e-Science, Information Infrastructure,
and Digital Libraries
Libraries in the Digital Age
Dubrovnik
30 May 2005
Christine L. Borgman
Professor & Presidential Chair in Information Studies
University of California, Los Angeles
&
Oxford Internet Institute (2004-05)
Outline of talk
• What are we building, for whom, and why?
– e-Science / e-Social Science / Cyberinfrastructure
– Digital libraries
• Case studies
– Alexandria Digital Earth Prototype (ADEPT)
– Center for Embedded Networked Sensing (CENS)
• Summary of issues
Goals of Cyberinfrastructure
• Cyberinfrastructure (U.S. initiative)*
– “technology … now make(s) possible a comprehensive
“cyberinfrastructure” on which to build new types of scientific and
engineering knowledge environments and organizations and to
pursue research in new ways and with increased efficacy.”
– “NSF must ensure that the exponentially growing amounts of data
are collected, curated, managed, and stored for broad, long-term
access by scientists everywhere.”
*Atkins, D., Revolutionizing Science and Engineering Through Cyberinfrastructure: Report of the National
Science Foundation Blue-Ribbon panel on Cyberinfrastructure. 2003, January. Executive summary.
http://www.communitytechnology.org/nsf_ci_report/
Goals of e-Science
• e-Science (U.K. initiative)
– “e-Science: large scale science … carried out through distributed
global collaborations enabled by the Internet.” *
– “collaborative scientific enterprises … require access to very large
data collections, very large scale computing resources and high
performance visualisation” *
– “e-Science data generated from sensors, satellites, high-performance
computer simulations, high-throughput devices, scientific images …
will soon dwarf all of the scientific data collected in the whole
history of scientific exploration.””**
*About the UK e-Science Programme. http://www.rcuk.ac.uk/escience/
**Hey & Trefethen, 2003, The data deluge: an E-science perspective.
Digital Libraries
• Systems that support searching, use, creation of content
• Institutions with people, digital collections, and services
• Repositories of digital data and documents, as a component
of cyberinfrastructure, e-research, e-science, e-social
science, e-learning…
– Institutional repositories
– Open archives
– Data collections
Digital libraries and e-Science: Architecture
• JISC/NSF working group on digital libraries and
e-science / cyberinfrastructure
– Layered model of cyberinfrastructure
– Venn diagram model
Images courtesy of Norman Wiseman, JISC
Cyberinfrastructure: An Evolving View
Applications
Space
Content
Digital Libraries
Scientific DBs
Information & knowledge layer
Middleware
services layer
ITC Infrastructure
Processors, memory, network
User
Interfaces &
Tools
The Focus for DL
Infrastructure:Version 2
E-Science
Or
E-Research
E-Learning
Hybrid
Libraries
E-resources: common
to all, need
demonstrators of how it
will work
Digital libraries and e-Science: Content
• Scholarly communication: Secondary resources
–
–
–
–
Conference proceedings
Journal articles
Working papers, manuscripts, pre-prints
Books
• Research data: Primary sources
– Raw data from research
• Instrumented data collection (labs, sensor networks)
• Field notes
– Archival sources
• Unique documents
• Records of individuals and organizations
• Value chain: linking data, documents, and related sources
E-bank image courtesy of Jeremy Frey and Liz Lyon; Crystallography image courtesy of Tony Hey
eBank Project
Virtual Learning
Environment
Undergraduate
Students
Digital
Library
E-Scientists
E-Scientists
Reprints
PeerReviewed
Journal &
Conference
Papers
Grid
Technical
Reports
Preprints &
Metadata
E-Experimentation
Publisher
Holdings
Graduate
Students
Institutional
Archive
Local
Web
Certified
Experimental
Results &
Analyses
Data,
Metadata &
Ontologies
5
Entire E-Science Cycle
Encompassing
experimentation,
analysis, publication,
research, learning
Crystallographic e-Prints
 Direct Access to Raw Data
from scientific papers
Raw data sets can be very large
and these are stored at National
Datastore using SRB server
Digital libraries and e-Science: Curation
•
Curation: maintenance of data over its entire life-cycle and over time for current and
future generations of users. Involves active management and appraisal… adding
value… to a trusted body of information.*
•
Scholarly communication: Secondary resources
–
–
–
–
•
Libraries
Disciplinary repositories (arXiv)
Institutional repositories (ePrints, Dspace)
Publishers
Research data: Primary sources
– Archives (arts and humanities)
– Disciplinary repositories (e.g., IVOA, BADC, IRIS, BIRN, NEON, GEON)
– Research groups
*What is digital curation? (2004). Edinburgh, Digital Curation Center. 2004.
Image of British Atmospheric Data Centre courtesy of Bryan Lawrence
Complexity + Volume + Remote Access = Grid Challenge
British Atmospheric
Data Centre
Simulations
British
Oceanographic Data
Centre
Assimilation
Digital libraries: Uses of secondary resources
• Community orientation of researchers
– “open science” depends upon sharing, transparency, rapid
dissemination
– documents may be self-archived, deposited, shared with peers
– incentive and reward system based on publication
– researchers contribute to digital collections via publication
• Individual orientation of students
– searchers of digital collections, not contributors
– reliant upon search mechanisms and bibliographic control
• Scholarly publications: inputs and outputs of research
• Digital libraries: “boundary objects” between experts and novices
Digital libraries: Uses of primary sources
• Community orientation of researchers
– e-Science goal to facilitate creation and use of research data by
collaborating groups
– Sharing practices vary widely by research topic and discipline
– Agreements made for access to data, credit for publications
• Data: input and output of research
• Providing context to interpret data
– Scholarly publications provide context
– Digital libraries remove context
Digital libraries: Sharing primary sources
