'data decade' bright green

By Hannah Redler Hawes, Director, Data as Culture Art Programme/Associate Curator, and Julie Freeman, Artist and ODI Art Associate

Data surrounds us and shapes our world. It is infrastructure – as essential as the roads, railways and electricity we use every day. And it can help us to understand and address some of the biggest challenges we face globally, from infectious diseases to climate change. As part of the Data Decade, at the Open Data Institute (ODI), we are further exploring this through 10 stories from different data perspectives. The first, Data in Culture, examines how data can inspire art, and indeed, be an art material of and in itself.

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This is Data Decade, a podcast by the ODI.

Emma Thwaites: Hello there! Welcome to Data Decade. I’m Emma Thwaites, and in this series I’ll be looking at the past and the future of data, how it surrounds us and how it shapes our world. Data touches every part of our daily lives, and it can help us to understand and address some of the biggest challenges we face globally – from infectious diseases, to climate change.

In the last decade, the amount of data created globally has grown exponentially. It’s become the lifeblood of businesses, communities and society. And with that of course comes enormous possibility but also anxiety about how data is used and by whom.

So in this podcast series, we’ll look at the last 10 years of data and how it’s changed the world, and also the next decade ahead and the transformational possibilities for data in the future. What is the true value of data? Welcome to Data Decade.

Emma Thwaites: So then thanks for listening. And we’ve got a great panel joining us for our first episode, as we explore data in culture. Now, when we think of data, we usually think of statistics, information and facts, but should we also think of data as art or art material or the subject matter for creative minds?

This is a really interesting area and over the next 30 minutes or so, we’re going to explore the role of data in art and culture over the last decade and the way it helps us to understand the world around us. Joining me to talk through all of this are four very distinguished guests.

First of all, the founder of the Data as Culture art programme at the ODI and artist, Julie Freeman; the artist and curator, Antonio Roberts; Hannah Redler Hawes, the director of the Data as Culture art programme at the ODI; and joining us down the line from Delhi, the visual artist Rohini Devasher. It’s great to have you here with us. It’s really exciting actually, to have Rohini from Delhi.

Rohini Devasher: Lovely to be here.

Emma Thwaites: What’s the weather like there?

Rohini Devasher: Very, very hot. Very hot.

Emma Thwaites: You lucky thing.

Um, I’m actually going to start this conversation with Hannah, our current Data as Culture curator. And actually Data as Culture has come at the sort of end of, you know, what has been an incredibly illustrious career in art and in culture and in some really interesting settings that you’ve worked. I wonder what changes particularly you’ve seen around the role of data in art and data in culture over the past 10 years, perhaps even beyond that to the beginning of your career – how you got into all of this?

Hannah Redler Hawes: Well, it’s a bit of a long story, but I started as a fine artist and I graduated during the recession of the 1990s and began, um, a digital media company with friends. And through that, I ended up working at the Science Museum as a curator focusing on media art and software art, digital technology.

And the first thing I commissioned for the Science Museum was a series of four artworks by different international artists that engaged with different aspects of digital technology. But looking back, it’s really interesting to see that those works obviously did use data in different ways, but nobody called it data art.

In fact, some of them anticipated some of the privacy issues we have today with David Rokeby’s ‘Watched and Measured’; they anticipated AI with Tessa Elliot and Jonathan Jones-Morris’s piece ‘Machination’, which looked at different machine vision systems; and various others.

But the main point of works like that – and they were part of a much broader area of practice, you know, across the world – was exploring the impact of digital technologies and the technologically-mediated landscape.

So the first time I came across anybody identifying data as an art material was when I became aware of the ODI and Julie Freeman’s work founding the programme in 2012, where suddenly Julie’s research beginning to think about what data is as an art material, and how you might break it down to be more specific and more granular than just digital, was raising some really interesting questions.

Emma Thwaites: I’m going to ask Julia a little bit about that in a second. Like whether you see yourself as a pioneer in that domain, but before I do: this idea of digital art being kind of slightly prescient and being able to predict the future or a version of the future, is that something that you’ve seen a lot of over the years?

