eScience: A Transformed Scientific Method

• 1.eScience -- A Transformed Scientific MethodJim Gray ,eScience Group,Microsoft Researchhttp://research.microsoft.com/~Gray in collaboration withAlex SzalayDept. 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 (wikis for each article and dataset)

3. eScience:What is it?

• Synthesis ofinformation technology and science.

• Sciencemethodsare 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 wasempirical

• • describing natural phenomena

• Last few hundred years:theoreticalbranch

• • using models, generalizations

• Last few decades:acomputationalbranch

• • simulating complex phenomena

• Today: data exploration(eScience)

• • unify theory, experiment, and simulation

• • Data captured by instruments Orgenerated by simulator

• • Processed by software

• • Information/Knowledge stored in computer

• • Scientist analyzes database / files using data management and statistics

5. X-Info

• The evolution of X-Info and Comp-X for each discipline X

• How to codify and represent our knowledge

• Data ingest

• Managing a petabyte

• Common schema

• How to organize it

• How tore organize 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

The Generic Problems Experiments & Instruments Simulations facts facts answers questions Literature Other Archives facts facts ? 6. Experiment Budgets Software

• 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

7. Experiment Budgets Software

• 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

Action item Foster Tools andFoster Tool Support 8. Project Pyramids In most disciplines there area few giga projects,several mega consortiaand 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 facilitiesto use instrument + dataInternational Multi-Campus Single Lab 9. Pyramid Funding

• Giga Projects need Giga Funding Major Research Equipment Grants

• Need projects at all scales

• computing example: supercomputers, + departmental clusters+ lab clusters

• technical+ social issues

• Fully fund giga projects,fund of smaller projects they get matching fundsfrom other sources

• Petascale Computational Systems: Balanced Cyber-Infrastructure in a Data-Centric World , IEEEComputer , V. 39.1, pp 110-112, January, 2006.

10. Action item Invest in toolsat all levels 11. Need Lab Info Management Systems(LIMSs)

• Pipeline Instrument + Simulator datato archive & publish to web.

• NASA Level 0 (raw) dataLevel 1 (calibrated) Level 2 (derived)

• Needs workflow tool to manage pipeline

• Build prototypes.

• Examples:

• • SDSS,LifeUnderYourFeet MBARI Shore Side Data System.

12. Need Lab Info Management Systems(LIMSs)

• Pipeline Instrument + Simulator datato archive & publish to web.

• NASA Level 0 (raw) dataLevel 1 (calibrated) Level 2 (derived)

• Needs workflow tool to manage pipeline

• Build prototypes.

• Examples:

• • SDSS,LifeUnderYourFeet MBARI Shore Side Data System.

Action item Foster generic LIMS 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 N 2 , likelihood techniques N 3

• We can only doN logN

• Must accept approximate answers New 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. Data Delivery: Hitting a Wall

• 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 needindicesto limit search paralleldata search and analysis

• This is wheredatabasescan help

• You can FTP 1 MB in 1 sec

• FTP 1 GB / min (~1 $/GB)

• 2 daysand 1K$

• 3 years and 1M$

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

• 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.

Action item Foster Data Management Data AnalysisData VisualizationAlgorithms &Tools 19. Let 100 Flowers Bloom

• Comp-X has some nice tools

• • Beowulf

• • Condor

• • BOINC

• • Matlab

• These tools grew from the community

• Its HARD to see a common pattern

• • Linux vs FreeBSDwhy 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 (wikis for each article and dataset)

21. All Scientific Data Online

• Many disciplines overlap anduse data from other sciences.

• Internet can unifyall literature and data

• Go from literatureto computationto databack to literature.

• Information at your fingertips For everyone-everywhere

• Increase Scientific Information Velocity

• Huge increase in Science Productivity

Literature Derived andRe-combined data Raw Data 22. Unlocking Peer-Reviewed Literature

• Agencies and Foundations mandatingresearch be public domain.

• • NIH (30 B$/y, 40k PIs,) (seehttp:// 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 documentshttp://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 PMCIhttp://ukpmc.ac.uk/

• Each site can accept documents

• Archives replicated

• Federate thru web services

• Working to integrateWord/Excel/ with PubmedCentral e.g. WordML,XSD ,

• To be clear:NCBI is doing 99.99% of the work.

25. Overlay Journals

• Articles and Data inpublic archives

• Journal title page in public archive.

• All covered by Creative Commons License

• • permits:copy/distribute

• • requires: attribution

• • http://creativecommons.org/

Data Archives articles Data Sets 26. Overlay Journals

• Articles and Data inpublic archives

• Journal title page in public archive.