• Incentives to share data
–
–
–
–
–
Tradition of “open science” to promote access, transparency
Establish trust and reciprocity within a research group
Ability to mine large data sets, compare results
Ability to replicate experiments, studies
Requirement of some funding agencies
• Incentives not to share data
–
–
–
–
–
Rewards for publication, not for data management
Benefits of contributing data may accrue to other parties
Risks of misinterpretation of your data
Risks of losing control over data
Risks of loss of intellectual property
Digital libraries, e-Science and e-Learning
• Case studies in the design of scientific digital
libraries that will support research and teaching
applications
– Alexandria Digital Earth Prototype (ADEPT)
• http://is.gseis.ucla.edu/adept/
– Center for Embedded Networked Sensing (CENS)
• http://www.cens.ucla.edu
Alexandria Digital Earth ProtoType
What information do you have about her?
Museum Artifacts
Earth Art
Alexandria DL of Distributed
Spatial Information Objects
Other Digital Archives
Zoological Habitat Study
If it has a latitude
and longitude then
it can be in ADL
Botanical Study
Ocean Science Data
Archeological Dig
<#>
Data models for habitat monitoring and sensor
Contaminant Transport Group
networks
“backbone”
network
•
•
•
adapted from
CA DWR website
Multimedia, Multiscale problems (time and space)
Multidisciplinary (current and as yet unknown) problems
Management, visualization, exploration of massive,
heterogeneous data streams
Center for Embedded Networked Sensing:
Education and Data Management Projects
• Goals
• Make sensor data useful for scientists
• Make sensor data useful for teaching high school science
• Facilitate inquiry learning with scientific data
• User communities
• Research scientists (habitat ecology, seismology)
• High school science teachers (biology and physics)
• High school students
• Activities to be supported
• Scientific data management by scientists
• Enable students to formulate and test hypotheses
• Enable students to design experiments “tasking” sensors
Some CENS Results-1
• CENS committed in principle to sharing data
• Data management practices vary widely by discipline
• Seismic: High experience, use of community
repository (IRIS) and metadata format (SEED)
• Habitat ecology: Low experience, exploring new
community repository (Morpho) and metadata
(Environmental Metadata Language)
• Avian biology (localization of birdsongs): High
experience across multiple disciplines
• Education: Standards exist for educational modules
but not for data
Some CENS Results-2
•
No single metadata model or crosswalk will serve all CENS
scientific applications
• Metadata for individual disciplines, e.g.,
• Environmental Metadata Language: biocomplexity data
• SEED: seismic data
• Metadata for technical devices, e.g., Sensor Markup
Language
• Geospatial coordinates required for most applications
• Geospatial data standards exists for 2D points
• Context descriptors also needed (distance from sea
level, local distance from ground, above/below leaf,
north/south side of tree)
METADATA FOR SENSOR DATA FOR HABITAT MONITORING
CENS Schema
SensorML
EML 2.0
CENS_Node.Node_Name
Name of Node
Sml:IdentifiedAs
(2.2.2)
CENS_Node.Node_Desc
Description of Node
AssetDescription
:
sml:description
(2.2.12)
CENS_Location.Location_ID
Unique location ID
CrsID (2.2.5)
Eml-Coverage
(2.4.4)
CENS_Location.X_Pos
(Position on X axis)
HasCRS (2.2.5)
ObjectState
(3.3.6)
Eml-CoverageGeographicCovera
ge
(2.4.4)
CENS_Location.Time_Recor
ded
Time location was captured
Eml-CoverageTemporalCoverage
(2.4.4)
CENS_Location.Time_Type
_ID
Refers to type of time of
Time_Type ID table
Eml-Coverage
(2.4.4)
METADATA FOR EDUCATION MODULES FOR HABITAT MONITORING
LOM
GEM
ADN
Educational-Typical Age
Range
(5.7)
Audience-Age
Audience
Life Cycle-Contribute
(2.3)
Creator
Resource Creator
General-Coverage
(1.6)
Coverage-Spatial,
Temporal
Coverage (spatial and
temporal)
Life Cycle-Date (2.3.3)
DateTime (8)
Date
Creation date Accession
date
General-Description
(1.4)
Description
Description
Educational
(5)
Pedagogy
Educational
CENS Research Directions
• Infrastructure goals for CENS
• Support scientists in collecting, managing, preserving,
sharing data
• Develop modular, extensible metadata architecture (XMLbased)
• Develop filtering tools to extract and visualize scientific
data for educational use
• Conduct behavioral studies of scientists, teachers, and
students
•
•
•
•
•
How do they determine their data requirements?
What are their criteria for selecting, preserving data?
How do they use scientific data?
How do their uses evolve over time?
What are their incentives and disincentives to contribute
data to repositories?
Summary
• Technical infrastructure of e-Science under construction
• Social aspects of e-Science infrastructure poorly understood
• Value chain of e-Science requires linkage between data and documents
– Scholarly communication infrastructure evolving, but exists
– Data infrastructure fragmented across disciplines, communities
• Digital libraries: intersection of e-science, e-learning, and libraries
–
–
–
–
Data practices vary more widely than do publication practices
Communities may represent, organize, and use the same data differently
Individuals may use the same content differently for different purposes
Research on digital libraries and e-learning can identify some of the
challenges for building an e-science infrastructure
Acknowledgements
• ADEPT is funded by National Science Foundation grant
no. IIS-9817432, Terence R. Smith, University of
California, Santa Barbara, Principal Investigator.
http://is.gseis.ucla.edu/adept/
– ADEPT collaborators: Gregory Leazer, Anne Gilliland, Richard
Mayer
• CENS is funded by National Science Foundation
Cooperative Agreement #CCR-0120778, Deborah L.
Estrin, UCLA, Principal Investigator.
http://www.cens.ucla.edu
– CENS collaborators: Jonathan Furner, William Sandoval, Noel
Enyedy, Michael Hamilton
Alexandria Digital Earth ProtoType
ADEPT Project: Geospatial digital libraries

