The document discusses the development of data services to support eScience/eResearch. It provides an overview of eScience, including that it involves large-scale collaborative science enabled by the internet using digital data. Characteristics of eScience include being data-driven, distributed, collaborative, and trans-disciplinary. Libraries are important to eScience because it involves large data sets, collections, and repositories. The document also discusses how science paradigms have shifted to become more computational and data-focused.

1. School
of
Information
Studies
Syracuse
University
Developing
Data
Services
to
Support
eScience/eResearch
2012
Priscilla
M.
Mayden
Lecture
eScience
and
the
Evolution
of
Library
Services
Jian
Qin
School
of
Information
Studies
Syracuse
University
http://eslib.ischool.syr.edu/
February
22,
2012

2. School
of
Information
Studies
Syracuse
University
The
morning
ahead
An
environmental
scan
• E-­‐Science,
cyberinfrastructure,
and
data
• What
do
all
these
have
to
do
with
me?
Case
study:
The
gravitational
wave
research
data
management
Group
work:
Role
play
in
developing
data
management
initiatives
Priscilla M. Mayden Lecture 2012, Utah 2

3. School
of
Information
Studies
Syracuse
University
An
environmental
scan
• E-­‐Science,
cyberinfrastructure,
and
data
• What
do
all
these
have
to
do
with
me?
Overview
of
E-­‐Science
and
Data
Characteristics
of
e-­‐science
Data
sets,
data
collections,
and
data
repositories
Why
does
it
matter
to
libraries?

4. School
of
Information
Studies
Syracuse
University
E-­‐Science
“In
the
future,
e-­‐Science
will
refer
to
the
large
scale
science
that
will
increasingly
be
carried
out
through
distributed
global
collaborations
enabled
by
the
Internet.
”
National e-Science Center. (2008). Defining e-Science.
http://www.nesc.ac.uk/nesc/define.html
Priscilla M. Mayden Lecture 2012, Utah 4

5. School
of
Information
Studies
Syracuse
University
Characteris>cs
of
e-­‐science
• Digital
data
driven
• Distributed
• Collaborative
• Trans-­‐disciplinary
• Fuses
pillars
of
science
– Experiment
– Theory
Greer,
Chris.
(2008).
E-­‐Science:
Trends,
Transformations
&
Responses.
In:
– Model/simulation
Reinventing
Science
Librarianship:
Models
for
the
Future,
October
2008.
– Observation/correlation
http://www.arl.org/bm~doc/
ff08greer.pps
Priscilla M. Mayden Lecture 2012, Utah 5

6. School
of
Information
Studies
Syracuse
University
Shi?
in
Science
Paradigms
Thousand
A
few
hundred
A
few
decades
Today
years
ago
years
ago
ago
Data exploration (eScience)
unify theory, experiment, and
simulation
A computational -- Data captured by
approach instruments or generated by
simulating simulator
Theoretical complex -- Processed by software
branch phenomena -- Information/Knowledge
using models, stored in computer
generalizations -- Scientist analyzes
Science was database/files using data
empirical management and statistics
describing natural Gray,
J.
&
Szalay,
A.
(2007).
eScience
–
A
transformed
scienti_ic
method.
phenomena http://research.microsoft.com/en-­‐us/um/people/gray/talks/NRC-­‐CSTB_eScience.ppt
Priscilla M. Mayden Lecture 2012, Utah 6

7. School
of
Information
Studies
Syracuse
University
Gray,
J.
&
Szalay,
A.
(2007).
eScience
–
A
transformed
scienti_ic
method.
http://research.microsoft.com/en-­‐us/
X-­‐Informa>cs
um/people/gray/talks/NRC-­‐CSTB_eScience.ppt
• The
evolution
of
X-­‐Informatics
and
Computational-­‐X
for
each
discipline
X
• How
to
codify
and
represent
our
knowledge
Experiments &
Instruments
Other Archives facts questions
Literature facts ? answers
Simulations
The Generic Problems
• Data
ingest
• Query
and
Visualization
tools
• Managing
a
petabyte
• Building
and
executing
models
• Common
schema
• Integrating
data
and
Literature
• How
to
organize
it
• Documenting
experiments
• How
to
reorganize
it
• Curation
and
long-­‐term
preservation
• How
to
share
with
others
Priscilla M. Mayden Lecture 2012, Utah 7

