Be be happy to have her today with us.
She's learned that co-director of the exhibition Center
for the Study of the Life Sciences at Denny's,
where she leads that other study research strand.
She's also doing fellow what they are doing institute
in London and an editor in chief of the International Journal
of Journal History and Philosophy of the Life Sciences.
And she's currently based
in Berlin as a fellow of the patient shop
Connect to Berlin, which I understand she's speaking from today.
And Professor of Genetics Research, Spencer Fields,
the fields of history, emptiness, be a biology, science, biotechnology
studies and general tenacity of science
as she she's currently investigating the philosophy,
history and social studies of faith in science,
especially the impact of big and open data and research
on why there's a society responsible that the management
infrastructures and the construction of semantics to enable
the linkage for automatic mining in the plant sciences and biomedicine.
She has received many awards and an honor.
She is now
the she's not a
member of the academy, the measurement center that she just did a science
at the Academy that kept academia and the real society of biology.
She has also done a lot of enormous service to the profession.
She's a vice president of the European Philosophy of Science Association
as an advocate of first open science.
She was she has been a plan as ambassador and a member
of the open science policy platform of the European Commission.
Today, she'll be presenting
big and open data for development, the gaps.
And it's
our pleasure to have her here the first year studying that.
Thank you very much, Maria.
And thanks, everybody, for the invitation and for having me here
and was really enormously interested in the discussion
you were having on that as a result of the project.
And is wonderful to be able to have a dialog with so many experts
in this area.
So as Maria has said, I mean, I should really point out
I'm not an expert in the study development but I'm hoping that
what I can do is bring a few reflections,
which I think intersects heavily
with some of the issues you already touched upon
that come from the study of what's going on within
academic research and how people are thinking about the
dissemination of data how this is potentially transforming
the landscape, but also some of the problems
and some of the potential challenges that are right now in this field.
So let me just
So just
to summarize some of the basic starting points for thinking about
what are the novelty,
the advantages of being an open data within the sciences.
Many of them were already mentioned in the previous discussion.
So we are looking at a situation where for many years
in data were not really very often
disseminated in and of themselves across researchers.
People tended to communicate the research in the form of papers
but now more and more
actual communication of the data themselves has become very relevant.
And that's what a lot of initiatives are focusing on.
And this is also because not only we have technologies
that allow us to produce more and more digital traces
of our interactions with the world, but we can actually integrate them
more and more effectively with data that come from social services
or commercial services.
And you already had quite a lot about this today.
So the
idea is that we can hopefully increase the use of this data.
We can use them over and over again for a variety of different purposes.
And we can also decrease the waste, the loss of data
that are produced sometimes at a very high cost,
but maybe then don't get circulated or exploited in us.
There's also a strong expectation that one can
basically use automated or semi-automated
and tools to be able to analyze the data.
And so one can have one.
But can some people have defined Data-Driven discovery
and of course, to try and enable this, there's a lot of work happening
on how one classifies the data, what kind of semantic systems,
what kind of standards can we use it to make sure that data
that are produced in a particular part of the world,
a particular group of people, can actually not only be received,
then accessed by people based in other places,
but can also be meaningfully understood.
And they compare to, for instance, or integrated
with data produced elsewhere.
So of course
that also meant that certainly within the sciences
we have seen the emergence of many new institutions
in modes of working around data And again, this, I think was very,
very well exemplified by the sessions that preceded this
So obviously, this is providing an opportunity
that some people associate with the opportunity to improve
the very relationship between science and society, providing a platform by
which people actually are questioning what counts as science.
Are we only looking at scientific papers and reports,
or what does it mean
to consider data themselves as a formal scientific output?
How important are scientific infrastructures
and the various regulation of data travel
and data mobility to the production of knowledge?
How should the production of results be credited in
how should results be disseminated?
But also, what kinds of incentives do we offer for people who may be in
possession of relevant data?
But perhaps reluctant to share them with other stakeholders?
