Northernlands 2 - Four principles for a responsible data collaboration



This transcript comes from the captions associated with the video above. It is "as spoken".

Good afternoon everyone. My name is Stefania Milan and I work as

associate professor media and digital cultures at the

University of Amsterdam. Today I have come to you from this

beautiful garden and in preparing this talk I thought

I'd rather share with you some nice flowers and greenery

instead of yet another white wall that has been accompanied

most of our many, many conference calls and video

presentations over the last few

months. So I really apologize in advance for any occasional bird

or dog or car passing by that might somewhat hinder the audio

of this presentation.

I'm a sociologist, by formation. I've been doing quite

some work at the intersection, sociology, political science,

media studies, and data science, and mostly rather

interdisciplinary and cross disciplinary work. I'm very

passionate in particular about what people do with technology.

I consider data as yet another type of meaningful technology.

Over the last couple of months I've dedicated myself in

particular at understanding how the Covid Pandemic is narrated

through data with a specific focus on those communities and

individuals that have been left at the margins.

So I'm thinking about those that work, for example in

informal economy. People with disabilities but also sex

workers, poor families, deprived children, you name it. The many

precarious and gig workers around our communities both in

the global North and in the global South. What I would like

to share with you today however are some reflections on how do

we try to foster meaningful data collaborations and data

collaborations that become

precious. Not only to us, the researchers. Not only to the

funders, but are directly shaped and become useful also to the

communities on the ground. So to the people and those practices

that we are directly observing.

Today, in particular, I would like to share with you some

reflections. That I did a few years back and not interested in

the context of data-fication of data-fied society

With a co-author on mine, Ciara Milan, currently Marie Curie,

fellow at the University of Gratz in Austria.

With Ciara, we observe comparing our field notes in various

fields that very often

field work on the ground yields very very interesting theory

development. Smashing datasets. Often we do not make apparent

how the data were collected and not just from the methodological

point of view, but from the epistemological and ontological

POV. And in particular we fail to make clear how we develop

relationships with our research subject - with those that are

being researched. For us, and this is the departure of our

reflections, communities are not just research objects. Research

subjects if you prefer. They are rather active agents with their

own values, preferences, visions, strategies, their own

modes and languages of engagement, their very own

specific needs. So how can we make sure we expect then, that

we do not, as researchers overly shape the research in a way that

just benefits us. How can we repurpose the goal and the

sometimes very professional language of researchers. The

professional language of the founders of policymakers in a

way that the research becomes accessible also to those that are

being researched. How do we share our resources? How do we very

importantly consider the communities on the ground? The

individuals on the ground that participate in our research as

valuable resources and consider

also the effort that they make

to collaborate with us and how can we built in the research

process the data collaboration in this case, some operational

mechanisms that warrant that we listen and are accountable to

the communities on the ground. In these reflections were very much

inspired by the amazing work of a urban sociologist from the

United States of America. Boston in particular, Charlotte Ryan.

Charlotte has been working for many, many years with

communities on the ground. The United States.

And at the borders between the United States and Mexico,

as well as in Mexico and other Latin American countries,

in a very seminal piece of work

co-authored with one of these activists that she collaborated

with Karen Jeffrey - a Union organizer - they reflect on

treating or understanding moments and communities on the

ground as skilled learners. So as agents as individuals as groups

that have the capacity themselves on making sense of

their activities and reflect on them. And in this way she

encouraged us researchers. They encourage us researchers to

leverage this ability of communities on the ground to

learn from their own practices

and reflect on said practice. It's in this framework that Ciara and

I came up with a very simple framework for research called

the strap framework. In nature I mean not in this nature, but in

reality a strap is a piece of clothing usually that connects

two items to parts of a bag of a bag or up to parts of a jacket

or a bag to another bag or you name it.

A Strap is also very much

integral to the objects it tries to connect, the objects

it belongs to.

So strap for us in this case, in the research process, stays for

it's an acronym and I'm gonna tell you in a minute what this

acronym hides. But we really want to use the analogy of the

strap in real word to connect

to research and try to make the case that is very important

to building these mechanisms that we offer to

your perusal in order to ensure that data collaborations are

meaningful to both sides of the equation. So strap stays for the

The 'S' stays for sharing.

The 'T' stays for translation.

They 'R' indicates relevance. The 'A' stays for accountability

And finally 'P' is for power. So I repeat, sharing,

translation, relevance, accountability and power.

So let's now go briefly over these five different items that often

hide more complex items. So let's start from sharing.

