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1.
Logistics for Webinar
You Must Call In ForAudio:
866-740-1260 access code 9870179#
Participants muted
Ask questions in chat any time
20 minutes for Q&A
Recording & slides, schedule of webinars:
blog.dmptool.org/webinar-series
DMPToolWebinar Series 6: Health Sciences & the DMPTool
Sponsored by IMLS
16 July 2013
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2.
Health Sciences
and the DMPTool
#dmptwebinar
FromFlickr–Username:phalinn
Lisa Federer | @lisafederer
University of California-LosAngeles, Louise M. Darling Biomedical Library
DMPToolWebinar Series 6: Health Sciences & the DMPTool
Sponsored by IMLS
16 July 2013
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3.
28 May Introduction to the DMPTool
4 June Learning about data management: Resources, tools, materials
18 June Customizing the DMPTool for your institution
25 June Environmental Scan:Who's important at your campus
9 July Promoting institutional services; EZID Outreach Made Simple!
16 July Health Sciences & DMPTool - Lisa Federer, UCLA
23 July Digital humanities and the DMPTool - Miriam Posner, UCLA
13 Aug Data curation profiles and the DMPTool – Jake Carlson, Purdue
How to give the data management sales pitch to various audiences
Other tools and resources that work with/complement the DMPTool
Beyond funder requirements: more extensive DMPs
Case studies 1 – How librarians have successfully used the tool
Case studies 2 – How librarians have successfully used the tool
Outreach Kit Introduction
Certification program introduction
blog.dmptool.org/webinar-series
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4.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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5.
Webinar Goals
• Introduce requirements
relevant to health
sciences researchers
• Provide an overview of
info to include in plans
• Demonstrate how to
use the DMPTool to
support NIH
• Point to resources for
further help
From Flickr– Username: AG Gilmore
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6.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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7.
NIH Sharing Policies
• NIH Data Sharing Policy
• NIH Grants Policy
Statement (2.3.11.2.3
Access to Research
Data)
• For full list of NIH
policies, see
http://grants.nih.gov/gr
ants/sharing.htm
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8.
Data Sharing Policy - Applicability
• applicants seeking
>$500,000 in annual
direct costs
• applications submitted
after October 1, 2003
• final research data for
research purposes
• all types of research data
(clinical/basic science),
especially unique data
From Flickr– Username: bengrey
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9.
Data Sharing Policy - Applicability
• applicants seeking
>$500,000 in annual
direct costs
• applications submitted
after October 1, 2003
• final research data for
research purposes
• all types of research data
(clinical/basic science),
especially unique data
From Flickr– Username: bengrey
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10.
Data Sharing Policy - Applicability
• applicants seeking
>$500,000 in annual
direct costs
• applications submitted
after October 1, 2003
• final research data for
research purposes
• all types of research data
(clinical/basic science),
especially unique data
From Flickr– Username: bengrey
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11.
Data Sharing Policy - Applicability
• applicants seeking
>$500,000 in annual
direct costs
• applications submitted
after October 1, 2003
• final research data for
research purposes
• all types of research data
(clinical/basic science),
especially unique data
From Flickr– Username: bengrey
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12.
Components of the NIH Application
• Data Sharing Plan (or
explanation of why data
sharing is not possible)
• Budget/Budget
Justification
• Background and
Significance Section
• Human Subjects Section
From Flickr– Username: Raphael Moser
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13.
Components of the NIH Application
• Data Sharing Plan (or
explanation of why data
sharing is not possible)
• Budget/Budget
Justification
• Background and
Significance Section
• Human Subjects Section
From Flickr– Username: Raphael Moser
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14.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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15.
NIH Guidance
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16.
Data Sharing Plan
• schedule for sharing
• data formats
• documentation and/or
analytic tools provided
with datasets
• data-sharing
agreement
• mode of data sharing
From Wikimedia Commons – German Federal Archives
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17.
Data Sharing Plan
• schedule for sharing
• data formats
• documentation and/or
analytic tools provided
with datasets
• data-sharing
agreement
• mode of data sharing
From Wikimedia Commons – German Federal Archives
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18.
