February 10, 2024 • 28 mins
This time my guest is the intriguing Quinn Dombrowski, whom I have been keeping an eye on via social media for ages due to their Textile Makerspace project and general all-round niceness and humanity. We had a fascinating chat about some non-traditional approaches to data visualisation. You can even study data visualization with Quinn at Stanford.
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Episode Transcript
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(00:00):
Welcome to another episode of the Data Revolution podcast.
(00:15):
Today, my guest is Quinn Dombrowski, who is an interesting person who is going to introduce
us themselves.
Hi, I'm Quinn Dombrowski.
I am an academic technology specialist in the division of literatures, cultures and languages
and in the Center for Interdisciplinary Digital Research in the library at Stanford University,
(00:35):
which is probably the most acronym-tastic title of anyone you've had on the show.
That is a mouthful, that's why I didn't even want to try and say that one.
So what are we going to talk about today?
Today we're going to be talking about data visualization with textiles.
That's an interesting angle.
So what prompted you to be interested in this?
(00:58):
I don't even really know how I got into this to begin with.
I have been teaching this data visualization with textiles class at Stanford this past
spring, and I'm teaching it again this upcoming spring.
And as I was starting to plan for the class, I dug through my closet and realized that
about 10 years ago, I had made a data visualization on the occasion of my husband graduating with
(01:23):
his PhD.
And it was this elaborate quilting thing where every single color represented something different,
whether it was the number of classes he had taken or the classes he had taught or the
languages he had learned or the chapters of his dissertation, all kind of pieced together
very carefully.
But in the end, the thing did not turn out to be a square.
(01:44):
And I was so frustrated that it wasn't going to realize my dreams of a quilt or a pillowcase.
And so I balled it up and threw it in a closet and forgot about it for 10 years until one
day I found myself doing this.
But I mean, when you say data visualization with textiles, people automatically go to
the temperature blanket, which seems to be my brain weight too.
So I've seen it so often.
(02:05):
So often.
And I mean, the temperature blanket is great as a way in if that's your kind of thing.
It's very clear and clean data.
I mean, you do have to make some choices about like, are you doing high for the day and low
for the day or just one of the two?
I mean, it's pretty clear and unambiguous and like you translate it directly to colors,
(02:26):
you know, just sort of map your colors to your data.
It's a pretty simple thing to do.
But I always hate to see it end with that.
There are so many other things and more things you can do to sort of take advantage of the
affordances of textiles as something really different than, you know, just sort of throwing
(02:46):
some data in Excel and hoping that it turns out or Tableau or any of your pointy clicky
things.
And yeah, I mean, I feel like I've come to understand the data that I work with this
way in a really like rich and meaningful way that wouldn't have been possible with a spreadsheet.
It sounds like a really tactile way to interact with data, which sounds like a very strange
(03:07):
thing to me because, you know, you're used to it being on a screen, but you're actually
touching it and interacting with data physically, which does that change how you think about
it?
How you feel about it?
I think it does.
So I mean, there's like, there's a difference between, you know, making a pie chart that
says, you know, you know, something happens twice as often as one other thing.
(03:31):
You can represent it as a pie chart, you can represent it as, you know, a bar chart.
There's lots of ways that you can represent these things graphically.
And I think on some level, your brain's like, oh, okay, all right, yeah, this, this happens,
you know, a lot more than this other thing.
But it really is a different experience to, you know, start a project with like, you know,
two balls of yarn of roughly equal size, and then by the time you're done, you know,
(03:54):
have one of them be, be almost out or like a tiny little fluffy thing, and the other
be, you know, still something sizable, like, you know, understanding kind of like how,
how we get to twice as much as some other value through number of, you know, rows of
knitting or sort of like cranks of a knitting machine or rows of weaving.
(04:15):
It's really satisfying and, and yeah, I feel like I have a better, a better sense of like
what this is once I've done something with it.
So how do the students react to these, these courses?
How do they come in and how do they leave?
Have they somehow brainwashed this out of this?
Well, so I run a textile maker space at Stanford.
(04:37):
It started off its life as a computer lab that no one loved.
And so I inherited it when I started my job and I asked around and no one, no one was
very excited about this computer lab.
It had dying computers and dying digitization equipment.
So I got rid of all of that and just out of pocket bought some sewing machines and, you
know, you know, wrote out a sign declaring it the textile maker space.
(04:59):
And so it was.
And thanks to, you know, some small bits and pieces of money here and there, you know,
an anonymous alum donation and then some funding from an initiative at Stanford to bring together
all the maker spaces called Making at Stanford, where now I have like student staff, you know,
we were able to create like a pretty thriving space.
(05:19):
But one of the goals of this Making at Stanford program is to have all undergrads have some
kind of making be part of their undergrad experience, you know, be it.
Oh, wow.
Yeah, it's really cool.
And there's all kinds of maker spaces.
Like, you know, ones with big industrial machines and lots of safety training.
There's like an, you know, an educator oriented space with like they have like a laminator.
(05:40):
Of course, the teachers have the laminator and the combining machines and like it's fantastic.
You know, lots of laser cutters, lots of 3D printers and things like that.
And I'm the one that's scared towards textiles, sort of like the odd cousin of the bunch.
I don't think we've got a text at our uni.
I don't think we've got a textile making space.
I've got to go and ask some questions.
(06:01):
Because we've got all the other kinds.
It's a great, you know, and it's the funny thing is they did a survey and they showed
that the second most common piece of equipment across all of these spaces was after the 3D
printer, of course, was a sewing machine.
But the problem was in a lot of the places it was a sewing machine that was picked up
kind of like an afterthought and no one really knew how to use it and no one knew what to
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do with it.
