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Adaptive Visualisation Tools for e-Science Collaboration (ADVISES)

Adaptive Visualisation Tools for e-Science Collaboration (ADVISES). Alistair Sutcliffe (PI) Oscar De Bruijn, Jock McNaught Sarah Thew, Colin Venters, School of Informatics, Iain Buchan NIHBI, Rob Proctor NCESS University of Manchester

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Adaptive Visualisation Tools for e-Science Collaboration (ADVISES)

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  1. Adaptive Visualisation Tools for e-Science Collaboration (ADVISES) Alistair Sutcliffe (PI) Oscar De Bruijn, Jock McNaught Sarah Thew, Colin Venters, School of Informatics, Iain Buchan NIHBI, Rob Proctor NCESS University of Manchester EPSRC E-Science Usability  programMay 2006- April 2009

  2. 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

  3. Our Domain- Epidemiology Research questions See the effects of different Analyses- in context (space, time. distribution in population, etc) Interactive visualisation Understanding Childhood obesity Causal analysis from complex multivariate spatio- temporal evidence Multi-variate statistical analyses- differences between cohorts over time, between areas

  4. 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

  5. Gene Features SNP Names LD √ √ √ √ rs1243 0.001 Gene Name rs2684 0.0023 rs5387 0.05 Pathway ID - 124463 rs367 0.001 rs9877 0.002 2-Aceto-2 hydroxybutanoate 3-hydroxy-2 oxypentanoate 2,3 Dihydro 3 methypentanoate rs1354 0.05 6.2.34.6 2.3.4.2 rs3243 0.04 6.2.34.6: FRA1 – RS1234 p = 0.012 Prototypes and Storyboards Chromosome overview level Population differences Zoom in to find Mutation DNA allele Gene detail (SNPs) Link to see effect on Mutation Effect on Protein/ Enzyme production Metabolic Pathways

  6. PCT prototype- Epi-maps Multiple representations Analysis controls Interactive Map display Quick win prototype- more complex controls and functions added later

  7. 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

  8. Progress to date • Requirements analysis nearly complete- research questions & workflows • Storyboards and prototypes developed for 2 sub projects PCT prototype- Epi-Maps Genetic 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

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