Adding valuethroughdatacuration

Adding Value through Data Curation
APLIC
April 29, 2015
San Diego, California
Libbie Stephenson, UCLA (libbie@ucla.edu)
Jared Lyle, ICPSR (lyle@umich.edu)

UCLA Social Science Data Archive
Established mid-1960’s
Small domain-specific archive of data for use in
quantitative research
Surveys, enumerations, public opinion polls,
administrative records
Three full time staff; part time student interns
Holdings are partly files deposited by faculty
Services include: Reference, statistical, data
management plans, research project support

• 50+ years of
experience
• Data stewardship
• Data management
• Data curation
• Data preservation
ICPSR

Recent Federal Data Sharing Initiatives
• NIH: 2003 – data sharing plans
• NSF: 2011 – data management plans
• OSTP: 2013 – Memo with subject “Increasing
Access to the Results of Federally Funded
Scientific Research”

https://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf

http://sites.nationalacademies.org/DBASSE/CurrentProjects/DBASSE_082378

http://guides.library.oregonstate.edu/federaloa

http://bit.ly/FedOASummary

Data Portion of Memo - 13 Elements
• The elements are also summarized online
within ICPSR’s Web site:
http://icpsr.umich.edu/content/datamanagement/ostp.html

“It saves funding and avoids
repeated data collecting efforts,
allows the verification and
replication of research findings,
facilitates scientific openness,
deters scientific misconduct, and
supports communication and
progress.”
Niu (2006). “Reward and Punishment Mechanism for Research Data Sharing.”
http://www.iassistdata.org/downloads/iqvol304niu.pdf

UK results on data sharing attitudes
• In 2011 survey, 85% of researchers said they
thought their data would be of interest to
others.
• Only 41% said they would be happy to make
their data available.
• Only a third had previously published data.
Source: DaMaRO Project, University of Oxford
http://www.slideshare.net/DigCurv/15-meriel-patrick

Data Sharing Status
Federal
Agency
Shared
Formally,
Archived
(n=111)
Shared
Informally,
Not
Archived
(n=415)
Not
Shared
(n=409)
NSF
(27.3%)
22.4% 43.7% 33.9%
NIH
(72.7%)
7.4% 45.0% 47.6%
Total 11.5% 44.6% 43.9%
Pienta, Gutmann, & Lyle (2009). “Research Data in The Social Sciences: How Much is Being Shared?”
http://ori.hhs.gov/content/research-research-integrity-rri-conference-2009
See also: Pienta, Gutmann, Hoelter, Lyle, & Donakowski (2008). “The LEADS Database at ICPSR:
Identifying Important ‘At Risk’ Social Science Data.”
http://www.data-pass.org/sites/default/files/Pienta_et_al_2008.pdf
Pienta, Alter, & Lyle (2010). “The Enduring Value of Social Science Research: The
Use and Reuse of Primary Research Data”. http://hdl.handle.net/2027.42/78307

Adding Value through Data Curation
1.Maximize access
2.Protect confidentiality and privacy
3.Appropriate attribution
4.Long term preservation and sustainability
5.Data management planning

Maximize Access (Data Curation)

Discoverable
http://www.flickr.com/photos/papertrix/38028138/

Accessible
http://www.guardian.co.uk/science/grrlscientist/2012/mar/29/1

A well-prepared data collection
“contains information intended to
be complete and self-explanatory”
for future users.

