data driven science: some lessons learned · 2012. 5. 10. · 3 managed by ut-battelle for the u.s....

Data Driven Science: Some Lessons

Learned

Data Symposium 2012 SeWHIP & CTSI

John W. Cobb, Ph.D. Milwaukee, WI March 1, 2012

2 Managed by UT-Battelle for the U.S. Department of Energy Data Driven Science

Acknowledgement and collaborators

• DataONE http://www.dataone.org/

• Cal Dig. Lib. http://www.cdlib.org/

• Cornell Lab of Ornithology http://www.birds.cornell.edu/ http://ebird.org/content/ebird/

•  TeraGrid (now XSEDE) https://www.xsede.org/

• National Science Foundation


Outline

• Distinctions between cyberinfrastructure, information technology, computer science, computational science – a personal view

• Requirements analysis for interoperable long-term data archive and curation service (a datanet) with focus on ecological, biological, and environmental sciences

• Description of DataONE datanet services and related services as an exemplar (bulk of talk)


Outline





Data driven science

A computing center or a data center?

Rorshach test: What is This?

Images courtesy of the NCCS, ORNL

“A supercomputer is just one more source of petabytes of data”


A (personal) taxonomy of computing research activities

Computer Science (CS) •  Algorithm development •  Compu6ng architecture development •  Language research •  …

Computa6onal Science (Computa6onal Sci) •  PDE solver algorithms •  Finite math representa6ons of

con6nous PDE’s •  Numerical linear algebra •  …

Cyberinfrastructure (CI) •  Developing methods for accessing

compu6ng services •  Virtual organiza6on research •  …

Informa6on Technology (IT) •  Research about IT opera6ons •  Provisioning •  Produc6on networking (internal and

external connec6ons) •  Cybersecurity •  …

In addition to research efforts IT, CI, and Computational science often have coupled operational tasks.


Contrasts: CI and IT

CI •  Enables research initiatives

•  Expands topline •  Seeks new services •  Externally focused •  Project partner

IT •  Enables enterprise

integration •  Optimizes bottom line •  Optimizes current services •  Internally focused •  Project component

However, pragmatically, •  university CIO’s often are charged with CI and IT responsibilities as well as a

partner in computational science provisioning. Usually computer science is not under CIO function. Library science is also separate although occasionally academic library operations sometimes also fall under the CIO function.

•  Some CI and IT services can look quite similar (system administration, network security, …)


My focus today: CI

“Like the physical infrastructure of roads, bridges, power grids, telephone lines, and water systems that support modern society, cyberinfrastructure refers to the distributed computer, information and communication technologies combined with the personnel and integrating components that provide a long-term platform to empower the modern scientific research endeavor.” (Atkins Report, NSF Blue-Ribbon Advisory Panel on Cyberinfrastructure 2003)


Outline





What is needed to build a robust set of data services?

• Archiving • Management • Metadata regularization • Curation • Retention • Practioner education • Socio-cultural barriers • …


Poor data practice “data entropy”

Info

rmat

ion

Con

tent

Time

Time of publication

Specific details

General details

Accident

Retirement or career change

Death

(Michener et al. 1997)

In what sense is modern science reproducible?

cf. Brian Athey and Clifford Lynch this morning


Data management plans now required

NSF Data Management Plan Requirements “Beginning January 18, 2011, proposals submitted to NSF must include a supplementary document of no more than two pages labeled "Data Management Plan" (DMP) . This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. Proposals that do not include a DMP will not be able to be submitted.” http://www.nsf.gov/eng/general/dmp.jsp

Other agencies have or are instituting requirements


Data deluge and interoperability “the flood of increasingly heterogeneous data”

•  Data are heterogeneous –  Syntax

•  (format) –  Schema

•  (model) –  Semantics

•  (meaning)

Jones et al. 2007

By hand is time-consuming and brittle


Baseline assessment: scientists (2010)

• Demographics

social sciences 16%

computer science/

engineering 9%

physical sciences

12%

environmental sciences 18%

atmospheric science 4%

biology 14%

ecology 18%

medicine 2%

other 7%

academic 80%

government 13%

commercial 2%

non-profit 3% other 2%

n=1315 n=1317

Work Sector Discipline

Tenopir, C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, Manoff M, Frame M. 2011. Data Sharing by Scientists: Practices and Perceptions. PLoS ONE. 6(6)


12 21 26 95 95 96 97

266

676

DIF DwC DC EML FGDC Open GIS

ISO My Lab none

Metadata language

What standard do you currently use?


