eScience -- A Transformed Scientific Method

1. eScience -- A Transformed Scientific Method Jim Gray, eScience Group, Microsoft Research http://research.microsoft.com/~Gray in collaboration with Alex Szalay Dept. Physics & Astronomy Johns Hopkins University http://www.sdss.jhu.edu/~szalay/

2. Talk Goals Explain eScience (and what I am doing) & Recommend CSTB foster tools for • data capture (lab info management systems) • data curation (schemas, ontologies, provenance) • data analysis (workflow, algorithms, databases, data visualization ) • data+doc publication (active docs, data-doc integration) • peer review (editorial services) • access (doc + data archives and overlay journals) • Scholarly communication (wiki’s for each article and dataset)

3. eScience: What is it? Synthesis of information technology and science. Science methods are evolving (tools). Science is being codified/objectified.How represent scientific information and knowledge in computers? Science faces a data deluge.How to manage and analyze information? Scientific communication changing publishing data & literature (curation, access, preservation)

4. Science Paradigms Thousand years ago: science was empirical describing natural phenomena Last few hundred years: theoretical branch using models, generalizations Last few decades: a computational branch simulating complex phenomena Today:data exploration (eScience) unify theory, experiment, and simulation Data captured by instrumentsOr generated by simulator Processed by software Information/Knowledge stored in computer Scientist analyzes database / filesusing data management and statistics

5. Experiments & Instruments facts questions facts Other Archives facts answers Literature facts ? Simulations X-Info • The evolution of X-Info and Comp-X for each discipline X • How to codify and represent our knowledge The Generic Problems • Data ingest • Managing a petabyte • Common schema • How to organize it • How to reorganize it • How to share with others • Query and Vis tools • Building and executing models • Integrating data and Literature • Documenting experiments • Curation and long-term preservation

6. Software for Instrument scheduling Instrument control Data gathering Data reduction Database Analysis Modeling Visualization Millions of lines of code Repeated for experiment after experiment Not much sharing or learning CS can change this Build generic tools Workflow schedulers Databases and libraries Analysis packages Visualizers … Experiment Budgets ¼…½ Software

7. Software for Instrument scheduling Instrument control Data gathering Data reduction Database Analysis Modeling Visualization Millions of lines of code Repeated for experiment after experiment Not much sharing or learning CS can change this Build generic tools Workflow schedulers Databases and libraries Analysis packages Visualizers … Experiment Budgets ¼…½ Software Action item Foster Tools and Foster Tool Support

8. Project Pyramids In most disciplines there are a few “giga” projects, several “mega” consortia and then many small labs. Often some instrument creates need for giga-or mega-project Polar station Accelerator Telescope Remote sensor Genome sequencer Supercomputer Tier 1, 2, 3 facilities to use instrument + data

9. Pyramid Funding • Giga Projects need Giga FundingMajor Research Equipment Grants • Need projects at all scales • computing example: supercomputers, + departmental clusters + lab clusters • technical+ social issues • Fully fund giga projects, fund ½ of smaller projectsthey get matching funds from other sources • “Petascale Computational Systems: Balanced Cyber-Infrastructure in a Data-Centric World ,” IEEE Computer,  V. 39.1, pp 110-112, January, 2006.

10. Action item Invest in tools at all levels

11. Need Lab Info Management Systems (LIMSs) • Pipeline Instrument + Simulator data to archive & publish to web. • NASA Level 0 (raw) data Level 1 (calibrated) Level 2 (derived) • Needs workflow tool to manage pipeline • Build prototypes. • Examples: • SDSS, LifeUnderYourFeetMBARI Shore Side Data System.

12. Need Lab Info Management Systems (LIMSs) Action item Foster generic LIMS • Pipeline Instrument + Simulator data to archive & publish to web. • NASA Level 0 (raw) data Level 1 (calibrated) Level 2 (derived) • Needs workflow tool to manage pipeline • Build prototypes. • Examples: • SDSS, LifeUnderYourFeetMBARI Shore Side Data System.

13. Science Needs Info Management • Simulators produce lots of data • Experiments produce lots of data • Standard practice: • each simulation run produces a file • each instrument-day produces a file • each process step produces a file • files have descriptive names • files have similar formats (described elsewhere) • Projects have millions of files (or soon will) • No easy way to manage or analyze the data.