Hannah Redler Hawes: Well, I think there’s this thing that’s been happening throughout the 20th and 21st century, which is industry has evolved and artists have responded to that industry. But a really interesting thing that’s happened in digital technology and data technologies is that artists have often been pioneer researchers within those fields.

So there’s been a really symbiotic relationship between the new spaces that industry has created and the ways that artists have occupied those spaces and anticipated and raised questions about how those spaces will affect us as humans existing in those spaces.

Emma Thwaites: Excellent. Thank you. And I’m going to come back now to that question about pioneers of data art and the idea of data as an art material, which was very new to me back in the early days of the ODI 10 years ago, and it seems very new to Hannah as well.

Julie, did you think of yourself as a pioneer?

Julie Freeman: No, I don’t think any pioneers think of themselves as a pioneer, but I did know that I was working with very new technologies and new combinations of technologies at the time. That is always interesting to me. It’s really- I’m really curious about the combination of computer science and fine art.

And for me to use digital technology to sort of extend our sensory perception, to understand the natural world and living systems more is what’s been my main focus. And underlying all of that is data. Data is the communication between hardware and software, is the thing that communicates between computers and humans, between computers and computers themselves.

So data has always been at the core of my work. And I think when the penny dropped or that, that was what was there, then it became really obvious to me that data is the core art material. And then, I think the research that’s flowed out of that and from the past sort of 10 years comes from that moment.

Emma Thwaites: That connection between data and technology and the natural world is really interesting. How have you explored that in your work?

Julie Freeman: I’ve always been fascinated by how in nature, everything is constantly flowing and moving, and I wanted to try and capture in my art practice that sort of consistent ebb and flow and dynamic movement.

And for me, data is a really good way of representing that because when you’re using real time living data, you can add that into the artwork itself. And so suddenly you’ve got something that is kind of alive or a representation of something that’s alive right here and right now. I think understanding the natural world through these data streams is something that we couldn’t do without technology.

And that’s interesting to me as well, that it’s not using technology for the sake of it – it’s using it because it does extend something that we couldn’t do otherwise.

Hannah Redler Hawes: Like revealing invisible structures. That’s something that a lot of artists who’ve been working with data over the years have done is sort of reveal patterns, reveal invisible structures, like Julie’s piece ‘We Need Us’ which I’ll let Julie talk about.

Emma Thwaites: I was thinking about that and I was also thinking about the naked mole rats. So tell us about both of those.

Julie Freeman: So then the naked mole rats is a project I worked on called ‘RAT Systems’, which is Rodent Activity Transmission Systems, and that uses the data from a colony of naked mole rats, Colony Omega. And there’s 28 naked mole rats, we’ve followed their movements 24 hours a day and use that data for many reasons. So we use the data for scientific purposes – to analyse their behaviour, but also to create artworks that were a digital abstract animation, kinetic silicon sculptures, which were kind of like soft, robotic sculptures, and also data visualisation as well.

So the idea between the RAT Systems project was to collect a really unique set of data, a live set of data, and then use it in multiple ways for science, for design, for communication and for art. And it showed that data is really manipulatable and really flexible. And it kind of provokes questions around data as truth.

If one set of data can produce all of these different outputs, then what can we do with other sets of data and how can it be manipulated? So there’s this kind of balance between playing with data and using it as a really serious evidential subject matter.

Emma Thwaites: And ‘We Need Us’?

Julie Freeman: And ‘We Need Us’ is, um, I mean, I really love ‘We Need Us’ because one of the things that I’m proud about is that it’s been running since 2014.

And so it’s eight years old this year. And it’s been using an open data stream from the Zooniverse – the Zooniverse citizen science website where scientists upload their datasets, and then they ask citizens all over the world to classify that data in different ways. So they might be looking at images of animals from the Serengeti, or they might be looking at microscopic images of cells.

And one of the interesting things about Zooniverse is that all of those datasets need human intervention. So the data that needs to be classified can’t be done by machine learning or an algorithm. It has to have humans, you know, actually looking at it – or listening to it if it’s bat sounds – and then classifying that.

