA? (Another personal question that surfaced for me, and surprised me, in the same way that the watering can/plant life numbers surfaced an insight when I was contemplating the some stuff in my living room data ditty.) As for broader interpretations - those relating to a world further afar and with important implications and ramifications - the ratio of used clothing to new in my purchasing habits speaks to the ecosystem of clothes: consumption; supply chains, labor conditions; textile waste; closed loops; upcycling; etc., as well as speaking to the social status and pressures culture and capitalism can impose.
I collected a lot more data than what's represented here. Some of it is pretty insignificant, like sleeve and hem length--although that too offers promise in a guided conversation like those I do with students on gallery tours. For example: if the data ditty tells us that my closet is full of short-sleeved and sleeveless items, I probably don't live in a northern climate. This speaks quietly to bits of meta-data about the maker, on an interpretive level speaking more to art history and an artist's biography. So though not really speaking to any kind of broader global impact, the sleeves reading is valuable anyway in exercising close looking, from which more parts of a story can be brought into the light and discussed.
Here's an example of this same close-reading re: clothing (and a bit more) in a painting called Subway, made in 1935 of a busy NYC subway car. As the Wolfsonian's education manager and teaching artist, it's really fun to activate this piece with high school students in Miami. And it goes like this: I first remind the kids that the painting was made in 1935. Then I ask them to imagine that Miami had a subway in 1935 (it didn't). Next I ask: Is this what you would see in a subway car in Miami in 1935? Eventually they surface two significant reasons why this would not be a subway scene in Miami in 1935, and their responses usually come in this order: 1/ People are wearing felt hats and heavy coats, which nobody wears in Miami (these are tells just like those about shortsleeved and sleeveless garments--although the climatic significance works in the inverse); and, 2/ Miami was still segregated and so black folk and white folk would not be traveling together in the same subway car. Clearly these two observations carry very different weight, yet both speak to the power of close looking and what details tell us, some significant socially and some significant in other ways, even apparent minutiae like clothing.
As for some other closets count numbers I collected but didn't include, some return us to the ecosystem of clothes and more consequential data re: global impact, like fiber content (in my case, cotton, 72%; linen 5%; silk 9%; silk-cotton batiste, 6%; wool; 5%; other, 1%), which is the kind of counting that counts in all kinds of ways.
What's still missing is the legend—the key to reading some of the data points through the symbols I chose. Because I'm embedding everything within a photograph I wish I didn't have to place some of the story elements outside the frame. If I come up with more explicit representations that tell us what certain data means, there would be no need for an external key.
So, in this closets count piece, what isn't totally and explicitly clear is the way I counted the amount of time these items have been in my closets. I used a bit of a flower motif to do that. In the lower right corner, laid over a diminishing road are three sets of small circles, and each small circle represents one year. It reads like this: the yellow part, -O/YUL, means less than 1 year, which is my time in Montreal; the blue part means 2-5 years, the length of time I lived in Miami; and the red part means more than 6 years, which is how long ago I lived in Vancouver (but doesn't further specify exactly how much older than 6 years those items are).
While closets count is still not where I want my data ditties to be as visual communication and story-telling, I'm making progress. Learning by doing.
February 2022
Since it's still frightful weather outside, I continue to look to what I can count indoors, at home. So I turned to my closets.
There's an important caveat to fess up to at the outset: I'm a clothing designer and pattern-maker (now as a hobby, albeit a serious one) and clothing is a serious subject to me. Relatedly (maybe), I have a lot of clothes. Nonetheless, the closet count yielded some pretty interesting results, and again I found myself surprised by some of the information revealed. For example: I thought I wore a lot more solids than patterns. (I was also a bit shocked by how much is in my closets.)
As for close-looking and the deeper stories a bit of critical thinking can surface, I noticed this: How come so little clothing activity--purchases and production--in YUL compared to YVR and MIA? (Another personal question that surfaced for me, and surprised me, in the same way that the watering can/plant life numbers surfaced an insight when I was contemplating the some stuff in my living room data ditty.) As for broader interpretations - those relating to a world further afar and with important implications and ramifications - the ratio of used clothing to new in my purchasing habits speaks to the ecosystem of clothes: consumption; supply chains, labor conditions; textile waste; closed loops; upcycling; etc., as well as speaking to the social status and pressures culture and capitalism can impose.
I collected a lot more data than what's represented here. Some of it is pretty insignificant, like sleeve and hem length--although that too offers promise in a guided conversation like those I do with students on gallery tours. For example: if the data ditty tells us that my closet is full of short-sleeved and sleeveless items, I probably don't live in a northern climate. This speaks quietly to bits of meta-data about the maker, on an interpretive level speaking more to art history and an artist's biography. So though not really speaking to any kind of broader global impact, the sleeves reading is valuable anyway in exercising close looking, from which more parts of a story can be brought into the light and discussed.