Protect confidentiality and privacy
• It is critically important to protect the identities of research
subjects
• Disclosure risk is a term that is often used for the possibility
that a data record from a study could be linked to a specific
person
• Data with these risks can be shared via a secured virtual
environment
• Data concerning very sensitive topics can also be shared via
a secured environment

Restricted-use data can be effectively
shared with the public
• ICPSR has been sharing restricted-use data for
over a decade. Three methods are used:
– Secure Download
– Virtual Data Enclave
– Physical Enclave
• ICPSR stores & shares over 6,400 restricted-
use datasets associated with over 2,000
‘active’ restricted-use data agreements

Appropriate Attribution
• Properly citing data encourages the replication of
scientific results, improves research standards, guarantees
persistent reference, and gives proper credit to data
producers.
• Citing data is straightforward. Each citation must include
the basic elements that allow a unique dataset to be
identified over time: title, author, date, version, and
persistent identifier (e.g., DOI).
• Resources: ICPSR's Data Citations page , IASSIST's Quick
Guide to Data Citation, DataCite.

Long term preservation and sustainability

“Digital information lasts forever or five years,
whichever comes first”.
-Jeff Rothenberg

https://flic.kr/p/arHsh4

http://www.flickr.com/photos/blude/2665906010/

Data Management Planning
• Data management plans describe how researchers
will provide for long-term preservation of, and
access to, scientific data in digital formats.
• Data management plans provide opportunities for
researchers to manage and curate their data more
actively from project inception to completion.

ICPSR’s Data Management & Curation Site
http://www.icpsr.umich.edu/datamanagement/

Purpose of Data Management Plans
• Data management plans describe how researchers
will provide for long-term preservation of, and
access to, scientific data in digital formats.
• Data management plans provide opportunities for
researchers to manage and curate their data more
actively from project inception to completion.

Elements of a Data Management
Plan
Element Description Recommended?
Data description Provide a brief description of the
information to be gathered; the
nature, scope and scale of the
data that will be generated or
collected.
Highly recommended.
Generic Example 1:
This project will produce public-use nationally representative survey data for
the United States covering Americans' social backgrounds, enduring political
predispositions, social and political values, perceptions and evaluations of
groups and candidates, opinions on questions of public policy, and participation
in political life.

Plan
collected.
Highly recommended.
Generic Example 2:
This project will generate data designed to study the prevalence and correlates
of DSM III-R psychiatric disorders and patterns and correlates of service
utilization for these disorders in a nationally representative sample of over 8000
respondents. The sensitive nature of these data will require that the data be
released through a restricted use contract.

Plan
collected.
Highly recommended.
ICPSR:
[Provide a brief description of the information to be gathered -- the nature,
scope, and scale of the data that will be generated or collected.] These data,
which will be submitted to ICPSR, fit within the scope of the ICPSR Collection
Development Policy. A letter of support describing ICPSR's commitment to the
data as they have been described is provided.

Plan
Access and
sharing
Indicate how you intend to archive
and share your data and why you
have chosen that particular option.
Highly recommended.
Generic Example 1:
The research data from this project will be deposited with [repository] to ensure
that the research community has long-term access to the data.

Plan
Access and
sharing
Highly recommended.
Generic Example 2:
The project team will create a dedicated Web site to manage and distribute the
data because the audience for the data is small and has a tradition of interacting
as a community. The site will be established using a content management
system like Drupal or Joomla so that data users can participate in adding site
content over time, making the site self-sustaining. The site will be available at a
.org location. For preservation, we will supply periodic copies of the data to
[repository]. That repository will be the ultimate home for the data.

Plan
Access and
sharing
Highly recommended.
Generic Example 3:
The research data from this project will be deposited with [repository] to ensure
that the research community has long-term access to the data. The data will be
under embargo for one year while the investigators complete their analyses.

Plan
Access and
sharing
Highly recommended.
Generic Example 4:
The research data from this project will be deposited with the institutional
repository on the grantees' campus.

Plan
Access and
sharing
Highly recommended.
ICPSR:
The research data from this project will be deposited with the digital repository
of the Inter-university Consortium for Political and Social Research (ICPSR) to
ensure that the research community has long-term access to the data. The
integrated data management plan proposed leverages capabilities of ICPSR
and its trained archival staff.