And so I was interested in taking that metadata of that ecosystem – who is classifying data, what data would they classifying – and turning it into an abstract living artwork. And that’s what ‘We Need Us’ is, and it’s still functioning today. And there are, you know, over 2 million people classifying that data on the Zooniverse website.

So it’s a really nice example of an ongoing project that is representative of the life of open data.

Emma Thwaites: And again, to Hannah’s point, it’s this sort of hidden story, the activity that takes place online, or the story that exists within data that you don’t see unless an artist or a researcher chooses to reveal it, which is really fascinating.

Um, I’d like to come back to, to both of you in a little while and talk about the role of humour in data art. Um, but that’s not to say you’re not humorous Antonio, you are very humorous.

Antonio Roberts: Well, we’ll see.

Emma Thwaites: You have also been a guest curator at the ODI, um, for our Rules of Engagement exhibition, which was a couple of years ago, but you’re an artist in practice too.

And you’ve, you’ve shown with us several times. So I’d also like you to talk about two works, if you would – particularly ‘data.set’ and ‘Algorave’.

Antonio Roberts: Yeah, definitely. I can talk about those for sure. Um, ‘data.set’ – I believe that was probably a 2016 artwork. I was working with a dataset as it were-

Emma Thwaites: Funny that!

Antonio Roberts: I know – that’s where the name comes from.

Um, and it was about digital inclusion. And I was trying to think about how to represent the data, because I guess when you’re looking at data, it can’t just be like groups of numbers, abstract, it’s about things. And in a way that can be quite overwhelming sometimes looking at all of these numbers, like what do they represent? Is that number that you’re seeing a big number? Is it a small number? If so, what effect does it have?

And so with ‘data.set’, I was trying to really represent the overwhelmingness of working with data. So as it’s presented, it’s uh, I think it’s like 52 aluminium, very small aluminium, um, panels, presenting the data in a quite abstract way where it just looks quite, like, colourful and glitchy almost – looks like it’s quite kind of like a broken screen.

And by separating it into those individual chunks, hopefully it becomes a little bit more digestible and that’s in a way, like again, what sort of artists are trying to do when they’re working with data is trying to separate into chunks that you can kind of understand a bit, that you can uncover it a bit more where it isn’t just so much all at once. It’s tiny little pieces.

So that’s sort of how I was working with data – that was back in 2016. And uh-

Emma Thwaites: I’m suddenly struck by the way that – here we are, we’re sitting, talking about artworks so far, mainly visual artworks, trying to describe them. They are really arresting pieces. So I definitely recommend people go and have a look at them, but ‘Algorave’ is slightly different.

Antonio Roberts: Yes. ‘Algorave’ is very different. So it’s more- it’s audio, it’s artwork for your ears.

Yeah so ‘Algorave’, it’s basically people making music and visuals using programming, and they do it live on stage. So if you ever see a performer with electronic music or electronic equipment, you often don’t know what they’re doing. Like they’re operating it onstage and they’re pressing buttons. And it’s really amazing, but there’s sort of this mystery about what’s happening, sort of they’re manipulating a black box of mysterious wonders and you don’t get to see what’s happening.

So with ‘Algorave’, what it kind of does is it uncovers what is happening within the machine.

So people who are making the music using programming, they’re typing out the code. And you can see what they’re typing, it’s being projected behind them. So that allows an audience watching to have an insight into what’s happening. They can see a one-to-one relationship of that person has typed this code, and now there’s a sound happening. Well, now there’s some visuals happening.

And by no means is everyone who attends one of these events or even plays at one of these events, a coder, a computer scientist. But it just means that you can see that, I guess, computers are made by people. That even, especially when you think about narratives around artificial intelligence taking over the world, there is still a sense that there is a human behind all of this.

So even these complex systems, there are people who are making moves and they can be understood by humans. So that’s how I interpret what ‘Algorave’ is doing. And making good music and good visuals as well. Like it’s still a bit of a party at the end of it, but there’s more to that.

Emma Thwaites: I love that cause that- that’s another theme that’s coming through in the conversation so far, this idea that the role of art is partly to demystify.