So it sort of sat gathering dust in a corner.
And the feedback that we got from other people was like, yeah, I've seen sewing machines
around, but like when I ask people how they use it, like no one can help me.
So we found our way here.
So yeah, I run this maker space and, you know, when the funders say that they would love
to see more making courses, you know, you can take a hint.
(06:44):
And I put a course on the book.
You know, I assure the department chair that this would take like no time at all.
You know, it was going to be a small independent study.
Five students max.
This was not even going to be something that she would have to worry about like eating
my life.
And so we got it, we got it registered.
And on the first day of students signing up for classes, I realized pretty quickly that
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something had gone wrong in the course system because all of a sudden I had 20 people signed
up.
So I couldn't say no to 15 people and just basically reimagined the whole thing completely
on the fly.
You know, the textile maker space can't fit 20 people at the same time.
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I mean, you can pack 30 in there, but it's like a fire hazard.
You can't turn around.
So so, you know, we set it up so that people could come in during any of our open hours
and me or the other staff would teach them how to how to use whatever method or method
they were interested in.
And the students just made these utterly delightful projects and you kind of all at their own
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pace working on whatever kinds of data they wanted.
We had a check in in the middle.
And that was a that was a great moment to actually have a conversation with some of these students
and find out from them.
Some of them came in with like, you know, very academic, you know, data sets that they
were, you know, going to be working on or so they assured me.
Yes, you're tracking tracking the reading that I'm doing over time and you know, oh,
(08:13):
you know, things in my thesis and so forth.
But the more you get them talking about it and kind of drawing them out on the subject,
you know, more often than not, there was like something else that they actually wanted to
do that like data that they thought was like weird and fun and like they wish that they
could do but like kind of with the academic rigor mindset of, you know, a university like
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that's not where they went to first.
But eventually we got them thinking along those lines.
And that's that's the project that many of them ended up making.
There was a student who had tracked the number of cats that she had seen on campus and made
a sweatshirt where the tail was the data about the cats over time.
And all of us, she managed to sort of turn into a data set from her photos.
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It was just a delight.
Fascinating to think about students learning to interact with data visually and going from
the very academic to their real passion.
And that's that's a pretty unusual trajectory for a university course.
How did the administration view this?
(09:17):
I mean, I didn't tell them.
I mean, we just did it.
I mean, one of the nice things about Stanford is is kind of culturally it's very entrepreneurial.
And there's a lot of space to just sort of like go and try things as long as it's sort
of within within, you know, generally reasonable boundaries.
(09:37):
And I think it's it's fairly well understood from the administration side that, you know,
there are mental health challenges, you know, among the students.
It's a high stress environment.
And a lot of the, you know, creative art, you know, personal essay writing classes, I
mean, they fill up, you know, within five minutes of registration opening.
(09:58):
There's really a need for this kind of creative self expression.
And there's not as many avenues for this as as one would hope.
And I think that that's that's one angle of the making a Stanford program, trying to kind
of make that more accessible and in reach for for everyone kind of regardless of what
shape that takes exactly.
(10:20):
That's really interesting because in Australia, our government, our previous conservative
government tried to stop people from doing arts degrees by putting the price up for a
real lot.
But people can't see that.
Yeah.
So there's this real hunger for students to do things that aren't STEM.
And they really, really want to do it, even if you double the price.
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So there's not a lot of price sensitivity in the student space.
It's really quite interesting.
And even now, the Labor government, they're like the Democrats, the Americans, they, they've
kept those fee structures and they're studying.
But why are people not studying STEM?
It's because like nobody, not everybody has to study STEM.
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And there are things that you can do in the STEM space like this, which I find really
interesting.
And one of, because one of my big challenges is how you visualize data and how you can
make it accessible to normal human beings.
Because there was this crazy idea for a long time that every person in a business wanted
to be a citizen data scientist.
And I'm like, no, they don't.
(11:26):
They just want to do their jobs.
Yes.
No, it's, it's, it's really interesting.
And I mean, one of the things that has surprised me about running the maker space and, and
this course has been, that it's not the students that I expected that, you know, take to it
so easily.
It is in fact the engineers who are probably the most, the most common department of the
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students who come by the maker space is, is engineering.
And it takes a lot of effort and kind of encouragement and congealing to get the students in, in my
department, the literature department to, to come and make things.
And what I, what I realized eventually, even though I wouldn't have guessed it at first,
is that, you know, once you are, once you are used to making one kind of thing, it's
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not a big leap to make another kind of thing.
So, you know, if, if you, you know, are spending your days in a lab making like microcontrollers,
like why not go spend some yarn or like sew a thing, you know, the, the, the, the transferability
of that is, is pretty high.
But it's the students who have spent their entire lives basically reading and writing
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things for whom like the idea of like using your hands to do something besides turning
a page or like typing some things is, is kind of alien and, you know, needs, needs some
translation.
That, that actually aligns with my experience because, you know, every engineering student
has to do stuff in the shop.
So they're in a workshop somewhere making like you said circuit boards or welding, or welding
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off and, you know, we've got some pretty fun labs in our engineering faculty.
So it would, wouldn't make sense that they find the tactile nature.
It's, it's a really, I just find it so utterly fascinating that, that there is such a resistance
from some of these students.
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So because there's so much in their head and it tells me that there's, there's probably
something wrong with our whole education process that they get so much into their head that
they can't associate with, with tactile.
And we were chatting before we started recording about my past when I was in high school and
one of the art mediums that I used was weaving, which is from, from your reaction was pretty
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unusual.
Yeah, I haven't, I haven't seen that so commonly in schools here.
Maybe when I got, so I decided kind of on impulse to buy a giant standing loom.
This thing is almost as long as I am tall.