8. School
of
Information
Studies
Syracuse
University
Useful
resources
Part 2: Health and Wellbeing
• The healthcare singularity and the age of
semantic medicine
• Healthcare delivery in developing countries:
challenges and potential solutions
• Discovering the wiring diagram of the brain
• Toward a computational microscope for
neurobiology
• A unified modeling approach to data-intensive
http://research.microsoft.com/en- healthcare
us/collaboration/fourthparadigm/
• Visualization in process algebra models of
biological systems
Priscilla M. Mayden Lecture 2012, Utah 8

9. School
of
Information
Studies
Syracuse
University
What
are
data?
What
are
some
of
the
major
data
formats?
Why
data
formats?
FUNDAMENTALS
OF
DATA
Priscilla M. Mayden Lecture 2012, Utah 9

10. School
of
Information
Studies
Syracuse
University
What
are
data?
(1)
An
artist’s
conception
(above)
depicts
fundamental
NEON
observatory
instrumentation
and
systems
as
well
as
potential
spatial
organization
of
the
environmental
measurements
made
by
these
instruments
and
systems.
http://www.nsf.gov/pubs/2007/nsf0728/nsf0728_4.pdf
Priscilla M. Mayden Lecture 2012, Utah 10

11. School
of
Information
Studies
Syracuse
University
What
are
data?
(2)
Priscilla M. Mayden Lecture 2012, Utah 11

12. School
of
Information
Studies
Syracuse
University
Medical
and
health
data
Standardization
Compliance
Security
http://www.weforum.org/issues/charter-health-data
Priscilla M. Mayden Lecture 2012, Utah 12

13. School
of
Information
Studies
Syracuse
University
The
mul>-­‐dimensions
of
data
Research orientation
Data types
Data formats
Levels of
processing
Priscilla M. Mayden Lecture 2012, Utah 13

14. School
of
Information
Studies
Syracuse
University
Scien>fic
data
formats
Common
data
format
Image
formats
Matrix
formats
Microarray
_ile
formats
Communication
protocols
Priscilla M. Mayden Lecture 2012, Utah 14

15. School
of
Information
Studies
Syracuse
University
Scien>fic
&
medical
data
formats
• Medical
and
Physiological
Data
• Chemical
Formats
Formats
– XYZ
—
XYZ
molecule
geometry
_ile
– BDF
—
BioSemi
data
format
(.xyz)
(.bdf)
– MOL
—
MDL
MOL
format
(.mol)
– EDF
—
European
data
– MOL2
—
Tripos
MOL2
format
(.mol2)
format
(.edf)
– SDF
—
MDL
SDF
format
(.sdf)
• Molecular
Biology
data
Formats
– SMILES
—
SMILES
chemical
format
– PDB
—
Protein
Data
Bank
(.smi)
format
(.pdb)
• Bioinformatics
Formats
– MMCIF
—
MMCIF
3D
molecular
model
format
(.cif)
– GenBank
—
NCBI
GenBank
sequence
format
(.gb,
.gbk)
• Medical
Imaging
– FASTA
—
bioinformatics
sequence
– DICOM
—
DICOM
annotated
format
(.fasta,
.fa,
.fsa,
.mpfa)
medical
images
(.dcm,
.dic)
– NEXUS
—
NEXUS
phylogenetic
data
format
(.nex,
.ndk)
Priscilla M. Mayden Lecture 2012, Utah 15

16. School
of
Information
Studies
Syracuse
University
Why
data
formats?
• Archiving
• Transmission
– Preservation
for
– delivery
across
posterity
• hardware
• Storage
• software
– Availability
for
• administrative
“arbitrary”
access
– system
boundaries
• Analysis
– availability
for
processing
Priscilla M. Mayden Lecture 2012, Utah 16

17. School
of
Information
Studies
Syracuse
University
Summary
• Scienti_ic
data
formats
are
closely
tied
to
scienti_ic
computing
– Data
structure,
model,
and
attributes
– Self-­‐descriptive
with
header/metadata
– API
for
manipulating
the
data
– Interoperability:
conversion
between
different
formats
• No
one-­‐format-­‐_its-­‐all
standard
• Each
standard
has
one
or
more
tools
for
creating,
editing,
and
annotating
dataset
Priscilla M. Mayden Lecture 2012, Utah 17

