Presenters : Libbie Stephenson, Jared Lyle
This session discusses the value of and methods for curating data, especially in light of recent government and academic initiatives. Special attention will be paid to data management plans.
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Adding Value to Data through Curation
1. Adding Value through Data Curation
APLIC
April 29, 2015
San Diego, California
Libbie Stephenson, UCLA (libbie@ucla.edu)
Jared Lyle, ICPSR (lyle@umich.edu)
2. 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
3. • 50+ years of
experience
• Data stewardship
• Data management
• Data curation
• Data preservation
ICPSR
5. 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”
11. 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
12. “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
13. 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
14. 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
15. 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
19. A well-prepared data collection
“contains information intended to
be complete and self-explanatory”
for future users.
20. 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
21. 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
22. 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.
29. 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.
31. 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.
32. 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.
33. 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 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.
34. 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.
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.
35. Elements of a Data Management
Plan
Element Description Recommended?
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.
36. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
37. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
38. Elements of a Data Management
Plan
Element Description Recommended?
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 4:
The research data from this project will be deposited with the institutional
repository on the grantees' campus.
39. Elements of a Data Management
Plan
Element Description Recommended?
Access and
sharing
Indicate how you intend to archive
and share your data and why you
have chosen that particular option.
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.
40. Elements of a Data Management
Plan
Element Description Recommended?
Access and
sharing
Indicate how you intend to archive
and share your data and why you
have chosen that particular option.
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.
41. Elements of a Data Management
Plan
Element Description Recommended?
Access and
sharing
Indicate how you intend to archive
and share your data and why you
have chosen that particular option.
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).
42. Elements of a Data Management
Plan
Element Description Recommended?
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.
43. Elements of a Data Management
Plan
Element Description Recommended?
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 2:
The clinical data collected from this project will be documented using CDISC
metadata standards.
44. Elements of a Data Management
Plan
Element Description Recommended?
Metadata A description of the metadata to
be provided along with the
generated data, and a discussion
of the metadata standards used.
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.
45. Elements of a Data Management
Plan
Element Description Recommended?
Metadata A description of the metadata to
be provided along with the
generated data, and a discussion
of the metadata standards used.
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.
46. Elements of a Data Management
Plan
Element Description Recommended?
Metadata A description of the metadata to
be provided along with the
generated data, and a discussion
of the metadata standards used.
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.
47. Elements of a Data Management
Plan
Element Description Recommended?
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.
48. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
49. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
50. Elements of a Data Management
Plan
Element Description Recommended?
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.
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.
51. Elements of a Data Management
Plan
Element Description Recommended?
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.
52. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
53. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
54. Elements of a Data Management
Plan
Element Description Recommended?
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.
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
55. Elements of a Data Management
Plan
Element Description Recommended?
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.
56. Elements of a Data Management
Plan
Element Description Recommended?
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 2:
Digital video data files generated will be processed and submitted to the
[repository] in MPEG-4 (.mp4) format.
57. Elements of a Data Management
Plan
Element Description Recommended?
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.
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.
58. Elements of a Data Management
Plan
Element Description Recommended?
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.
59. Elements of a Data Management
Plan
Element Description Recommended?
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 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.
60. Elements of a Data Management
Plan
Element Description Recommended?
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.
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.
61. Elements of a Data Management
Plan
Element Description Recommended?
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.
62. Elements of a Data Management
Plan
Element Description Recommended?
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.
63. Elements of a Data Management
Plan
Element Description Recommended?
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...
64. Elements of a Data Management
Plan
Element Description Recommended?
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.
65. Elements of a Data Management
Plan
Element Description Recommended?
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.
66. Elements of a Data Management
Plan
Element Description Recommended?
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.
67. Elements of a Data Management
Plan
Element Description Recommended?
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.
68. Elements of a Data Management
Plan
Element Description Recommended?
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.
69. Elements of a Data Management
Plan
Element Description Recommended?
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.
70. Elements of a Data Management
Plan
Element Description Recommended?
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.
71. Elements of a Data Management
Plan
Element Description Recommended?
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.
72. Elements of a Data Management
Plan
Element Description Recommended?
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.
73. Elements of a Data Management
Plan
Element Description Recommended?
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.
78. And still more guidelines after the
project is awarded:
• Guide emphasizes
preparation for data
sharing throughout
the project
• Available online and
via download (pdf)
81. 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!
