Meeting Federal Research Requirements
62
From Data Sharing to Data Stewardship: Meeting Federal Data Sharing Requirements ACRL 2015 Thursday, March 26, 2015 ICPSR – University of Michigan Hashtag: #icpsr
Transcript of Meeting Federal Research Requirements
From Data Sharing to Data Stewardship: Meeting Federal Data Sharing Requirements
ACRL 2015
Thursday, March 26, 2015
ICPSR – University of Michigan
Hashtag: #icpsr
https://www.flickr.com/photos/29261037@N02/8896766525
https://www.flickr.com/photos/shawnhoke/6040690284
Direct identifiers
• Addresses, including ZIP and other postal codes
• Telephone numbers, including area codes
Indirect identifiers
• Exact dates of events (birth, death, marriage)
• Detailed income
• Detailed geographic information (e.g., county)
“The study is composed of about 180,000 autopsy x-ray image files taken of 58 corpses. The images originally arrived on DVD and are formatted to comply with the Digital Imaging and Communications in Medicine (DICOM) standard…. The images are the data of the study, the images files themselves contain metadata (metadata on the images) scrubbed of identifiers but there isn't much in terms of documentation.”
http://www.wired.com/wp-content/uploads/2014/04/480815249-660x672.jpg
Today
• History (brief!) of federal data sharing requirements
• What is good data sharing? How do you achieve data stewardship?
• Public data sharing services – tours & take-away tips
• Resources for creating data management plans and funding quotes
You should leave this session with -
• Keen understanding of several sustainable data sharing models
• Ability to assess data sharing services
– Through review of several services
– Walk-away tips for evaluating
• Knowledge (a portal) of resources for creating data management plans for grant applications
• 50+ years of experience
• Data stewardship
• Data management
• Data curation
• Data preservation
ICPSR
ICPSR Archives/Repositories
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://www.icpsr.umich.edu/files/ICPSR/ICPSRComments.pdf
http://guides.library.oregonstate.edu/federaloa
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
1.Maximize access
2.Protect confidentiality and privacy
3.Appropriate attribution
4.Long term preservation and sustainability
5.Data management planning
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 Oxfordhttp://www.slideshare.net/DigCurv/15-meriel-patrick
Data Sharing Status
Federal Agency
Shared Formally, Archived(n=111)
Shared Informally, NotArchived(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
What is good data sharing - the basis of data stewardship?
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.
Do no harm.
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
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.
• 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.
• See ICPSR's resource: Guidelines for Effective Data Management Plans
The Status of Data Sharing
– Good data sharing exists!
– Good data sharing requires funding -sustainable funding!
– Sustainable funding for free public access remains a challenge
Sustainable Data Sharing Models –Three to Explore
• Fee for access model (subscription model)
• Agency model (agency or foundation funds public access)
• Fee for deposit model (researcher writes fee into grant and pays at deposit to fund public access)
I. Fee-for-Access Data Sharing• Funding is maintained by annual subscription fees charged to
institutions; individuals at subscribing institutions have free (open) access to data
• Pooled (ongoing) subscriber fees are used to acquire, curate, and maintain the service
• The service, open to everyone, is thus sustained by subscribers, but agencies indicate these models are not ‘open enough’ because of the access fees
II. Agency-funded Data Sharing• Agency sponsors/funds (ongoing) data curation & sharing enabling the
public to access without charge
• The archive is hosted with a curation entity like ICPSR where the public can easily discover and access data and restricted-use data can also be securely shared
• Agency directs data selection and compliance policies
III. Fee-for-Deposit Data Sharing• Depositor (individual or entity) pays for data to be
curated and stored – a fee at deposit
• Deposit fees should be written into the grant application
• Incoming deposit fees sustain the service and the professionals behind it
• Sustainability risk fairly high in this model as it depends upon:– Continuous influx of deposit fees
– Depositors to put allocated fees towards curation & sharing
• Data tends to be bit-level (not curated): WIDIWYG
Fee for Deposit Services Arriving Daily! (tips for evaluating coming shortly)
First: A Side-Note on Sharing Restricted-Use Data
• Data with disclosure risk –potential to identify a research subject
• Data with highly sensitive personal information
What is Restricted-Use Data?