It didn't actually fit to the door of my office.
We had to partially disassemble it.
I got it because it was on sale and I'm like, all right, clearly, like I need to buy this
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loom and then step two was figure out what to do with it.
And I asked around and I only managed to find one student who, you know, went to an unusual
like sort of rural private school and happened to know like using this kind of loom, like
how it works and how you set it up.
Yeah, that definitely seems less, less common here at least.
I know it's pretty, it's pretty uncommon here to confession.
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I went to a private school here and we had a lot of facilities and you could pretty much
do whatever you wanted in the art space.
But I think because, because I'm one of those people who really lives in my head, you know,
translates through the keyboard.
So it was really interesting to make things with clay or to make things with looms.
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And I haven't done it since I was in high school.
So it's just a really interesting thing for me about how do we as educators, and I think
the maker space is a really great idea.
And then there's this whole challenge of how do we, how do we think differently about data,
which is one of the things that I'm really interested in is how do we visualize data
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differently?
And I think your approach with textiles is a really interesting one.
Do you have, have you also, because it always seemed in my mind that weaving and knitting
are correlated.
Have you also adopted that?
Yeah.
So about a year ago is when I just started getting into like all of the yarn based arts.
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You know, I've been sewing for, I don't know, 15 years.
And at this point, I make like literally all of my clothes except for socks.
I sew them all myself.
But sort of I had socks up there is like this, this holy grail.
Like if only I could get myself a pair of socks, then I could say, you know, I've done,
I've done the whole, the whole thing.
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And also, I mean, there's, there's, there's some appeal to the, the, you know, knitting
crochet, even spinning with some of the modern portable e-spinner is like the electric e-wheel
wheel that can fit in the lunchbox.
And you can do it on a train to have a creative hobby that's more portable than sewing.
Because I mean, sewing is pretty infrastructure heavy.
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Like you can't just throw a sewing machine like in your backpack and do it on the train
the same way you could, you know, pick up, pick up some knitting needles.
On the train.
Yeah.
For the train.
Yeah, exactly.
So yeah, I started, I started exploring those and like spinning my own yarn.
And you know, I got some folks to teach me how to crochet.
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Crochet is, is more than knitting.
Crochet is kind of surprisingly popular among college students these days.
There's a lot of people who do it.
You know, there's, there's plenty of people who come by the makerspace to like, you know,
pick up a different size crochet hook than they happen to have on them or like sort of
see what we have in our donated stash of yarn and then make wonderful things and send us
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pictures and it's, it's just a delight.
So yeah, I mean, it's, it's, but yeah, crochet, crochet is a little bit different.
It's a little bit more kind of geometrical and, and kind of 3D ish than the knitting
where kind of in learning to weave and reading books about it.
I've seen far more in terms of like translating knitting terminology into weaving terminology
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than the equivalent for crochet.
Yeah, yeah, they seem to me to be quite different.
I know all of them, but I don't do them.
It's kind of making me feel weird about why, why don't I do these things anymore?
Because I read books all the time.
So this is, this is my hack.
I've been listening to audiobooks while I, while I knit and weave and it, I feel like
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I can do that easily and enjoy them both at the same time.
So, so what, what sort of, cause to Stanford, when you were talking about the computer lab,
I was like, everyone at Stanford must have a decent computer and not need an actual computer
lab.
So it makes sense that you were able to create the maker space.
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But is this mainly undergraduate students that are coming through the maker space?
What do the post-grad come through to?
It's everyone.
It's, it's undergrads, it's grad students, it's staff.
There's even some faculty who, who come by.
You know, sometimes it's just like really basic thing is like they want to mend something,
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you know, or they want to make a present for someone.
We have an embroidery machine and that thing is just like humming 24 seven, especially
right before the holidays and before the, the end of the school year when people want
to like embroider things as, you know, graduation gifts for friends or, you know, other mementos.
It really is just a wonderful space of, of kind of encounter across these different administrative
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divisions between people at the university and different, you know, ranks and, and roles.
And, you know, it's not, it's not really clear, you know, who's going to be the expert in
this space.
Like you, you may have a tenured faculty member, like, you know, coming in with like,
you know, vague memories of having knit, you know, several decades before and wanting to
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catch up again, you know, and a 17 year old can, can get them on the right track.
Like it's, it's a really beautiful thing.
So it sounds like a really great collaborative space.
I'm actually wondering how we can do more of these kinds of things with data.
How, how it might be translatable from, from the textile space into other spaces.
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Like one of the things we've got for our researchers is we do get-togethers where people do,
do stats work together.
Because you know, a lot of people find stats statistics very confronting.
I personally did.
Because I was an arts girlie, not a math girlie.
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But, but, you know, having these collaborative spaces where you can sit together and work
together, I think is a big way to make a lot of this stuff more accessible.
And it's just really interesting to me about how we can, how we can take this kind of great
idea about the makerspace into other spaces.
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Like we're already doing it with, with all the makerspaces in engineering.
We've got so many makerspaces.
Every time you turn around a corner, there's another makerspace that's cropped up because
on campus, you know, there's so much demand for them.
And we've got a lot of students.
We've got about 60,000 students.
So there's a lot of demand, but one of the interesting things for me is how do we, how
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do we do more collaborative learning outside the classroom space?
Because I think that's where a lot of the magic happens.
I suspect in your makerspace, a lot of the magic's happening because you're bringing
people together with an interesting object in front of them.
So they're sort of side by side looking at the object together.
Yeah.
Well, I think one of the, the great innovations of the, the Making at Stanford initiative,
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because I mean, it's a similar situation, right?
Where, you know, makerspaces everywhere, like no one knows who's running other makerspaces.
