Usability Challenges for e-Science
Rob Procter
National Centre for e-Social Science & UTF
rob.procter@ncess.ac.uk
www.ncess.ac.uk
26-27 January, 2006
Usability Workshop, NeSC
The e-Science Vision

A globally connected, scholarly community
promoting the highest quality scientific
research.
“e-Science is about global
collaboration in key areas
of science and the next
generation of infrastructure
that will enable it.” John
Taylor, Director General of
Research Councils, UK
Office of Science and
Technology
“The goal of cyberinfrastructure is to
provide an integrated, high-end system of
computing, data facilities, connectivity,
software, services, and instruments that
enables all scientists, engineers and
educators to work in new ways on
advanced research problems that would
not otherwise be solvable.” Peter Freeman,
Director, CISE, NSF
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Realising the Vision

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Now that early adopters are all on
board and focus shifts from concept
demonstrators to generic tools
supporting real users, and users’
experiences of pilot projects is
absorbed, important challenges for
usability research are beginning to
emerge.
We can no longer assume that “if we
build it, they will come.”
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Individual and Group Usability

Design usable research environments:
– Simple but powerful user interfaces providing
integrated access to tools, services and data.

Support collaboration:
– Local and distributed, small and large scale, in
real-time and over time.

Support evolution of research methods and
tools:
– Track and respond to change, provide training.
5
Organisational Usability

Help build manageable infrastructures:
– Easily configurable solutions.

Provide dependable authorisation,
authentication and access control
mechanisms:
– Simple to apply and to police.

Support research governance:
– Negotiating, representing and administering
policies.
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Community Usability

Create incentives for sharing data:

Develop sustainable models of
technology supply:
– Recognise and reward different forms of
contribution.
– Open source or proprietary?

Support development of community
knowledge repositories:
– Standards for metadata and ontologies

Respond to research drivers:
– Demand for evidence-based research.
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Work: The ‘Missing What’ in e-Science?

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Tendency to assume that there is only one kind of knowledge, only one
kind of science and one scientific method.
Knorr-Cetina (1999) has shown that scientific cultures are very
diverse. She illustrates this by two detailed studies: high energy
physics and molecular biology:
– HEP has a history of large scale, trans-national collaboration because the
science demands hugely expensive infrastructure which is only affordable if
shared. This ‘collectivity of instruments’ is matched by a collectivity of
physicists who collaborate in the design and running of experiments, and
share the data.
– Molecular biology is an individually oriented lab-bench science conducted in
small laboratories, highly competitive and fragmented. Molecular biology’s
‘tools of the trade’, once big and expensive, became small, cheap and widely
dispersed.


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HGP was molecular biology’s first attempt at ‘big science’, involving
contributions from more than 350 laboratories.
Impact of digital artefacts on scientific practice:
– Astronomy’s reconfiguration from an observational field science to an image
processing lab science.
Are we witnessing a paradigm shift in molecular biology to a
‘theoretical’ science that manipulates masses of sequence data rather
than biological samples and reagents; in silico experiments rather than
wet science?
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Challenge: Global Collaboration
e-Science seeks to foster and enhance
awareness ofcommunities.
globally distributed
research
colleagues’ ‘presence’
Key questions include:
 Devising
mechanisms
toolsrealtotime
support
virtual
meetings and
mapping
Compendium
formation of dynamic, distributed
research
discussions/group
sensemaking
communities.
recovering information
 Investigating
and understanding
from meetings
enacting decisions/
requirements for collaboratories,
coordinating activities
organizational entities that span distance,
Replay
synthesising
artifacts interaction
support rich
and recurring
oriented to a common research area, and
provide access to data sources, artifacts and
tools.

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Challenge: Trust

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e-Science raises significant concerns about
trust in technologies, trust in data and trust
between collaborators. Key research
questions include:
Understanding what makes technologies
trustable and how to provide ‘trust
affordances’ in e-infrastructure.
Understanding how distributed communities
impact formation of ‘cultures of trust’ and
how to develop practices to deal with this.
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Trust and Mobile Data

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Work of manipulating physical artefacts such as
paper records affords natural, locally visible account
of itself.
Introducing digital artefacts can change visibility
and accountability of work practices with implications
for:
– Trust in processes of data collection.
– Trust in colleagues as interpreters of data.