14. Data Analysis • Looking for • Needles in haystacks – the Higgs particle • Haystacks: Dark matter, Dark energy • Needles are easier than haystacks • Global statistics have poor scaling • Correlation functions are N2, likelihood techniques N3 • We can only do N logN • Must accept approximate answersNew algorithms • Requires combination of • statistics & • computer science

15. Analysis and Databases • Much statistical analysis deals with • Creating uniform samples – • data filtering • Assembling relevant subsets • Estimating completeness • Censoring bad data • Counting and building histograms • Generating Monte-Carlo subsets • Likelihood calculations • Hypothesis testing • Traditionally performed on files • These tasks better done in structured store with • indexing, • aggregation, • parallelism • query, analysis, • visualization tools.

16. You can GREP 1 MB in a second You can GREP 1 GB in a minute You can GREP 1 TB in 2 days You can GREP 1 PB in 3 years Oh!, and 1PB ~4,000 disks At some point you need indices to limit searchparallel data search and analysis This is where databases can help You can FTP 1 MB in 1 sec FTP 1 GB / min(~1 $/GB) … 2 days and 1K$ … 3 years and 1M$ Data Delivery: Hitting a Wall FTP and GREP are not adequate

17. Accessing Data • If there is too much data to move around, take the analysis to the data! • Do all data manipulations at database • Build custom procedures and functions in the database • Automatic parallelism guaranteed • Easy to build-in custom functionality • Databases & Procedures being unified • Example temporal and spatial indexing • Pixel processing • Easy to reorganize the data • Multiple views, each optimal for certain analyses • Building hierarchical summaries are trivial • Scalable to Petabyte datasets active databases!

18. Analysis and Databases Action item Foster Data Management Data Analysis Data Visualization Algorithms &Tools • Much statistical analysis deals with • Creating uniform samples – • data filtering • Assembling relevant subsets • Estimating completeness • Censoring bad data • Counting and building histograms • Generating Monte-Carlo subsets • Likelihood calculations • Hypothesis testing • Traditionally performed on files • These tasks better done in structured store with • indexing, • aggregation, • parallelism • query, analysis, • visualization tools.

19. Let 100 Flowers Bloom • Comp-X has some nice tools • Beowulf • Condor • BOINC • Matlab • These tools grew from the community • It’s HARD to see a common pattern • Linux vs FreeBSD why was Linux more successful?Community, personality, timing, ….??? • Lesson: let 100 flowers bloom.

20. Talk Goals Explain eScience (and what I am doing) & Recommend CSTB foster tools and tools for • data capture (lab info management systems) • data curation (schemas, ontologies, provenance) • data analysis (workflow, algorithms, databases, data visualization ) • data+doc publication (active docs, data-doc integration) • peer review (editorial services) • access (doc + data archives and overlay journals) • Scholarly communication (wiki’s for each article and dataset)

21. All Scientific Data Online • Many disciplines overlap and use data from other sciences. • Internet can unify all literature and data • Go from literature to computation to data back to literature. • Information at your fingertipsFor everyone-everywhere • Increase Scientific Information Velocity • Huge increase in Science Productivity

22. Unlocking Peer-Reviewed Literature • Agencies and Foundations mandating research be public domain. • NIH (30 B$/y, 40k PIs,…)(see http://www.taxpayeraccess.org/) • Welcome Trust • Japan, China, Italy, South Africa,.… • Public Library of Science.. • Other agencies will follow NIH

23. How Does the New Library Work? • Who pays for storage access (unfunded mandate)? • Its cheap: 1 milli-dollar per access • But… curation is not cheap: • Author/Title/Subject/Citation/….. • Dublin Core is great but… • NLM has a 6,000-line XSD for documents http://dtd.nlm.nih.gov/publishing • Need to capture document structure from author • Sections, figures, equations, citations,… • Automate curation • NCBI-PubMedCentral is doing this • Preparing for 1M articles/year • Automate it!

24. Pub Med Central International • “Information at your fingertips” • Deployed US, China, England, Italy, South Africa, Japan • UK PMCI http://ukpmc.ac.uk/ • Each site can accept documents • Archives replicated • Federate thru web services • Working to integrate Word/Excel/…with PubmedCentral – e.g. WordML, XSD, • To be clear: NCBI is doing 99.99% of the work.