Plan
Access and
sharing
Highly recommended.
ICPSR:
ICPSR will make the research data from this project available to the broader
social science research community. Public-use data files: These files, in which
direct and indirect identifiers have been removed to minimize disclosure risk,
may be accessed directly through the ICPSR Web site. After agreeing to Terms
of Use, users with an ICPSR MyData account and an authorized IP address
from a member institution may download the data, and non-members may
purchase the files. Restricted-use data files: These files are distributed in those
cases when removing potentially identifying information would significantly
impair the analytic potential of the data. Users (and their institutions) must apply
for these files, create data security plans, and agree to other access controls.

Plan
Access and
sharing
Highly recommended.
ICPSR (continued):
Timeliness: The research data from this project will be supplied to ICPSR before
the end of the project so that any issues surrounding the usability of the data
can be resolved. Delayed dissemination may be possible. The Delayed
Dissemination Policy allows for data to be deposited but not disseminated for an
agreed-upon period of time (typically one year).

Plan
Metadata A description of the metadata to
be provided along with the
generated data, and a discussion
of the metadata standards used.
Highly recommended.
Generic Example 1:
Metadata will be tagged in XML using the Data Documentation Initiative (DDI)
format. The codebook will contain information on study design, sampling
methodology, fieldwork, variable-level detail, and all information necessary for
a secondary analyst to use the data accurately and effectively.

Plan
Highly recommended.
Generic Example 2:
The clinical data collected from this project will be documented using CDISC
metadata standards.

Plan
Highly recommended.
ICPSR:
Substantive metadata will be provided in compliance with the most relevant
standard for the social, behavioral, and economic sciences -- the Data
Documentation Initiative (DDI). This XML standard provides for the tagging of
content, which facilitates preservation and enables flexibility in display. These
types of metadata will be produced and archived:
Study-Level Metadata Record. A summary DDI-based record will be created
for inclusion in the searchable ICPSR online catalog. This record will be
indexed with terms from the ICPSR Thesaurus to enhance data discovery.

Plan
Highly recommended.
ICPSR (continued):
Data Citation with Digital Object Identifier (DOI). A standard citation will be
provided to facilitate attribution. The DOI provides permanent identification for
data & ensures that they will always be found at the URL.
Variable-Level Documentation. ICPSR will tag variable-level information in DDI
format for inclusion in ICPSR's Social Science Variables Database (SSVD),
which allows users to identify relevant variables &studies of interest.

Plan
Highly recommended.
ICPSR (continued):
Technical Documentation. The variable-level files described above will serve
as the foundation for the technical documentation or codebook that ICPSR will
prepare and deliver.
Related Publications. Resources permitting, ICPSR will periodically search for
publications based on the data and provide two-way linkages between data
and publications.

Plan
Intellectual
property rights
Entities or persons who will hold
the intellectual property rights to
the data, and how IP will be
protected if necessary. Any
copyright constraints (e.g.,
copyrighted data collection
instruments) should be noted.
Highly recommended.
Generic Example 1:
The principal investigators on the project and their institutions will hold the
copyright for the research data they generate.

Plan
Intellectual
property rights
Highly recommended.
Generic Example 2:
The principal investigators on the project and their institutions will hold the
copyright for the research data they generate but will grant redistribution rights
to [repository] for purposes of data sharing.

Plan
Intellectual
property rights
Highly recommended.
Generic Example 3:
The data gathered will use a copyrighted instrument for some questions. A
reproduction of the instrument will be provided to [repository] as
documentation for the data deposited with the intention that the instrument be
distributed under "fair use" to permit data sharing, but it may not be
redisseminated by users.

Plan
Intellectual
property rights
Highly recommended.
ICPSR:
Principal investigators and their institutions hold the copyright for the research
data they generate. By depositing with ICPSR, investigators do not transfer
copyright but instead grant permission for ICPSR to redisseminate the data
and to transform the data as necessary to protect respondent confidentiality,
improve usefulness, and facilitate preservation.