When data becomes mobile, can provenance
information substitute for ordinary, everyday
practices by which trust is achieved in co-located
work settings?
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Challenge: Representations for VOs



Can Virtual Organisations (evolving
ensembles of collaborating
organisations and agreements) be made
visible for members?
Can users gain a sense of the
implicativeness of their actions? How
can contributors of data be assured
that data is used in a prescribed
fashion?
Codifying of policies and auditing of
interactions provides an opportunity to
visualise VOs.
S. Carlson, Uni. of Essex
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Challenge: Representing Knowledge

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Production of new forms of knowledge is a central
feature of the e-Science vision. Key research
questions include:
Investigating new forms of reasoning and their
impact on requirements for tools.
Understanding use of representations, how
representations mediate research and how emerging
forms are used in practice.
Identifying new forms representations, including
migration of existing techniques for visualisation to
new communities.
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Why don’t biologists modularise OWL ontologies properly?
public-semweb-lifesci@w3.org
“I don't blame them [MGED/PSI community] because to truly
comprehend RDF/OWL is not an easy task, it takes not just the
understand of technology itself but more so the vision on how
things should and can work in SW.”
“One thing we have to remember is that biologists are
building ontologies to do a job of work. They are not
produced as some end of CS or SW research”
“Principles are all well and good, but we should know from
decades of software engineering that saying "do it properly"
isn't a solution. We need tooling and methodologies that do not
in themselves hinder a domain specialist. In many cases it is
easier to re-develop than re-use or even cut-and-paste from an
existing ontology than it is to muck around “doing it properly””
“There is a gap between the view of ontology for CS people and
for biological people. The ontology in biologists’ eyes are more
of a treaty than logical representation, CS has the the reverse
of that view. It needs dialog to bring the view to a middle
ground and mechanisms to stretch to both directions.”17
C. Goble, Uni of
Manchester
Challenge: Methodologies


A significant set challenges are centered on methodologies for
designing and building e-infrastructure and tools.
Building on efforts of early adopters:
– Processes for turning bespoke prototypes into generically usable
tools.

Devising new methods for requirements capture, including
requirements for work practices that are only as yet imagined:
– Scalability of approaches which emphasize detailed investigation.
– User involvement in design and development processes.
– Co-realising tools through situated design and development.

“We know of no scalable methods of requirements analysis that
document the needs of vastly different user populations,
continue to document changing needs over decades, coordinate
investigation at multiple sites of use, design for large
distributed entities, and absorb transformative changes in
practice.” (Zimmerman and Nardi, 2006)
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What is NCeSS is Doing?

Entangled data project (Essex):
– Case studies of three networks of researchers:
• Distributed group of physicists.
• Distributed users of a complex sociological dataset.
• An ethnographic archiving project.

Oxford e-Social Science (OeSS) - Ethical, Legal and
Institutional Dynamics of Grid Enabled-Science:
– Focusing on the social, institutional, ethical and legal issues
surrounding e-Science infrastructures and research
practices.

Many NCeSS projects have made usability an explicit
goal.
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MiMeG
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Mimio receiver
Tools to analyse audio-visual
qualitative data and related
materials collaboratively over
the Grid.
Co-located researchers are able
to see analyst prepare to
produce a stroke in front of the
screen, researchers at remote
sites are only aware of the
stroke at the time it is being
produced.
Plan to start conveying where
annotating devices are with
respect to display, through
tracking pens’ positions around
intervening space along with
appropriate remote visualisation.
Mimio pen
Image projected to screen
Boundary microphone
Speaker
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Conclusions
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Realising e-Science vision requires understanding and
addressing needs of individual users, groups, organisations and
communities.
Calls for multidisciplinary efforts conducted on an
unprecedented scale, involving users, usability researchers and
developers.
Must be coordinated and must look to plan beyond immediate
funding opportunities.
Should exploit collaboration technologies now available.
Above all, usability must be strongly embedded in the e-Science
programme.
If it is not, how can we change that?
CHI workshop, Montreal, April 22nd, 2006
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