• All covered by Creative Commons License

• • permits:copy/distribute

• • requires: attribution

• • http://creativecommons.org/

Journal Management System Data Archives articles title page Data Sets 27. Overlay Journals

• Articles and Data inpublic archives

• Journal title page in public archive.

• All covered by Creative Commons License

• • permits:copy/distribute

• • requires: attribution

• • http://creativecommons.org/

Journal Management System Journal Collaboration System Data Archives articles title page comments Data Sets 28. Overlay Journals

• Articles and Data inpublic archives

• Journal title page in public archive.

• All covered by Creative Commons License

• • permits:copy/distribute

• • requires: attribution

• • http://creativecommons.org/

Journal Management System Journal Collaboration System Data Archives Action item Do for other sciences what NLM has done for BIO Genbank-PubMedCentral articles title page comments Data Sets 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 conferencepeer-reviewsystem (~300 conferences)

• • Form committee

• • Accept Manuscripts

• • Declare interest/recuse

• • Review

• • Decide

• • Form program

• • Notify

• • Revise

31. Publishing Peer Review

• Add publishing steps

• • Form committee

• • Accept Manuscripts

• • Declare interest/recuse

• • Review

• • Decide

• • Form program

• • Notify

• • Revise

• • Publish

• & improveauthor-reader experience

• Manage versions

• Capture data

• Interactive documents

• Capture Workshop

• • presentations

• • proceedings

• Capture classroomConferenceXP

• Moderated discussionsof published articles

• Connect to Archives

32. Why Not a Wiki?

• Peer-Review isdifferent

• • It is very structured

• • It is moderated

• • There is a degree of confidentiality

• Wiki is egalitarian

• • Its a conversation

• • Its completely transparent

• Dont get me wrong:

• • Wikis are great

• • SharePoints are great

• • But..Peer-Review is different.

• • And, incidentally: review of proposals, projects,is more like peer-review.

• Lets have Moderated Wiki re published literaturePLoS-One is doing this

33. Why Not a Wiki?

• Peer-Review isdifferent

• • It is very structured

• • It is moderated

• • There is a degree of confidentiality

• Wiki is egalitarian

• • Its a conversation

• • Its completely transparent

• Dont get me wrong:

• • Wikis are great

• • SharePoints are great

• • But..Peer-Review is different.

• • And, incidentally: review of proposals, projects,is more like peer-review.

• Lets have Moderated Wiki re published literaturePLoS-One is doing this

Action item Foster new document authoring and publicationmodels and tools 34. So What about Publishing Data?

• The answer is42 .

• But

• • What are the units?

• • How precise? How accurate 42.5.01

• • Show your workdataprovenance

35. Thought Experiment

• You have collected some data and want to publish science based on it.

• How do you publish the dataso that others can read it andreproduce your resultsin 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:

• • • Whats a planet, star, galaxy,?

• • • Whats 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

• This requires agreement about

• • Units: cgs

• • Measurements:who/what/when/where/how

• • CONCEPTS:

• • • Whats a planet, star, galaxy,?

• • • Whats 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

Warning! Painful discussions ahead: The O word: Ontology The S word: Schema The CV words:Controlled Vocabulary Domain experts do not agree 38. The Best Example: Entrez-GenBank http:// 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

Nucleotide sequences Protein sequences Taxon Phylogeny MMDB 3 -D Structure PubMed abstracts Complete Genomes PubMed Entrez Genomes Publishers Genome Centers 39. Publishing Data

• Exponential growth:

• • Projects last at least 3-5 years

• • Data sent upwards only at the end of the project

• • Data willneverbe centralized

• More responsibility on projects

• • Becoming Publishers and Curators

• Data will reside with projects

• • Analyses must be close to the data

Roles Authors Publishers Curators Consumers Traditional Scientists Journals Libraries Scientists Emerging Collaborations Project www site Bigger Archives Scientists 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 digitalcollected via electronic sensors or 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. The Best Example: Entrez-GenBank http:// 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

Action item Foster Digital Data Libraries (not metadata, real data) and integration with literature Nucleotide sequences Protein sequences Taxon Phylogeny MMDB 3 -D Structure PubMed abstracts Complete Genomes PubMed Entrez Genomes Publishers Genome Centers 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 (wikis for each article and dataset)

44. backup 45. Astronomy

• Help build world-wide telescope

• • All astronomy data and literatureonline and cross indexed

• • Tools to analyze the data

• Built SkyServer.SDSS.org

• Built Analysis system

• • MyDB

• • CasJobs (batch job)

• OpenSkyQuery Federation 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 Telescope Virtual Observatory http://www.us-vo.org/ http://www.ivoa.net/

• Premise:Most data is (or could be online)

• So, the Internet is the worlds 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(noworking at night, no clouds no moons no..).