We used to talk a lot at the ODI in the early days about the ‘Wizard of Oz’ effect that you know, with data and technology more broadly, in the general population, certainly of which I am a member, there’s a view that it’s all terribly mysterious and complex and you know, not to be understood, not our business to understand it.

And actually when you pull back the curtain, it isn’t so. And, and I wonder what you think about the notion of artists and art in that role of demystifying and making what look like quite complex concepts understandable to the majority.

Antonio Roberts: I think that is part of the role of what artists are doing when they’re working with data or just anything really is, yeah, for sure, demystifying it, but especially in regards to data, it is kind of communicating what that data means overall.

Because, as I was saying earlier, like it’s so very easy to just look at the data as just numbers, but there are usually people behind the numbers and those numbers can also affect people as well.

Like what, what effect does the data on digital inclusion have on actual people? And so sometimes those stories are what the artist is telling. So even if they’re not explaining each minute detail of the data and at least talking about the effect on it. And I feel sometimes people are able to relate to it better in that way.

And that might be talking about it through music, sound, performance, whatever, or whatever methods they’re doing. So, yeah, I feel that sometimes artists are good at doing that. Bringing in some ways, the people back into what the data is talking about, like speaking about the emotion and the people.

Emma Thwaites: Absolutely. Yeah. Hannah, I think you had something to say about-

Hannah Redler Hawes: Yeah, I think that there’s not an explanation role, and engaging with art about data can demystify some of the ideas that data and talking about data and working with data is only for experts and isn’t for real people. So it definitely achieves that. But I think it also creates layers of mystery and poetry and aesthetics and new ideas that are really, really important for the art world as well as for the data world.

But this thing about also coming back to the humans is one of the things – I think the strongest thing – that most of the art that I’ve really loved working with or experiencing with data, is that work that’s challenging the assumptions that technology isn’t messy like humans, and technology will be fair, and technology will be better than human, and more human than human, or more humane than human.

And so what we’re seeing with a lot of work is it’s challenging the sort of investment of sort of like false ideals we place in technology as well.

Emma Thwaites: And that is a really good segue actually into Rohini, who’s been waiting patiently, but is our most recent member of our ODI artists club, um, or our Data as Culture artists club, and is currently the artist in residence and working on a brand new piece for the ODI.

So Rohini, I’m really interested to hear how you got into the practice of combining art and data, and kind of your journey over the past 10 years, um, and your exploration of data as a, as an art material and the subject matter for your work.

Rohini Devasher: Yeah. Thank you so much. Um, that’s a great question, actually. So I’m an artist, I’m an amateur astronomer, and a lot of the work that I’ve done over the past year- 10 years, is sort of looked at the history, the processes of observation and the field or the site.

And I collect and I make material and recordings of different kinds, which includes videos, you know, images, drawings, and sound. But I never used the word data at all. And I think one of the reasons, is something that Hannah’s already talked about a little bit, is that I had a very specific definition or a set of connections in my head around the word.

So for instance, data equals authority. Data equals science with a big S. Data equals truth. Data equals objectivity, et cetera. But conversations with the amazing people at the ODI, Hannah, Julie, and many others at home as well, has sort of exploded this monolithic idea of data. So data is everywhere. Data is many things, you know, and Julie, for instance, just talked about it again, this idea of data being playful, of being malleable.

At the summit, I remember someone was talking about data as a mirror, data as a cloud, you know, and as Antonio just said, data is also fundamentally about people. So I think what changes when you put data at the core, or at least when you articulate it more clearly, is that I now see that all the forms of data that are created and collected during my engagement with the site or an object becomes a way to read the natural world and the environment.

And this reading is obviously a form of mediation, right? A way to try and make sense of what is happening. Because there is no such thing as a pure or objective observation, and mediation is integral to this process and this mediation is technological, but it’s also embodied. It is very much about the people.

So I think just to end, data is everything, then. For me, it’s stories, it’s histories, it’s interviews, it’s drawings, it’s video, it’s photographic records of the sky, which are then sort of repurposed, refracted, and then looked at through different prisms and lenses or, you know, speculative frames, scientific frames, et cetera, which then go onto produce the work.

