2012 Fall Data Management Planning Workshop

CREATING A DATA
MANAGEMENT PLAN
NOVEMBER 5, 2012

Lizzy Rolando, Research Data Librarian

Why Data Management?
2

 Good for You
 Good for Science

 Required by Funding Agencies

Funding Agency Requirements
3

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 programs require a data management plan
Some requiring that all grants include a data sharing plan, which
• must also be shared
All environmental data should be made visible, accessible and
• All data should be made visible, users
independently understandable toaccessible and independently
understandable to users, within 2 years of end of grant
NASA • Data should be made freely and widely available.
NASA •
• Data should be plan and evidence of anyavailable.
A data sharing made freely and widely past sharing activities
• A databe included as part of the technicalpast sharing activities
should sharing plan and evidence of any proposal
should be included as part of the technical proposal
CDC • All data are released and/or shared as soon as feasible
CDC • All data should be released and/or shared as soon as feasible

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.

Basic DMP Components
5

 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.

Data Description
6

 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?

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.

Data and Metadata Formats
8

 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?

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.

Data Access and Sharing Policies
10

 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?

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

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?

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.

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?

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.

DMPTool
17

 Developed by a number of academic universities in
response to funding agency mandates
 https://dmp.cdlib.org/

Step 1: Sign In
18

Choose Georgia Tech

Step 2: Create a Plan
20

Select a Funding Agency
Email is sent to
Georgia Tech
Library

Let’s Talk About Names
21

Strongly Recommend
Naming Plan “[Insert
Proposal Title Here]
Data Management
Plan”

Downloadable Templates
22

Clicking on
“Funder
Requirements”
will lead to a
page with a list
of all funding
agency
requirements

Step 3: One Section at a Time
23

Sections are
different
depending on
funding
source.
Georgia Tech
and DataONE
Enter your have resources
answers here available for
every section

Some Sections Have Extra Advice
24

Georgia Tech
specific help
text

Almost There
25

You should
save after
every section,
but definitely You’re so close
save at the to the end!
very end.

Step 4: Export
26

Now that you have
the content, you can
export your plan.

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.

Step 6: Finish and Start Research!
28

 Add plan to proposal or distribute among
research team
 Start your newly funded research!

Other Data Management Plan Resources
29

 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

Questions?
30

Lizzy Rolando
Research Data Librarian
lizzy.rolando@library.gatech.edu
404.385.3706
http://libguides.gatech.edu/research-data

2012 Fall Data Management Planning Workshop

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