CREATING A DATA

MANAGEMENT PLAN

NOVEMBER 5, 2012

Lizzy Rolando, Research Data Librarian

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

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

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.

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.

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

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.

Never Fear! 16

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

Shibboleth… 19

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

have resources

available for

every section

Enter your

answers here

Some Sections Have Extra Advice 24

Georgia Tech

specific help

text

Almost There 25

You should

save after

every section,

but definitely

save at the

very end.

You’re so close

to the 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

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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

Questions?

Lizzy Rolando

Research Data Librarian

lizzy.rolando@library.gatech.edu

404.385.3706

http://libguides.gatech.edu/research-data

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