2012 fall data management planning workshop
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These are the slides from the Data Management Planning Workshop, taught on November 5, 2012, in the Georgia Tech Library.TRANSCRIPT
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CREATING A DATA
MANAGEMENT PLAN
NOVEMBER 5, 2012
Lizzy Rolando, Research Data Librarian
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Why Data Management?
Good for You
Good for Science
Required by Funding Agencies
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Funding Agency Requirements
Funding Agency Requirement
NSF* • Must include DMP in proposal
• Materials collected during research should be shared
NIH • Papers must be submitted to PubMed
• Projects with over $500,000 funding must share data and include
Data Sharing Plan in proposal
USDA • National Institute of Food and Agriculture requires all data to be
submitted to public domain without restriction
NOAA • Some programs require a data management plan
• All environmental data should be made visible, accessible and
independently understandable to users
NASA • Data should be made freely and widely available.
• A data sharing plan and evidence of any past sharing activities
should be included as part of the technical proposal
CDC • All data are released and/or shared as soon as feasible
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Funding Agency Requirement
NSF* • Must include DMP in proposal
• Materials collected during research should be shared
NIH • Papers must be submitted to PubMed
• Projects with over $500,000 funding must share data and include
Data Sharing Plan in proposal
USDA • National Institute of Food and Agriculture requires all data to be
submitted to public domain without restriction
NOAA • Soon requiring that all grants include a data sharing plan, which
must also be shared
• All data should be made visible, accessible and independently
understandable to users, within 2 years of end of grant
NASA • Data should be made freely and widely available.
• A data sharing plan and evidence of any past sharing activities
should be included as part of the technical proposal
CDC • All data should be released and/or shared as soon as feasible
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Exciting News! 4
Beginning January 14, 2013, the Biographical
Sketch(es) for an NSF grant proposal will include
a section on “Products,” and no longer
“Publications.” This way, applicants can include not
just publications, but also datasets, software,
patents and copyrights.
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Basic DMP Components
Data Description
Data and metadata standards
Data access and sharing policies
Data re-use and re-distribution
Data preservation and archiving *Depending on the funding source and the directorate/division/program, data
management plan requirements may differ.
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Data Description
What kinds of data will you produce?
Numerical data, simulations, text sequences, etc.
Experimental, observational, simulation
Raw, derived
How will you acquire the data?
How will you process the data?
How much data will you collect?
Are you using any existing data?
What QA/QC procedures will you use?
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Recommendations 7
A short description of your project helps to give context to why you are collecting the data.
Two people should record and enter data separately.
Notes about the data (metadata) should be recorded alongside the data by the data collectors.
Make sure you record units and have headers for rows and columns in your tables.
Keep all raw data separate from analyzed data, and maintain versions of data during analysis.
Survey existing data sources.
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Data and Metadata Formats
What metadata will you create/include with data?
i.e. What does someone else need to know about your
data in order to reuse them?
Where will this be recorded? How? What format?
Will you use a community metadata standard?
Will you conform to community terminology?
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Recommendations 9
Use metadata standards common in your discipline. i.e. Ecological Metadata Language for Ecology
Always include a “readme.txt” file that describes the who, what, where, when and why of the data, at a bare minimum.
Make sure you have recorded the information that you would need if you were trying to use someone else’s data.
Check with the data repository where you hope to store your data – sometimes they require a particular metadata standard.
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Data Access and Sharing Policies
Are your data sensitive, so access by others needs
to be restricted?
What license or publishing model will you use for
your data?
How will you make your data accessible to others?
What data will you make available and at what
stage of your research?
Do you have protocols, such as IRB, that you need to
comply with? If so, how will you do so?
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Recommendations 11
Apply an open license to data that you will share.
Explain why you cannot share data, if that is the
case.
For example, the data are proprietary.
Anonymize or de-identify any sensitive data
Use a repository that can mediate data sharing if data
cannot be sufficiently anonymized
Comply with IRB restrictions
That should be obvious, but we’ll say it anyways
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Data Re-use and Re-Distribution 12
Who do you expect will want to or can reuse your
data?
Should there be restrictions on who or how your
data can be reused?
How should others indicate that they have used your
data?
How long will your data be available to others for
reuse?
Does your institution have rules about data?
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Recommendations 13
Imagine the broadest possible audience for your data.
Place as few restrictions on your data as you can.
Check with your chosen repository to make sure they provide a data citation.
You want credit when someone else uses your data!
Link your published articles to the data underlying those data.
Use a repository that can make your data available far into the future.
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Data Preservation and Archiving 14
What formats for your data will you use? Are they
preservation friendly?
What repository or data archive can take your
data when you are finished?
How do they preserve/share your data?
What are their access policies?
Is any extra work needed to prepare data for the
repository?
Who will be responsible for final preservation?
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Recommendations 15
Appraise your data, selecting those with long-term value, and document your choices.
Use preservation friendly digital formats.
Non-proprietary, commonly used
You may need to transform data into new format.
Find a repository that will take your data, and plan to comply with their policies early on.
Look into using SMARTech!
P.I.’s should ultimately be responsible for dealing with the final disposition of the data.
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Never Fear! 16
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DMPTool 17
Developed by a number of academic universities in
response to funding agency mandates
https://dmp.cdlib.org/
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Step 1: Sign In 18
Choose Georgia Tech
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Shibboleth… 19
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Step 2: Create a Plan 20
Select a Funding Agency
Email is sent to
Georgia Tech
Library
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Let’s Talk About Names 21
Strongly Recommend
Naming Plan “[Insert
Proposal Title Here]
Data Management
Plan”
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Downloadable Templates 22
Clicking on
“Funder
Requirements”
will lead to a
page with a list
of all funding
agency
requirements
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Step 3: One Section at a Time 23
Sections are
different
depending on
funding
source.
Georgia Tech
and DataONE
have resources
available for
every section
Enter your
answers here
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Some Sections Have Extra Advice 24
Georgia Tech
specific help
text
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Almost There 25
You should
save after
every section,
but definitely
save at the
very end.
You’re so close
to the end!
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Step 4: Export 26
Now that you have
the content, you can
export your plan.
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Step 5: Share plan 27
Send your plan to the Research Data
Librarian (Me!) to look over your plan.
Have your colleagues look at your plan.
Do you know your grant officer? Maybe
they will look at it.
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Step 6: Finish and Start Research! 28
Add plan to proposal or distribute among
research team
Start your newly funded research!
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Other Data Management Plan Resources
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Digital Curation Centre -
http://www.dcc.ac.uk/resources/data-management-plans
ICPSR – while made for Social Science data, it has great
resources for anyone:
http://www.icpsr.umich.edu/icpsrweb/content/datamanage
ment/dmp/plan.html
UK Data Archive - http://www.data-
archive.ac.uk/media/2894/managingsharing.pdf
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Questions?
Lizzy Rolando
Research Data Librarian
404.385.3706
http://libguides.gatech.edu/research-data
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