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Developing a Strategy for e-Science Indiana University Malcolm Atkinson Director e-Science Institute UK e-Science Envoy www.nesc.ac.uk 5 th February 2008.

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Presentation on theme: "Developing a Strategy for e-Science Indiana University Malcolm Atkinson Director e-Science Institute UK e-Science Envoy www.nesc.ac.uk 5 th February 2008."— Presentation transcript:

1 Developing a Strategy for e-Science Indiana University Malcolm Atkinson Director e-Science Institute UK e-Science Envoy 5 th February 2008

2 Outline What is e-Science What we gained from an e-Science initiative Why we need a strategy What should the strategy achieve What computing research do we need Theory & pioneering steer each other Realistic models Sustainable farming for the e-Science Ecosphere The global challenge

3 Definition of e-Science Computing has become a fundamental tool in all research disciplines, which often proceed by assembling and managing large data collections and exploiting computer models and simulations (a topic called e-Science)

4 Strengths of e-Science Research using e-Science Research enabling e-Science Communities and e-Infrastructure supporting research and innovation

5 e-Science Centres in the UK Oxford Edinburgh Belfast Cambridge STFC Daresbury Manchester LeSC Newcastle Southampton Cardiff STFC RAL Glasgow Leicester UCL Birmingham White Rose Grid White Rose Grid Lancaster Reading Access Grid Support Centre Access Grid Support Centre Digital Curation Centre National Grid Service National Grid Service National Centre for e-Social Science National Centre for e-Social Science National Centre for Text Mining National Centre for Text Mining National Institute for Environmental e-Science National Institute for Environmental e-Science Open Middleware Infrastructure Institute Open Middleware Infrastructure Institute SheffieldSheffield YorkYork LeedsLeeds Coordinated by: Directors’ Forum & NeSC Coordinated by: Directors’ Forum & NeSC

6 Web: OMII-UK: For all kinds of users Taverna: effortless workflows for scientists OGSA-DAI: data integration for service providers PAG: AG video- conferencing for anyone Campus Grid Toolkit: easy to install grid for job submission SAGA: abstraction & code mobility

7 NGS & Partners, 2007

8 ESI Themes Slide from Dr Anna Kenway Theme 8: Trust and Security in Virtual Communities Theme 4: Spatial Semantics for Automating Geographic Information Processes Theme 5: Distributed Programming Abstractions Theme 6: e-Science in the Arts and Humanities Theme 7: Neuroinformatics and Grid Techniques to Build a Virtual Fly Brain Theme 9: Provenance

9 Outline What is e-Science What we gained from an e-Science initiative Why we need a strategy What should the strategy achieve What computing research do we need Theory & pioneering steer each other Realistic models Sustainable farming for the e-Science Ecosphere The global challenge

10 Official UK Research Goals

11 Tremendous global challenges

12 Scale, Urgency, Complexity, …

13 The 21st Century This is the century of information PM G. Brown, University of Westminster, 25 October 2007 We can collect it We can generate it Can we move it? We can store it Can we use it? Dramatic increase in data from sensors Dramatic drop in cost of computation Web-scale effects Ubiquitous digital communications Community intelligence Global challenges Transforming research, design, diagnosis, social behaviour, …

14 セキュリティ GRID/ ペタコン ユビキタス ITS ではない 情報系アンブレラ …And then there is now the Information Explosion 988EB (2010) 161EB (2006 by IDC) = 1ZB Slide: Satoshi Matsuoka

15 Outline What is e-Science What we gained from an e-Science initiative Why we need a strategy What should the strategy achieve What computing research do we need Theory & pioneering steer each other Realistic models Sustainable farming for the e-Science Ecosphere The global challenge

16 High-Level Goals for CIR New world-leading research New methods & new technology High impact (transformative) Sustained rapid transfer from invention to wide use Much wider engagement => More Research & Innovation Cultural changes Effective transfer between business & academia Cost effective Shared e-Infrastructure (Cyberinfrastructure) Shared support for developing advances in  Tools  Services  Trust

