1. Preparing
eScience
Librarians
for
Managing
Research
Data
RDAP
2012,
New
Orleans,
LA
Jian
Qin
School
of
InformaCon
Studies
Syracuse
University
2. NoCons
of
eScience
librarianship
ProacCve
training
for
data
literacy
ConsultaCve
Leader
in
services
for
eScience
data
use
and
iniCaCves
management
AcCve
players
and
contributors
of
data
Part
of
team
curaCon
transcending
disciplinary
boundaries
RDAP
2012,
New
Orleans
2
3. EducaCng
the
new
type
of
workforce
• ScienCfic
data
literacy
(SDL)
project
(hNp://sdl.syr.edu),
2007-‐2009
• E-‐Science
Librarianship
Curriculum
project
(eSLib
hNp://eslib.ischool.syr.edu),
2009-‐2012,
in
partnership
with
Cornell
University
Library
RDAP
2012,
New
Orleans
3
4. A
curriculum
for
eScience
librarianship
• Overall
learning
objecCves:
– Ability
to
arCculate
eScience
and
to
plan
and
develop
eScience
librarianship
projects
– Competency
in
scienCfic
data
management
– Competency
in
cyberinfrastructure
technologies
– Ability
to
collaborate,
communicate,
and
lead
in
eScience
librarianship
projects
RDAP
2012,
New
Orleans
4
5. Ability
to
• ArCculate
eScience
process
and
data
lifecycle
arCculate
• IdenCfy
user
needs
and
translate
eScience
and
to
the
needs
into
system
requirements
plan
and
develop
• Make
plans
for
eScience
eScience
librarianship
project
iniCaCon
and
librarianship
implementaCon
• Conduct
research
on
data
related
projects
issues
such
as
insCtuConal
data
policy,
support
services,
and
technology
adopCon
• Write
grant
proposals
for
obtaining
funding
to
support
eScience
librarianship
projects
RDAP
2012,
New
Orleans
5
6. • ArCculate
data
Competency
in
characterisCcs
scien-fic
data
• Analyze
domain
data
sets
management
and
develop
data
models
• Define
metadata
element
sets
• Develop
specialized
metadata
for
data
curaCon,
preservaCon,
and
access
• Create
metadata
records
for
scienCfic
data
sets
RDAP
2012,
New
Orleans
6
7. • Maintain
informaCon
Competency
in
retrieval
interfaces
cyberinfrastruct • Maintain
informaCon
ure
technologies
exchange
networks
• Program,
write
code,
and
manipulate
scripts
• Use
content
management
systems
• IdenCfy
and
model
data/
work
flows
• Assess
research
needs
for
and
performance
of
CI
tools
RDAP
2012,
New
Orleans
7
8. Ability
to
• Develop
partnership
with
collaborate,
internal
and
external
communicate,
and
organizaConal
units
and
lead
in
eScience
collaborators
librarianship
• Communicate
with
projects
administrators
and
researchers
• Engage
researchers
in
data
management
processes
• IniCate
and
lead
in
eScience
librarianship
projects
RDAP
2012,
New
Orleans
8
9. The
curriculum
Courses
Primary
learning
outcomes
in
eScience
librarianship
projects
Ability
to
collaborate,
communicate,
and
lead
ScienCfic
Data
Competency
in
scienCfic
data
Management
(core)
management
Competency
in
Cyberinfrastructure
(core)
cyberinfrastructure
technologies
Ability
to
arCculate
eScience
and
to
Data
services
(capstone)
plan
and
develop
eScience
librarianship
projects
Database
systems
(required
elecCve)
Metadata
(elecCve)
RDAP
2012,
New
Orleans
9
10. Theme
1:
building
fundamentals
1
2
Case
studies
that
use
Overview
of
scienCfic
data
pracCcal
examples
to
guide
management
that
covers
students
step-‐by-‐step
in
data
and
metadata
data
analysis
and
fundamentals
management
3
Using
scienCfic
data,
which
involves
discussions
of
data
quality,
data
repositories
and
discovery,
data
analysis
and
presentaCon,
and
ethics
and
intellectual
property
issues
RDAP
2012,
New
Orleans
10
11. Building
fundamentals:
data
formats
Overview
of
scienti.ic
data
management
that
covers
data
and
metadata
fundamentals
Data
NASA’s
de-inition
of
data
Processing
level
level
processing
levels
Level
4
Self-‐descripCve
informaCon
existed
as
Level
Reconstructed
unprocessed
instrument
0
data
at
full
resolutions.
Level
3
header
of
the
data
file
Level
Reconstructed,
unprocessed
instrument
Level
2
1A
data
at
full
resolution,
time
referenced,
and
annotated
with
ancillary
information,
Common
Data
Format
(CDF)
Level
1B
Flexible
Image
Transport
System
(FITS)
but
not
applied
to
the
Level
0
data.
GRid
In
Binary
(GRIB)
Level
Level
1A
data
that
has
been
processed
to
Level
1A
Hierarchical
Data
Format
(HDF)
1B
sensor
units.
Not
all
Network
Common
Data
Format
(netCDF)
instruments
will
have
a
Level
1B
Level
0
equivalent.
Major
scienCfic
data
format
RDAP
2012,
New
Orleans
11
12. Building
fundamentals:
Understanding
data
and
metadata
Data
formats
Processing
levels
Data
collecCons
Some
formats
contain
self-‐
Lineage
vital
to
descripCve
metadata
assessing
data
Metadata
standards
need
quality
to
be
adjusted
for
local
descripCon
needs
RDAP
2012,
New
Orleans
12
13. Building
fundamentals:
data
literacy
IL:
ACRL.
(2010).
DL:
Finn,
Charles,
W.P.
(Tech
&
Learning,
2004)
SDL:
Qin,
J.
&
J.
D’Ignazio,
(Journal
of
Library
Metadata,
2010)
RDAP
2012,
New
Orleans
13
14. Theme
2:
Analysis
and
generalizaCon
Analysis
of
data
problems
is
an
analysis
of
domain
data,
requirements,
and
workflows
that
will
lead
to
the
development
of
soluCons.
RDAP
2012,
New
Orleans
14
15. Analysis
and
generalizaCon:
engaging
in
real
research
projects
• Engage
students
in
research
and
service
projects
– Data
policy
analysis
– Data
management
consultaCon
– Interviews
and
survey
design
• Course
projects
– Real-‐world
data
management
problems
RDAP
2012,
New
Orleans
15
16. Theme
3:
collaboraCon
and
communicaCon
• Community
of
pracCce
• InsCtuConalizaCon
of
data
services
– Data
policies
– Compliance
to
funding
agency
policies
and
mandates
– Infrastructural
data
services
at
insCtuConal,
community,
and
naConal
levels
• Awareness,
incenCves,
and
training
RDAP
2012,
New
Orleans
16
17. CollaboraCon
and
communicaCon
• Mentoring
by
Cornell
librarians,
led
by
Gail
Steinhart
• Internships
in
academic
libraries
and/or
research
centers
• Guest
speakers
to
classes
• Engaging
students
in
research
and
service
projects
RDAP
2012,
New
Orleans
17
18. Evolving
curriculum
CAS
in
Data
Science
Required
courses:
• Database
• Applied
Data
Science
Data
storage
Data
Data
Systems
and
analyCcs
visualizaCon
management
management
RDAP
2012,
New
Orleans
18