Plan
Ethics and
privacy
A discussion of how informed consent
will be handled and how privacy will be
protected, including any exceptional
arrangements that might be needed to
protect participant confidentiality, and
other ethical issues that may arise.
Highly recommended.
Generic Example 1:
For this project, informed consent statements will use language that will not
prohibit the data from being shared with the research community.

Plan
Ethics and
privacy
Highly recommended.
Generic Example 2:
The following language will be used in the informed consent: The information in
this study will only be used in ways that will not reveal who you are. You will not
be identified in any publication from this study or in any data files shared with
other researchers. Your participation in this study is confidential. Federal or
state laws may require us to show information to university or government
officials [or sponsors], who are responsible for monitoring the safety of this
study.

Plan
Ethics and
privacy
Highly recommended.
Generic Example 3:
The proposed medical records research falls under the HIPAA Privacy Rule.
Consequently, the investigators will provide documentation that an alteration or
waiver of research participants' authorization for use/disclosure of information
about them for research purposes has been approved by an IRB or a Privacy
Board.

Plan
Ethics and
privacy
Highly recommended.
ICPSR:
Informed consent: For this project, informed consent statements, if applicable, will not
include language that would prohibit the data from being shared with the research
community. Disclosure risk management: The research project will remove any direct
identifiers in the data before deposit with ICPSR. Once deposited, the data will undergo
procedures to protect the confidentiality of individuals whose personal information may
be part of archived data. These include: (1) rigorous review to assess disclosure risk, (2)
modifying data if necessary to protect confidentiality, (3) limiting access to datasets in
which risk of disclosure remains high, and (4) consultation with data producers to
manage disclosure risk. ICPSR will assign a qualified data manager certified in
disclosure risk management to act as steward for the data while they are being
processed. The data will be processed and managed in a secure non-networked

Plan
Format Formats in which the data will be
generated, maintained, and made
available, including a justification
for the procedural and archival
appropriateness of those formats.
Highly recommended.
Generic Example 1:
Quantitative survey data files generated will be processed and submitted to the
[repository] as SPSS system files with DDI XML documentation. The data will
be distributed in several widely used formats, including ASCII, tab-delimited (for
use with Excel), SAS, SPSS, and Stata. Documentation will be provided as
PDF. Data will be stored as ASCII along with setup files for the statistical
software packages. Documentation will be preserved using XML and PDF/A.

Plan
Highly recommended.
Generic Example 2:
Digital video data files generated will be processed and submitted to the
[repository] in MPEG-4 (.mp4) format.

Plan
Highly recommended.
ICPSR:
Submission: The data and documentation will be submitted to ICPSR in
recommended formats. Access: ICPSR will make the quantitative data files
available in several widely used formats, including ASCII, tab-delimited (for use
with Excel), SAS, SPSS, and Stata. Documentation will be provided as PDF.
Preservation: Data will be stored in accordance with prevailing standards and
practice. Currently, ICPSR stores quantitative data as ASCII along with setup
files for the statistical software packages, and documentation is preserved
using XML and PDF/A.

Plan
Archiving and
preservation
The procedures in place or envisioned
for long-term archiving and
preservation of the data, including
succession plans for the data should
the expected archiving entity go out of
existence.
Highly recommended.
Generic Example 1:
By depositing data with [repository], our project will ensure that the research
data are migrated to new formats, platforms, and storage media as required by
good practice.

Plan
Archiving and
preservation
existence.
Highly recommended.
Generic Example 2:
In addition to distributing the data from a project Web site, future long-term use
of the data will be ensured by placing a copy of the data into [repository],
ensuring that best practices in digital preservation will safeguard the files.

Plan
Archiving and
preservation
existence.
Highly recommended.
ICPSR:
ICPSR is a data archive with a nearly 50-year track record for preserving and
making data available over several generational shifts in technology. ICPSR
will accept responsibility for long-term preservation of the research data upon
receipt of a signed deposit form. This responsibility includes a commitment to
manage successive iterations of the data if new waves or versions are
deposited. ICPSR will ensure that the research data are migrated to new
formats, platforms, and storage media as required by good practice in the
digital preservation community. Good practice for digital preservation requires
that an organization address succession planning for digital assets. ICPSR has
a commitment to designate a successor in the unlikely event that such a need
arises.