17 Elements of CIR Establish an Office of Strategic Coordination of Century-of-Information Research Support the continuous innovation of research methods Provide easily used, pervasive and sustained e-Infrastructure for all research Enlarge the productive research community who exploit the new methods fluently Generate capacity, propagate knowledge and develop a culture via new curricula

18 Enable extreme e-Science Sustain support for interdisciplinary teams Breakthroughs depend on talented research leaders Plus strong supporting teams Provide an environment of composable components Significant advances from familiar components Composed in new ways Provide powerful tools and services With licence to experiment Inject energy through challenges & long-term funds

19 CIR Sustain method invention Applied Scientist e-Scientist Researcher communities using e-Science Methods e-Sciencee-Infrastructure Computer Science Evidence Methods Models & challenges Algorithms Models Notations Methods Technology Supports Challenges Ideas Models Tests Uses Deploys Evaluates Adapts Infrastructure Provision and Support Infrastructure Development Adoption Challenges Challenges & supports Operational data Slide from John Darlington with modifications Real invention has more complex interactions

20 CIR Enable fluent mass use Applied Scientist e-Scientist Researcher communities using e-Science Methods e-Sciencee-Infrastructure Computer Science Evidence Methods Models & challenges Algorithms Models Notations Methods Technology Supports Challenges Ideas Models Tests Uses Deploys Evaluates Adapts Infrastructure Provision and Support Infrastructure Development Adoption Challenges Challenges & supports Operational data

21 Balancing Three Strands of CIR Pioneering Invention of new data & computational methods Advances in the ways they are used Advances in the technology that supports them Provision e-Infrastructure or Cyberinfrastructure  support, consultancy, training, tools, services  Curated digital data resources, Computation,  Communication networks & CSCW Education & cultural change Preparing graduates to flourish in the digital economy Developing a culture & trust that enables data sharing

22 scientists Local Web Repositories Digital Libraries Graduate Students Undergraduate Students Virtual Learning Environment Technical Reports Reprints Peer- Reviewed Journal & Conference Papers Preprints & Metadata Certified Experimental Results & Analyses experimentation Data, Metadata Provenance Workflows Ontologies The social process of science Slide: Dave De Roure

23 Web ServicesRESTful APIscmd linessshhttp Web BrowserMobile phoneiPodCarEquipmentPDA P2P mashups workflows services applications Subject ICT experts Computer Scientists Software Companies Workflow tools Ruby on Rails ecosystem Scientists open source Software Engineers nesc Slide: Dave De Roure

24 scientists Local Web Repositories Graduate Students Undergraduate Students Virtual Learning Environment Technical Reports Reprints Peer- Reviewed Journal & Conference Papers Preprints & Metadata Certified Experimental Results & Analyses experimentation Data, Metadata Provenance Workflows Ontologies Digital Libraries The social process of science 2.0 Slide: Dave De Roure

25 Organised Sharing? Application researchers choose / lead Leads to diversity & little fluent use Sustaining & improving community effects Group & subject community cultures Sharing advantageous Costs of development, deployment, operations Costs of improvement, scaling & green computing What and when to share Low-level services & libraries shared across disciplines Curated digital resources across discipline groups Tools may be discipline specific or widely used

26 Developing Trust Researchers share networks & computing And trust them Will they trust a shared storage service? How would you build such trust System, model and data complexity increase How can we build trust in the results they give? Much data is personal, medical or financial Blunders happen How do we get the public to trust research use

27 Education and Training Training Targeted Immediate goals Specific skills Building a workforce Education Pervasive Long term and sustained Generic conceptual models Developing a culture Both are needed Organisation Skilled Workers TrainingServices & Applications Invests PreparesDevelop Strengthens Society Graduates EducationInnovation Invests PreparesCreate Enriches

28 Outline What is e-Science What we gained from an e-Science initiative Why we need a strategy What should the strategy achieve What computing research do we need Theory & pioneering steer each other Realistic models Sustainable farming for the e-Science Ecosphere The global challenge

29 Matrix to analyse e-Science Observation Modelling Analysis Action Collaboration Anthropology Archaeology Astronomy Biology Biochemistry Chemistry Demography Economics Engineering Geography Scholarship Design Diagnosis Exploitation