Emma Thwaites: So just to bring that to life a little more, the work that you have created for the ODI – and I was very drawn to something that Hannah said about the work that is created using data as an art material is often poetic and often very beautiful. And so is the case with your piece. I wonder if you could just describe to the listener what the piece involves and how you came to make it and what it looks like to experience, what it feels like to experience it.

Rohini Devasher: Yeah. I, I love this work, if I’m allowed to say that.

Emma Thwaites: You absolutely are. We love it too.

Rohini Devasher: I really do – thoroughly enjoyed making it and I’m just living in it. So essentially, I’ll describe it a little bit first, and then go a little bit backwards into how it came to be, but, um-

Emma Thwaites: I should say it’s called ‘One Hundred Thousand Suns’. Hannah’s just prompted me, reminded me to say that. So yeah.

Rohini Devasher: Yeah. So the piece, ‘One Hundred Thousand Suns’, is a kind of rendering of the sun, very simply. It’s assembled from data that is historical, also contemporary. And it explores the notion that there are, you know, again, as many people have said already, multiple readings and outliers of data, depending on the site, the observer, the mode and method of observation, and collection, and preservation.

And I think the reason I came to it is that, one of the things that really struck me over the research phase of the residency at the ODI, was this idea of digital twins. Which is very simply, you know, a digital twin is a virtual representation, which serves as a kind of real-time counterpart to a physical object. Right?

But then there are also conversations now about a digital twin of the earth. And apparently there’s already a digital twin of the ocean, which was sort of blowing my mind because then it suggests that all of these processes are noble, right? And that they can be modelled. And it really got me thinking about what seems to me to be this huge distance between this truth of a model and what it actually is trying to embody.

So at the heart of ‘One Hundred Thousand Suns’ is a physical space, is a site, which is in the south of India. It’s the Kodaikanal Solar Observatory in Tamil Nadu, where everyday – weather permitting – since 1904, the staff at the observatory have recorded images of the sun. And this data spans almost 120 years and it comprises almost 157,000 distinct portraits of our nearest star.

You know, and the range of these observations is from drawings on discs of paper, to photographic glass plates, and now to more, you know, sort of contemporary digital H-alpha images. But all of these thousands of suns are sort of, you know, they’re really incredible archetypes, you know, they’re sort of a conjunction of direct experience and observation on one hand, but also information and data on the other.

So, what I did was I took this data, and I combined it with, uh, my own data, which includes video, photographs and interviews collected at Kodaikanal. But also interviews with eclipse chasers, eclipse data collected during my eclipse chases. And further layered it with datasets from the NASA Goddard Space Flight Center, scientific visualization studio, you know, this is all material that is incredibly in the public domain.

So very briefly we’re looking at four paradigms, different ways of metaphoric, speculative, poetic renditions of the sun.

Emma Thwaites: Wow. That’s a lot. It’s fascinating. I mean, I’m quite- what I’m really interested in, because of the historical nature of, of the data that you’ve used to create this work and the fact that it goes back – Did you say 120 years?

Rohini Devasher: Almost, yes.

Emma Thwaites: Yeah. About 120 years. I wonder what you think either the artwork itself or the practice, the process of creating it, teaches you, potentially teaches us, about our world, our planet over that timespan?

Rohini Devasher: Mm oh, that’s an amazing question. Well, I think deep time is something I’ve been interested in for a long time. This idea that there are timescales, which are beyond perhaps imagination and thought.

But what is really incredible about Kodaikanal is the consistency, the sort of- the fact that there are these sort of consistent 120 years of observations. And the fact that this is also a family tradition for the staff at the observatory.

So many of the people who work at Kodaikanal are fourth generation staff. So there are people who have literally devoted, you know, their lives to this kind of ritual, this daily ritual of waking up and 7:00 AM taking a photographic image of the sun.

I think it does so many interesting things. Also just thinking about space ,about site, because I think for me, one of the things that I’ve realised is that data and site are completely entangled and contingent, you know, on each other.