82. 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
83. 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
84. 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
86. 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
87. 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
88. 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
89. 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
92. 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
Hinweis der Redaktion
http://www.aplici.org/conferences/48-1/conference-program/
This session will discuss the value of and methods for curating data, especially in light of recent government and academic initiatives. Special attention will be paid to data management plans.
Source of slide: Myron Gutmann’s IDF Meeting (June, 2007)
ICPSR exists to preserve and share research data to support researchers who:
Write research articles, books, and papers
Teach or utilize quantitative methods
Write grant/contract proposals (require data management plans)
Current archives/collections/repositories already meeting public access requirements regarding data
NACDA – NACJD – SAMHDA: examples of long term sustainability
NAHDAP – SAMHDA – DSDR: examples of sharing of confidential data
NACJD – example of depository/researcher compliance (holding 10% of funding to PI)
LGBT – MET: unique infrastructure and dissemination
Research Connections: reports and data dissemination; audiences including policymakers
In January 2011, the National Science Foundation released a new requirement for proposal submissions regarding the management of data generated using NSF support. All proposals must now include a data management plan (DMP). (NIH has similar DMP requirements.)
The plan is to be short, no more than two pages, and is submitted as a supplementary document. The plan needs to address two main topics:
What data are generated by your research?
What is your plan for managing the data?
The OSTP Memo
This memo directed funding agencies with an annual R&D budget over $100 million to develop a public access plan for disseminating the results of their research
concern for investment: “Policies that mobilize these publications and data for re-use through preservation and broader public access also maximize the impact and accountability of the Federal research investment.”
Federal agencies with over $100 M annually in R&D expenditures to develop plans to support increased public access to the results of research funded by the Federal Government
The OSTP Memo – Overview
Released February 22, 2013
A concern for investment: “Policies that mobilize these publications and data for re-use through preservation and broader public access also maximize the impact and accountability of the Federal research investment.”
Federal agencies with over $100 M annually in R&D expenditures to develop plans to support increased public access to the results of research funded by the Federal Government
“Maximize access, by the general public and without charge, to digitally formatted scientific data created with Federal funds…”
Public comments were solicited.
Now, many federal agencies are starting to post their initial responses. This LibGuide at Oregon State University keeps an updated list of agency public access plans.
Some in the library community are also crowdsourcing a Google Doc to track the agency responses.
The data portion of the OSTP memo references 13 distinct elements. ICPSR created an overview page discussing each of the 13 elements in detail.
But first, some context about data sharing – what researchers are doing today and what impact this may have on funder mandates (and vice versa).
We know that data sharing is a good thing…
Yet, many researchers never share their data. These results come from the UK.
Source of slide: http://www.slideshare.net/DigCurv/15-meriel-Patrick
In the U.S., ICPSR surveyed NSF and NIH awardees in 2010 about their data sharing behaviors. The results were similar to the UK findings.
4,883 NIH & NSF PIs emailed a survey
1,217 responses (24.9% response rate)
1,003 valid (collected data, not disseratation)
We attempted to invite all 4,883 of these Pis.
The PI survey consisted of consisted of questions about research data collected, various methods for sharing research data, attitudes about data sharing and demographic information. PIs were also asked about publications tied to the research project including information about their own publications, research team publications, and publications outside the research team. We received 1,217 responses (24.9% response rate). For the analytic sample we select PIs and their research data if (1) they confirm they collected research data (86.6% of the responses), (2) they did not collect data for a dissertation award (n=33), or (3) they were missing data on the dependent variable.
Back to the 5 core elements from the OSTP memo. These core elements form the basis of data stewardship.
First is maximizing access through data curation. There are two aspects to curation…
First is to make it discoverable. This includes documenting and describing the data, creating well-formed metadata, and listing the metadata in searchable and findable catalogs.
Second is to make the data accessible. You might be able to find the data, but if it’s in rough shape or ill-described, you won’t be able to use it.
This is an example data file with lots of missing and ill-described values.
The ultimate litmus test– the data collection is a complete and self-explanatory package. Future users will be able to find and make use of the files without any need to contact the original PI.
[Quote is from the National Longitudinal Survey of Youth’s explanation of its documentation (see: http://www.nlsinfo.org/nlsy97/97guide/chap3.htm#threethree).]
