Hello, good afternoon from my side, my name is Raman Ganguly and we heard today in the
keynote how important it is for user experience to work with a multidisciplinary team and
I like now to present you experience working with multidisciplinary teams and I like to
give you in the beginning a little bit of background why we did this and what's about.
So this was a project from our university, it was from the pharmaceutical chemistry department
and they came to us in the really early stage of the project and because they were in planning
of the project and they told us they want to do life science to make more fair because
a lot of data in the life science are hidden in some attached hard copies of papers or
on hard drives or something like this and negative results of the studies are very rarely
available.
And from the project perspective they want to change this and for these they applied
to a big Austrian funding agency, it is the biggest national funding agency, the FWF and
so they applied for a project and they came to us and you can see here an old logo from
our repository, it's called FEDRA and when you see this logo you don't think about life
science so this was not our tradition, it was not where we have the most data in this,
we came more from the humanities side but we want to get more outreach to other disciplinaries
and also to the life science because it was completely different and we said yes we like
to apply also together to this fund and work to get on a project and to make also our repository
infrastructure more feasible also for life science.
So the data should be fair, this was one of the main issues and the focus of the project
was not to run to build the coal infrastructure in one, it was also to make a prototype, how
it can do this and also for this prototype you have to focus on some specifics so we
don't want to cover the whole life science in the beginning, we just want to focus what's
in from the pharmaceutical chemistry and also there we focus on various specific things so
we want to have data from bio activities and also we want to describe it with rich ontologies
and this is from the beginning, it was a multidisciplinary approach so because from the
pharmacy side they work with chemistries and they work with biologists and both they understand
what's in this repository and both should find the data and should work with the data.
So by the project we are now in that we have these department of the pharmaceutical chemistry,
we have the team which is for data management from our university, this is a part at the library
and the part at the computer center and then we also bring in some expert from the technical
university, they are computer scientists from the department of the computer scientists which have
specialized on preserving data. So this is these multidisciplinary team what we are talking about
and everybody from depending on which team brings their own point of view to the project. So when
we look to the pharmaceutical chemistry they see all their data, so they have the structure of the
data, this is this what they are working with them, this is they want to make this what they
are in their head fair and open and make the outreach to the world and so but they have no
clue about data management, they have no clue about preserving the data, they're researchers
and they want to make their research more visible. So then we came from the data management, the
repository management, we have a strict workflow view, so the data is born somewhere then they are
transferred and enriched so that's possible to bring it into a repository, then it's in a repository,
we do all this stuff what we are doing with repository and then there's the reuse and this
is the workflow and this was our point of view and also from the technical university, their team,
they had also a point of view of making a recommendation for preservation, they work
very close with RDA, they have a lot of recommendations there and for them there
is about data citation, how you can cite data, how you can bring it more to the papers, how you can
make it more visible there and so. So you have these approaches and all these three point of
views must now fit together, must now bring together in one project that we have. In the
end an infrastructure which everybody happy with and which the people can use. So going also back
to a little bit more of the background, so what we have already from the infrastructure side
because we didn't start from the scratch, we have in our university an infrastructure which is based
on a data layer, this is mainly a repository infrastructure, on this repository infrastructure
we have an API, based on the on top of the API we have some services for data visualization like
an image server that you can present the pictures and so on and so on and also everything you can
access over an APR and then we have a standard user interface and the standard user interface
is also on top of the of the APIs, it's just one view of the data and it's possible to make
additional user interfaces. So this was also what we thought it is possible for making customization
for the life science. So this was also one idea and now it was time to bring all the teams together
and working across functional teams means also that you have to get known each other,
finding coming in languages. So if you have seen this point of views from the beginning what I
presented from the data, which kind of data there is, our workflow approach and the approach of the
recommendations, you talk a lot and then after some meetings you see okay you have talked a lot
but you cannot understand what the other people are really mean. So this is also a big process
and we learned a lot of doing this process, finding these common languages, finding this
common language. This was very very important and also defining a common understanding of the goal
because also here you have different point of views what should be the outcome and also
defining here a common a common goal is very very important. The next what is also was very
challenging for us and not for the people from the pharmacy. This was for us understanding the
data. We talked a lot about the data and we talked and we talked and we draw some pictures of the data
and then we end up with this. This was not really this what we was defining as a usual data and the
data what we are work what we like to work with. It was more or less something like this and also
we had a big process to find what are the data and then how we can preserve this data. When we
now found it what we should preserve, how we can preserve it, how we can cite it and how we can
make it fair because this was the main approach and this was why we get the funding to make the
data and life science fair. What we did is we started with a long process with a long period of
defining some kind of infrastructure. We did a lot of mock-ups. So we had a tool, an online tool,
balsamic. I think some people noticed. We worked with this. We draw a lot of mock-ups and throw a
lot of from them away. We saw that we started maybe a little bit too fast with the mock-ups.
Maybe not. It was a defining process and we learned also that going too fast with the user
interfaces is good because you see something also when you have these mock-ups but it doesn't really
help with understanding of the data. Then we had a lot of meetings doing a lot of scratches
on whiteboards. We have I think a bunch of photo collection of these images.
This also was something that we really need in the whole process to understand what we really want
because that was the biggest challenge to define what we like to do, what we want,
what the outcome should be. Then we had these big, huge spreadsheets where we also defined
the ontologies, defined some ways to find user interfaces, the menus and so on. There are also,
I think, a bunch of them we had. This was a really long process. I think we worked
more than three-quarters of a year for this defining process.