And the idea, of course, is that in
improving the science society relationship along these lines
actually would mean improving research and potential interventions
towards development goals broadly concede
for instance, by integrating data sources to provide reliable indicators
and assess the impact of potential interventions
and also expanding the reach of scientific methods
to social groups
into geographical regions that haven't been very well represented so far.
It partly because maybe we lacked the opportunity to really cover
those areas in a meaningful sense.
So all of this sounds very, very good.
And and we immediately then look at some of the potential challenges
with this kind of optimistic view of how one thinks
about big data exerted potentially also results from development.
And this is the example of the COVID 19 pandemic.
And now I'm sure you're familiar with arguments to the effect
that the ways in which in the pandemic response
has been organized have been really dependent on the open
science initiatives in ways novel ways of disseminating and sharing data.
So there's been a lot of emphasis on the fact that thanks to commercial
publishers actually releasing papers for free is a priority,
thanks to open data initiatives in data platforms and allowed
very quickly to share data among different research groups,
but has been an acceleration of discovery and
is been much, much faster to try and make some inroads
into trying to understand how the virus worked.
And that actually has led
to very important trials for science in this area.
For instance, of course, most of all, the development of effective vaccines
very quickly after the emergence of the pandemic.
So there's been an argument
that this really is a sort of vindication of the value
of big data platforms and initiatives and an almost unassailable
demonstration of the value and of being an open data space.
And so I
actually think that this is not entirely the case.
And another way to read it, what the pandemic is brought to us
is, in fact, in looking at how research as
especially biomedical research has worked under the pandemic
as brought to the fore in ways actually were very useful.
Many of the problems that we're still having
in trying to circulate data and make sense of that.
So for one thing, there have been enormous
technical issues with actually sharing data, particularly
when we were looking at data that were coming from frontline
medical services, doctors, patient groups and people who had expertize
that were not
necessarily experts in biomedicine or recognized aspects in biomedicine.
And this is something that we have explored in England
with a report that it was commissioned by the Alan Turing Institute,
where we assessed
the extent to which the UK was data ready for the pandemic.
So how fast, how efficient it was for researchers in the UK
to exploit existing data to try and come up with
solutions and ways of tackling the pandemic.
And in fact, what we found was pretty disastrous
that by and large it was extremely difficult to
integrate particular data coming from public health authorities
at the local level.
And that created huge gaps in how
researchers could
in fact model and predict it being in the curve
of the pandemic because of the pandemic across the country.
It also something that has come out quite strongly during
the COVID 19 pandemic is the fact that many of the technologies
proposed to try and produce and truck data were relatively expensive.
And a lot of the tracing programs
that were devised to try and follow the spread of the pandemic,
for instance, related to the use of digital devices
and this potentially actually a de facto widening
rather than reducing the existing digital divide around the world.
So people that didn't have access to mobile phones
or were not very good at using these devices and of course
and thinking about children, but also elderly people
in both categories, especially elderly people
being very vulnerable to the virus, not quite being able to participate
in this kind of data collection exercise as you would expect.
Also, I think one of the things that the pandemic showed quite clearly
is a general lack of clarity over what are their rights
and what are the obligation that pertain to this kind
of very widespread and very fast data sharing.
And one of the examples I've been looking at is the
database
that was originally devised in 2008
to try and help researchers to disseminate data
about a genomic data, actually about influenza.
And a particularity of this database was that
rather than just making it available to anybody,
it people who were accessing what accessing these databases,
in fact need to subscribe to a set of rules
about how they meet the use the data that they get access to.
And these rules include,
for instance, crediting the people that actually produced this data.
And arguably this was very successful.
These actually led to many people, particularly
in the Global South, contributing data and with the idea that
in fact they could rely on the fact that maybe people
accessing this data would credit their work properly.
And but in fact, very interestingly, and it was because it was so effective
and it was transformed or redeployed into that
had these focused on genomic data about COVID.
And this actually was very successful in this first year potentially.
One could say that
one of the principal tools was one of the principal tools
for biomedical research
on COVID, certainly in the first year of the pandemic,
but into the second year of the pandemic, 2021,
this database came under huge attack.