So one of the things I try and do when I think now, when I articulate it as data, right, is I try and keep myself as open as possible about what the data might be. Because of course the space, the, the people, the weather, your camera, everything is completely, everything is unexpected. You have to let yourself be open to surprise.

I mean, that’s not quite answering your question, but I think there is this kind of sense of being both in the moment, but also having some sense of a sort of long view, if you like.

Emma Thwaites: Mm, I was very struck by, um, when I saw the original concept for the work, the different mediums that you described for making these recordings and how those have changed over time.

I think that’s, that’s absolutely fascinating. And the beautiful writing on some of them, you know, back in the day. Really, really lovely. Um, Julie – you had a thought, I think?

Julie Freeman: Yeah, just as Rohini was talking – Rohini, always inspiring – but it just made me think about reflecting on 10 years of the data decade that we were talking about, all of the models that are created are only- are always restricted by the current observation and the observational tools that we’re using at the time.

So one of the things that data does, and all of the datasets we have, they’re kind of like moments in time of what we’re able to collect and what we’re able to think of in that moment. So biological data, the type of sensors, even the size of the battery that you can put on a fish to track its fish data, that is really limited compared to even 10 years ago, it’s limited to what we’ve done now.

And then, what’s interesting in a cultural terms at any one time, as to where money is plowed and therefore what data is collected, because what is interesting politically or culturally. And so I think those kinds of, this trail of data that we’ve got gives us an indication of what we were interested in as a society over time.

Emma Thwaites: That’s really fascinating. And this is an anecdote which seems to be relevant. I read it years ago and I can’t give you a source for it, but it was the fascinating idea that a person living in America in the 17th century – so one of the early, earlier settlers – only had to digest the same amount of information in their lifetime, as one could read today in one edition of the New York Sunday Times.

So I think that’s what it’s called. Well maybe it’s the New York Times Sunday edition. Anyway, doesn’t matter, you know, it’s a thick beast of a newspaper at the weekend, but that all of that information in that one edition was equal or is equal – how you work that out, I have absolutely no idea, but it just struck me as, you know, that’s a real illustration of what you were talking about, actually.

How, you know, every year, the amount of data and the variety of data and the depth of the data that we can collect grows exponentially. So thank you for that.

I’m going to ask you all to do a little bit of crystal ball gazing now, if that’s okay.

Um, so we’ve talked about the past. We’ve certainly talked about the last 10 years, but I wonder what trends you all see as emerging in data art over the next 10 years, and in the use of data as an art material, how you see this field evolving.

I’m going to come to Hannah first.

Hannah Redler Hawes: Okay. Well, I’m in such great company. I’ve probably got these ideas from the three other people around the table, but I think what we’re seeing artists doing is challenging the assumptions of big tech.

And we’re seeing artists try to reclaim the capabilities of technology and the opportunities of technology to serve humanity.

And obviously, the tools are quite democratic, actually. Artists are sort of really challenging the way that big tech has been built and how we might build different structures to, I don’t know, perhaps create different situations that are more structured in a more artistic way.

Emma Thwaites: That’s interesting. Julie?

Julie Freeman: I think, um, I think the more that artists learn about data, the more we have access to different datasets. So there’s- more than ever, there’s more API, APIs for us to work with data now. But I think it- a little bit, like Hannah said, it turns it on its head. So the more it’s available, the more you want to criticise it and look at it in different ways.

So I think there’ll be a rise of people working with synthetic datasets, to kind of like, push back against a real, evidential dataset.

So synthetic data is something that is really interesting.

Emma Thwaites: Yeah. Tell us a little bit more about it. Cause it’s not, I’m not sure I know what it is.

Julie Freeman: So you can, you can create synthetic datasets by using sets of parameters and sort of machine learning. So you can generate a set of data that might be your idea of an ideal scenario of a population or, of a whatever.

So you take kind of like descriptors of a traditional dataset and then use it to run your own data. So rather than using real data, you just do a mock-up, like a synthetic one. And by using that, some of, I think some of the emergence of that is to try and iron out some of the bias from real datasets that are only collecting certain things, but obviously there’s a bias in the synthetic data too.

