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The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey Corporate Vice President Microsoft External Research The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey Corporate Vice President Microsoft External Research This work is licensed under a Creative Commons Attribution 3.0 United States License. TonyTony HeyHey –– AnAn IntroductionIntroduction Commander of the British Empire This work is licensed under a Creative Commons Attribution 3.0 United States License. The Fourth Paradigm This work is licensed under a Creative Commons Attribution 3.0 United States License. AA DigitalDigital DataData DelugeDeluge inin ResearchResearch • Data collection – Sensor networks, satellite surveys, high throughput laboratory instruments, observation devices, supercomputers, LHC … SensorMap Functionality: Map navigation • Data processing, Data: sensor-generated temperature, video analysis, visualization camera feed, traffic feeds, etc. – Legacy codes, workflows, data mining, indexing, searching, graphics … •Arc hiving – Digital repositories, libraries, preservation, … This work is licensed under a Creative Commons Scientific visualizations Attribution 3.0 United States License. NSF Cyberinfrastructure report, March 2007 EmergenceEmergence ofof aa FourthFourth ResearchResearch ParadigmParadigm 1. Thousand years ago – Experimental Science – Description of natural phenomena 2. Last few hundred years – Theoretical Science – Newton’s Laws, Maxwell’s Equations… 3. Last few decades – Computational Science – Simulation of complex phenomena Astronomy has been one of the first disciplines to embrace data‐intensive science with the Virtual Observatory (VO), enabling highly efficient access to data and analysis tools 4. Today – Data-Intensive Science at a centralized site. The image shows the – Scientists overwhelmed with data sets Pleiades star cluster form the Digitized Sky Survey from many different sources combined with an image of the moon, synthesized within the WorldWide Telescope service. • Data captured by instruments • Data generated by simulations • Data generated by sensor networks ¾ eScience is the set of tools and technologies Science must move from data to to support data federation and collaboration information to knowledge • For analysis and data mining • For data visualization and exploration • For scholarly communication and dissemination This work is licensed under a Creative Commons Attribution 3.0 United States License. With thanks to Jim Gray This work is licensed under a Creative Commons Attribution 3.0 United States License. This work is licensed under a Creative Commons Attribution 3.0 United States License. http://research.microsoft.com/fourthparadigm/ • “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science." • — Bill Gates, Chairman, Microsoft Corporation • “One of the greatest challenges for 21st‐century science is how we respond to this new era of data‐intensive science. This is recognized as a new paradigm beyond experimental and theoretical research and computer simulations of natural phenomena—one that requires new tools, techniques, and ways of working.” • — Douglas Kell, University of Manchester • “The contributing authors in this volume have done an extraordinary job of helping to refine an understanding of this new paradigm from a variety of disciplinary perspectives.” • — Gordon Bell, Microsoft Research This work is licensed under a Creative Commons Attribution 3.0 United States License. Listed 7 key areas for action by Funding Agencies: 1.Fund both development and support of software tools 2.Invest at all levels of the finding ‘pyramid’ 3.Fund development of ‘generic’ Laboratory Information Management Systems 4.Fund research into scientific data management, data analysis, data visualization, new algorithms and tools This work is licensed under a Creative Commons Attribution 3.0 United States License. Remaining three key areas for action relate to the future of Scholarly Communication and Libraries: 5. Establish Digital Libraries that support the other sciences like the NLM does for Medicine 6. Fund development of new authoring tools and publication models 7. Explore development of digital data libraries that contain scientific data (not just the metadata) and support integration with publishedThis work is licensed under literature a Creative Commons Attribution 3.0 United States License. Developing a Sustainable e-Infrastructure This work is licensed under a Creative Commons Attribution 3.0 United States License. AcceleratingAccelerating timetime toto insightinsight withwith AdvancedAdvanced ResearchResearch ToolsTools andand ServicesServices Our goal is to accelerate research by collaborating with academic communities to use advanced computer science research technologies Aim to help