And so people sought particularly many researchers in the global north
thought that it was actually very problematic,
that one couldn't just access all the data databases
and bulk download them and and not to actually seek
permission and in fact, all these kind of governance tools.
And this generated a big debate which is still ongoing around disease.
There's also been other databases
set up to try and provide an alternative service.
But certainly I think it shows the extent to which in different ways
of thinking about what open this means and what data dissemination means
and how one actually effectively transforms data into knowledge,
continue to hold and research,
including that important resource such as the one on COVID 19.
Another thing that I think has been worried me a lot
during the pandemic has been to see a general disregard
for the diverse sources and diverse perspectives
that actually can be associated to data.
So we've heard a lot about the fact that in integrating big data
actually brings in potentially
many different perspectives of many different voices
to the production of research, which is great.
But in fact, during the pandemic what we saw is that many researchers
tended to be very conservative
about which data they were in fact trusting and focused on
for instance, genomic data, data about the biology of
the virus was reputed to be generally quite reliable.
While there was much less effort, certainly the beginning
and the pandemic to look into integrating that more biological data
within socioeconomic data, for instance,
with the coming of public health and in the medical system
and in fact,
this was
partly because it became quite obvious that at least in many domains,
there was a lack of venues and a lack of instruments
to try and collaborate and consult various different communities
and disciplines that actually had some expertize
to offer even respect to the pandemic response.
But requires that it be to work
or to be integrated into this kind of research.
At the same time, we saw again and partly
because this idea of emergency was evoked in
quite a lot of projects, which are basically just scraping
data it from wherever it was possible to take them, it was very little
regard for ethical guidelines and ethical considerations.
And some people talk about this phenomenon
as a form of digital feudalism, where people who have the power to do
data analysis and think, you know, feel entitled to it,
pick up as much data as they possibly can,
and partly because, of course, they're very well intentioned,
they're trying to use data for the public good in.
But you can imagine that this also brings a lot of problems with it,
not least in terms of really thinking through questions of representation
and of actually
who is represented in the datasets and which kinds of knowledge is
are allowed to contribute to interpretation of this data.
And of course, not least,
there's been lots of polemics around how to govern data.
How does one access data particularly transnationally across borders?
And what we seeing there, which again, I think is a big and maybe
central concern for particularly blue skies research is a failure
of transnational solidarity in really thinking about
how does one transform the knowledge acquired by disseminating,
sharing and integrating big data into mass products
such as, for instance, vaccines and other drugs.
In the case of the pandemic, it became very clear
that despite the great solidarity manifested among researchers
that were working on developing a vaccine,
that was in fact very literal issues between that
and the way in which the vaccines are being commercialized
and the limits around which around it actually could access the vaccine.
So I think this brings me to just try and pick on at least
some of the challenges that we're seeing more generally
with the use of big data, which I think particularly
when thinking about development, it becomes very relevant.
So there are arguments I think were justified
that at least in some domains, what is happening through
the use of data is basically a form of mass surveillance,
a surveillance of human behavior
that appears at individual as well as community levels,
and whose social implication political implication, economic
implications for the communities that being under surveillance
are not particularly well looked after and thought about
And there are also very serious concerns
with the fact that in many IT types of data
analysis and data technologies in that actually expanding existing divides
and in fact silencing knowledge that comes from places
where there's maybe not many resources to produce datasets
or if the researchers were working on
on certain topics are not as visible and don't produce data, which is quite
as tractable as the data that in fact being disseminated the physical way
within research.
There is also quite a lot of concern around
what does it mean
to put such a strong emphasis on the cost of the use of the data?
What does it mean
for actually creating new datasets whenever it's needed?
Also, what are the implications of it not only focusing on reuse,
but actually trying to think about what does it mean to maintain
the quality of the data over time, which as many of you are very aware,
actually is a very, very big problem for many databases to think about.
You know, does this data actually stand the test of time
or do they become less and less reliable as technologies change
and ways of conceptualizing the data and classifying them also changes?
That is, of course, a lot of worry, especially within in
research environments around the effect of trust
in digital technologies and sometimes standardized software packages.
