Fiona Johnstone

13 February 2019

Fiona Johnstone

13 February 2019

Data Portraits

One of the aims of People Like You is to understand how people relate to their data and its representations. Scott Wark has recently written about ‘data selves’ for this blog; an alternative (and interconnected) way of thinking about persons and their data is through the phenomenon of the data portrait.

A quick Google of ‘data portraits’ will take you to a website where you can purchase a bespoke data portrait derived from your digital footprint. Web-crawler software tracks and maps the links within a given URL; the information is then plotted onto a force directed graph and turned into an aesthetically pleasing (but essentially unrevealing) image. Drawing on a similar concept, Jason Salavon’s Spigot (Babbling Self-Portrait) (2010) visualises the artist’s Google search history, displaying the data on multiple screens in two different ways; one using words and dates, the other as abstract bands of fluctuating colour. The designation of the work as a self-portrait raises interesting questions about agency and intentionality in relation to one’s digital trace: as well as referring to identities knowingly curated via social media profiles or personal websites, the data portrait can also suggest a shadowy alter-ego that is not necessarily of our own making.

Erica Scourti’s practice interrogates the complex interactions between the subject and their digital double: her video work Life in AdWords (2012-13) is based on a year-long project where Scourti regularly emailed her personal diary to her G-mail account, and then performed to webcam the list of suggested ad-words that each entry generated. A ‘traditional’ portrait in the physiognomic sense (formally, it consists of a series of head-and-shoulders shots of the artist speaking directly to camera), Life in AdWords is also a portrait of the supplementary self that is created by algorithmically generated, ‘personalised’ marketing processes. Pushing her investigation further, Scourti’s paperback book The Outage (2014) is a ghost-written memoir based on the artist’s digital footprint: whilst the online data is the starting point, the shift from the digital to the analogue allows the artist to probe the gaps between the original ‘subject’ of the data and the uncanny doppelgänger that emerges through the process of the interpretation and materialisation of that information in the medium of the printed book.

Other artists explore the implications of representation via physical tracking technologies. Between 2010 and 2015, Susan Morris wore an Actiwatch, a personal health device that registers the body’s movement. At the end of each year she sent the data to a factory in Belgium, where it was translated into coloured threads and woven into a tapestry on a Jacquard loom (a piece of technology that was the inspiration for Babbage’s computer), producing a minute-by-minute data visualisation of her activity over the course of that year. Unlike screen-based visualisations, the tapestries are highly material entities that are both physically imposing (SunDial:NightWatch_Activity and Light 2010-2012 (Tilburg Version) is almost six metres long) and extremely intimate, with disruptions in Morris’s daily routine clearly observable. Morris was attracted to the Actiwatch for its ability to collect data not only during motion, but also when the body is at rest; the information collected during sleep – represented by dark areas on the canvas – suggests an unconscious realm of the self that is both opaque and yet quantifiable.

Susan Morris, SunDial:NightWatch_Activity and Light 2010-2012 (Tilburg Version), 2014. Jacquard tapestry: silk and linen yarns, 155 x 589cm.  © Susan Morris.

Katy Connor is similarly interested in the tensions between the digital and material body. Using a sample of her own blood as a starting point, Connor translates this biomaterial through the scientific data visualisation process of Atomic Force Microscopy (AFM), which imagines, measures and manipulates matter at the nanoscale. Through Connor’s practice, this micro-data is transformed into large 3D sculptures that resemble sublime landscapes of epic proportions.


Katy Connor, Zero Landscape (installation detail), 2016.
Nylon 12 sculpture against large-scale risograph (3m x 12m); translation of AFM data from the artist’s blood.  © Katy Connor.

One strand of the People Like You project focuses particularly on how people relate to their medical data. Tom Corby was diagnosed with Multiple Myeloma in 2013, and in response begun the project Blood and Bones, a platform for the data generated by his illness. The information includes the medical (full blood count / proteins / urea, electrolytes and creatinine); the affective (mood, control index, physical discomfort index, stoicism index, and a ‘hat track’ documenting his headwear for the duration of the project); and financial data (detailing the costs to the NHS of his treatment). Applying methods from data science to the genre of illness blogging, Corby’s project is an attempt to take ownership of his data creatively, and thus to regain a measure of control over living with disease.

In the final pages of his influential (although now rather dated) book, Portraiture, the art historian Richard Brilliant envisaged a dystopian future where the existence of portraiture (as mimetic ‘likeness’) is threatened by ‘actuarial files, stored in some omniscient computer, ready to spew forth a different kind of personal profile, beginning with one’s Social Security number’ (Brilliant 1991). Brilliant locates the implicit humanism of the portrait ‘proper’ in opposition to a dark Orwellian vision of the individual reduced to data. Writing in 1991, Brilliant could not have foreseen the ways in which future technologies would affect ideas about identity and personhood; comprehending how these technologies are reshaping concepts of the person today are one of the aims of People Like You.