Here's an example of this same close-reading re: clothing (and a bit more) in a painting called Subway, made in 1935 of a busy NYC subway car. As the Wolfsonian's education manager and teaching artist, it's really fun to activate this piece with high school students in Miami. And it goes like this: I first remind the kids that the painting was made in 1935. Then I ask them to imagine that Miami had a subway in 1935 (it didn't). Next I ask: Is this what you would see in a subway car in Miami in 1935? Eventually they surface two significant reasons why this would not be a subway scene in Miami in 1935, and their responses usually come in this order: 1/ People are wearing felt hats and heavy coats, which nobody wears in Miami (these are tells just like those about shortsleeved and sleeveless garments--although the climatic significance works in the inverse); and, 2/ Miami was still segregated and so black folk and white folk would not be traveling together in the same subway car. Clearly these two observations carry very different weight, yet both speak to the power of close looking and what details tell us, some significant socially and some significant in other ways, even apparent minutiae like clothing.
As for some other closets count numbers I collected but didn't include, some return us to the ecosystem of clothes and more consequential data re: global impact, like fiber content (in my case, cotton, 72%; linen 5%; silk 9%; silk-cotton batiste, 6%; wool; 5%; other, 1%), which is the kind of counting that counts in all kinds of ways.
What's still missing is the legend—the key to reading some of the data points through the symbols I chose. Because I'm embedding everything within a photograph I wish I didn't have to place some of the story elements outside the frame. If I come up with more explicit representations that tell us what certain data means, there would be no need for an external key.
So, in this closets count piece, what isn't totally and explicitly clear is the way I counted the amount of time these items have been in my closets. I used a bit of a flower motif to do that. In the lower right corner, laid over a diminishing road are three sets of small circles, and each small circle represents one year. It reads like this: the yellow part, -O/YUL, means less than 1 year, which is my time in Montreal; the blue part means 2-5 years, the length of time I lived in Miami; and the red part means more than 6 years, which is how long ago I lived in Vancouver (but doesn't further specify exactly how much older than 6 years those items are).
While closets count is still not where I want my data ditties to be as visual communication and story-telling, I'm making progress. Learning by doing.
data ditty 3 ...
things are picking up momentum
and now reveal more about the importance of what we chose to count
(plus what it reveals, and so might impact)
my IG story
March 2022
Next, something a bit more consequential. Call it meta-humor, NO pun intended. I'm happy with what I learned. First, still more learning about counting - this time centered on learning more about how to capture what I want to count from a tech point of view. And then pretty amazing learning in terms of what the counting revealed about IG, its algorithms, and how much of my time they harvest. I wish I could say I was surprised to see what my IG story told me. But I'm not. I've long loathed the term "consume content" and even though I won't use that language myself, I guess there's no way of escaping its application in my life, like it or not. As for the visual activation of the information, it's pretty underwhelming to me. It's just ok.
Because I designed my IG story for direct posting to IG--as a post, not as what they call a "story"--all the data and interpretive background required to understand this ditty's story is contained within the set of 9 layouts. I toyed with the idea of describing my whole process - what tech I used to acquire all the data - but then decided it wasn't all that important to the front-end experience. That said, my motivation to include all that info was to demonstrate the overall integrity of the methodology--did my data collection process have integrity? Is the data reliable and valid? (Basically, to explicitly introduce more STEAM stuff.)
After collecting and crunching the numbers, I spent a lot of time on how to represent my findings. As I said, it looks ok. But barely. Now, more than ever, I'm so super appreciating the art of graphic design and all the skills and knowledge, experience and talent that go into making the discipline sing.
SO much for me to learn.
March 2022
Next, something a bit more consequential. Call it meta-humor, NO pun intended. I'm happy with what I learned. First, still more learning about counting - this time centered on learning more about how to capture what I want to count from a tech point of view. And then pretty amazing learning in terms of what the counting revealed about IG, its algorithms, and how much of my time they harvest. I wish I could say I was surprised to see what my IG story told me. But I'm not. I've long loathed the term "consume content" and even though I won't use that language myself, I guess there's no way of escaping its application in my life, like it or not. As for the visual activation of the information, it's pretty underwhelming to me. It's just ok.
Because I designed my IG story for direct posting to IG--as a post, not as what they call a "story"--all the data and interpretive background required to understand this ditty's story is contained within the set of 9 layouts. I toyed with the idea of describing my whole process - what tech I used to acquire all the data - but then decided it wasn't all that important to the front-end experience. That said, my motivation to include all that info was to demonstrate the overall integrity of the methodology--did my data collection process have integrity? Is the data reliable and valid? (Basically, to explicitly introduce more STEAM stuff.)
After collecting and crunching the numbers, I spent a lot of time on how to represent my findings. As I said, it looks ok. But barely. Now, more than ever, I'm so super appreciating the art of graphic design and all the skills and knowledge, experience and talent that go into making the discipline sing.
SO much for me to learn.
data ditty 4 ...
inspired by #3 and the focus on social media, I present
What I Discovered from Spotify's Discovery Weekly playlist
The full piece includes a short video that I'm unable to upload here so please have a look at my IG post to view it (oh, the irony).
The full piece includes a short video that I'm unable to upload here so please have a look at my IG post to view it (oh, the irony).