As statistical analysis packages so strongly and sometimes over
and beyond the expertize
of people who have been working in certain fields for many, many years,
even if they were necessarily working on big data policy.
And also there is quite a lot to worry around the opportunity
that big data analysis poses to actually markets.
And if you want alternative facts, fake news data,
which are in fact not reliable data that are misleading data
but have been putting puts out because there are vested interests
in actually making them look like they are reliable data
because again, it is being
really interesting interventions in the previous session around
what can, for instance, national statistics offices do to try and
control this phenomenon.
So I guess one of the
questions that I'm trying to ask it together
with my research group at University of Exeter is
how does one
go from thinking about big data and the opportunities of open data
in theory to thinking about the kinds of very practical problems
that emerge when one implements those ideas in practice?
So on the one hand, there is idea that in using this data can open up
new species, it can help us to challenge
traditional communication channels and power structures.
It can encourage an expansion of participation
in research, and particularly participation from below, from people
who haven't been as well represented and so far.
But at the same time, that is also justified concern
that relying on these kinds of data may in fact enhance conservatism
in May it may actually make existing biases worse.
It can exclude in some contributor systematically.
It can itself actually discriminate and can cause further inequity
and inequality.
So in
one of the exercises that I did when I was working together
with the European Commission
over the last three, four years was to try and think about
what are the problems faced by people who would like to contribute to data,
to research efforts, and why is it that some people manage
to contribute data to existing databases, to pieces of research
so easily while others actually find that very, very hard?
You know, there is this illusion sometimes that just because
we have access to the Internet, then everybody can contribute equally.
But that's certainly not what we have found it, particularly in
doing a lot of research, together with informants in Central
Africa, South Africa, in Bangladesh and in parts of Latin America,
So what we found is that
especially for people who are working within the academic system,
there are very serious issues with how research is being evaluated
and the fact that when one tries to put a lot of work
into informative data and curating data
so that it can actually be usable by others it can be understood and
by other people that are appropriate meta data capacity data.
Well, actually, these people don't really get
any reward from their institutional settings.
In fact, they're penalized very often
for wasting time on actually curating the data.
And this obviously creates very strong disincentives to do it well.
There's also already within the research environment,
a very big problem in trying to recognize the fact
that there are very different types of research cultures and research
methods and in fact very different kinds of data.
So there's a very strong emphasis at the moment
on quantitative measurements, and this comes
to the expense of qualitative observations
which however continue to be a very, very important complement
for quantitative analysis and is very, very difficult.
And for people who are producing particularly positive data
to try and communicate that data as effectively
as people who are working more on the quantitative side,
there are obviously big costs and also accountabilities that come with it,
making data available and try to provide data as a resource.
It is often a quite expensive exercise in a possible ways
and some institutions certainly don't have the opportunity
to be able to do this.
I don't think is a coincidence that certainly within the world
of biological research for instance and by far
most of the main databases providing access to biological data
are based in the global north and are run by very powerful
institutions who have a very expansive and financial opportunities
that are of course also very big problem
skills in the country that is needed for people to be able to actually work
with data and in ways that are careful and
allow them to ask their own questions
with the data that they're dealing with
There are huge issues around intellectual property regimes, around
data, and very often this is partly because very different
kinds of legal frameworks are applicable to the same datasets.
Somebody may produce a dataset for a particular working
within a particular institution.
And so some of the rules that at that institution,
maybe a university have on who governs the data would apply.
But at the same time, that same person may also
be sponsored by a particular international funder,
say, the Gates Foundation, which has a very different set of rules
for how one should disseminate data and who owns the data.
And again, that person may be subject to national regulations around
how does one deal with state
data, which have been produced by research institutions
and so on and so forth.
And this creates a lot of confusion
and a lot of problems for researchers on the ground.
There are all sorts of questions also with the semantics of data,
in other words,
the ways in which they are classified and what kinds of terminologies.
In fact, very often what kind of language is used
in to classify and disseminate the data.