Plan
Storage and
backup
Storage methods and backup
procedures for the data, including
the physical and cyber resources
and facilities that will be used for
the effective preservation and
storage of the research data.
Highly recommended.
Generic Example 1:
[Repository] will place a master copy of each digital file (i.e., research data
files, documentation, and other related files) in Archival Storage, with several
copies stored at designated locations and synchronized with the master
through the Storage Resource Broker.

Plan
Storage and
backup
Storage methods and backup
procedures for the data, including
the physical and cyber resources
and facilities that will be used for
the effective preservation and
storage of the research data.
Highly recommended.
ICPSR:
Research has shown that multiple locally and geographically distributed copies
of digital files are required to keep information safe. Accordingly, ICPSR will
place a master copy of each digital file (i.e., research data files,
documentation, and other related files) in ICPSR's Archival Storage, with
several copies stored with partner organizations at designated locations and
synchronized with the master.

Plan
Existing data A survey of existing data relevant
to the project and a discussion of
whether and how these data will
be integrated.
Generic Example 1:
Few datasets exist that focus on this population in the United States and how
their attitudes toward assimilation differ from those of others. The primary
resource on this population, [give dataset title here], is inadequate because...

Plan
Existing data A survey of existing data relevant
to the project and a discussion of
whether and how these data will
be integrated.
Generic Example 2:
Data have been collected on this topic previously (for example: [add
example(s)]). The data collected as part of this project reflect the current time
period and historical context. It is possible that several of these datasets,
including the data collected here, could be combined to better understand how
social processes have unfolded over time.

Plan
Data organization How the data will be managed
during the project, with
information about version control,
naming conventions, etc.
Generic Example 1:
Data will be stored in a CVS system and checked in and out for purposes of
versioning. Variables will use a standardized naming convention consisting of a
prefix, root, suffix system. Separate files will be managed for the two kinds of
records produced: one file for respondents and another file for children with
merging routines specified.

Plan
Quality
Assurance
Procedures for ensuring data
quality during the project.
Example 1:
Quality assurance measures will comply with the standards, guidelines, and
procedures established by the World Health Organization.

Plan
Quality
Assurance
Procedures for ensuring data
quality during the project.
Example 2:
For quantitative data files, the [repository] ensures that missing data codes are
defined, that actual data values fall within the range of expected values and
that the data are free from wild codes. Processed data files are reviewed by a
supervisory staff member before release.

Plan
Security A description of technical and
procedural protections for
information, including confidential
information, and how
permissions, restrictions, and
embargoes will be enforced.
Example 1:
The data will be processed and managed in a secure non-networked
environment using virtual desktop technology.

Plan
Responsibility Names of the individuals
responsible for data management
in the research project.
Example 1:
The project will assign a qualified data manager certified in disclosure risk
management to act as steward for the data while they are being collected,
processed, and analyzed.

Plan
Budget The costs of preparing data and
documentation for archiving and
how these costs will be paid.
Requests for funding may be
included.
Example 1:
Staff time has been allocated in the proposed budget to cover the costs of
preparing data and documentation for archiving. The [repository] has estimated
their additional cost to archive the data is [insert dollar amount]. This fee
appears in the budget for this application as well.

Plan
Legal
requirements
A listing of all relevant federal or
funder requirements for data
management and data sharing.
Example 1:
The proposed medical records research falls under the HIPAA Privacy Rule.
Consequently, the investigators will provide documentation that an alteration or
waiver of research participants' authorization for use/disclosure of information
about them for research purposes has been approved by an IRB or a Privacy
Board.