No one knows who has what equipment and things, and things like that.
But even, I mean, certainly like having access to, you know, sort of a pool of funding and
grants and things helps a lot.
But, you know, there's, there's also this concept of like the maker council where it's
(21:43):
the people who run, you know, all of the kind of major and minor spaces on their radar.
And we get together once a quarter and just like talk about things and plan workshops
together.
And, you know, I volunteered to like, you know, anyone who wants to activate all of those
sewing machines, they have like gathering dust in the corner, like, let me know, we'll
schedule a time, I'll go, I'll go show all your students, Dad, how to do it.
(22:05):
And I think like, you know, similarly, like I wouldn't, you know, know my way around a
3D printer if my life depended on it.
But like now, now I know who to ask and, you know, sort of building up these like sort
of friendly networks of expertise where like, you don't necessarily have to know everything,
but like, you know, the guy who will know the thing and, and you can sort of help route
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people to the right thing is a really powerful thing on on campuses that tend to be like
so siloized around individual disciplines and different methods.
And, you know, like as it turns out, the person who had like a stash of other looms, you know,
was a was a physics professor. And, you know, never would have guessed this, but it's it's
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just delightful to make so many new friends in different parts of campuses that I'd never
even been to before.
It's fascinating, you know, because you get you go to the place where you do your classes
and you just hang around there.
So getting people to go to other parts of the campus is always an interesting challenge.
You need to have a reason to do it.
(23:11):
I'm interested to wonder, though, how this might be translated into the workplace. What
sort of ideas have you got about that? Because, you know, it's an interesting idea.
Yeah, well, so the the show that I'm wearing right now actually was one of my first weaving
projects that is very much like a workspace like data project where this this is three
(23:36):
months of slack messages from my immediate group in the library. Wow. Yeah, between August
and November 2022. And, you know, one of my first weaving projects, I just sort of pulled
up the history of our slack messages and, you know, just sort of assigned one color of yarn
to each basically like role or job title. The the bosses in purple, the developers are
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in black, people in my job are the sort of turquoisey color. And I just I just wove, you
know, one, one weft yarn for every message, like I wasn't trying to factor in, you know,
amount of time that elapsed between the messages or like length of messages, there's other
things you could do to try to do that as well. But literally just like who is saying what
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and then who is replying over the course of time. And, you know, I actually finished
this exercise feeling really good about the group that I work for within the library that
like, you know, we're actually a pretty functional place, like people talk to each other. You
know, there's there's like, friendly chit chat, but it's not like forced or weird. Our
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boss has a habit of like making announcements, which I hadn't really appreciated before.
But like, you know, kind of as as, you know, a way to kind of understand the kind of the
relationships between people and the interactions within a group, I imagine there are other
groups in the library that were I did you a similar exercise for it would look very,
very different. And then thinking about kind of like what what do these differences in
(25:06):
dynamic like tell us about the group? Like are there are there issues that we're seeing
within the group that, you know, manifest in other ways that like communication style
might have, you know, play some role here. You know, that that's been that's been kind
of really useful for me, like within the context of this of this organization. Yeah, I mean,
(25:26):
another another thing that we've done at the makerspace that, you know, people seem to
really enjoy is our guestbook that we actually do use for stats, I count the the yarns when
people ask well, how many people have visited the makerspace like all right, let me go get
a pencil and like count all these tiny things. So people people weave a different color of
yarn depending on what they come for they hear just so or embroider or knit, so forth.
(25:52):
And then there's a key with different colors of beads. And optionally, they can add colors
for how they're feeling that day and the slide on as many beads as they they they feel like
sharing. And so at the end of the year, we hang this up as a tapestry. And you can kind
of like see the ups and downs of the quarter, you know, where people are getting stressed,
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you know, obviously finals week is terrible and everyone's exhausted. But like, as a way,
you know, to sort of like anonymously capture in some way that like still feels cathartic
and meaningful, you know, workplace morale in a way that's not like, you know, sending
out a survey where people are probably going to lie, it's it's it's harder to lie with
the yarn. It's yeah, we get we get those surveys. And it's just like, oh, no, I don't want to
(26:37):
tell you how I'm feeling. Right? If you could if you could basically anonymously like go
up to a loom and slide on, you know, some yarn and some beads. I wonder, I wonder if
at least in some work cultures, that might be a useful way to kind of like get a pulse
on on how people are doing in a way that's that's like less awkward and and sort of satisfying.
(27:02):
Yeah, because it's one of the things in our agile process with my development team, you
know, that we always want to sort of take a temperature check of how people are feeling.
And we just we just, you know, we've tried various methods. But it depends on the high
on the levels of trust, like we've got a high level of a trust in the team. So people are
(27:24):
willing to just say, I don't feel good today. I don't feel good about the last sprint. I
don't, you know, and stuff like that. But not everybody has that high level of trust.
So I think that sort of technique could be really useful to get insights into a lot,
a lot of different workplaces.
Yeah, there's there's no, you know, handwriting to give you away. There's no demographics,
(27:46):
you know, it's just, you know, plus plus it's just kind of satisfying to like grab a piece
of yarn, chop it off and just go over under over under and slide some beads on. Yeah.
So this has been a really interesting chat. Thank you so much for your time. Thank you
for persisting through my internet problems. Thank you NBN for breaking down yet again.
(28:10):
Always a pleasure. Thank you so much, Quinn. I've really enjoyed our chat.
Yeah, thanks so much for having me.
And that is it for another episode of the Data Revolution podcast. I'm Kate Crothers.
Thank you so much for listening. Please don't forget to give the show a nice review and a
like on your podcast app of choice. See you next time.
Welcome to another episode of the Data Revolution podcast.