41%

76%

78%

81%

0% 20% 40% 60% 80% 100%

Willing to place all of my data into a central data repository with no

restric6ons

Appropriate to create new datasets from shared data

Willing to place at least some of my data into a central data repository

with no restric6ons

Willing to share data across a broad group of researchers

Many are interested in sharing data

Percent agree


Collect

Assure

Describe

Deposit

Preserve

Discover

Integrate

Analyze

How do I preserve my

data?

What tools do I use?

Will I get credit for my work?

How much will it cost?

What is a data management

plan?

Who can help me?

What is metadata?

Where do I preserve my

data?

Needs of scientists: the data lifecycle


eBird pilot project exploration and visualization

Spa6o-‐Temporal Exploratory Model iden6fies factors affec6ng pa^erns of migra6on

Diverse bird observa6ons and environmental data from 300,00 loca6ons in the US integrated and analyzed using High Performance Compu6ng Resources

Land Cover

Meteorology

MODIS – Remote sensing data

•  Examine pa^erns of migra6on

•  Infer how climate change may affect bird migra6on

Model results

Occurrence of Indigo Bun=ng (2008)

Jan Sep Dec Jun Apr

19 Managed by UT-Battelle for the U.S. Department of Energy Data Driven Science 19 19

Secretary Salazar on Birds (May 3, 2011): “The State of the Birds report is a measurable indicator of how well we are fulfilling our shared role as stewards of our nation’s public lands and waters.”

Acadian Flycatcher Distribution – eBird.org


Dec

reas

ing

Spa

tial C

over

age

Incr

easi

ng P

roce

ss K

now

ledg

e

Adapted from CENR-OSTP

Remote sensing

Intensive science sites and experiments

Extensive science sites

Volunteer & education networks

“Building the Knowledge Pyramid” 90:10 à 10:90

Multiple Scales


Tracing requirements

• Multiple scales •  Interoperable across repositories • Cross organizational (VO’s) • Multiple identities • Data heterogeneity • Manage disparate rights policies • Support all phases of the data life cycle •  Include education and outreach to change community

practies


Outline





DataONE Movie (with Sound)


DataONE is Cyberinfrastructure

Three major components form a flexible, scalable, sustainable network

Member Nodes •  diverse institutions •  serve local community •  provide resources for

managing their data •  retain copies of data

Coordinating Nodes •  retain complete

metadata catalog •  indexing for search •  network-wide services •  ensure content

availability (preservation)

•  replication services

Investigator Toolkit

Source: DataONE/Michener

25 Managed by UT-Battelle for the U.S. Department of Energy Data Driven Science 25 25

25"

Examples of data holdings

Data Archive Types of Data Managed Metadata Standard(s)

Biodiversity, taxonomic, ecological BDP, DwC, DC, OGIS

Biogeochemical dynamics, terrestrial ecological Earth observation imagery

DIF, BDP, ECHO

Ecological, biodiversity, biophysical, social, genomics, and taxonomic

EML

Avian populations and molecular biology DwC

Biological and taxonomic DC subset

Biophysical, biodiversity, disturbance, and Earth observation imagery

EML

Biodiversity, biotic structure, function/process, biogeochemical, climate, and

hydrologic

EML

Metadata Interoperability Across Data Holdings

EML=Ecological Metadata Language

BDP=Biological Data Profile DwC=Darwin Core

DC=Dublin Core ECHO=EOS ClearingHOuse

OGIS=OpenGIS

DC subset=Dublin Core subset

DIF=Directory Interchange Format


Initial member nodes ORNL-‐DAAC Dryad KNB

Community Agency repository Journal consor6um Research network

Data Ecology and biogeochemical dynamics

Biosciences Biodiversity, ecology, environment

Size 900 data products, ~ 1 TB

~ 1,000 data products, ~ 3 GB

20,000 data products, 100s GBs

Services Tools for data preservation, replication, discovery, access, subsetting, and visualization

Tools for data preservation, replication, discovery and access

Tools for data preservation, replication, discovery, access, management, and visualization

Metadata stds. FGDC subset Dublin Core applica6on profile

EML, FGDC

Degree of cura6on High Medium Low

Data submission Agency-approved, staff-assisted submission and curation of final data product

Web-based data submission at time of journal article submission

Self-submission via desktop tool at any time

Sponsor NASA NSF/JISC, socie6es, publishers

NSF


Preserve Data and Metadata

•  Metadata copied to Coordinating Nodes

•  Mirrored between CNs •  Data replicated between

Member Nodes •  CNs manage copies •  Checksums recorded •  Promote quality

metadata

*

data driven science: some lessons learned · 2012. 5. 10. · 3 managed by ut-battelle for the u.s....

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