Certainly in the western parts of the world,
we have by now pretty much conceded the fact that English
is the main language to be disseminated,
but have certainly looked at many situations,
particularly for instance in and trying to in share data
about crops in in various parts of Africa where
try to translate local languages into English
comes with all sorts of problems and very often one loses
a lot of very important information in nuance
by trying to make that translation.
Obviously, there's also ethical concerns.
There's questions, as I mentioned, around
Watchlist the resources to do this kind of work, and therefore
there's also questions around
the inequity and discrimination in the infrastructures
that different kinds of researchers have access to.
So actually, I'm going
to probably skip this because I want to take too long.
And of course we'll be
I to have a discussion, but I think I more or less mentioned this
and in this light that just wanted to show you a study that we did in.
Oh, sorry.
I thought that was something happening.
I hope you can still hear me.
But so in this like this, I wanted to show a study that we did
a few years back looking at at least trying to classify,
not a list, all of them, but just classify the types of data
repositories and data services that people who are working in plant
science should be aware of when they're trying
to share the data and also to reuse other people's data.
And as you can see here,
there's an enormous amount of just different types of data resources.
And that, of course,
makes very, very difficult for people who are working on the ground
to understand how these very complex data landscape moves.
So those of you who are students and entering this space
for the first time, don't worry if you're incredibly confused.
I have be working with these kinds of things for many, many years,
and I absolutely cannot keep up with what is going on most of the time.
So this is a situation where one has to a trust collaborators
working in different parts of one's field to try and keep up somehow
know how one needs to actually be able to manage data properly.
Of course, there's also very serious governance challenges as to one,
how utterly and one manages this data.
And here I just want to mention some of the issues
associated with your fair data, data which are findable,
accessible, interoperable and reusable.
These are principles that have become very popular
and of course are actually incredibly important in trying to think about
what does it mean to actually share data.
But one of the things that we've been worrying about and many,
many other people, of course, have pointed to the same issue,
is that despite the acronyms fair,
there is very little consideration about what does it mean
to operate ethically and responsibly in relation to the fact
this is actually not a consideration that's included
in the set of principles at all.
So just to give you a quick example of that,
this is one project that the collaborating with,
which is the attempt to try and produce data sets over
and field trials on particular crops which are cultivated around the world.
In this case, this field which is in Ibadan and across
Lagos in Nigeria is a field where researchers
are cultivating in cassava roots a very important food staple.
Of course,
and they're analyzing these different kinds of fruits, different varieties,
and they want to be able to disseminate the data produced
through these efforts around the world.
So that can be compared
and some knowledge can be acquired around and better known
as can be acquired around how these plants develop.
For instance,
when they're exposed to different climates,
which of course is very important,
we're trying to deal with climate change,
and this work is done very often through standardized
field books, which allow technicians to collect data directly in the field
as they're actually conducting this research, these field trials,
and to export these data directly on their local servers in Nigeria,
which then basically again, pretty much instantaneously almost
disseminated to international databases
and basically it makes it possible
for these data to travel all over the world to
many other institutes around the world by doing similar kinds of research
as well as many universities, international organizations
like the Food and Agriculture Organization and parts
of the agricultural and tech business.
One of the issues with
pushing for data sharing at this scale
is, of course, that on the one end is very important to think about crop
data as good something which is really, really important
for researchers to access, because by analyzing this data, one
can better understand how humans can survive in a changing planet.
But at the same time, if this database is actually
it is very, very difficult within these kinds of data infrastructures
to recognize the rights of indigenous groups and local breeders
who actually very often are producing the varieties of crops that are then
in, analyze and measured and in
in all this knowledge that is then transferred into databases.
And there are very interesting
attempts to try and incorporate this kind of knowledge in databases.
For instance, in the seed, you are communities of practice,
which I was a part of.
But generally speaking, this is a very very much like behind
as an effort to the idea of just disseminating
as much of this data as possible and hoping that it can be useful.
