Yeah, no, I, oh, this is really loud when you're, you're up here.
Yeah, I should say I'm in the school of public administration and I'm a management researcher
who comes to digital humanities in that way and I guess we're going to take us from the
large infrastructure through e-research down to the local infrastructure.
And what we find, and I was struck again looking at the abstracts, the number of co-authored
papers here at the conference, in fact, I'd be pretty hard pressed to come up with a few
that are single authored, and we're seeing this increasing collaboration in our academic
projects.
And it's important to clearly define this because it's a term that we toss around quite
a bit without necessarily really nailing down what we mean by it.
And if we look in the literature, collaboration and team research as opposed to groups, it's
this working together to achieve joint goals with joint accountability to each other.
And we'll come back to that idea of accountability through the nature of this talk.
But this is more than just maybe doing something together.
It's actually answering to each other and to other stakeholders about what we're doing.
So we also know in our research projects that it happens at different levels within
a research project.
In digital humanities right now we have several very, very large projects where clusters within
a project collaborate, but we also have quite a few projects that are collaborating across
themselves to achieve something bigger.
We often within academic projects talk about collaborating between peers.
Now we know this becomes very problematic when we begin to think about how do we incorporate
students within this because by some measures in academic circles, they're not quite our
peers yet.
We're training them to become our peers.
But in digital humanities projects, we also have our professional staff, the project managers,
programmers, developers, and some of those kinds of positions.
And how do we in fact begin to incorporate them as true collaborators within this, an
idea of joint accountability?
And we've certainly been undertaking several initiatives to begin to define how we recognize
the different kind of work that goes on in our project.
And the last thing to highlight again is this idea of the necessity for accountability mechanisms.
That when we begin to engage in work together, we always have to be reporting back to each
other answering for how we're doing the work, when we're doing it, and that kind of thing.
Now as I mentioned, and of course as we all know, digital humanities is collaborative
by definition.
And it is very much a departure from our typical humanities work patterns where the single
scholar toiling away in the library, like Craig's comment, I am my infrastructure.
When we get into digital humanities, we are looking at a larger kind of thing.
And with that, there becomes a need to develop new work patterns and necessary skills and
knowledge and not limited to, but definitely including project management, financial and
human resource management, communication, conflict resolution, negotiation, et cetera.
And certainly not typical skills developed in graduate training, and I'm quite interested
in the panel this afternoon about DH as a degree, whether we'll begin to address some
of those things.
Now we can look to other parts of campus to give us some guidance on in fact how to
work in these new work patterns.
And sciences and applied sciences where there's been a long history of labs can provide a
guidance and models for organization and management within these kind of collaborations.
Now within the sciences and applied sciences, we tend to see a couple of organizational structures.
The one certainly that we maybe are most familiar with is the faculty-directed lab,
where there's a lead researcher who creates his or her research vision, finds the funding,
and works with their staff to carry it out, and staff in a very broad definition here.
This is graduate students, postdocs, the technicians, administrative staff, all the rest.
And this lead researcher is responsible and accountable for that research vision, the
results and the use of the funds.
And when we think about that accountability, we see that it actually gets expressed on
quite a few different levels.
There's not just within the department, but they're also accountable to the faculty, university,
funders, and even to the discipline on what happens.
And we look at traditional measures of productivity within this, and this will be the number of
articles that are published in some of the sciences and in digital humanities.
We're seeing a number of patents that may come forward, lines of codes that are written,
conference paper, the typical things that those of us who are academics have to account
for each year in our activity reports.
And in this faculty-directed lab, that lead researcher again acts as leader, he or she
is mentor with the graduate students and postdocs, and also acts like a senior management within
that setting.
And the collaboration then within this kind of lab occurs through the work as laid out
by that lead researcher with their vision.
So the consensus on how to actually meet that research vision becomes the area for that
collaboration.
And the group then, that staff if you want to call it, decides in consensus on how in
fact they're going to reach that research vision.
And the challenges we have within that of course are how to recognize, coordinate, integrate
the different kind of still skills, status, and experience, and something certainly we're
struggling with with our DH projects.
And there's often a tension felt around the desire for independence and the desire still
to be accountable and report to that lead researcher.
Now in thinking about this issue, I started doing some looking around and it turns out
there are lots of guides and books available out there for that newly minted scientist
on how to set up and manage this type of lab, people, and resources.
In fact in some of these manuals it even tells how to write a job description.
And I realize maybe we need similar kinds of things within the digital humanities.
The other model that we're seeing increasingly is what we call the collaboratory, and this
is often found in the big science and again the large infrastructure, but also increasingly
in digital humanities.
And we can really break down this collaboratory into two different component parts.
There's the co-laboratory, which is a group of faculty directed labs that come together
to share the infrastructure of many kinds.
So in big science this is sharing the Hydron Collider in Switzerland is one example.
There's equipment that may be too expensive or too big to be housed in more than one location.
And even data, and there are a few big data initiatives in history right now that would
fit within this co-laboratory setting.
And these are called centers without walls, and this idea that we can create a lab that
in fact transcends a physical place.
So inherent within the term collaboratory is the collaboratory.
Had to practice saying these different words.
And this is where we actually get the interrelationship between the researchers working on their joint
research projects.
So in this case they're collaborating together, but each of the research involved brings their
own funding to the project.
And I should say within the co-laboratory it may be centrally funded, but often researchers
have to pay for access to some of these resources.
Now the challenges in the collaboratory is it really requires group cohesion, clarity
and understanding of common vision.