Sharing is fairly intuitive, right. I as a researcher as the

initiator of data collaboration I might want to share my

findings. Very often however this sharing is not even written in

our research project. It's taken for granted or is simply not

really practiced because sharing entails relation building. Data

collection as well very often entails relation building or a

list. Good data collection

hides very careful relation building. However, we tend to

really underscore this part because this process

are very time consuming.

They often come with the very end of the research project or

research process. When we are ready in the publication phase,

or very simply, we.

Sorry about this mosquito. Very often we simply equate sharing

with sharing simply the publication of final report. A

final academic article. However, this is really not good enough

we argue. For a good data collaboration sharing entails

or has to entail also transparency.

What do we mean by transparency? Way to make the

research process transparent to the research subjects and we

have to share this project from the early on. This connects to the

R letter is going to come up next

But sharing really means trying to understand in the

various phases of the research projects the various phases of

the research collaboration, when can we involve the

research subjects. Also, for example in determining what are

the needs, the desires, and the preferences of the subjects.

But also for example, their research questions.

So we have seen the 'S' of sharing now it's time to briefly

look at the 'T' of STRAP - the second letter. The 'T' stays

in for translation. What do I mean by translation? Well,

translation might entail sometimes translating between

different language between Dutch, Italian, Dutch and

English. But what we really try to signal here is much, much

deeper and goes to the bottom of relation building. Translation

indicates really the conversion, the active effort of converting

my agenda into the agenda of someone else. My language, which

is often a disciplinary language, a professional language, into the

language of the research subject.

But it is not a simply a Mechanical Act. It really

speaks to relation building once more and it points to a very

very subtable but very important distinction that we would like

to introduce here, which is the very key distinction between

research with and research about research about is often what we

practice. Why? Because it is we want to research.

You want to know more about a specific community for example.

I have that the research about supposes some distance.

Entails you know, completely

detachment scientific. Very typical scientific objectivity.

That, you know, make sure that I am the, objective

observer. The distant observer, the expert observer. Now we know

very well, and I'm not arguing against this. This is very

important, very important element of scientific process

- scientific inquiry. However for meaningful data

collaboration to happen and with data this is very specific

need. Because data

opens up before it an immense amount of possibilities of

researching with citizens research subjects. It's very, very

important to go to the bottom of the relationship that tries to

involve the citizens in the process. So here we argue really

with a 'T' of translation for moving away from researching

mainly about into researching with. This researching with

might not concern every phase of research project. Might not entail

or concern every aspect of a

research question. Or very complex research project. But we

do argue that this is very important. It's very important

to at least sometimes for some parts of research project to

build in this relation that is different. That gives agency

back and power back to those that are actively being

researched by us. I give you an example, there's a very

interesting group of what we may call data activist in Milan in

northern Italy that are involving citizens in data

collection about a pollutant. About polluting agents released

into the air by, for example,

you know cars or central heating and other

polluting forces. And what they're trying to do is they not

only give citizens a little device to install, you know, in

their terrace out of their window in the garden, but they actually

involve the citizens from early on in building this very simple

devices. Although wait for those interested citizens that are

interested in able to participate to involving them

into the data analysis, such introducing them to some simple

mechanisms of data analysis and really giving them

power into shaping the research relationship and the research

project. The project I'm talking

about is done by the Off Topic Lab in Milan, but there

are many, many others of this kind throughout the word in

the global South and in the industrialized world.

Now let's move on to the 3rd letter of the strap.

Relevance. We do have to make sure we argue that our research

is relevant not only to us as your developers as data analyst,

as policymakers; those on the powerful side of the equation,

those with the expertise at least in data collection and analysis,

but also to those that are being researched. This entails

starting from and developing mechanisms and research methods

to involve in the research process from day one.

Even from day zero, really.

From the very same research idea that we start with

citizens and people on the ground

Interestingly, a few years back at the University of

Amsterdam, we also did a couple of events where we

involved a civil society organization from the Amsterdam

area in particular into what we call data for the social good

experiments. So we played around with an innovative methodology

that started from the needs so

started from the needs of the communities on the ground from

the society organizations and we tried to meet midway in making

our research questions intersect their research question and needs.

And it was a very interesting form of dialogue, very similar

to what Charlotte Ryan and Karen Jeffrey experimented in the US

for the last 20 or plus years

and I'm sure there are many, many other

groups that practice... the effort of making data collaborations

relevant also to research subjects, so making sure that our

research questions - at least some of them - get closer to what

those who are being researched also care about.