And that means that all the people who are involved in producing
the plants in which data are collected are potentially being exploited, and
particular by companies that then use this data to produce
the fertilizers and seeds that then get commercialized
back to the people who have offered that knowledge for free
and also that are big problems to the extent in the extent to which
data production, for instance by through farming instruments
used to actually a farm is being privatized with this database
which has been appropriated by Agritech companies.
So the question becomes for these kinds of issues, well,
data governance is important, but along who is a right participant
in data governance and who establishes these kinds of rules?
So I guess
trying to conclude and so hopefully we can open for discussion
in one simplistic vision, I think a bit intensive research
is to think that these are about transparency and the best we can do
is to make research elements as widely accessible as possible.
If the idea that it is very much about digital transformation,
social and information and communication technologies
like these databases, like the Internet, and are
the main tools that we can use to do that intensive research
and the idea that this is good research,
that by making all of this data available,
we pretty much automatically improve the content of science
because we make more materials available for study
and also to improve the researchers working conditions.
The idea that a little research is global,
it can reach anybody
with any interest in research and an Internet connection,
and it is possible to provide skills to training
and it doesn't really matter what people are based at.
And of course, related to this idea, that's a bit intensive.
Research can facilitate equity in research, production and consumption
by making previously
inaccessible resources available to those who may wish to use them.
Now, this is not quite what we are found in our research
and I think an alternative view that we could consider
is that in fact in data intensive research appropriate,
that intensive research, data sharing, open data
are not so much about just providing access to the data,
but about trying to ensure that data are used responsibly.
In effect, having access to data turns out to be pretty hard for
unless people have the skills, the infrastructure, the governance
structure, the administrative support to foster responsible use
also this idea that
the only thing that matters is only things that matter.
The only data that matter
are the data digitized and more that can be put in a beta format.
And I think that needs to be very carefully scrutinized.
There's lots of different fields of expertize and knowhow
that actually much more difficult to put in a digital format
than, for instance, genomic sequencing data.
And that is not really obvious to me.
In fact, I think we will not the case that this knowledge is less valuable
for research than easily quantifiable knowledge.
And so we need to be very careful around this.
I think also it's very important to just acknowledge the fact that
a bit intensive research typically is good for some and not others.
That is very, very new solutions that are good for everybody.
And I think
many people working in the data science world by now accept the fact
that there is no neutral way of in disseminating data.
When you develop data infrastructures, you pretty much always
make value judgments
and you always make choices about what you think are the most reliable
the priority data, and also what you think your main audience would be
and what you think the uses of the data
that you envisage happening maybe.
And so in given this is very important to make value judgments and choices
explicitly so that we address issues of inequity in diversity in research.
And of course in social life.
This also means that while of course
many data intensive solutions have a global impact, very often
they need to really go through localized situated solutions.
And this means looking at it, which users are actually privileged
by anyone with intensive solution and do that with a transparent.
And I think that's really important for actually making this a
if a such as more trustworthy and more reliable in themselves,
it makes it easier for people
access than to understand where they're coming from
and what they're supposed to be used for.
And finally, rather than thinking about equity as an immediate
and obvious result of intensive infrastructures and data sharing,
I think it's important to think about the question of fairness,
to investigate empirically it which research
was actually best served by the distributions of hand and how
systems can be rebalanced to address at research needs and goals.
Now, I could say a bit more.
I have a few more slides,
but I think it may be a good time to stop it
so that we can actually have a discussion.
Thank you very much for your attention.
Thanks so much, Sabina.
So we're now going open to that
start for questions from the audience
and we have a bit I don't know
if this is a question, so I think we have a question
by Alex from Alex is that.
But this must be a new question, right?
Okay.
So Alex, you may ask about.
Thank you.
Thanks to being and and thank you so much. Yes.
Thank you so much for that very measured
Wide-Ranging and nuanced discussion.
Are you covered so much within a very, very short space of time
and it raises so many interesting
questions for for academics,
for those working in the third sector, for our students,
if I have to focus on one, which I do, I guess
leading towards the end, I was very impressed with the way
you summed up
some of the recommendations and what we need to be looking for
and looking out for and and the different types of approaches.
