Adaptive Visualisation Tools for e-Science Collaboration (ADVISES) Alistair Sutcliffe (PI) Oscar De Bruijn, Jock McNaught Sarah Thew, Colin Venters, School.

Adaptive Visualisation Tools for e-Science Collaboration (ADVISES)

Alistair Sutcliffe (PI)

Oscar De Bruijn, Jock McNaughtSarah Thew, Colin Venters,

School of Informatics,

Iain BuchanNIHBI,

Rob ProctorNCESS

University of Manchester

EPSRC E-Science Usability  programMay 2006- April 2009

Objectives

• To analyse users’ research methods and questions using sub-language – research questions drive workflow

• To develop a prototype, configurable visualisation-data analysis system driven by research questions

• To evaluate the prototype with researchers in the medical e-science community.

• To develop a user-centred requirements analysis and design method for e-science.

The Vision-

Research Questions are the E-science interface

Interactive Visualisation allows you to see the effect of your question AND you can interpret the results in context

Our Domain- Epidemiology

UnderstandingChildhoodobesity

Causal analysisfrom complexmultivariatespatio- temporalevidence

Multi-variate statistical analyses- differences between cohortsover time, between areas

Interactivevisualisation

See the effects of differentAnalyses- in context (space, time.distribution in population, etc)

Researchquestions

Requirements Analysis- Approach

• Ethnographic studies- observing research practices

• Interviews for background domain knowledge

• Language analysis- analysing published papers and recorded conversations (Research Questions)

• Scenarios and Storyboards- early designs for-Primary Care Trusts- visualisation of epidemiology of childhood obesity - Genetic Epidemiology- visualisations linking population

level genetic markers to disease profiles and metabolic pathways

• Requirements workshops and demonstrations

Prototypes and Storyboards

Gene Name

rs1243

rs2684

rs5387

rs367rs9877

rs1354

rs3243

0.001

0.0023

0.05

0.0010.002

0.05

0.04

SN

PN

ames

LDG

eneF

eatures

√√

√√

3-hydroxy-2oxypentanoate

2.3.4.2

2,3 Dihydro 3 methypentanoate

6.2.34.6

Pathway ID - 124463

6.2.34.6: FRA1 – RS1234 p = 0.012

2-Aceto-2hydroxybutanoate

Chromosome overview level

Gene detail (SNPs)

Metabolic Pathways

Populationdifferences

MutationDNA allele

MutationEffect on Protein/Enzymeproduction

Zoom in tofind

Link to seeeffect on

PCT prototype- Epi-maps

Analysiscontrols

InteractiveMap display

Multiple representations

Quick win prototype- more complex controls and functions added later

Problems encountered(and lessons learned)

• Limited user/domain expert availability-

- diversify use base

- engage users with storyboards and prototypes early

- go with the flow- follow your users’ enthusiasm

• Understanding the domain– background reading– appropriate expertise on the team

• Prioritising Requirements

- cost/benefit analysis for trade offs

- look for quick wins for user engagement

Progress to date

• Requirements analysis nearly complete- research questions & workflows

• Storyboards and prototypes developed for 2 sub projectsPCT prototype- Epi-MapsGenetic Epidemiology Visualisation (storyboards)

• Moving onto 2nd version prototypes with evaluation studies

• Developing method and design framework for e-science visualisation

• Refining requirements analysis method- Question driven requirements