This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016

1. LIBBIE STEPHENSON, DATA ARCHIVIST (RETIRED)
UCLA SOCIAL SCIENCE DATA ARCHIVE
LIBBIE@G.UCLA.EDU
HTTPS://DATAVERSE.HARVARD.EDU/DATAVERSE/SSDA_UCLA
Data Curation for Quantitative
Social Science Research:
A Case Study
NISO Virtual Conference: Data
Curation – Cultivating Past Research
Data for Future Consumption
August 31, 2016

2. DISCLAIMER
 I am retired from UCLA so my
comments reflect my own experience
and expertise. They do not necessarily
reflect the ideas, opinions or practices
of anyone at UCLA.
 These materials are free for you to
use, but please cite accordingly.
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3. OVERVIEW
 About the Archive
 About the data we manage
 What we are trying to do
 What we actually do
 Some illustrations
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4. ABOUT THE ARCHIVE
 Operating since 1964 -- before email, PC’s, Internet,
laptops, smart phones; Manage survey/quantitative
data stored on media from punch cards to cloud
 Staff have library science degrees; statistical and
technical expertise; quantitative social science
background
 Serve all UCLA quantitative researchers: Provide
reference, cataloging/metadata, long term archiving;
support in data rescue, management, security.
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4https://dataverse.harvard.edu
/dataverse/ssda_ucla

5. SURVEY/QUANTITATIVE
RESEARCH
 Carried out in the U.S. since 1940’s -- post
WW2
 1960’s -70’s -- ICPSR & academic archives
 1970’s -- growth of data oriented professional
associations (IASSIST, APDU, IFDO, CESSDA)
 Focused on society and social norms
 Predict outcomes; test assumptions; study
change over time; run experiments
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Note: in any
discipline we
also need to
understand
the work
flow of the
research and
the way
individuals
approach
their work.

6. CURATION GOALS
 Researcher driven philosophy of open access,
data sharing, reuse
 Collaborative, multi-unit or multi-institutional
 Ensure data conservation and long term usability,
as well as discovery and access
 Processes and work flows support disaster
planning
 Use of best and trusted digital repository
policies, models, practices, and work flows
 Reflect values of accountability and integrity
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7. POLICIES SUPPORT PRACTICE
 Foundational, essential to a strong data curation
infrastructure.
 Encompasses what is acquired/collected, curation
levels and scope, ensures long term usability, drives
processes and work flows
 Social Science Data Archive policy
 TOOL : Policy-making for Research Data in
Repositories by Ann Green, Stuart Macdonald and
Robin Rice.
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8. OUR STEPS IN CURATION
 Initial contact
 Data Quality Review and Appraisal
 Ingest
Verification
Metadata
Physical storage
 Access
 Preservation
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9. INITIAL CONTACT
 Data Curation Profile
 Data Management Plan
 Guide to Social Science Data Preparation
and Archiving
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10. APPRAISAL
 Archival Collection Policy
 Also depends on:
 Resources to process
 Long term resources
 Fitness, usefulness
 Data Deposit Form signatures and
completeness; commitment to share
data; privacy and confidentiality
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11. DATA QUALITY REVIEW
Use of statistical packages, emulator, Adobe Pro, Excel,
Colectica, Text editor
 Verify deposit package, check sums, freq’s,
compare data to documentation
 Completeness of codebook, question text,
sampling, weighting, recodes, methods
 Disclosure analysis, check for personal identifiers
and assess privacy/confidentiality of respondents
 Documentation converted to PDF/A
11
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12. EXAMPLE: WHAT KIND OF DATA?
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13. CODEBOOK DOCUMENTS THE
COLUMNS
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5002 01 01 302000 001 101 10004B121068965
Each item is
called a variable.
We refer to the
numeric content
of each item as a
value.

14. COMPARE FREQS TO CODEBOOK
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VALUES
VALUE LABELS
VARIABLE

15. RUN MARGINALS/FREQUENCIES
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Sex of Respondent
Frequency Percent Valid Percent Cumulative Percent
Valid MALE 856 45.1 45.1 45.1
FEMALE 1041 54.9 54.9 100.0
Total 1897 100.0 100.0
What is your race - ethnicity
Frequency Percent Valid Percent Cumulative Percent
Valid White 618 32.6 32.6 32.6
Hispanic 475 25.0 25.0 57.6
Black 474 25.0 25.0 82.6
Asian or Pacific Islander 282 14.9 14.9 97.5
Native American or Alaskan native 17 .9 .9 98.4
Identifies more than one of the above groups 20 1.1 1.1 99.4
DON'T KNOW 2 .1 .1 99.5
REFUSED 9 .5 .5 100.0
Total 1897 100.0 100.0