Sophie Day

14 January 2019

Sophie Day

14 January 2019

2018-2019 Science Cafes are launched at Maggie’s West London

Our series was formally launched with introductions from Kelly Gleason, Cancer Research UK senior research nurse, and Iain McNeish, Head of Division, Cancer, (both at Imperial College London & Imperial College Healthcare NHS Trust). Later we heard from Adam Taylor (National Physical Laboratory) about work in the Rosetta Team under Josephine Bunch which is supported to map cancer through the first round of CRUK Grand Challenges so as to improve our understanding of tumour metabolism (

To begin with, we learned about the breakthrough presented by tamoxifen in the development of personalised cancer medicine before hearing more about the infinite complexity of cancer biology. Twenty years ago, treatments were given to everyone with an anatomically defined cancer. This was frustrating since staff knew from experience that the treatment wouldn’t work for most people and many patients were disappointed. The introduction of tamoxifen led to stratification based on a common oestrogen receptor. Later, in ovarian cancer, it became clear that PARP inhibitors could be used successfully on approximately 20% of patients, who had inherited particular susceptibilities (in BRCA-1 and BRCA-2). Nonetheless, sub-group or stratified medicine is a long way from the goal of delivering unique treatment to everyone’s unique cancer.

This complexity is clear from the preliminary application of a range of integrated techniques by physicists, chemists and biologists in the Rosetta Team, as Adam then explained. Collaborators map and visualise tumours as a whole in their particular environments along with their constituents down to the level of individual molecules in cells. In combination, these measures give both a detailed picture of different tumour regions and a holistic overview. Amongst the many techniques are AI methods that we have encountered through Amazon or Tesco platforms which find patterns through reducing complexity. For example, 4,000 variables are reduced to three coloured axes that label different chemical patterns in one application of varied mass spectrometry techniques. You can find regions of similarity in the data by colour coding, and explore their molecular characteristics.

Amazon has applied non-negative matrix factorisation to predict how likely we are to buy a particular item once we have bought another specific item. A similar approach enabled McNeish’s group to find patterns among samples of ovarian cancer that had all looked different. The team traced 7 patterns driven by 7 mechanisms among these samples.

Embedded in the study of cancer’s biology and chemistry, data scientists ‘know that these are not just numbers. They know where the numbers come from and the biological and technical effects of these numbers.’ Non-linear methods such as t-SNE help in the analysis of very large data sets. Neural networks have also been developed to use in a hybrid approach where a random selection of data is analysed with t-SNE (stochastic neighbourhood embedding) to provide a training set for neural network applications which are then validated using t-SNE methods on another randomly selected chunk of data.

This approach combines fine-grained detail with broad pattern recognition in different aspects of tumour metabolism. It might lead to the development of a ‘spectral signature’ to read the combined signature of thousands of molecules at diagnosis.

At the end of the evening, most of us revealed anxieties about the attribution of a wholly singular status through personalising practices. Those affected by cancer wanted the ‘right’ treatment for them but we were reassured by the recognition that we also share features with other people. We appreciated the sense of combining and shifting between the ‘close up’, which renders us unique, and a more distant view, where we share a great deal with others.

Many thanks to Maggie’s West London for their hospitality.


Scott Wark

2 January 2019

Scott Wark

2 January 2019

You and Your (Data) Self

You might have seen these adverts on the TV or on a billboard: a man and his doppelgänger, one looking buttoned up and neat and the other, somehow cooler. “Meet your Data Self”, says the poster advert on the tube station wall I often stare at when I’m waiting for the next train. In smaller type, it explains: “Your Data Self is the version of you that companies see when you apply for things like credit cards, loans and mortgages”. And then: “You two should get acquainted”.

This advert has bothered me for quite a while. I’m sure that’s partially intentional—whether I find it funny or whether I find it irritating, its goal is to make the brand it’s advertising, Experian PLC, stick in my mind. I find the actor who plays this everyman and his double, Marcus Brigstocke, annoying—score one to the advert. Beyond Brigstocke’s cocked brow, what bothers me is that this advert raises far more questions than it answers.

Who is this “Data Self” it’s telling me to get acquainted with? Is this person really like me, only less presentable? What impact does this other me have on what the actual me can do? And—this question might come across as a little odd—who does this other me belong to?

Experian is a Credit Reference Agency, so presumably the other ‘me’ is a representation of my financial history: how good I am at paying my bills on time; whether I’ve been knocked back for a credit card or overdraft; even if I’ve been checking my credit history a lot lately, which might come across as suspicious. Banks, credit card companies, phone companies, car dealers—anyone who might extend you credit so you can get a loan or pay something off over time will check in with agencies like Experian to see if you’re a responsible person to lend to.

As a recently-finished PhD student, I’ve no doubt that my other me is not so presentable, to use the visual metaphor presented by this advert’s actor/doppelgänger. A company like Experian might advise another company, like a bank, to not front me money for the long summer holiday I’m dreaming of taking to Northern Italy as I wait for the next packed tube. This “me” might not be trustworthy. Or, to put it another way, this “me” might not indicate trustworthiness.

The point of this advert is to get me to order a credit report from Experian so that I can understand my credit history and so that I can build it up or make it better. This service is central to the contemporary finance industry, which has to weigh the risk of lending money or extending credit to someone like me against the reward they get when I pay it back. If I want to be a better me, it suggests, I ought to get better acquainted with myself—or rather, my data self. If I want that holiday, its visual metaphor suggests, I’d better straighten my data self’s tie.