Why does this matter? 1) Others will be able to independently use/understand data, 2) Data will be readable (i.e., in useable formats) in the future, 3) It makes your life less complicated once you’re finished with the data collection -- you don’t need to continually explain, reformat, revise, etc.
This isn’t rocket science, but it’s still important. I recognize that many watching this Webinar have extensive data backgrounds, so I’m going to convey the information as quickly and directly as possible.
Second is protecting the confidentiality and privacy of respondents. Many data collections involve human subjects. Even biological and physical science data have disclosure risks.
There are ways to deal with disclosive data. ICPSR, for example, has been sharing restricted-use data for over 10 years. We’re currently adding about 50 new agreements each month.
Third in the core elements is appropriate attribution. It’s important that we give credit where credit is due. This helps researchers want to share. It helps agencies understand the impact of their funding. And it helps repositories tell the full story about the data, since primary and secondary use can be seen as an extension of the original metadata.
Data citation is straightforward.
Here’s a sample data citation from an ICPSR study.
Fourth is preservation and sustainability.
Many cuneiform tablets have been preserved for thousands of years.
Media: What is the prospect for 3.5” floppies?
Software: EBCDIC format?
The fifth (and final) core element is data management planning, which actual should be implemented prior to the other four elements. Proper planning greatly improves the chance that a data collection will be accessible, attributed, and preserved.
A collection of resources (links) to assist in data management plans for grant proposals
Tools to prepare plans (templates & sample plans)
Contact information for plan advice
In all, the existing websites generally concurred that there are 8 elements that a data management plan should cover. I am going to talk about these 8 highly recommended elements first. For each of the 8 elements - I will give a couple of examples of what kinds of text might be written. And, I will show you what to write if the data will be archived with ICPSR.
Data description is important to include because it will help reviewers understand the characteristics of the data, their relationship to existing data, and any disclosure risks that may apply.
The second example describes a data collection that is also nationally representative, but also mentions the sensitive nature of the data and the need for using a restricted-use data agreement to disseminate the data.
The next element – access and sharing – is important to include in a DMP to specify how the data will be accessed and shared. Sharing data helps to advance science and to maximize the research investment. A recent paper [link: http://deepblue.lib.umich.edu/handle/2027.42/78307] reported that when data are shared through an archive, research productivity increases and many times the number of publications result as opposed to when data are not shared.With respect to timeliness of data deposit, archival experience has demonstrated that the durability of the data increases and the cost of processing and preservation decreases when data deposits are timely. It is important that data be deposited while the producers are still familiar with the dataset and able to transfer their knowledge fully to the archive.
Self-dissemination example:
Describe and justify this
Still indicate a plan to preserve the data.
Describe a delayed dissemination plan.
Designate a institutional repository on one’s campus.
If you intend to use ICPSR you can first include this text…
It designates ICPSR as the intended archival home for the data.
It indicates our dissemination commitment to both public and restricted-use data files. It also covers ICPSR’s terms of use for these kinds of data.
It covers our expectation that the data be deposited before the end of the project to ensure continuity of data management. It also mentions delayed dissemination policy.
Why this is importantGood descriptive metadata are essential to effective data use. Metadata are often the only form of communication between the secondary analyst and the data producer, so they must be comprehensive and provide all of the needed information for accurate analysis.Structured or tagged metadata, like the XML format of the Data Documentation Initiative (DDI) standard, are optimal because the XML offers flexibility in display and is also preservation-ready and machine-actionable.
The first example references this ideal.
The second example talks about clinical data and references the metadata standard common to clinical data which is called CDISC.
This is the language you can use if depositing with ICPSR. It references our reliance on the DDI standard. That study-level descriptions of the data will be created.
Our use of a standard data citation and a persistent identifier to locate the data.
And, our documentation of data down to the variable-level.
Why this is importantGood descriptive metadata are essential to effective data use. Metadata are often the only form of communication between the secondary analyst and the data producer, so they must be comprehensive and provide all of the needed information for accurate analysis.Structured or tagged metadata, like the XML format of the Data Documentation Initiative (DDI) standard, are optimal because the XML offers flexibility in display and is also preservation-ready and machine-actionable.
In order to disseminate data, archives need a clear statement from the data producer of who owns the data. The principal investigator's university is usually considered to be the copyright holder for data the PI generates.
The first example makes this clear.
Many archives do not ask for a transfer of copyright but instead just request permission to preserve and distribute the data.