25. articles Data Sets Overlay Journals • Articles and Data in public archives • Journal title page in public archive. • All covered by Creative Commons License • permits: copy/distribute • requires: attribution http://creativecommons.org/ Data Archives

26. title page articles Data Sets Overlay Journals • Articles and Data in public archives • Journal title page in public archive. • All covered by Creative Commons License • permits: copy/distribute • requires: attribution http://creativecommons.org/ JournalManagement System Data Archives

27. title page comments articles Data Sets Overlay Journals • Articles and Data in public archives • Journal title page in public archive. • All covered by Creative Commons License • permits: copy/distribute • requires: attribution http://creativecommons.org/ JournalCollaboration System JournalManagement System Data Archives

28. Action item Do for other scienceswhat NLM has done for BIOGenbank-PubMedCentral… title page comments articles Data Sets Overlay Journals • Articles and Data in public archives • Journal title page in public archive. • All covered by Creative Commons License • permits: copy/distribute • requires: attribution http://creativecommons.org/ JournalCollaboration System JournalManagement System Data Archives

29. Better Authoring Tools • Extend Authoring tools to • capture document metadata (NLM tagset) • represent documents in standard format • WordML (ECMA standard) • capture references • Make active documents (words and data). • Easier for authors • Easier for archives

30. Conference Management Tool • Currently a conference peer-review system (~300 conferences) • Form committee • Accept Manuscripts • Declare interest/recuse • Review • Decide • Form program • Notify • Revise

31. Publishing Peer Review • & improve author-reader experience • Manage versions • Capture data • Interactive documents • Capture Workshop • presentations • proceedings • Capture classroom ConferenceXP • Moderated discussions of published articles • Connect to Archives • Add publishing steps • Form committee • Accept Manuscripts • Declare interest/recuse • Review • Decide • Form program • Notify • Revise • Publish

32. Why Not a Wiki? • Peer-Review is different • It is very structured • It is moderated • There is a degree of confidentiality • Wiki is egalitarian • It’s a conversation • It’s completely transparent • Don’t get me wrong: • Wiki’s are great • SharePoints are great • But.. Peer-Review is different. • And, incidentally: review of proposals, projects,… is more like peer-review. • Let’s have Moderated Wiki re published literature PLoS-One is doing this

33. Action item Foster new document authoring and publication models and tools Why Not a Wiki? • Peer-Review is different • It is very structured • It is moderated • There is a degree of confidentiality • Wiki is egalitarian • It’s a conversation • It’s completely transparent • Don’t get me wrong: • Wiki’s are great • SharePoints are great • But.. Peer-Review is different. • And, incidentally: review of proposals, projects,… is more like peer-review. • Let’s have Moderated Wiki re published literature PLoS-One is doing this

34. So… What about Publishing Data? • The answer is 42. • But… • What are the units? • How precise? How accurate 42.5 ± .01 • Show your work data provenance

35. Thought Experiment • You have collected some dataand want to publish science based on it. • How do you publish the data so that others can read it and reproduce your results in 100 years? • Document collection process? • How document data processing (scrubbing & reducing the data)? • Where do you put it?

36. Objectifying Knowledge • This requires agreement about • Units: cgs • Measurements: who/what/when/where/how • CONCEPTS: • What’s a planet, star, galaxy,…? • What’s a gene, protein, pathway…? • Need to objectify science: • what are the objects? • what are the attributes? • What are the methods (in the OO sense)? • This is mostly Physics/Bio/Eco/Econ/... But CS can do generic things

37. Objectifying Knowledge Warning!Painful discussions ahead: The “O” word: Ontology The “S” word: Schema The “CV” words: Controlled Vocabulary Domain experts do not agree • This requires agreement about • Units: cgs • Measurements: who/what/when/where/how • CONCEPTS: • What’s a planet, star, galaxy,…? • What’s a gene, protein, pathway…? • Need to objectify science: • what are the objects? • what are the attributes? • What are the methods (in the OO sense)? • This is mostly Physics/Bio/Eco/Econ/... But CS can do generic things

38. PubMed Entrez Genomes PubMed abstracts Complete Genomes Publishers Genome Centers Taxon 3 -D Structure Phylogeny MMDB Nucleotide sequences Protein sequences The Best Example: Entrez-GenBankhttp://www.ncbi.nlm.nih.gov/ • Sequence data deposited with Genbank • Literature references Genbank ID • BLAST searches Genbank • Entrez integrates and searches • PubMedCentral • PubChem • Genbank • Proteins, SNP, • Structure,.. • Taxonomy… • Many more

39. Roles Authors Publishers Curators Consumers Traditional Scientists Journals Libraries Scientists Emerging Collaborations Project www site Bigger Archives Scientists Publishing Data • Exponential growth: • Projects last at least 3-5 years • Data sent upwards only at the end of the project • Data will never be centralized • More responsibility on projects • Becoming Publishers and Curators • Data will reside with projects • Analyses must be close to the data

40. Data Pyramid • Very extended distribution of data sets: data on all scales! • Most datasets are small, and manually maintained (Excel spreadsheets) • Total volume dominated by multi-TB archives • But, small datasets have real value • Most data is born digital collected via electronic sensorsor generated by simulators.