Goals
 Add services to Alexandria Digital Library for teaching
undergraduate courses in geography
 Facilitate inquiry learning by providing access to primary sources

User communities
 Faculty, as researchers
 Faculty, as teachers of undergraduate courses
 Undergraduate students
Activities to be supported
 Information searching and retrieval
 Composing lectures that incorporate text, concepts, and objects
 Constructing learning modules in which students can formulate
and test hypotheses
<#>

Alexandria Digital Earth ProtoType
Some ADEPT Results (1999-2004)-1

Technology use by geographers
 Research: sophisticated users of IT; mapping software,
atmospheric simulations, sensor networks
 Teaching: minimal users of IT; rely on overhead projectors, chalk,
paper maps, boxes of rocks, lecturing from hand-written notes

Information seeking by geographers
 Research: typical library use, online searching
 Teaching: irregular, non-directed, often a by-product of research
activities
Information resources used by geographers
 Research: varies by specialty; all want maps and images
 Teaching: varies by course; all want maps and images
<#>

Alexandria Digital Earth ProtoType
Some ADEPT Results (1999-2004)-2

Search queries of geographers
 Research: concept, place (place name, latitude/longitude)
 Teaching: concept, place, process (examples of erosion, population
movements, etc.)

Selection of digital resources for teaching
 Clarity of presentation more important than familiarity of location
 Desire to annotate, highlight, resize, for presentation
Use of primary data in instruction
 Preference for use of own research data
 Tools to manage own research data would make DL teaching
services more attractive
 Most prefer to hold their data privately; want control over personal
digital libraries of their teaching and research resources
<#>