30 Climate, Observation Satellite & ground based imaging, ocean buoys, atmospheric, ocean & coastal surveys, robotic mobile devices, distributed urban, rural & river sensors Past from trees, corals, ice, sediments, geology, … Long-term phenomena Observations decades to centuries Data used for centuries Large & sustained data flows Economic long-term data storage / management Complexity, variety of data >40 ISO standards (OGC+) Stability & change, calibration & normalisation Sufficient coverage & resolution Speed for exceptional environmental events (E3) Dependable accuracy Data discovery, understanding metadata & ontologies Source: Next Generation Science for Planet Earth: NERC strategy

31 Climate, Modelling Many interacting subsystems: solar, atmospheric physics & chemistry, oceans, air+water interface, cryosphere, air+ice interface, biosphere+ air+land interface, land surface, fires, volcanoes, human activity, … Interacting models Multiple versions Large (global) team efforts Dependent on many parameters (estimates) No one understands fully even one model Constructing trusted models - mathematics to hindcasting Composing models Combining data & observation Computational power Managing & using data produced Curation, cataloguing & metadata Managing & tracking model revision Rapid execution for E3 Making models usable

32 Climate, Analysis Identify & bring together multiple data sets Transform them to align & expose information Statistical comparisons Visualisations Finding, accessing & transforming data Moving data reduction steps to data Necessary data movement Tools that cope with the scale: statistics, data handling & visualisation Curating, cataloguing results Agreeing trusted analysis methods Automating analysis Stability & change

33 Climate, Action Scholarship: papers, contribution to national & international reports. Advice & policy: planning for & response to E3 Planning agriculture, epidemiology & coastal retreat Public outreach Prediction services Traditional quality of results / arguments With 10-year time to truth Cross-discipline for Socio-economic impact data Privacy & ethics Recognition & responsibility Many model & data sources & contributors Rational debate about validity and significance of results Multi-disciplinary effects

34 Climate, Collaboration Already International (UN, INSPIRE, scholarly) collaboration Economic, social & political drivers Usual CSCW Skype, Blogs, tele/video conferencing, wiki, facebook, telepresence, OptIPort, … Shared data resources Quality metadata Shared code development & testing Ontologies & standards Multi-site computational steering & spatio-temporal visualisation Business case to support the research

35 Climate, pervasive How do you build & sustain the business case Stern report helps How do you provide security without inhibiting collaboration, open inspection, alternative interpretations Cost reductions Pooling data collection Pooling storage Sharing responsibilities Pooling model development But diversity for safety Security Prevent damage to data Prevent misuse of resources

36 Please join me in the Matrix Populate columns you care about Music, fine art, chemistry, linguistics, … Integrate & digest the list of requirements Identify the current barriers Think up strategies for overcoming them Start communities following those strategies

37 Outline What is e-Science What we gained from an e-Science initiative Why we need a strategy What should the strategy achieve What computing research do we need Theory & pioneering steer each other Realistic models Sustainable farming for the e-Science Ecosphere The global challenge

38 Data is the Key e-Science is different We are responsible for our data We curate it / select it / throw it away Our program executions build & reshape it We need a safe model for fluent mass use Transactional & idempotent Safety - avoiding accidental data loss / corruption Realistic - nothing is perfect: S/W, H/W, People, Organisations We need eXtreme e-Science Smart engineers working with extreme care Ramp & flow between mass use and eXtreme e-Science Foundation requires careful engineering How do we manage it? How do we move it? How do we protect it? How do we trust it?

39 Careful Engineering Requires Good quality models Specifying realistic target behaviours Stochastic Pi Calculus? Computer scientists, mathematicians & statisticians wanted Benchmarks & Measurement Long-term, multi-purpose & realistic scale Agreed measurement against the models Shaped by & shaping standards Foundations for trust Engineering effort Collaborative & Competitive worldwide Expect incremental progress not magic We’ve come a long way We have much further to go

40 Questions Photographer: Kathy Humphry


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