18. School
of
Information
Studies
Syracuse
University
What
is
a
dataset?
What
are
some
of
the
metadata
standards
for
describing
datasets?
What
is
data
management?
DATASETS,
METADATA,
AND
DATA
MANAGEMENT
Priscilla M. Mayden Lecture 2012, Utah 18

19. School
of
Information
Studies
Syracuse
University
Dataset
classifica>on
Volume
Large-­‐volume
Small-­‐volume
Priscilla M. Mayden Lecture 2012, Utah 19

20. School
of
Information
Studies
Syracuse
University
Ecological data example: Instantaneous streamflow by watershed
http://www.hubbardbrook.org/data/dataset.php?id=1
Priscilla M. Mayden Lecture 2012, Utah 20

21. School
of
Information
Studies
Syracuse
University
Diabetes data
and trends—
Country level
estimates:
http://apps.nccd.cdc.gov/
DDT_STRS2/
NationalDiabetesPrevale
nceEstimates.aspx?
mode=PHY ;
Diabetes Data &
Trends home page:
http://apps.nccd.cdc.gov/
ddtstrs/default.aspx
Priscilla M. Mayden Lecture 2012, Utah 21

22. Clinical trials data management:
School
of
Information
Studies
Syracuse
University
http://www.clinicaltrials.gov/ct2/show/NCT00006286?term=TADS
+NIMH&rank=1
Priscilla M. Mayden Lecture 2012, Utah 22

23. School
of
Information
Studies
Syracuse
University
Common
in
the
examples
• Attributes
of
a
dataset
tell
users/managers:
– What
the
dataset
is
about
– How
data
was
collected
– To
which
project
the
data
is
related
– Who
were
responsible
for
data
collection
– Who
you
may
contact
to
obtain
the
data
– What
publications
the
data
have
generated
– ??
Priscilla M. Mayden Lecture 2012, Utah 23

24. School
of
Information
Studies
Syracuse
University
Metadata
standards
in
medical
&
health
sciences
Structure
Semantics
Medical
Bioinfomatics
NCBI Taxonomy
Healthcare
images
NCBO Bioportal
UMLS
MeSH (Medical Subject
GenBank
Headings)
GenBank
HL7
DICOM
GenBank
SNOMED CT (Systematized
Nomenclature of Medicine--
Clinical Terms)
Priscilla M. Mayden Lecture 2012, Utah 24

25. School
of
Information
Studies
Syracuse
University
Priscilla M. Mayden Lecture 2012, Utah 25

26. School
of
Information
Studies
Syracuse
University
Research
data
collec>ons
Size Metadata Management
Standards
Larger,
Multiple, Organized
discipline-­‐ comprehensive Institutionalized,
based
Heroic
individual
Smaller,
None or inside the
team-­‐based
random team
Priscilla M. Mayden Lecture 2012, Utah 26

27. School
of
Information
Studies
Syracuse
University
Research
collec>ons
• Limited
processing
or
long-­‐term
management
• Not
conformed
to
any
data
standards
• Varying
sizes
and
formats
of
data
_iles
• Low
level
of
processing,
lack
of
plan
for
data
products
• Low
awareness
of
metadata
standards
and
data
management
issues
Priscilla M. Mayden Lecture 2012, Utah 27

28. School
of
Information
Studies
Syracuse
University
Resource
collec>ons
• Authored
by
a
community
of
investigators,
within
a
domain
or
science
or
engineering
• Developed
with
community
level
standards
• Life
time
is
between
mid-­‐
and
long-­‐term
Priscilla M. Mayden Lecture 2012, Utah 28

29. School
of
Information
Studies
Syracuse
University
Reference
collec>on
• Example:
Global
Biodiversity
Information
Facility
– Created
by
large
segments
of
science
community
– Conform
to
robust,
well-­‐established
and
comprehensive
standards,
e.g.
• ABCD
(Access
to
Biological
Collection
Data)
• Darwin
Core
• DiGIR
(Distributed
Generic
Information
Retrieval)
• Dublin
Core
Metadata
standard
• GGF
(Global
Grid
Forum)
• Invasive
Alien
Species
Pro_ile
• LSID
(Life
Sciences
Identi_ier)
• OGC
(Open
Geospatial
Consortium)
Priscilla M. Mayden Lecture 2012, Utah 29