(00:15):
Today, my guest is Quinn Dombrowski, who is an interesting person who is going to introduce
us themselves.
Hi, I'm Quinn Dombrowski.
I am an academic technology specialist in the division of literatures, cultures and languages
and in the Center for Interdisciplinary Digital Research in the library at Stanford University,
(00:35):
which is probably the most acronym-tastic title of anyone you've had on the show.
That is a mouthful, that's why I didn't even want to try and say that one.
So what are we going to talk about today?
Today we're going to be talking about data visualization with textiles.
That's an interesting angle.
So what prompted you to be interested in this?
(00:58):
I don't even really know how I got into this to begin with.
I have been teaching this data visualization with textiles class at Stanford this past
spring, and I'm teaching it again this upcoming spring.
And as I was starting to plan for the class, I dug through my closet and realized that
about 10 years ago, I had made a data visualization on the occasion of my husband graduating with
(01:23):
his PhD.
And it was this elaborate quilting thing where every single color represented something different,
whether it was the number of classes he had taken or the classes he had taught or the
languages he had learned or the chapters of his dissertation, all kind of pieced together
very carefully.
But in the end, the thing did not turn out to be a square.
(01:44):
And I was so frustrated that it wasn't going to realize my dreams of a quilt or a pillowcase.
And so I balled it up and threw it in a closet and forgot about it for 10 years until one
day I found myself doing this.
But I mean, when you say data visualization with textiles, people automatically go to
the temperature blanket, which seems to be my brain weight too.
So I've seen it so often.
(02:05):
So often.
And I mean, the temperature blanket is great as a way in if that's your kind of thing.
It's very clear and clean data.
I mean, you do have to make some choices about like, are you doing high for the day and low
for the day or just one of the two?
I mean, it's pretty clear and unambiguous and like you translate it directly to colors,
(02:26):
you know, just sort of map your colors to your data.
It's a pretty simple thing to do.
But I always hate to see it end with that.
There are so many other things and more things you can do to sort of take advantage of the
affordances of textiles as something really different than, you know, just sort of throwing
(02:46):
some data in Excel and hoping that it turns out or Tableau or any of your pointy clicky
things.
And yeah, I mean, I feel like I've come to understand the data that I work with this
way in a really like rich and meaningful way that wouldn't have been possible with a spreadsheet.
It sounds like a really tactile way to interact with data, which sounds like a very strange
(03:07):
thing to me because, you know, you're used to it being on a screen, but you're actually
touching it and interacting with data physically, which does that change how you think about
it?
How you feel about it?
I think it does.
So I mean, there's like, there's a difference between, you know, making a pie chart that
says, you know, you know, something happens twice as often as one other thing.
(03:31):
You can represent it as a pie chart, you can represent it as, you know, a bar chart.
There's lots of ways that you can represent these things graphically.
And I think on some level, your brain's like, oh, okay, all right, yeah, this, this happens,
you know, a lot more than this other thing.
But it really is a different experience to, you know, start a project with like, you know,
two balls of yarn of roughly equal size, and then by the time you're done, you know,
(03:54):
have one of them be, be almost out or like a tiny little fluffy thing, and the other
be, you know, still something sizable, like, you know, understanding kind of like how,
how we get to twice as much as some other value through number of, you know, rows of
knitting or sort of like cranks of a knitting machine or rows of weaving.
(04:15):
It's really satisfying and, and yeah, I feel like I have a better, a better sense of like
what this is once I've done something with it.
So how do the students react to these, these courses?
How do they come in and how do they leave?
Have they somehow brainwashed this out of this?
Well, so I run a textile maker space at Stanford.
(04:37):
It started off its life as a computer lab that no one loved.
And so I inherited it when I started my job and I asked around and no one, no one was
very excited about this computer lab.
It had dying computers and dying digitization equipment.
So I got rid of all of that and just out of pocket bought some sewing machines and, you
know, you know, wrote out a sign declaring it the textile maker space.
(04:59):
And so it was.
And thanks to, you know, some small bits and pieces of money here and there, you know,
an anonymous alum donation and then some funding from an initiative at Stanford to bring together
all the maker spaces called Making at Stanford, where now I have like student staff, you know,
we were able to create like a pretty thriving space.
(05:19):
But one of the goals of this Making at Stanford program is to have all undergrads have some
kind of making be part of their undergrad experience, you know, be it.
Oh, wow.
Yeah, it's really cool.
And there's all kinds of maker spaces.
Like, you know, ones with big industrial machines and lots of safety training.
There's like an, you know, an educator oriented space with like they have like a laminator.
(05:40):
Of course, the teachers have the laminator and the combining machines and like it's fantastic.
You know, lots of laser cutters, lots of 3D printers and things like that.
And I'm the one that's scared towards textiles, sort of like the odd cousin of the bunch.
I don't think we've got a text at our uni.
I don't think we've got a textile making space.
I've got to go and ask some questions.
(06:01):
Because we've got all the other kinds.
It's a great, you know, and it's the funny thing is they did a survey and they showed
that the second most common piece of equipment across all of these spaces was after the 3D
printer, of course, was a sewing machine.
But the problem was in a lot of the places it was a sewing machine that was picked up
kind of like an afterthought and no one really knew how to use it and no one knew what to
(06:23):
do with it.
So it sort of sat gathering dust in a corner.
And the feedback that we got from other people was like, yeah, I've seen sewing machines
around, but like when I ask people how they use it, like no one can help me.
So we found our way here.
So yeah, I run this maker space and, you know, when the funders say that they would love
to see more making courses, you know, you can take a hint.
(06:44):
And I put a course on the book.
You know, I assure the department chair that this would take like no time at all.
You know, it was going to be a small independent study.