So it’s an interesting field. And I also think there’s going to be a lot of people thinking about low carbon data and data deduplication, where we begin to push back on- we shouldn’t be replicating data just because it’s easy, because there’s a carbon footprint associated with all digital. And so the idea of reduced data, I think is interesting for me anyway.

And I think that’s gonna be. That is emerging more and more.

Emma Thwaites: Less data?

Julie Freeman: Less data. I’m sorry. I know.

Emma Thwaites: How very dare you! Antonio?

Antonio Roberts: Yeah. I think there’s just going to be a general push back against data collection and even just the idea about the data that we’re being presented with is the truth or is actually representative of everything.

Um, yeah, so I think, yeah, I can see there’s all the artists themselves as well, especially when talking of things about decarbonisation and NFTs, they are doing their, like their own data collection. If anything, just to put that power in their hands as well, to be able to put the tools which are using for data collection in their hands to then be able to kind of amplify what they’re talking about.

But yeah, I think generally there is going to be some sort of push back, I feel, against the amount of data that ever is and what it’s collecting and what it says about us. Yeah. Gen- generally, I think that’s, yeah, the way that it’s going.

Emma Thwaites: It feels a bit like a rising tide at the moment.

Antonio Roberts: I feel so. Yeah, definitely like whether that’s technology in general or just data is yet to be seen, but yeah, I can see a bit of pushback basically.

Emma Thwaites: And Rohini, how about you? What’s the future hold?

Rohini Devasher: I have no idea. I am surprised by so much of what is happening already. I expect the Metaverse will have some sort of impact on both, but what kind and what shape and form it’ll take, I have no idea. It’s just all up in the air – everything is possible and nothing is possible.

Emma Thwaites: So you’re not making yourself a hostage to fortune.

Rohini Devasher: Nope!

Emma Thwaites: Absolutely not.

Well, listen, that’s been so fascinating. We could probably talk for another hour, I would think. Unfortunately, we don’t have the time. So I would like to thank first of all, Rohini Devasher joining us from Delhi and also, uh, Hannah Redler Hawes, Julie Freeman, and Antonio Roberts.

Thanks very much, guys!

Julie Freeman: Thanks Emma, it’s been great.

Emma Thwaites: That’s all for episode 1 of Data Decade, looking at Data in Culture and the fascinating insight into the role of data in art and creative works.

If you want to find out more about this, head over the theodi.org, where we continue the conversation around how data use has changed in the last 10 years. And also, where it’s heading in the future.

And if you’ve enjoyed the podcast, of course, please do subscribe for updates. In the next episode, we’ll be looking at data in the built environment.

I’m Emma Thwaites, and this has been Data Decade from the ODI.

When we think of data, we usually think of statistics, information and facts. But it’s far more than that – we can also think of data as art, or art material, or the subject matter for creative minds. Over the last decade, data’s role in culture has transformed – from a relatively unknown idea, to an artistic practice that helps us to understand the world around us.

From its inception, artists have interrogated digital technology and the role it plays in culture. We can see this with software art like David Rokeby’s Watched and Measured (2000), which touches on the privacy issues we contend with today; or Tessa Elliott and Jonathan Jones-Morris’s Machination (2000), which anticipates ideas around artificial intelligence (AI). Looking back, it’s interesting to see that these artworks clearly used data in various ways, but weren’t labelled as ‘data art’. The role of these works was to explore the impact of digital more broadly, but over the last decade we’ve seen data art develop as its own practice – with exhibitions like Somerset House’s Big Bang Data, curated by Olga Subirós and José Luis de Vicente (2015), or art programmes like the ODI’s own Data as Culture which focuses more specifically on the role of data and pioneering ideas around data as an art material.

In my practice as an artist, I never used the word ‘data’ at all. I had a very specific definition or set of conditions around the word – data equals authority, data equals science, data equals truth, data equals objectivity. But conversations with the amazing people at the ODI have expanded this monolithic idea of data. Data is everywhere, data is many things – it can be playful and malleable. And data is fundamentally about people.