There’s lots more that might be said about how credit agencies inform the choices we can make and handle our data. One of the more straightforward comments we might make about them is also one that interests us most: This other, data “me” isn’t me. This is perhaps obvious—the advert’s doppelgänger is a metaphor, after all. It’s a person like me, it’s constructed from data about me, and it influences my life, but it’s not me. But this also means that This other, data “me” isn’t mine.

This advert presents just one example of the many data selves produced when we consciously or inadvertently give up our data to other companies. In this case, we agree to our data being passed on to credit rating agencies like Experian every time we get given credit. What’s interesting about this data self is that whilst it isn’t you, it has an effect on a future version of you—in my offhand example, a you who might be holidaying in Italy; or, more problematically, a you who might need an overdraft to make ends meet month-to-month. To riff on our project’s title, these data selves are, quite literally, people like you. They might not be you, but they have a real effect on your life.

We need todo a lot more work researching who these datafied versions of ourselves actually are and what effect they have on being a person in our big data present. As Experian point out in another campaign fronted by food writer and austerity campaigner Jack Monroe, several million U.K. residents are “invisible” to the country’s financial services because they don’t have a credit profile. Conversely, we might ask, what does it mean to be a person in our big data present if who we are is judged on our data doppelgängers? What does it mean when my other “me” isn’t mine—when it’s opaque, confusing, and sold to me as a service?

Countless other digital platforms and services create both fleeting and lasting “data selves” that are used to try to sell us products, for instance, or to better tailor services to our needs. This process is called “personalisation”. One of the things we want to ask as part of our research project is this: who are we when who we are is determined by who we are like? Credit Reference Agencies and the “data selves” they produce make this tangled question tangible, but it applies to many other areas of contemporary life—from finance to medicine, from our participation in digital culture to our status as individuals, actors, citizens, and members of populations. This question raises others about what it means to be a “me” in the present. These are the questions, I think, that bind this project together.


For more information about Credit Reference Agencies, see the Information Commissioner’s Office information page.

William Viney

26 November 2018

William Viney

26 November 2018

What is Personalisation?

Personalisation is at once ubiquitous in contemporary life and a master of disguise. Its complexity hides in plain sight. Personalisation may mean producing products and services to ideas of individual demand, but it also means much more than this. Personalisation connects diverse practices and industries such as finance and marketing, medicine and online retail. But it also goes by many aliases – patient-centred, user-oriented, stratified and segmented – in ways that can make it hard to follow. It’s not always clear what personalised products and services share in common.

The ‘People Like You’ project does not shy from this diversity. It works across the fields of medicine, data science, and digital culture to understand the differences in each of these domains, as well as how people and practices work across them. One challenge of understanding emerging practices that are forming within and between particular industries is that histories of personalisation may be contested, sensitive, or rapidly developing. We want to find ways to explore different meanings of the term ‘personalisation’ in the United Kingdom, among people from different working backgrounds: academic and commercial scientists in the biomedical, biotechnology and pharmacology; public policy; advertising and public relations; communications; logistics; financial analysis. So we have designed a study that might be the first of its kind in the UK – an oral history of personalisation. 

The ‘What is Personalisation?’ study uses stakeholder interviews to establish how and why each industry personalises, and with what techniques of categorisation, monitoring, tracking, testing, retesting, aggregation and individuation. These interviews are in-depth and semi-structured. They usually last an hour or more. Interviews allow us an opportunity to understand how a particular individual views their work, industry, profession or experience.

A wide range of policy makers, activists, scientists, technologists, and healthcare professionals have already participated, detailing how they see the emergence of personalisation affecting their lives. Striking themes have revealed just some of the connective aspects of personalised culture: the links between standardisation, promise and failure; how languages of democratic and commercial empowerment contest state, regulative, or market legislative and economic power; how products or services can treat prototyping as a continuous process; the influence of management and design consultancies; and the way mobile technologies interpretr data in real time to produce ‘unique’ experiences for users. These are just some of the ideas that we have talked about during our interviews. We also get to discuss when and how these ideas emerged and became popular in a given industry, field or policy area.

The connections that can be made across different fields, practices, or industries can be contrasted to the highly specific emergence of personalisation in some areas. For instance, the special confluence of disability and consumer rights activism that formed alongside and, at times, in opposition to deregulation in healthcare systems in the late 1980s created individual (later personalised) health budgets, now an important policy instrument used by the National Health Service’s personalised care services. The challenge is to understand the historical and social formation of a particular patch in [personalisation’s history, its various actors and networks, to recognise adjacent and comparable developments. We are doing this whilst recognising broader patterns that are germane to other contemporary figures of personalisation. One of these may be the specific inclusion and exclusion factors that prevent a personalised service becoming a mass standardised service.  Another is to understand whether or not personalisation is being heralded as a success or as a response to failure – not the best of all available options but an alternative to foregone possibilities].

Our work takes patience and a lot of help from those who are passionate experts in their field. If you feel you have an experience of personalisation that would make an important contribution to this study then please get in touch with William Viney (