Five students max.
This was not even going to be something that she would have to worry about like eating
my life.
And so we got it, we got it registered.
And on the first day of students signing up for classes, I realized pretty quickly that
(07:08):
something had gone wrong in the course system because all of a sudden I had 20 people signed
up.
So I couldn't say no to 15 people and just basically reimagined the whole thing completely
on the fly.
You know, the textile maker space can't fit 20 people at the same time.
(07:28):
I mean, you can pack 30 in there, but it's like a fire hazard.
You can't turn around.
So so, you know, we set it up so that people could come in during any of our open hours
and me or the other staff would teach them how to how to use whatever method or method
they were interested in.
And the students just made these utterly delightful projects and you kind of all at their own
(07:50):
pace working on whatever kinds of data they wanted.
We had a check in in the middle.
And that was a that was a great moment to actually have a conversation with some of these students
and find out from them.
Some of them came in with like, you know, very academic, you know, data sets that they
were, you know, going to be working on or so they assured me.
Yes, you're tracking tracking the reading that I'm doing over time and you know, oh,
(08:13):
you know, things in my thesis and so forth.
But the more you get them talking about it and kind of drawing them out on the subject,
you know, more often than not, there was like something else that they actually wanted to
do that like data that they thought was like weird and fun and like they wish that they
could do but like kind of with the academic rigor mindset of, you know, a university like
(08:35):
that's not where they went to first.
But eventually we got them thinking along those lines.
And that's that's the project that many of them ended up making.
There was a student who had tracked the number of cats that she had seen on campus and made
a sweatshirt where the tail was the data about the cats over time.
And all of us, she managed to sort of turn into a data set from her photos.
(08:57):
It was just a delight.
Fascinating to think about students learning to interact with data visually and going from
the very academic to their real passion.
And that's that's a pretty unusual trajectory for a university course.
How did the administration view this?
(09:17):
I mean, I didn't tell them.
I mean, we just did it.
I mean, one of the nice things about Stanford is is kind of culturally it's very entrepreneurial.
And there's a lot of space to just sort of like go and try things as long as it's sort
of within within, you know, generally reasonable boundaries.
(09:37):
And I think it's it's fairly well understood from the administration side that, you know,
there are mental health challenges, you know, among the students.
It's a high stress environment.
And a lot of the, you know, creative art, you know, personal essay writing classes, I
mean, they fill up, you know, within five minutes of registration opening.
(09:58):
There's really a need for this kind of creative self expression.
And there's not as many avenues for this as as one would hope.
And I think that that's that's one angle of the making a Stanford program, trying to kind
of make that more accessible and in reach for for everyone kind of regardless of what
shape that takes exactly.
(10:20):
That's really interesting because in Australia, our government, our previous conservative
government tried to stop people from doing arts degrees by putting the price up for a
real lot.
But people can't see that.
Yeah.
So there's this real hunger for students to do things that aren't STEM.
And they really, really want to do it, even if you double the price.
(10:41):
So there's not a lot of price sensitivity in the student space.
It's really quite interesting.
And even now, the Labor government, they're like the Democrats, the Americans, they, they've
kept those fee structures and they're studying.
But why are people not studying STEM?
It's because like nobody, not everybody has to study STEM.
(11:05):
And there are things that you can do in the STEM space like this, which I find really
interesting.
And one of, because one of my big challenges is how you visualize data and how you can
make it accessible to normal human beings.
Because there was this crazy idea for a long time that every person in a business wanted
to be a citizen data scientist.
And I'm like, no, they don't.
(11:26):
They just want to do their jobs.
Yes.
No, it's, it's, it's really interesting.
And I mean, one of the things that has surprised me about running the maker space and, and
this course has been, that it's not the students that I expected that, you know, take to it
so easily.
It is in fact the engineers who are probably the most, the most common department of the
(11:51):
students who come by the maker space is, is engineering.
And it takes a lot of effort and kind of encouragement and congealing to get the students in, in my
department, the literature department to, to come and make things.
And what I, what I realized eventually, even though I wouldn't have guessed it at first,
is that, you know, once you are, once you are used to making one kind of thing, it's
(12:16):
not a big leap to make another kind of thing.
So, you know, if, if you, you know, are spending your days in a lab making like microcontrollers,
like why not go spend some yarn or like sew a thing, you know, the, the, the, the transferability
of that is, is pretty high.
But it's the students who have spent their entire lives basically reading and writing
(12:37):
things for whom like the idea of like using your hands to do something besides turning
a page or like typing some things is, is kind of alien and, you know, needs, needs some
translation.
That, that actually aligns with my experience because, you know, every engineering student
has to do stuff in the shop.
So they're in a workshop somewhere making like you said circuit boards or welding, or welding
(13:03):
off and, you know, we've got some pretty fun labs in our engineering faculty.
So it would, wouldn't make sense that they find the tactile nature.
It's, it's a really, I just find it so utterly fascinating that, that there is such a resistance
from some of these students.
(13:25):
So because there's so much in their head and it tells me that there's, there's probably
something wrong with our whole education process that they get so much into their head that
they can't associate with, with tactile.
And we were chatting before we started recording about my past when I was in high school and
one of the art mediums that I used was weaving, which is from, from your reaction was pretty
(13:52):
unusual.
Yeah, I haven't, I haven't seen that so commonly in schools here.
Maybe when I got, so I decided kind of on impulse to buy a giant standing loom.
This thing is almost as long as I am tall.
It didn't actually fit to the door of my office.
We had to partially disassemble it.
I got it because it was on sale and I'm like, all right, clearly, like I need to buy this
(14:15):
loom and then step two was figure out what to do with it.