– Rohini Devasher, ODI Artist in Residence 2021–22

Over the last decade of our work with data as an art material, one particularly interesting idea that’s emerged is how data can connect us to the natural world. In nature, everything is constantly moving and flowing. Data can capture this dynamic movement, and by adding real-time, living data to an artwork, you can directly represent something that is alive right here and right now.

Take for example Rodent Activity Transmission (RAT) systems by Julie Freeman (2016–2021) which uses real-time data from a colony of naked mole-rats to analyse their behaviour and create dynamic artworks – including an abstract digital animation, kinetic sculptures and data visualisation. Or, Freeman’s We Need Us (2014–ongoing), which uses metadata from the Zooniverse – a citizen-science portal which enables millions of people to participate in citizen-science projects by classifying data about science, history and nature – to create a live, online animated artwork.

Data enables us to understand the natural world in a way we couldn’t before – it reveals hidden structures and patterns, and expands our appreciation of the life that is around us. But the world of data itself can be complex and hard to comprehend.

Over the last decade, data artists have been working to demystify some aspects of data – to make it visible and accessible – while creating layers of poetic mystery, aesthetics and new ideas that further artistic practice. We can see this with works by artist and curator Antonio Roberts. Roberts’s data.set (2016) is an abstract representation of a dataset about digital inclusion using noisy colours that mimic a glitching or broken screen. For this piece, Roberts wanted to represent the overwhelmingness of working with data while also breaking down the data in a way that feels more digestible. Roberts is an original part of the scene which hosts live events called Algoraves (2012–ongoing). These feature performers creating music and visuals using programming live on stage. The live code is projected behind the performer, enabling the audience to witness the link between the code and the effect it has on the music and visuals, uncovering the mystery that surrounds both electronic performers and data.

Artists are demystifying, but also communicating what that data means. It’s very easy to look at data as just numbers, but there are people behind the numbers, and those numbers can affect people as well.

– Antonio Roberts

Algorave, and all of the work artists are doing with data, reminds us of something that can be forgotten while working with spreadsheets or charts – that data is fundamentally human. Algorave facilitates a one-to-one relationship between the audience and the performer, highlighting the human behind the machine. This idea is also central to other works we’ve touched on, like ‘We Need Us’, which requires humans – and not machines – to classify data; or ‘data.set’, which encourages us to remember that each data point can represent a person or can be used in a way that could have very real effects on someone’s life.

The act of data collection is also distinctly human, which means it can give a unique insight into culture. Data collection is always restricted to the tools and methods of observation that are available at that time; and the data we choose to collect indicates what was important to a person, culture or agenda at any given time.

This idea is reflected in Rohini Devasher’s new, arresting work ‘One Hundred Thousand Suns’. The piece is a rendering of the Sun, assembled from historical and contemporary data collected by the staff at the Kodaikanal Solar Observatory. For almost 120 years, staff at the observatory have recorded images of the Sun – from drawings on discs of paper, to photographic glass plates, to more contemporary digital images. Devasher has combined this with other data – including videos captured at Kodaikanal, interviews with eclipse chasers and datasets from the NASA Goddard Space Flight Center – to explore metaphoric, speculative and poetic renditions of the Sun.

One Hundred Thousand Suns explores the notion that there are multiple readings of data depending on the site, the observer, and the mode and method of observation, collection and preservation.

– Rohini Devasher, ODI Artist in Residence 2021–22

As we look to the next decade, it can be hard to predict where data artists may take us, and how data’s role in culture may transform. We are currently seeing artists challenge big tech to reclaim the capabilities of technology and the opportunities for technology to equitably serve humanity. The more that artists stretch data and the more access they have to it, the more it’ll be challenged, interrogated and turned on its head. This will likely continue long into the future – whether that’s addressing the power imbalance of data collection, eliminating bias in datasets, or tackling the carbon implications of data collection and the need for data deduplication.

We’re excited to see how data art continues to shift, reconfigure and critique this ever-changing landscape as we move into the next decade of data.