And I was wondering how you you talked about fairness,
but, you know, how you balance that tension between
equity and research, production and consumption.
And sort of the open access, if you like.
How do you balance the tension between
doing that while at the same time
making sure that those who are doing it have the skills and governance
structure, if you like, to ensure its responsible use?
So there's a real there is a real tension there.
And you pointed to fairness as
a conceptually as a way that you might be able to sort of bridge that.
And I just wondered if you could say
a little bit more about that, because I'm not quite there yet
with that concept of how fairness can can do it.
But I it's it's a tough call
and I don't know how we, you know, we bridged that.
But but now, of course, I mean, Alex, thanks so much for that.
Really.
Absolutely.
Central question and I agree with you.
I mean, this is really very difficult
to cut to cash out what factors may mean in practice
when dealing with these kinds of resources.
So my approach I mean, the way in which I started
across this question in my work is thinking about what fairness means
in developing technical specifications.
For data sharing in data use.
So rather than externalizing the idea of fairness and thinking
or is basically about trying to think about generally social conditions,
from which data may be collected. Right.
So the moment that precedes to arrive in a particular system,
who contributes and then thinking about how they are
used in the other end.
I think the spacing between of and actually
processing the data, making choices about how we're structuring
particular infrastructure or the research projects
to take advantage of some data, not others.
I think this is where it very, very important issues come out.
And so for me, I think one of the
challenges for me has been to work with a lot of scientific collaborators
in really thinking very much detail about what constitutes a fair choice
in the space of those very, very particular technical decision.
What makes during the research is so not going to go through that,
not because it will take too long, but for instance in
have a paper where we explore which is more than happy
to disseminate in the where we explore in emotional sadness in relation
to the re-use of social media data which I think is a very relevant
consideration because I heard many of you discussing this challenge.
Right, of, of, on the one hand having this incredible
a new set of insights potentially coming from people using social media,
but at the same time a big challenges which are both ethical, right?
I mean, these people are not necessarily agreeing
or consenting to information that they produce be used for research
purposes, even if this purposes are ostensibly good,
but also create scientific issues because precisely
because these are not reproduced with an eye to research
and they're very often very difficult to contextualize
and to really understand actually, how do we reliably interpret
what X was that people meant when they,
for instance, refer to not being able to breathe
right or things very, very important things of this kind.
So in that paper, in that study, we try to look specifically
at what people can do when they're designing the study.
Throughout the study,
and at the end to think about what may improve the situation.
I mean, there's always going to be tensions.
We're using these kinds of data and at the same time,
we certainly don't want to just give up
on using those sources because in some cases,
the fact that we have these new sources of information,
as many people already mentioned, could be lifesaving
for whole communities.
But I think having a still an eye on,
you know, under which conditions we do this is really important.
I mean, another,
I think quite important issue, a certain is come up a lot with working
with researchers and well, maybe more researchers in the global north.
But it's been the fact that
because a lot of people are struggling with where to store the data
and where to, you know, which kind of cloud services to use, for instance.
And there is a tendency to worry a lot maybe about SATs to use a bit less
about who actually ends up storing and sometimes
having some degree of control over your resources.
And so, you know, in particular, in a world where the cheapest services
approaches at the moment are provided by some of them,
you know, very big box corporations like Google, Amazon
Web Services and so on and so forth, And so again, questions around
what can be done to overcome this and can one look a little bit further
maybe into some of the services that actually do exist,
even if that may be less immediate,
that provide
a little bit more protection for the data that one is stewarding
and I think would be really very useful.
I guess I could go for a long time, but I think at least in my case,
thinking about what can be done, we don't know case to case basis
in to improve the situation, not to resolve it in any way,
but at least to take those tensions into account
throughout the study is really important.
Maybe another example, just very briefly,
is in biomedical research that is now quite a lot of emphasis,
which I think is wonderful on thinking whether in in order to do what
it intends to study, what really does need to have access to the data.
In fact, because it results in especially data being decently curated
and that are reliable databases that can provide access.
Very often what you really need is not necessarily even to have access
to the raw data or even to the economy and aggregated data.