So unlike in a faculty or lead researcher directed lab where it's that researcher's vision,
here in the collaboratory we've got to get a consensus of how in fact we'll work together
and only works well if everyone respects the rules and boundaries and contributes as
much as they take.
So this idea with big data, with some of the digital history projects, everybody has to
contribute data in relation to how they're taking the data out.
Now we can see that there are several keys to success in both these kind of models.
That first there's this explicit understanding of mandate.
Very clear on the kind of vision that the particular lab or collaboratory will go forward
with and the planning needed to support those initiatives.
We also need clear articulation of positions within this, documentation of the interrelationships
and how people in fact work together and report with each other and then the mechanisms to
in fact ensure that occurs.
And an understanding of those various accountability structures again and the behavior norms.
Again when this big data that everyone's contributing data that people can use.
And where leadership direction must be given and taken.
And within this, especially when we think of a collaboratory and the faculty directed
lab is they accept that the project leader level where they can perhaps be as eccentric
as they want and we certainly can probably come up with lots of examples that in fact
there becomes very little room for people to act contrary to that collaborative structure.
And in the collaboratory especially there's this understanding of the difference between
the shared infrastructure in space and that interaction that occurs within that.
And again having those mechanisms to make sure that everyone pulls together within that.
And in fact we've got hierarchy across the different areas.
And the challenge always within this is ultimately the failure to reach some of our research
targets and outputs within these settings can lead to reduced grant funding and ultimately
a lack of research space.
So ultimately you have to be productive, you have to produce to continue receiving funding
that allows you to move forward.
So this really brings some questions around as we think about digital humanities and as
we move forward with increasing size of the projects we do and even the number of projects
that we do is how do the science collaborative models actually work within digital humanities?
Can they work?
What do we need to adapt within them?
And I'm going to look today at an examination of one academic lab and it looked at how it
adapted these models, developed their collaboration, met the research objectives and then produced
the outcomes that could be evaluated.
And what were the lessons that it learned along the way?
So this lab has gone through kind of three phases.
The first phase it was really quite small, faculty directed lab.
In small I mean very a director, lab manager and a few GRAs and one or two programmer developers
and a very productive working relationship.
It had the lead researcher vision and funding and it was very productive by again those
traditional academic measures.
As it moved into phase two it grew in size, met more funding, more staff and began to
include postdocs within the lab environment and still was lead researcher vision with
their funding and scaling and building on those foundations.
Now as it moved in this second phase with these larger amounts of staff if you want
to call it within there, the staff began to request the opportunity to be more collaborative
or create a collaborative structure within the lab setting, in fact becoming more like
autonomous researchers than staff.
And in fact they wanted to move to consensus on the lab vision, consensual decision making
and leadership.
However there were few accountability mechanisms in place but the lab vision still sat with
the lead researcher funding.
The phase three it's now more like a structured co-laboratory.
The lead researchers returned to more of a typical hierarchical structure, formal leadership
but they've integrated with other projects that are funded by their own lead researcher
so in fact creating that common space for their projects to work.
And as the lab moved through these different phases they in fact collected data to assist
in understanding what was happening within that and again drawing on the standard funding,
standard and funding agency benchmarks.
So what did the lab learn?
Well it was actually quite productive in phase one and two, quite a few of the typical academic
outputs.
But they found as they moved into the second part of the phase two that they had trouble
implementing the co-laboratory principles for success.
That they found in fact the diffused accountability structures that were in place led to a drop
in lab productivity and team functionality.
That they didn't see quite the same number of traditional academic outputs, there was
an increase in personal complaints and in fact it wasn't even noticed at first because
of the way the nature of academic work, because we get so many things in the pipeline that
come out it for a while didn't necessarily reveal what was happening on the ground.
And within this they found that the team members had little direct accountability for the work
plans.
Again the lead researcher was the one who remained accountable to outside stakeholders.
And they hadn't necessarily when they put in the collaboration principles thought about
the accountability mechanisms along with it.
And ultimately in the end the work culture became less than ideally functional.
And this is just a quick graphic that shows the drop in productivity versus some of the
other internal human resource issues.
So what do we learn from all this?
Well that science models for collaboration and creation of well functioning labs can
in fact be applied to digital humanities.
But this experience can serve as a cautionary tale.
More digital humanities researchers are getting that opportunity to set up their own labs,
manage their own staff and research, create partnerships, but still needing to remain
accountable to the outside stakeholders.
And so to think about these things in advance of setting up.
That in a faculty directed lab some hierarchy is necessary for a productive lab, especially
when that lead researcher is responsible and accountable for getting the funds and
getting the results as they're related to the research vision.
So at times this individual must have the last word since it's their name on the grant
application.
However, consensus and active participation in discussions about ways to achieve that
research vision is productive in fact.
But that's where we can create these exciting and in fact quite intellectually productive
environment and working relationships that we can guide our GRA's and our post docs,
our programmers, developers and other staff that are working in that to create in fact
quite amazing outputs.
And we've got lots of digital humanities projects that have done just that.
And we can in fact measure that consensus and active participation through staff satisfaction,
but also through the standard academic metrics.
An engaged lab is a productive lab and there are various ways to support that.
And ultimately the roles must be supported through clear objectives, tasks, timelines
and then consequences for non-performance that in fact need to work with our staff then
once we've achieved that, understand how we're going to achieve the research vision to enact
it and work plans that the various members of the lab have to in fact follow through.
Thank you very much.