Plan
Audience Describe the audience of users
for the data.
Example 1:
The data to be produced will be of interest to demographers studying family
formation practices in early adulthood across different racial and ethnic
groups. In addition to the research community, we expect these data will be
used by practioners and policymakers.

Plan
Selection and
retention
periods
A description of how data will be
selected for archiving, how long the
data will be held, and plans for
eventual transition or termination of
the data collection in the future.
Example 1:
Our project will generate a large volume of data, some of which may not be
appropriate for sharing since it involves a small sample that is not
representative. The investigators will work with staff of the [repository] to
determine what to archive and how long the deposited data should be
retained.

Data Management
Plan Resources

Data Curation Profiles Toolkit
http://datacurationprofiles.org/

And still more guidelines after the
project is awarded:
• Guide emphasizes
preparation for data
sharing throughout
the project
• Available online and
via download (pdf)

http://www.data-archive.ac.uk/media/2894/managingsharing.pdf

http://www.dataone.org/sites/all/documents/DataONE_BP_Primer_020212.pdf

Involving ICPSR Further
 In addition to reviewing ICPSR’s website
materials, you may want to:
 Contact ICPSR to discuss whether a future data
collection fits within the ICPSR collection. Note:
The earlier the better!
 Request a Letter of Support for a grant
application from ICPSR indicating support for
archiving the data with ICPSR. Note: The earlier
the better!
 Determine if there are any costs to archiving the
data with ICPSR. Note: The earlier the better!

UCLA SSDA – What we know now
A Data Management Plan is not a static
document; it is a continuous process
Review plan over time – update, revise, prioritize
next steps
Focus on meeting needs of managing during and
after project – for researcher, team and future
users

UCLA SSDA – Where we are now
Training of librarians to do curation essential
Internships for students from information Studies
Redesigned workflow for appraisal and ingest
More focus on data quality review
Increase use of tools to create metadata

What is quality data?
Data creation
Relevant
Accurate
Ethical
Complete
Timely
First Use
Understandable
Reuse
Independently
understandable
Findable / Shared /
Open/ Public
Preserved
From: Peer, Green and Stephenson, IDCC, February 2014

Data Quality Review
From: Peer, Green and Stephenson, IDCC, February 2014

Libraries, Archives and Repositories –
Represent many different approaches
to data management
Who are the stakeholders?
What are their roles and responsibilities?
What are the policies for collection and archiving?
Goals for long term usability?
How do the policies govern the infrastructure?
Consider the type/format/discipline,
organizational environment, mission, staff
competencies, technical capabilities, finances

Standards and certification
Data Seal of Approval (DSA) – self-audit
ISO 16363/TDR – peer review
Trusted Repository Audit Certification (TRAC) –
complete external audit
Digital Repository Audit Method Based on Risk
Assessment (DRAMBORA) – carry out periodically

Important to note:
Managing data is not
just about models and
technology.
Requires an
organizational
ecology.
Involves people,
processes and
policies.
http://web.stanford.edu/group/dlss/pasig/PASIG_September2014/20
140916_Presentations/20140916_03_System_Architecture_for_Dig
ital_Preservation_Neil_Jefferies.pdf

Considerations
People – still need to build workforce capacity
Basic data curation skills
Using metadata schemas, software, sysadmin
External influences and pressures
Funder requirements
New kinds of data
Systems will need to evolve – will become obsolete
How will all this be financially sustained?
http://web.stanford.edu/group/dlss/pasig/PASIG_September2014/2014
0916_Presentations/20140916_03_System_Architecture_for_Digital_
Preservation_Neil_Jefferies.pdf

Thank you!
libbie@ucla.edu
lyle@umich.edu

UCLA SSDA – Curation Practices
Follow OAIS to appraise and ingest files
Data Quality Review
DDI Metadata
Data and metadata processed in Colectica
Carry out media format migration when necessary
Process for use with SDA
DataPASS deposit

Adding valuethroughdatacuration

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