16. INGEST – PHYSICAL FORMATS
 Virus check, run check sums, address
versioning, fixity, file naming conventions
 Convert files to archival formats if required
 Back copies to external media
 Copy datasets to Dataverse; Safe Archive tool
 Use of secure file transfer client
 SQL/PHP scripts for local holdings file
 Compression software (7-zip)
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Address
disaster plan
and file
access
(public and
local);
Security
requirements;
LOCKSS

17. INGEST– BIBLIOGRAPHIC METADATA
Bibliographic metadata enables search and
discovery:
 Establish bibliographic-level identity for unique
items
 Bibliographic record to WorldCat/Voyager
 Add record to holdings database (SQL)
 Create Dataverse record; Assign persistent
identifier
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Produce and review with investigator

18. WHAT ELSE DO WE NEED TO
KNOW ABOUT THE DATA?
 Description of the study
 Citation
 Funding source
 Methodology
 Sampling
 Publications
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19. EXAMPLE - DATAVERSE
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Links to tools to
manage collections
Navigate to and
search for studies
Studies can be downloaded or
analyzed online

20. VARIABLE LEVEL SEARCH
CAPABILITIES
 Enables searching across many studies at
once.
 Enables searching shared catalogs of multiple
archives
 TOOLS: Colectica Repository and NESSTAR
 Requires local or remote hosting of software.
 Can share the metadata files for repurposing.
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21. DATA DOCUMENTATION
INITIATIVE
Document, Discover, and Interoperate
 “International standard for describing data
that result from observational methods in
the social, behavioral, economic, and health
sciences”
 “Facilitates interpretation and understanding
-- both by humans and computers”
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http://www.ddialliance.org/

22. INGEST-VARIABLE LEVEL METADATA
Descriptive metadata of detailed information about the
data enables understandability and reuse:
 Create variable-level metadata, using Colectica or
NESSTAR to produce standardized metadata records
 Create DDI record; full DDI codebook
 Migrate DDI to Colectica Repository
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Produce and review with investigator
NESSTAR

23. EXAMPLE - IMPORTING DATA
 Use the
Data tab
to import
files from
SPSS or
STATA
formats.
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24. Label
Question
text
Numeric
values
Variable Details include variable name,
label, description or question text, and
types of coding.
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25. EXAMPLE DDI FROM COLECTICA
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DDI fields are in
red; used to
create
documentation;
can be
repurposed

26. PRESERVATION AND CURATION
 Continuous monitoring of file formats; migrate to new formats
when:
New operating system; New version of statistical software
New mode of file transfer; Code change
 Monitoring of database function; software updates or redesigns
 Monitoring of servers, external media health; replace as needed
 Data forensics; check sums; validation; authentication; version
control; format migration; refresh media; record preservation
metadata -- DDI
 Review disaster plan and collection policy at regular intervals
 Review new or revised regulations for intellectual property;
security; data producers/distributors; funding agencies
 Review with original depositor, their data management plans,
changes in access or user permissions
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Focus is on functional-level preservation and long term
usability through use of DDI and continuous review.

27. UNCOMFORTABLE TRUTHS
 Data management in institutions requires
high level administrative participation;
new, sustained funding; and differently
trained staff
 Data management planning is not a static
event but a continuous process to ensure
long term independently understandable
informed reuse of research
 There is an urgent need for standards, tools,
and best practice models for many different
file formats and disciplines
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28. NEXT STEPS FOR PRACTITIONERS
“Crucial metadata about data are not always
being captured or created and linked to data in
repositories. Storage and persistence of data
submissions isn't enough. We need data
archivists and librarians to commit to partnering
with researchers to curate data -- to review
incoming data for usability, confidentiality, and
completeness of descriptive information.”
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Ann Green (2016) Email communication
Used with permission

29. ANY QUESTIONS?
THANK YOU!
 Social Science Data Archive, UCLA
 Box 951484
Los Angeles, CA 90095-1484
310-825-0716
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30. LINKSSocial Science Data Archive dataverse.harvard.edu/dataverse/ssda_ucla
Data Seal of Approval www.datasealofapproval.org/en/
National Digital Stewardship Alliance
ndsa.org/activities/levels-of-digital-preservation/
Open Archival Information System
www.oclc.org/research/publications/library/2000/lavoie-oais.html
Social Science Data Archive Policy
data-archive.library.ucla.edu/SSDA_collectionAndArchivingPolicy.pdf?_ga=
1.3255478.786669706.1378228281
Data Curation Profile datacurationprofiles.org/
Data Management Planning at ICPSR
www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/index.html
ICPSR Guide to Data Preparation
www.icpsr.umich.edu/icpsrweb/content/deposit/guide/
Colectica www.colectica.com/
NESSTAR www.nesstar.com/index.html
DDI www.ddialliance.org/
Dataverse dataverse.org/
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