41. Data Sharing/Publishing • What is the business model (reward/career benefit)? • Three tiers (power law!!!) (a) big projects (b) value added, refereed products (c) ad-hoc data, on-line sensors, images, outreach info • We have largely done (a) • Need “Journal for Data” to solve (b) • Need “VO-Flickr” (a simple interface) (c) • Mashups are emerging in science • Need an integrated environment for ‘virtual excursions’ for education (C. Wong)

42. Action item Foster Digital Data Libraries(not metadata, real data)and integration with literature PubMed Entrez Genomes PubMed abstracts Complete Genomes Publishers Genome Centers Taxon 3 -D Structure Phylogeny MMDB Nucleotide sequences Protein sequences The Best Example: Entrez-GenBankhttp://www.ncbi.nlm.nih.gov/ • Sequence data deposited with Genbank • Literature references Genbank ID • BLAST searches Genbank • Entrez integrates and searches • PubMedCentral • PubChem • Genbank • Proteins, SNP, • Structure,.. • Taxonomy… • Many more

43. Talk Goals Explain eScience (and what I am doing) & Recommend CSTB foster tools and tools for • data capture (lab info management systems) • data curation (schemas, ontologies, provenance) • data analysis (workflow, algorithms, databases, data visualization ) • data+doc publication (active docs, data-doc integration) • peer review (editorial services) • access (doc + data archives and overlay journals) • Scholarly communication (wiki’s for each article and dataset)

44. backup

45. Astronomy • Help build world-wide telescope • All astronomy data and literature online and cross indexed • Tools to analyze the data • Built SkyServer.SDSS.org • Built Analysis system • MyDB • CasJobs (batch job) • OpenSkyQueryFederation of ~20 observatories. • Results: • It works and is used every day • Spatial extensions in SQL 2005 • A good example of Data Grid • Good examples of Web Services.

46. World Wide TelescopeVirtual Observatoryhttp://www.us-vo.org/http://www.ivoa.net/ • Premise: Most data is (or could be online) • So, the Internet is the world’s best telescope: • It has data on every part of the sky • In every measured spectral band: optical, x-ray, radio.. • As deep as the best instruments (2 years ago). • It is up when you are up.The “seeing” is always great (no working at night, no clouds no moons no..). • It’s a smart telescope: links objects and data to literature on them.

47. ROSAT ~keV DSS Optical IRAS 25m 2MASS 2m GB 6cm WENSS 92cm NVSS 20cm IRAS 100m Why Astronomy Data? • It has no commercial value • No privacy concerns • Can freely share results with others • Great for experimenting with algorithms • It is real and well documented • High-dimensional data (with confidence intervals) • Spatial data • Temporal data • Many different instruments from many different places and many different times • Federation is a goal • There is a lot of it (petabytes)

48. Time and Spectral DimensionsThe Multiwavelength Crab Nebulae Crab star 1053 AD X-ray, optical, infrared, and radio views of the nearby Crab Nebula, which is now in a state of chaotic expansion after a supernova explosion first sighted in 1054 A.D. by Chinese Astronomers. Slide courtesy of Robert Brunner @ CalTech.

49. SkyServer.SDSS.org • A modern archive • Access to Sloan Digital Sky SurveySpectroscopic and Optical surveys • Raw Pixel data lives in file servers • Catalog data (derived objects) lives in Database • Online query to any and all • Also used for education • 150 hours of online Astronomy • Implicitly teaches data analysis • Interesting things • Spatial data search • Client query interface via Java Applet • Query from Emacs, Python, …. • Cloned by other surveys (a template design) • Web services are core of it.

50. SkyServerSkyServer.SDSS.org • Like the TerraServer, but looking the other way: a picture of ¼ of the universe • Sloan Digital Sky Survey Data: Pixels + Data Mining • About 400 attributes per “object” • Spectrograms for 1% of objects