All you need is to provide a very precise question by
and then people who are running databases can run that question
can run related algorithms through the data
so that I can provide you with a reliable answer,
but without in fact the data ever leaving that space
and I think in for situations like the ones among allies
in the agricultural sector, where people really underestimate
the kind of ethical issues
that relate to the sharing of data and speculation in the raw data,
some of the solutions will be quite interesting
to think about a little bit more interesting.
Just just a quick, very quick reply
because I was thinking then about Indigo and its talk earlier
and represent one time and so on and how
you could get these big
adhesive, let's say, you know, doing the number crunching for
you and the importance of you just being able to ask the right question.
And of course
that then you have the issues around transparency and accessibility.
And it's always good to sort of have the, you know, different
maybe third sector organizations just checking in and just,
you know, having access to make sure that that data is being interpreted.
Yeah, no, I couldn't agree with you.
In fact, I mean, I suppose for me, the other the third area, which is maybe
in fact the most important is how does one construe
venues of dialog and consultation around these kinds of exercises.
And so the idea that I mean, certainly for researchers in academia,
one has to work in a transdisciplinary way, meaning
you have to have people who have expertize but are not
professionals and involved in this process are absolutely essential.
I mean, that's the case.
I mean certainly for pandemic research,
as I was talking about before, having patient groups involved
in frontline medical professionals involved really was essential.
And it happened very rarely.
And because they just weren't enough venues that were ready
to provide the kind of connections between people,
that I think is a huge concern.
And we can think about that in pretty much any role.
Thank you. Thanks.
So thanks a lot.
And if I may take two more minutes out of everybody's time
since we started a bit late, then there's another question
that is has been written on the chat by a lady seller.
I think she
has trouble with her microphone, so she says, very interesting talk.
Thanks so much.
Thinking mostly about students looking for good quality date that.
Is there any tool that serves the purpose of measuring the quality
of the item?
Various dimensions, something similar to what exists for better reviews
and meta analysis to assess the quality of typical evidence.
Yeah, so that's a great question.
And I'm afraid to say that's not there isn't.
I guess that's pretty much the short answer
in the sense that there's lots of different tools being developed
in different domains and in this certainly not universal methods.
I mean, pretty much
all experts of data quality would agree on this point.
And some tools may be actually very useful in very specific
domains in terms of helping you to assess the quality of the data.
But I think one of the criterion I actually very often use to try
and at least have a first impression when I put a new dataset in a new data
infrastructure especially is have a look at the care
with which the data has been in contextualized.
So I tend to be very
unhappy
with datasets where all you actually can access is the data themselves,
just a set of measurements because you know,
sometimes this happens because that's what people can do.
That's all they had the resources to do.
But in fact,
it makes it extremely difficult to even think about what you know,
whether those data are reliable or whether they provide quality
of to be able to make that pronouncement.
You typically have
to have quite a lot of data and you need to have information
about who produced the data in the first place,
and even better information about how they've been processed.
And since they've been inserted in a certain infrastructure,
so I think a very good sign, at least for whether data that you find
the line are reliable is whether you can easily find
information around what they're about how they were collected
and also how they were processed
and organized and according to which criteria,
and even with which goes by the people who are giving you access
to that data.
And I think that's at least a starting point.
Okay.
So thanks so much
for that.
Wonderful talk.
And you gave us a lot to to think about.
And we're now going to I don't think that I just don't have a clapping
oh. Thanks very much to all of you for the attention and of course,
what ability to communicate with people later.
And if you want to follow up in any way. Thank you.
Thanks so much.
Of I give now the floor back to Damian
yes. Well, wow, that was really super interesting.
And as you say, a lot of food for thought for for when the project ends.
Lots of things to read, to think about for other projects
and other work with data. So thank you very much.
I mean,
yes, we are running a little bit late,
but yeah, we're going to take maybe two or 3 minutes before we start
the next session with Kenny and to think about the implications
of all of these changes for teaching data skills in the university
and beyond.
So please stay with us and we're going to start at three ten.
Okay.
So just a small.