And I asked around and I only managed to find one student who, you know, went to an unusual
like sort of rural private school and happened to know like using this kind of loom, like
how it works and how you set it up.
Yeah, that definitely seems less, less common here at least.
I know it's pretty, it's pretty uncommon here to confession.
(14:39):
I went to a private school here and we had a lot of facilities and you could pretty much
do whatever you wanted in the art space.
But I think because, because I'm one of those people who really lives in my head, you know,
translates through the keyboard.
So it was really interesting to make things with clay or to make things with looms.
(15:02):
And I haven't done it since I was in high school.
So it's just a really interesting thing for me about how do we as educators, and I think
the maker space is a really great idea.
And then there's this whole challenge of how do we, how do we think differently about data,
which is one of the things that I'm really interested in is how do we visualize data
(15:23):
differently?
And I think your approach with textiles is a really interesting one.
Do you have, have you also, because it always seemed in my mind that weaving and knitting
are correlated.
Have you also adopted that?
Yeah.
So about a year ago is when I just started getting into like all of the yarn based arts.
(15:46):
You know, I've been sewing for, I don't know, 15 years.
And at this point, I make like literally all of my clothes except for socks.
I sew them all myself.
But sort of I had socks up there is like this, this holy grail.
Like if only I could get myself a pair of socks, then I could say, you know, I've done,
I've done the whole, the whole thing.
(16:08):
And also, I mean, there's, there's, there's some appeal to the, the, you know, knitting
crochet, even spinning with some of the modern portable e-spinner is like the electric e-wheel
wheel that can fit in the lunchbox.
And you can do it on a train to have a creative hobby that's more portable than sewing.
Because I mean, sewing is pretty infrastructure heavy.
(16:29):
Like you can't just throw a sewing machine like in your backpack and do it on the train
the same way you could, you know, pick up, pick up some knitting needles.
On the train.
Yeah.
For the train.
Yeah, exactly.
So yeah, I started, I started exploring those and like spinning my own yarn.
And you know, I got some folks to teach me how to crochet.
(16:50):
Crochet is, is more than knitting.
Crochet is kind of surprisingly popular among college students these days.
There's a lot of people who do it.
You know, there's, there's plenty of people who come by the makerspace to like, you know,
pick up a different size crochet hook than they happen to have on them or like sort of
see what we have in our donated stash of yarn and then make wonderful things and send us
(17:12):
pictures and it's, it's just a delight.
So yeah, I mean, it's, it's, but yeah, crochet, crochet is a little bit different.
It's a little bit more kind of geometrical and, and kind of 3D ish than the knitting
where kind of in learning to weave and reading books about it.
I've seen far more in terms of like translating knitting terminology into weaving terminology
(17:36):
than the equivalent for crochet.
Yeah, yeah, they seem to me to be quite different.
I know all of them, but I don't do them.
It's kind of making me feel weird about why, why don't I do these things anymore?
Because I read books all the time.
So this is, this is my hack.
I've been listening to audiobooks while I, while I knit and weave and it, I feel like
(18:00):
I can do that easily and enjoy them both at the same time.
So, so what, what sort of, cause to Stanford, when you were talking about the computer lab,
I was like, everyone at Stanford must have a decent computer and not need an actual computer
lab.
So it makes sense that you were able to create the maker space.
(18:20):
But is this mainly undergraduate students that are coming through the maker space?
What do the post-grad come through to?
It's everyone.
It's, it's undergrads, it's grad students, it's staff.
There's even some faculty who, who come by.
You know, sometimes it's just like really basic thing is like they want to mend something,
(18:41):
you know, or they want to make a present for someone.
We have an embroidery machine and that thing is just like humming 24 seven, especially
right before the holidays and before the, the end of the school year when people want
to like embroider things as, you know, graduation gifts for friends or, you know, other mementos.
It really is just a wonderful space of, of kind of encounter across these different administrative
(19:07):
divisions between people at the university and different, you know, ranks and, and roles.
And, you know, it's not, it's not really clear, you know, who's going to be the expert in
this space.
Like you, you may have a tenured faculty member, like, you know, coming in with like,
you know, vague memories of having knit, you know, several decades before and wanting to
(19:27):
catch up again, you know, and a 17 year old can, can get them on the right track.
Like it's, it's a really beautiful thing.
So it sounds like a really great collaborative space.
I'm actually wondering how we can do more of these kinds of things with data.
How, how it might be translatable from, from the textile space into other spaces.
(19:50):
Like one of the things we've got for our researchers is we do get-togethers where people do,
do stats work together.
Because you know, a lot of people find stats statistics very confronting.
I personally did.
Because I was an arts girlie, not a math girlie.
(20:11):
But, but, you know, having these collaborative spaces where you can sit together and work
together, I think is a big way to make a lot of this stuff more accessible.
And it's just really interesting to me about how we can, how we can take this kind of great
idea about the makerspace into other spaces.
(20:33):
Like we're already doing it with, with all the makerspaces in engineering.
We've got so many makerspaces.
Every time you turn around a corner, there's another makerspace that's cropped up because
on campus, you know, there's so much demand for them.
And we've got a lot of students.
We've got about 60,000 students.
So there's a lot of demand, but one of the interesting things for me is how do we, how
(20:57):
do we do more collaborative learning outside the classroom space?
Because I think that's where a lot of the magic happens.
I suspect in your makerspace, a lot of the magic's happening because you're bringing
people together with an interesting object in front of them.
So they're sort of side by side looking at the object together.
Yeah.
Well, I think one of the, the great innovations of the, the Making at Stanford initiative,
(21:22):
because I mean, it's a similar situation, right?
Where, you know, makerspaces everywhere, like no one knows who's running other makerspaces.