The 2nd example.
Copyright may also come into play if copyrighted instruments are used to collect data. In these cases, data producers should initiate discussions with archives in advance of data deposit.
This is the ICPSR text that can be included.
Protection of human subjects is a fundamental tenet of research and an important ethical obligation for everyone involved in research projects. Disclosure of identities when privacy has been promised could result in lower participation rates and a negative impact on science.
One of the most important considerations is that whenever possible – researchers collecting data should not include langue in the informed consent that prohibits data sharing.
The first example makes clear how to write this into a DMP.
This is specific language for an informed consent that can be mentioned in the DMP. ICPSR has been recommending this language for over a year.
This example pertains to HIPPA information being collected in studies.
The ICPSR text includes how the informed consent should be written. It also talks about ICPSR procedures that minimize disclosures and protect confidentiality that are part of our routine data processing.
Why this is importantDepositing data and documentation in formats preferred for archiving can make the processing and release of data faster and more efficient.Preservation formats should be platform-independent and non-proprietary to ensure that they will be usable in the future.
Video format data example. You can look at our website for more examples for other kinds of data formats.
ICPSR covers our preference of what format to receive data in.
It also covers access formats that we create.
It covers preservation formats for the data that we maintain.
Why this is importantDigital data need to be actively managed over time to ensure that they will always be available and usable. This is important in order to preserve and protect our investment in science. Preservation of digital information is widely considered to require more constant and ongoing attention than preservation of other media. Depositing data resources with a trusted digital archive can ensure that they are curated and handled according to good practices in digital preservation.
The first example here highlights the preservations being handled by a repository.
Even with self-dissemination of data - this should be covered in a DMP. This example highlights a copy being preserved by a archive or repository.
The ICPSR text highlights our 50year track record, our commitment to migrating data to viable future formats/technologies and describes our succession plan for our content in the event of the archive failing.
Why this is importantDigital data are fragile and best practice for protecting them is to store multiple copies in multiple locations.
The ICPSR text for a DMP describes our multiple copies held by partner organizations. It also mentions that the multiple copies are kept in synch.
More optional elements…
When the data are unique.
When the data relate to past data that have been collected.
Why this is importantIt is important to describe situations in which research data are in some way atypical with respect to how they will be organized. For example, some data collections are dynamically changing and version control is central to how the data will be used and understood by the scientific community.
Why this is importantProducing data of high quality is essential to the advancement of science, and every effort should be taken to be transparent with respect to data quality measures undertaken across the data life cycle.
Why this is importantSecurity for digital information is important over the data life cycle. Raw research data may include direct identifiers or links to direct identifiers and should be well-protected during collection, cleaning, and editing. Processed data may or may not contain disclosure risk and should be secured in keeping with the level of disclosure risk inherent in the data. Secure work and storage environments may include access restrictions (e.g., passwords), encryption, power supply backup, and virus and intruder protection.
Why this is importantTypically data are owned by the institution awarded a Federal grant and the principal investigator oversees the research data (collection and management of data) throughout the project period. It is important to describe any atypical circumstances. For example, if there is more than one principal investigator the division of responsibilities for the data should be described.
Why this is importantHow will the costs for creating data and documentation suitable for archiving be paid?
Why this is importantSome data have legal restrictions that impact data sharing—for example, data covered by HIPAA, proprietary data, and data collected through the use of copyrighted data collection instruments. How these issues might impact data sharing should be described fully in the data management plan.
Why this is importantThe audience for the data may influence how the data are managed and shared—for example, when audiences beyond the academic community may use the research data.
Why this is importantNot all data need to be preserved in perpetuity, so thinking through the proper retention period for the data is important, in particular when there are reasons the data will not be preserved permanently.
22 pages of guidelines and references even including a sample plan (boilerplate!) available for download.
Link to pdf document: http://www.icpsr.umich.edu/files/datamanagement/DataManagementPlans-All.pdf
https://dmp.cdlib.org/
Puts together the basic structure & form for your DMP. Note that it isn’t plug and go – the reasoning behind your management plans based on the discipline and/or data being collected should be added.
Pdf link to the data prep guide: http://www.icpsr.umich.edu/files/deposit/dataprep.pdf
More information on data preparation for archiving: http://www.icpsr.umich.edu/icpsrweb/content/deposit/guide/