• • Its a smart telescope:links objects and data to literature on them.

47. 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)

• • Spatialdata

• • Temporaldata

• Manydifferent instrumentsfrommanydifferent placesandmanydifferent times

• Federation is a goal

• There is a lot of it (petabytes)

IRAS 100 ROSAT ~keV DSS Optical 2MASS 2 IRAS 25 NVSS 20cm WENSS 92cm GB 6cm 48. Time and Spectral Dimensions The Multiwavelength Crab Nebulae X-ray,optical,infrared, andradioviews 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. Crab star1053 AD 49. SkyServer.SDSS.org

• A modern archive

• • Access to Sloan Digital Sky Survey Spectroscopic 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. SkyServer SkyServer.SDSS.org

• Like the TerraServer,but looking the other way:a picture of of the universe

• Sloan Digital Sky Survey Data: Pixels + Data Mining

• About 400 attributes per object

• Spectrograms for 1% ofobjects

51. Demo of SkyServer

• Shows standard web server

• Pixel/image data

• Point and click

• Explore one object

• Explore sets of objects (data mining)

52. SkyQuery ( http://skyquery.net/ )

• Distributed Query tool using a set of web services

• Many astronomy archives fromPasadena, Chicago, Baltimore, Cambridge (England)

• Has grown from 4 to 15 archives, now becominginternational standard

• WebService Poster Child

• Allows queries like:

SELECT o.objId, o.r, o.type, t.objId FROM SDSS:PhotoPrimary o,TWOMASS:PhotoPrimary t WHERE XMATCH(o,t)2 53. SkyQuery Structure

• Each SkyNode publishes

• • Schema Web Service

• • Database Web Service

• Portal is

• • Plans Query (2 phase)

• • Integrates answers

• • Is itself a web service

2MASS INT SDSS FIRST SkyQuery Portal Image Cutout 54. Schema (aka metadata)

• Everyone starts with the same schema Then the start arguing about semantics.

• Virtual Observatory: http:// www.ivoa.net /

• Metadata based on Dublin Core: http:// www.ivoa.net/Documents/latest/RM.html

• Universal Content Descriptors (UCD):http://vizier.u-strasbg.fr/doc/UCD.htx Captures quantitative concepts and their units Reduced from~100,000 tables in literature to ~1,000 terms

• VOtable a schema for answers to questions http://www.us-vo.org/VOTable/

• Common Queries: Cone Search and Simple Image Access Protocol, SQL

• Registry:http://www.ivoa.net/Documents/latest/RMExp.html still a work in progress.

55. SkyServer/SkyQuery Evolution MyDBand Batch Jobs

• Problem:need multi-step data analysis (not just single query).

• Solution:Allow personal databases on portal

• Problem: some queries are monsters

• Solution: Batch schedule on portal. Deposits answer in personal database.

56. Ecosystem Sensor Net LifeUnderYourFeet.Org

• Small sensor net monitoring soil

• Sensors feed to a database

• Helping build system tocollect & organize data.

• Working on data analysis tools

• Prototype for other LIMS Laboratory Information Management Systems

57. RNA Structural Genomics

• Goal:Predict secondary and tertiary structurefrom sequence. Deduce tree of life.

• Technique:Analyzesequence variations sharinga common structureacross tree of life

• Representingstructurally aligned sequencesis a key challenge

• Creating a database-driven alignment workbench accessing public and private sequence data

58. VHA Health Informatics

• VHA: largest standardized electronic medical records system in US.

• Design, populate and tune a ~20 TB Data Warehouse and Analytics environment

• Evaluate population health and treatment outcomes,

• Support epidemiological studies

• • 7 million enrollees

• • 5 million patients

• • Example Milestones:

• • • 1 Billionth Vital Sign loadedin April 06

• • • 30-minutes to population-wideobesity analysis (next slide)

• • • Discovered seasonality inblood pressure -- NEJM fall 06

59. HDR Vitals Based Body Mass Index Calculation on VHA FY04 Population Source: VHA Corporate Data Warehouse Total Patients 23,876 (0.7%) 701,089 (21.6%) 1,177,093 (36.2%) 1,347,098 (41.5%) 3,249,156 (100%)