And we move now to the 4th letter, in STRAP, the 'A'. The A

stays for a very, very informal, important component of any

research project: accountability. Now very often as researchers,

we understand accountability

towards our employers, but more importantly towards

our funders, right? We spend an enormous amount of

time with very often very tedious, but nonetheless very

important reports back to the funders, where we explain

very carefully how the money, was accurately spent, and how

we try to make the research process cost efficient. How we

really did the best possible choice given the situation.

But we don't spend enough time and enough energy thinking

about what we consider a very important aspect

of the research project of meaningful data collaborations,

but also meaningful research relations, which is the

accountability towards our research subjects. It's similar to ties into what we named translation. And it concerns

being accountable to the research subjects, so making

sure, for example, that we try not to harm very, very

important. Imperative that we derive from example from

the hacker culture but also from many other cultures as well. From,

for example, the culture of the Medical Sciences, but also from

the very important need of making sure that we are

accountable, not just at the very end of the project again,

but while the project.

For example. We should ask interrogate ourselves,

especially we do research with people on the ground that might

be, for example, human rights defenders or people that would

research or activities with communities that are potentially

harmed or endangered.

You know, by the state, by potential other enemies.

Then we make sure that we report back to them.

We involve them in the process, also assessing the

risks and this is what we mean by accountability. Assess the

risks of research projects throughout, not only the costs.

Financial cost if you want.

but especially

the risks in terms of for example repression or the risks

of exposing certain practices or you name it according to what is

your specific research situation of data collaboration that you

might have in mind. So what is very important is that this

becomes an activity of building bridges and that these bridges

across very time very often throughout the research

projects and that we disclosed what also our risk assessment is

because sometimes the enthusiasm over shadows

the risks also for the people in the ground.

And finally we get to the last letter of our STRAP framework,

the 'P'. 'P' stays in for power. What do we

mean by power?

Well, you know, often we are the power side of

the equation. We are those with research money with a research

grant to observe people on the ground. Sometimes the power is

not even, you know financial power or anything, simply the

expertise and ability to make

time. Make room for observing practices that people actually

lived in or spent their life fighting for. And you know, we

have the distance that we have the time with the resources that

allows us to do so compared to the people on the ground.

And this, you know, this really brings me to discuss power.

This expertise at this time. This money sometimes

results in very severe power imbalances between us and

those that we are researching. It's very important to get this

power... although very often we cannot really offset it...

To give this power adequate consideration and try to, in

a way or another, act against it or share resources if we can.

But in case of not being able to do so, at least acknowledging it.

Not just, you know, in the acknowledgement section of an

academic paper, but especially

when it comes to also designing and implementing

the more research project.

So to sum up.

The STRAP framework

Stands in for sharing, translation, relevance,

accountability and power.

We ask you to keep it into account as a source ready to use

and simple recipe to make sure that our research project and our

data collaboration is able to create many full connections

between the researchers and those being researched while

embedding the research process into an ongoing process or a

wolpe process of social change. Thank you for the attention.

Have a good rest of the day.

  • Stefania Milan

    Principal Invesitgator, DATACTIVE

    Stefania Milan
    © Stefania Milan

    Stefania Milan is Associate Professor of New Media and Digital Culture at the Department of Media Studies, University of Amsterdam. Her work explores the interplay between digital technology, activism and governance. She is the Principal Investigator of DATACTIVE, a project financed by the European Research Council exploring data- and algorithmic-mediated forms of political participation ( She is also the Project Leader of "Citizenship and standard-setting in digital networks", funded by the Dutch Research Council, and co-Principal Investigator in the Marie Curie Innovative Training Network "Early language development in the digital age".

    In 2018-20 she directed the Algorithms Exposed (ALEX) project, tasked with developing open source software tools for auditing personalization algorithms on social media and online shopping platforms. In 2017, she co-founded the Big Data from the South Research Initiative, a network of academics and practitioners critically investigating the impact of datafication and surveillance on communities at the margins. Stefania holds a PhD in Political and Social Science from the European University Institute. Prior to joining the University of Amsterdam, she worked at, among others, the Citizen Lab at the University of Toronto and the Central European University. Stefania is the author of Social Movements and Their Technologies: Wiring Social Change (Palgrave Macmillan, 2013/2016) and co-author of Media/Society (Sage, 2011). She enjoys experimenting with digital and action-oriented research methods and finding ways to bridge research with policy and action.


Nothernlands 2 is a collaboration between ODI Leeds and The Kingdom of the Netherlands, the start of activity to create, support, and amplify the cultural links between The Netherlands and the North of England. It is with their generous and vigourous support, and the support of other energetic organisations, that Northernlands can be delivered.

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