30. School
of
Information
Studies
Syracuse
University
Datasets,
data
collec>ons,
and
data
repositories
System for storing,
managing,
preserving, and
• Data
collections
are
built
for
providing access to
larger
segments
of
science
datasets
and
engineering
Data
• Datasets
repository
– typically
centered
around
an
A repository may
event
or
a
study
contain one or more
– contain
a
single
_ile
or
multiple
data collections
_iles
in
various
formats
A data collection may
– coupled
with
documentation
contain one or more
about
the
background
of
data
datasets
collection
and
processing
A dataset may
contain one or more
Priscilla M. Mayden Lecture 2012, Utah data files 30

31. School
of
Information
Studies
Syracuse
University
Data
management
for
science
research
• De_inition
from
Wikipedia:
http://en.wikipedia.org/wiki/Data_management
• Key
concepts
in
data
management:
– Data
ownership
– Data
collection
– Data
storage
How do they relate to
– Data
protection
responsible conduct of
– Data
retention
research?
– Data
analysis
http://ori.hhs.gov/images/
– Data
sharing
ddblock/data.pdf
– Data
reporting
Priscilla M. Mayden Lecture 2012, Utah 31

32. School
of
Information
Studies
Syracuse
University
An
aPempt
to
define
DM
• In
the
context
of
libraries:
– Data
management
is
a
process
in
which
librarians
plan,
design,
and
implement
data
services
to
support
eScience/
eResearch.
– Data
services
that
libraries
may
provide:
• Institutional
or
community
data
repositories
• Data
management
plan
for
pre-­‐
and
post-­‐award
of
grants
• Metadata
creation,
linking,
and
discovery
• Data
archiving,
preservation,
and
curation
• Consultation
for
research
group’s
data
management
projects
• Data
management
and
data
literacy
training
for
graduate
students
and
faculty
Priscilla M. Mayden Lecture 2012, Utah 32

33. School
of
Information
Studies
Syracuse
University
Ini>a>ves
in
research
libraries
Data support and Libraries involved in
services in supporting eScience:
institutions: 73%
45%
• Pressure
points:
– Lack
of
resources
– Dif_iculty
acquiring
the
appropriate
staff
and
expertise
to
provide
eScience
and
data
management
or
curation
services
– Lack
of
a
unifying
direction
on
campus
Source: Soehner, C., Steeves, C. & Ward, J. (2010). E-Science and data support services: A
study of ARL member institution. http://www.arl.org/bm~doc/escience_report2010.pdf
Priscilla M. Mayden Lecture 2012, Utah 33

34. School
of
Information
Studies
Syracuse
University
Data
preserva>on
challenges
• Data
formats
– Vary
in
data
types,
e.g.
vector
and
raster
data
types
– Format
conversions,
e.g.
from
an
old
version
to
a
newer
one
• Data
relations
– e.g.
there
are
data
models,
annotations,
classi_ication
schemes,
and
symbolization
_iles
for
a
digital
map
• Semantic
issues
– Naming
datasets
and
attributes
Priscilla M. Mayden Lecture 2012, Utah 34

35. School
of
Information
Studies
Syracuse
University
Data
access
challenges
• Reliability
• Authenticity
• Leverage
technology
to
make
data
access
easier
and
more
effective
– Cross-­‐database
search
– Integration
applications
– “Science-­‐ready”
datasets
Priscilla M. Mayden Lecture 2012, Utah 35

36. School
of
Information
Studies
Syracuse
University
Suppor>ng
digital
research
data
• Lifecycle
of
research
data
– Create:
data
creation/capture/gathering
from
laboratory
experiments,
_ield
work,
surveys,
devices,
media,
simulation
output…
– Edit:
organize,
annotate,
clean,
_ilter…
– Use/reuse:
analyze,
mine,
model,
derive
additional
data,
visualize,
input
to
instruments
/computers
– Publish:
disseminate,
create,
portals
/data.
Databases,
associate
with
literature
– Preserve/destroy:
store
/
preserve,
store
/replicate
/
preserve,
store
/
ignore,
destroy…
Priscilla M. Mayden Lecture 2012, Utah 36