No one knows who has what equipment and things, and things like that.
But even, I mean, certainly like having access to, you know, sort of a pool of funding and
grants and things helps a lot.
But, you know, there's, there's also this concept of like the maker council where it's
(21:43):
the people who run, you know, all of the kind of major and minor spaces on their radar.
And we get together once a quarter and just like talk about things and plan workshops
together.
And, you know, I volunteered to like, you know, anyone who wants to activate all of those
sewing machines, they have like gathering dust in the corner, like, let me know, we'll
schedule a time, I'll go, I'll go show all your students, Dad, how to do it.
(22:05):
And I think like, you know, similarly, like I wouldn't, you know, know my way around a
3D printer if my life depended on it.
But like now, now I know who to ask and, you know, sort of building up these like sort
of friendly networks of expertise where like, you don't necessarily have to know everything,
but like, you know, the guy who will know the thing and, and you can sort of help route
(22:27):
people to the right thing is a really powerful thing on on campuses that tend to be like
so siloized around individual disciplines and different methods.
And, you know, like as it turns out, the person who had like a stash of other looms, you know,
was a was a physics professor. And, you know, never would have guessed this, but it's it's
(22:50):
just delightful to make so many new friends in different parts of campuses that I'd never
even been to before.
It's fascinating, you know, because you get you go to the place where you do your classes
and you just hang around there.
So getting people to go to other parts of the campus is always an interesting challenge.
You need to have a reason to do it.
(23:11):
I'm interested to wonder, though, how this might be translated into the workplace. What
sort of ideas have you got about that? Because, you know, it's an interesting idea.
Yeah, well, so the the show that I'm wearing right now actually was one of my first weaving
projects that is very much like a workspace like data project where this this is three
(23:36):
months of slack messages from my immediate group in the library. Wow. Yeah, between August
and November 2022. And, you know, one of my first weaving projects, I just sort of pulled
up the history of our slack messages and, you know, just sort of assigned one color of yarn
to each basically like role or job title. The the bosses in purple, the developers are
(24:00):
in black, people in my job are the sort of turquoisey color. And I just I just wove, you
know, one, one weft yarn for every message, like I wasn't trying to factor in, you know,
amount of time that elapsed between the messages or like length of messages, there's other
things you could do to try to do that as well. But literally just like who is saying what
(24:22):
and then who is replying over the course of time. And, you know, I actually finished
this exercise feeling really good about the group that I work for within the library that
like, you know, we're actually a pretty functional place, like people talk to each other. You
know, there's there's like, friendly chit chat, but it's not like forced or weird. Our
(24:43):
boss has a habit of like making announcements, which I hadn't really appreciated before.
But like, you know, kind of as as, you know, a way to kind of understand the kind of the
relationships between people and the interactions within a group, I imagine there are other
groups in the library that were I did you a similar exercise for it would look very,
very different. And then thinking about kind of like what what do these differences in
(25:06):
dynamic like tell us about the group? Like are there are there issues that we're seeing
within the group that, you know, manifest in other ways that like communication style
might have, you know, play some role here. You know, that that's been that's been kind
of really useful for me, like within the context of this of this organization. Yeah, I mean,
(25:26):
another another thing that we've done at the makerspace that, you know, people seem to
really enjoy is our guestbook that we actually do use for stats, I count the the yarns when
people ask well, how many people have visited the makerspace like all right, let me go get
a pencil and like count all these tiny things. So people people weave a different color of
yarn depending on what they come for they hear just so or embroider or knit, so forth.
(25:52):
And then there's a key with different colors of beads. And optionally, they can add colors
for how they're feeling that day and the slide on as many beads as they they they feel like
sharing. And so at the end of the year, we hang this up as a tapestry. And you can kind
of like see the ups and downs of the quarter, you know, where people are getting stressed,
(26:12):
you know, obviously finals week is terrible and everyone's exhausted. But like, as a way,
you know, to sort of like anonymously capture in some way that like still feels cathartic
and meaningful, you know, workplace morale in a way that's not like, you know, sending
out a survey where people are probably going to lie, it's it's it's harder to lie with
the yarn. It's yeah, we get we get those surveys. And it's just like, oh, no, I don't want to
(26:37):
tell you how I'm feeling. Right? If you could if you could basically anonymously like go
up to a loom and slide on, you know, some yarn and some beads. I wonder, I wonder if
at least in some work cultures, that might be a useful way to kind of like get a pulse
on on how people are doing in a way that's that's like less awkward and and sort of satisfying.
(27:02):
Yeah, because it's one of the things in our agile process with my development team, you
know, that we always want to sort of take a temperature check of how people are feeling.
And we just we just, you know, we've tried various methods. But it depends on the high
on the levels of trust, like we've got a high level of a trust in the team. So people are
(27:24):
willing to just say, I don't feel good today. I don't feel good about the last sprint. I
don't, you know, and stuff like that. But not everybody has that high level of trust.
So I think that sort of technique could be really useful to get insights into a lot,
a lot of different workplaces.
Yeah, there's there's no, you know, handwriting to give you away. There's no demographics,
(27:46):
you know, it's just, you know, plus plus it's just kind of satisfying to like grab a piece
of yarn, chop it off and just go over under over under and slide some beads on. Yeah.
So this has been a really interesting chat. Thank you so much for your time. Thank you
for persisting through my internet problems. Thank you NBN for breaking down yet again.
(28:10):
Always a pleasure. Thank you so much, Quinn. I've really enjoyed our chat.
Yeah, thanks so much for having me.
And that is it for another episode of the Data Revolution podcast. I'm Kate Crothers.
Thank you so much for listening. Please don't forget to give the show a nice review and a
like on your podcast app of choice. See you next time.
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