Common Objection/Misperception: “My data are too sensitive to share. . .”
• 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
Reality: Restricted-use data can be effectively shared with the public
• Through the use of a virtual data enclave where the data never leave the server
• Where there is a process (and understanding!) to garner IRB approval from the requesting scientist’s university
• Where there is a system, technology, data professionals, and collaboration space in place to disseminate (expensive to build!)
• Because agencies do allow for an incremental charge to the data requestor to offset marginal costs
Review of Public Data Sharing Services• Overview of public data sharing services we have
reviewed
– Some key strengths of each
• Disclaimer: ICPSR has recently launched a public access service (hosted)
– You’ll likely notice some bias when we talk about the strengths of openICPSR
– And because we built the service, we know much more about it
– Still, ICPSR’s public access service isn’t for everyone –more on that shortly
Public Data Sharing Services
How is openICPSR unique?
openICPSR is a public data-sharing service:
• Where the deposit is reviewed by professional data curators who are experts in developing metadata (tags) for the social and behavioral sciences = discoverable
• With an immediate distribution network of over 750 institutionslooking for research data, that has powerful search tools, and a data catalog indexed by major search engines = usage
• Sustained by a respected organization with over 50 years of experience in reliably protecting research data = sustainable
• Prepared to accept and disseminate sensitive and/or restricted-use data in the public-access environment = protection of research subjects
How will openICPSR disseminate sensitive data to the public?
• The deposit of sensitive (restricted-use) data is similar to the deposit of non-sensitive data except that the depositor will indicate that the data should be for restricted-use only
• Dissemination of sensitive data will be through ICPSR’s virtual data enclave; in this environment, data never leave the secure server and analysis takes place in the virtual space
• Scientists desiring to access the data will need to apply for the data and will pay an access fee
• openICPSR has already received sensitive (restricted-use) and dissemination of these data has begun
openICPSR for Institutions and Journals
• Uses openICPSR platform
• Fully hosted in the ICPSR cloud – no tech or patches needed
• Branded with a logo and colors
• Deposits incorporated into ICPSR’s data catalog
• On-demand administrative usage tools
A final note: openICPSR accepts research data from a wide array of disciplines/fields, but not all
Tips for Evaluating a Data Sharing Service
• How will the service sustain itself? Does it have a long term funding stream?
• How will the service care for my data in the long term should the service fail? Is there a plan? A safety net?
• Can the service quickly maximize discoverability of my data? Does it explain how it will do so?
• Does the service have a network of interested researchers & students seeking data? Will my data get used?
• Does the service have knowledge of international archiving standards? • Does the service provide a DOI, data citation, and version control should I
need to update my files?• I have sensitive data or data with some disclosure risk to deposit. Does
the service understand how to secure it upon intake and when sharing? Does it have experience in this area?
Questions to consider when selecting a data sharing service:
Resources for Creating Data Management Plans for Grant
Applications
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.
Data Management Plan Resources
Guidelines for Download
And still more guidelines after the project is awarded:
• Guide emphasizes preparation for data sharing throughout the project
• Available online and via download (pdf)
ICPSR Data Curation Training Workshops
• 1-5 day workshops on data curation/data repository management decisions
– Participants learn about best practices and tools for data curation, from selecting and preparing data for archiving to optimizing and promoting data for reuse
• Available via ICPSR Summer Program (Ann Arbor – July 27-31, 2015) or onsite at your institution
Copies of these Slides & Use
• Feel free to share it; present it; cite it!
• Find copies of these slides on Slideshare.net
– Several notes and additional links are found in the notes view
Get More information• Visit ICPSR’s Data Management &
Curation site: http://www.icpsr.umich.edu/datamanagement/index.jsp
• Contact us:
– (734) 647-2200
• More on Assuring Access to Scientific Data: white paper –“Sustaining Domain Repositories for Digital Data”