scientists spend less time on IT issues and more time on science by creating open tools and services based on Microsoft platforms and productivity software This work is licensed under a Creative Commons Attribution 3.0 United States License. DataData AcquisitionAcquisition andand ModelingModeling The Swiss Experiment Life Under Your Feet •Powerful Software Improves Environmental •Researchers at The Johns Hopkins University Forecasting are deploying large arrays of wireless soil •Environmental scientists face many challenges sensors in a variety of environmental settings, in monitoring and understanding our planet’s including a park, an urban forest and a wetland. changing climate. Through an international The networks enable scientists to monitor collaboration called the Swiss Experiment, ecological changes on an unprecedented scale environmental scientists and computer science and offer insights into hydrology, greenhouse experts are deploying advanced sensor networks gases and the activity of organisms in the soil. and data management tools to improve environmental monitoring and forecasting. This work is licensed under a Creative Commons Attribution 3.0 United States License. CollaborationCollaboration andand VisualizationVisualization Research Information Center Collaboration and information sharing among SciScope ‐‐ Speeds Data Retrieval from researchers are among the most important but Multiple Repositories challenging aspects of scientific research. In For environmental scientists and engineers, recent years, scientists have begun using finding and retrieving relevant data can be a “virtual research environments” to exchange daunting and tedious task. Microsoft Research is information with colleagues in specific areas of developing an online search engine called study. Microsoft Research and The British SciScope that enables researchers to search Library are teaming up to build the Research multiple data repositories simultaneously and Information Centre. retrieve information in a consistent format. This work is licensed under a Creative Commons Attribution 3.0 United States License. AnalysisAnalysis andand DataData MiningMining PhyloD Trident •Statistical tool used to analyze DNA of HIV from •A Scientific Workflow Workbench Brings Clarity large studies of infected patients to Data •Typical job, 10 –20 CPU hours with extreme •Scientists at the University of Washington are jobs requiring 1K –2K CPU hours working with Microsoft External Research to – Very CPU efficient demonstrate how marrying visualization and workflow technologies can allow researchers to – Requires a large number of test runs for a better manage, evaluate and interact with even given job (1 –10M tests) the most complex scientific datasets. – Highly compressed data per job ( ~100 KB per job) This work is licensed under a Creative Commons Attribution 3.0 United States License. DisseminateDisseminate andand ShareShare Chem4Word •Chemistry Drawing in Word •Created in collaboration with University of Cambridge; Peter Murray‐Rust, et.al. Intent: Recognizes Author/edit 1D and 2D chemistry. chemical dictionary and Change chemical layout styles. ontology terms Relationships: Navigate and link referenced chemistry Data: Semantics stored in Chemistry Markup Language <?xml<?xml version="1.0"version="1.0" ?> <cml<cml version="3"version="3" convention="orgconvention="org‐synth‐report" xmlnsxmlns="http://www.xml="http://www.xml‐cml.org/schema"> <<moleculemolecule idid="m1">="m1"> <<atomArray>atomArray> <<atomatom idid="a1"="a1" elementTypeelementType="C"="C" x2x2="="‐2.9149999618530273" y2y2="0.7699999809265137"="0.7699999809265137" /> <<atomatom idid="a2"="a2" elementTypeelementType="C"="C" x2x2="="‐1.5813208400249916" y2y2="1.5399999809265137"="1.5399999809265137" /> <<atomatom idid="a3"="a3" elementTypeelementType="O"="O" x2x2="="‐0.24764171819695613" y2y2="0.7699999809265134"="0.7699999809265134" /> <<atomatom idid="a4"="a4" elementTypeelementType="O"="O" x2x2="="‐1.5813208400249912" y2y2="3.0799999809265137"="3.0799999809265137" /> <<atomatom idid="a5"="a5" elementTypeelementType="H"="H" x2x2="="‐4.248679083681063" y2y2="1.5399999809265137"="1.5399999809265137" /> <<atomatom idid="a6"="a6" elementTypeelementType="H"="H" x2x2="="‐2.914999961853028" y2y2="="‐0.7700000190734864" /> <<atomatom idid="a7"="a7" elementTypeelementType="H"="H" x2x2="="‐4.248679083681063" y2y2="="‐1.907348645691087E‐8" /> <<atomatom idid="a8"="a8" elementTypeelementType="H"="H" x2x2="1.0860374036310796"="1.0860374036310796" y2y2="1.5399999809265132"="1.5399999809265132" /> </</atomArray>atomArray> <<bondArray>bondArray> <<bondbond atomRefs2atomRefs2="a1="a1 a2" orderorder="1"="1" /> <<bondbond atomRefs2atomRefs2="a2="a2 a3" orderorder="1"="1" /> <<bondbond atomRefs2atomRefs2="a2="a2
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