Data Sharing Plan
• schedule for sharing
• data formats
• documentation and/or
analytic tools provided
with datasets
• data-sharing
agreement
• mode of data sharing
From Wikimedia Commons – German Federal Archives
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19.
Data Sharing Plan
• schedule for sharing
• data formats
• documentation and/or
analytic tools provided
with datasets
• data-sharing
agreement
• mode of data sharing
From Wikimedia Commons – German Federal Archives
-
20.
Data Sharing Plan
• schedule for sharing
• data formats
• documentation and/or
analytic tools provided
with datasets
• data-sharing
agreement
• mode of data sharing
From Wikimedia Commons – German Federal Archives
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21.
Schedule for Sharing
• “timely fashion”
• release no later than
acceptance for
publication of main
findings
• for epidemiological or
longitudinal studies,
release data in “waves”
as available
From Flickr– Username: Kate Tomlinson
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22.
Schedule for Sharing
• “timely fashion”
• release no later than
acceptance for
publication of main
findings
• for epidemiological or
longitudinal studies,
release data in “waves”
as available
From Flickr– Username: Kate Tomlinson
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23.
Schedule for Sharing
• “timely fashion”
• release no later than
acceptance for
publication of main
findings
• for epidemiological or
longitudinal studies,
release data in “waves”
as available
From Flickr– Username: Kate Tomlinson
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24.
Data Formats
• proprietary vs. open
formats
• consider formats
required by archives
• rationale for why this
format was selected
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25.
Data Formats
• proprietary vs. open
formats
• consider formats
required by archives
• rationale for why this
format was selected
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26.
Data Formats
• proprietary vs. open
formats
• consider formats
required by archives
• rationale for why this
format was selected
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27.
Data Documentation
metadata schemas
FromFlickr–Username:Sarah0s
codebook
ImagefromCDC
FromFlickr–Username:deltaMike
analytic tools
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28.
Data-sharingAgreements
• outline requirements
for ensuring privacy and
confidentiality
• stipulate provisions for
how shared data may
be used
• may include penalties
for violating agreement
From Wikimedia– Username: Lucas
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29.
Data-sharingAgreements
• outline requirements
for ensuring privacy and
confidentiality
• stipulate provisions for
how shared data may
be used
• may include penalties
for violating agreement
From Wikimedia– Username: Lucas
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30.
Data-sharingAgreements
• outline requirements
for ensuring privacy and
confidentiality
• stipulate provisions for
how shared data may
be used
• may include penalties
for violating agreement
From Wikimedia– Username: Lucas
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31.
Modes for Data SharingFromFlickr–Username:ichibod
FromWikimedia–Username:Dake
under the auspices of the PI in a data archive
FromFlickr–Username:NovartisAG
in a data enclave
FromFlickr–Username:loop_oh
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32.
Budget and Budget Justification
• include request for
funds for
sharing/archiving
• PIs may need
assistance on
estimating costs for
data preparation and
archiving From Flickr– Username: 401(K) 2013
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33.
Budget and Budget Justification
• include request for
funds for
sharing/archiving
• PIs may need
assistance on
estimating costs for
data preparation and
archiving From Flickr– Username: 401(K) 2013
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34.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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35.
Privacy of Human Subjects
• strip identifiers from
data
• indirect identifiers and
“deductive disclosure”
• restricted data enclaves
• data-sharing
agreements
From Wikimedia Commons– National Cancer Institute
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36.
Privacy of Human Subjects
• strip identifiers from
data
• indirect identifiers and
“deductive disclosure”
• restricted data enclaves
• data-sharing
agreements
From Wikimedia Commons– National Cancer Institute
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37.
Privacy of Human Subjects
• strip identifiers from
data
• indirect identifiers and
“deductive disclosure”
• restricted data enclaves
• data-sharing
agreements
From Wikimedia Commons– National Cancer Institute
-
38.