But in the end, we had really this understanding and we really know what we want. We think we know
what we want because then I will end up with some challenges in the next step.
But we had for us as a team now find the common language, the common goal. What we did now is to
build the infrastructure. To build the infrastructure, going back to the image in the beginning,
not in the beginning, so from the infrastructure. We have this data layer. We didn't need a lot of
the services what we already built because this was not useful for this use case. But we go
directly to the data layer, build an infrastructure we call then Federal Life Science. We started
because this was, depending on the time which was left, we need now a submit form that we can bring
the data into the repository. So a little bit background. In this repository, what we have in
this data layer, we have all kind of data. We have not only the data from the life science, we have
also the data from the humanities, what we saw before. And everything, what we have in data,
we are going to bring this into this data layer and then make some presentation layers on top
and some user interfaces. And also in this philosophy, we built this submit form.
Everything we can go over the API. And the nice thing is this, when you go directly to the
repository, you need a landing page. You always find this in the repository, but you also can't
access then in the future with search interface where you can really search special keywords
and special ontology in this topic. This is the prototype. So it's not really up and running at
the moment, but we have a few months left. In this few months, we will finalize the prototype
and we will have in the end, this is the interface for submitting. So in the end, this debugging will
be gone and it's only submitting. You saw here as a very simple way to guide the user through
the steps that they can submit the data, that they can describe the data, and also to describe
the context. Because this was a huge learning for us that the context of the data is very important,
especially in this use case, what they had. And this is what we saw from our point of view,
from the repository management, from the data management point of view. It's not that important,
but in this case, it's very important. And in the end, this will go to the repository.
So what was the key findings? The key findings is when you go to such a project,
working with teams, they have never worked before. They are very
the worst in what they've done before. You need this process of finding a language. You need this
process of working together and also that you have the same point of view of everything. That
you don't have these funny enamel of the data. That you have really an enamel of the data what
you can put into a repository. And what was also very challenging for us was that nobody on the team
was full-time. So they had also full-time contracts. But on the project itself,
it was not full-time. And this was challenging in time. Because when somebody worked and
throwing answers through the email, one was working in the morning, the other in the evening. So
coming to the answer is next time in the morning. So the conversation took sometimes very long.
And in this case, it was really important that we had this face-to-face meetings. So also meeting
each other on a regular base, it was very important. Not only discussing it per email,
not only discussing it per Skype, it was all in Vienna. So it's easy. So if we have a team,
which is through Europe or through the world, then it makes it more challenging. But in our
case, we were not in the same office. We were not in the same building. But we were all in the same
town. And so we had the possibilities to make also on a regular base, face-to-face meetings.
And this was something which really drives the project forward. And also the need of changing
the perspective. So nobody was right in this project. And nobody was wrong in this project.
So you have to align each perspective that you see the same vision, you see the same way.
And that's not an easy process because everybody is very keen of that. I know how the world looks
like. But working together from very different angles, it's very important to step back. And
also listen to the others see, to experience what their point of view is and how they see the world
and how you can bring this together. And don't forget to throw things away. So we done a lot
of things what we throw away. It was work in progress. And this was just one step of another
on the way that we now are here. But not everything is useful for the outcome.
It was sometimes really hard to do this because you had so many, you invested so many time in this
to build this up. And then you throw it away because you see, okay, it doesn't fit to the go.
You should do this. And the last thing what I like to say here in the key findings is
keep on asking questions. It's very important to ask the people the questions.
Even if you don't find the first right question in the beginning, keep on asking. Because you got
with each question and with each answer, you get more and more closer to the common understanding.
The project still goes on. But we already plan for the future. So the problem of a project is that
the project have an end. So the financing is it's also ending. And now is the question what we do
with the outcome. So one main thing is that we won't put the infrastructure into operation.
We do this in DAME that we from the beginning, the idea was that we work close together from
because we already know that will be a project and the budget is limited, also time limited.
So we integrate everything very close to our infrastructure and to our team. To our team,
I now speak from my perspective, from the perspective of data management. So we could
also manage that one developer. She was the main developer in this project. She is now part of our
team. She could be hired by the university and had a fixed contract. So this is some kind of
continuity, what we can do. But this is not the end of the story. Because bringing something into
operation, it's nice. As we know, software is never finished. And so we have also to do
more with this infrastructure to make more features, to create more features, to implement
more features, and also to enter data. Because a repository without data is not a good repository.
The main thing is that you have data in. So we're working again close with the pharmacy
that to bring the data in. And the people who are bringing the data in are not the people
who were in this project, who were developing the infrastructure. So also we have one view of the
whole infrastructure, of the whole thing. We now want to bring new users working with this
infrastructure and then ask questions to the user. How they define it? Is it easy to use?
Is it something what you can work with? Or is it a completely wrong way what we designed?
Also in this phase, we are still not in the end asking the question and working. Because now we're
doing the step to the user. And we need to get the feedback from the users. And as we saw,
new features should come in. We are now thinking about how we can define new projects around of this
life science, fair data in life science, and how we can bring these new features in.
So this will be our next step. I have five minutes, but I'm still in the end.
I will present now the team. These are the members who worked on this project.
It's not on a particular view. It's just from the surname, the rope. And if you have any questions,
also after this question, you can ask me now. There is a support address. You can throw all
a question also to the support address. Thank you very much.