37. School
of
Information
Studies
Syracuse
University
Suppor>ng
data
management
The data deluge Researchers need:
Numerical, image, video Specialized search
engines to discover
Models, simulations, bit the data they need
streams
Powerful data mining
XML, CVS, DB, HTML tools to use and
analyze the data
Priscilla M. Mayden Lecture 2012, Utah 37

38. School
of
Information
Studies
Syracuse
University
Research
data
management
Community
Institution
eScience
librarian
Financial and
policy support Science Data content User
domain idiosyncrasies requirements
Evolving and interconnecting –
Institutional
Community
National
International
repository
repository
repository
repository
Priscilla M. Mayden Lecture 2012, Utah 38

39. School
of
Information
Studies
Syracuse
University
Implica>ons
to
scholarly
communica>on
process
Publishing
Curation
Archiving
Data
publishing;
Maintaining,
preserving
The
long-­‐term
New
scholarly
publishing
and
adding
value
to
storage,
retrieval,
and
models—open
access,
digital
research
data
use
of
scienti_ic
data
institutional
and
throughout
its
lifecycle.
and
methods.
community
repositories,
self-­‐publishing,
library
publishing,
....
Priscilla M. Mayden Lecture 2012, Utah 39

40. School
of
Information
Studies
Syracuse
University
Summary
• E-­‐Science
development
has
raised
expectations
to
research
libraries
– Working
knowledge
and
skills
in
e-­‐Science
– Focus
on
process
(data
and
team
science)
rather
than
product
(reference
services)
– Proactive,
collaborative,
integrative,
and
interdisciplinary
Priscilla M. Mayden Lecture 2012, Utah 40

41. School
of
Information
Studies
Syracuse
University
Case
Study:
Learning
Data
Management
Needs
from
Scien>sts

42. School
of
Information
Studies
Syracuse
University
Gravita>onal
Wave
(GW)
Research
Priscilla M. Mayden Lecture 2012, Utah 42

43. School
of
Information
Studies
Syracuse
University
What
is
the
problem?
• Tracking
data
output
and
work_lows
is
dif_icult
due
to
lack
of
provenance
data
• Search
of
datasets
is
limited
due
to
lack
of
speci_ic
options
• Within
the
LIGO
community,
data
sharing
and
reuse
is
dif_icult
without
provenance
metadata
Data provenance case study 43

44. School
of
Information
Studies
Syracuse
University
Understand
the
research
workflow
• Interview
the
scientist
– Listening
(good
listening
skills)
– Asking
questions
(don’t
be
afraid
of
asking
questions)
– Use
your
librarian
brain
to
ingest
the
conversation:
• How
does
the
research
_low
from
one
point
to
next?
• What
consists
of
the
research
input
and
output
at
each
stage
of
research
in
terms
of
data?
Priscilla M. Mayden Lecture 2012, Utah 44

45. Mapping
out
the
knowledge
v0.1
School
of
Information
Studies
Syracuse
University
Priscilla M. Mayden Lecture 2012, Utah 45

46. Mapping
out
the
knowledge
v0.2
School
of
Information
Studies
Syracuse
University
Priscilla M. Mayden Lecture 2012, Utah 46

47. Mapping
out
the
knowledge
v1.0
School
of
Information
Studies
Syracuse
University
Priscilla M. Mayden Lecture 2012, Utah 47

48. School
of
Information
Studies
Syracuse
University
Lessons
learned
• Science
is
learnable
even
if
you
don’t
have
a
subject
background
– Learn
enough
to
understand
the
research
process
and
work_low
• Scientists
are
eager
to
get
help
• Librarians
need
to
be
technical-­‐minded
– Data,
metadata,
database
– Structures,
models,
work_lows
• Librarians
need
to
be
good
listeners
while
staying
good
conversation
leaders
– Know
when
and
how
to
lead
the
conversation
to
get
what
you
need
for
data
management
planning
and
implementation
– Do
your
homework
on
the
subject
so
that
you
can
be
an
intelligent
listener
Priscilla M. Mayden Lecture 2012, Utah 48