Privacy of Human Subjects
• strip identifiers from
data
• indirect identifiers and
“deductive disclosure”
• restricted data enclaves
• data-sharing
agreements
From Wikimedia Commons– National Cancer Institute
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39.
Withholding Proprietary Data
• Small Business
Innovation Research
(SBIR) grantees = up to
4 years
• Private sector co-
funding = 30-60 days
From Flickr– Username: m thierry
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40.
Withholding Proprietary Data
• Small Business
Innovation Research
(SBIR) grantees = up to
4 years
• Private sector co-
funding = 30-60 days
From Flickr– Username: m thierry
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41.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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42.
Creating a plan with the DMPTool
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43.
Road Map
Goals of thisWebinar
NIH policy overview
What to include in a plan
Special considerations
DMPTool and NIH Plans
Resources & more info
From Flickr– Username: Bastian
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44.
Resources
• NIH Data Sharing Overview:
http://grants.nih.gov/grants/policy/data_sharing/
• NIH Data Sharing Policy and Implementation Guidance:
http://grants.nih.gov/grants/policy/data_sharing/data_shari
ng_guidance.htm
• Full list of NIH sharing policies by Institute:
http://www.nlm.nih.gov/NIHbmic/nih_data_sharing_policies
.html
• Example data sharing agreements from NCI:
https://wiki.nci.nih.gov/display/DSIC/Data+Sharing+Agreem
ents
• Sample plan from NIH (Word doc):
http://grants.nih.gov/grants/sharing_example_data_sharing
_plan.doc
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45.
blog.dmptool.org/
webinar-series
From Flickr by Jeff Keacher
Next week:
Digital Humanities & the
DMPTool
Presenter: Miriam Posner,
UCLA
Tuesday 23 July @ 10am PT
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46.
blog.dmptool.org/webinar-series/
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47.
28 May Introduction to the DMPTool
4 June Learning about data management: Resources, tools, materials
18 June Customizing the DMPTool for your institution
25 June Environmental Scan:Who's important at your campus
9 July Promoting institutional services; EZID Outreach Made Simple!
16 July Health Sciences & DMPTool - Lisa Federer, UCLA
23 July Digital humanities and the DMPTool - Miriam Posner, UCLA
13 Aug Data curation profiles and the DMPTool – Jake Carlson, Purdue
How to give the data management sales pitch to various audiences
Other tools and resources that work with/complement the DMPTool
Beyond funder requirements: more extensive DMPs
Case studies 1 – How librarians have successfully used the tool
Case studies 2 – How librarians have successfully used the tool
Outreach Kit Introduction
Certification program introduction
blog.dmptool.org/webinar-series
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48.
Email
Twitter
Website
lmfederer@library.ucla.edu
@lisafederer
http://www.lisafederer.net
Questions?
Change Title, webinar #, and date in green text at top
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http://www.flickr.com/photos/ben_grey/4582294721/“Final research data are recorded factual material commonly accepted in the scientific community as necessary to document, support, and validate research findings. This does not mean summary statistics or tables; rather, it means the data on which summary statistics and tables are based. For most studies, final research data will be a computerized dataset.”
http://www.flickr.com/photos/ben_grey/4582294721/“Final research data are recorded factual material commonly accepted in the scientific community as necessary to document, support, and validate research findings. This does not mean summary statistics or tables; rather, it means the data on which summary statistics and tables are based. For most studies, final research data will be a computerized dataset.”
http://www.flickr.com/photos/ben_grey/4582294721/“Final research data are recorded factual material commonly accepted in the scientific community as necessary to document, support, and validate research findings. This does not mean summary statistics or tables; rather, it means the data on which summary statistics and tables are based. For most studies, final research data will be a computerized dataset.”
http://www.flickr.com/photos/ben_grey/4582294721/“Final research data are recorded factual material commonly accepted in the scientific community as necessary to document, support, and validate research findings. This does not mean summary statistics or tables; rather, it means the data on which summary statistics and tables are based. For most studies, final research data will be a computerized dataset.”
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