49. School
of
Information
Studies
Syracuse
University
Case
Discussions

50. School
of
Information
Studies
Syracuse
University
Case
Study
#1:
To
build
or
not
to
build
a
data
repository?
by the researchers in this institution.
A university library has developed an institutional repository for preserving and
providing access to the scholarly output
Now the new challenge arises from e-science research demanding data
management plan by the funding agency and the linking between publications
and data by the authors and users. You already know that some faculty use
their disciplinary data repository for submitting their datasets (e.g., GenBank for
microbiology research data). The problem you face now is whether an
institutional data repository should be built for those who do “small science” and
don’t have funding nor expertise to manage their data.
Questions to be addressed:
• What are the strategies you will use to approach the problem?
• What are the possible solutions for the problem?
• What are some of the tradeoffs for the solutions you will adopt?
Priscilla M. Mayden Lecture 2012, Utah 50

51. School
of
Information
Studies
Syracuse
University
Case
study
#2:
Developing
a
data
taxonomy
The concept of research data management is a stranger to many faculty as
well as your library staff. What is data? What is a data set? These seemingly
simple terms can be very confusing and have different interpretations in
different context and disciplines. As part of the data management strategies,
you decide to develop an authoritative data taxonomy for the campus research
community. This data taxonomy will benefit the creation and use of institutional
data policies, data repository or repositories, and data management plans
required of funding agencies.
Questions to be addressed:
• What should the data taxonomy include?
• What form should it take, a database-driven website or a static HTML page?
• Who should be the constituencies in this process?
• Who will be the maintainer once the taxonomy is released?
Priscilla M. Mayden Lecture 2012, Utah 51

52. School
of
Information
Studies
Syracuse
University
Case
study
#3:
Developing
a
data
policy
Data policies play an important role in governing how the data will be managed,
shared, and accessed. It is also an instrument that will fend off potential legal
problems. Data policies have several types: data access and use, data
publishing, and data management. Your university’s Office of Sponsored
Research has some existing policy on data, but it is neither systematic nor
complete. Many of the terms were defined years ago and did not cover the new
areas such as the embargo period of data. As the university has decided to
build a data repository for managing and preserving datasets, a data policy has
become one of the top priorities for both the institution and the data repository.
Questions to be addressed:
• What should the data policy include?
• Who should be the constituencies in this process?
• Who will be the interpretation authority for the data policy?
Priscilla M. Mayden Lecture 2012, Utah 52

53. School
of
Information
Studies
Syracuse
University
Case
study
#4:
Cataloging
datasets
Describing datasets is the process of creating metadata for datasets. In
scientific disciplines, several metadata standards have been developed, e.g.,
the Content Standard for Digital Geospatial Metadata (CSDGM), Darwin Core,
and Ecological Metadata Language (EML). Each of these metadata standards
contains hundreds of elements and requires both metadata and subject
knowledge training in order to use them. Besides, creating one record using
any of these standards will require a tremendous time investment. But you
library does not have such specialized personnel nor have the fund to hire new
persons for the job. The existing staff has some general metadata skills such as
Dublin Core. In deciding the metadata schema for your data repository, you
need to address these questions:
• Should I adopt a scientific metadata standard or develop one tailored to our
need?
• How can I learn what metadata elements are critical to dataset submitters and
searchers?
• What are some of the benefits and disadvantages for adopting a standard or
developing a local schema?
Priscilla M. Mayden Lecture 2012, Utah 53

54. School
of
Information
Studies
Syracuse
University
Case
study
#5:
Evalua>ng
data
repository
tools
Research data as a driving force for e-science is inherently a tool-intensive
field. Tools related to data management can be divided into two broad
categories: those for creating metadata records and those for data repository
management. An academic institution decided to build their own data repository
as part of the supporting service for researchers to meet the data management
plan requirement of funding agencies. This data repository development task
was handed down to the library. You the library director have to decide whether
to develop an in-house system or use an off-the-shelf software system. As
usual, you put together a taskforce to find a solution to this challenge. The
questions to be addressed by the taskforce include:
• What are the options available to us?
• What evaluation criteria are the most important to our goal?
• What are the limitations for us to adopt one option or the other?
• How will this option be interoperate with existing institutional repository
system? Or, can the existing repository system used for data repository
purposes?
Priscilla M. Mayden Lecture 2012, Utah 54