NISO Training Thursday Crafting a Scientific Data Management Plan

Thursday, February 26, 2015

Instructors:

Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries

Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries

http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/

Crafting a Scientific

Data Management Plan

Thursday, February 26, 2015

Instructors

Kiyomi D. Deards, MSLIS, Assistant

Professor, University of Nebraska-

Lincoln Libraries, kdeards2@unl.edu

Jenny Thoegersen, Data Curation

Librarian, University of

Nebraska-Lincoln Libraries,

jthoegersen2@unl.edu

Training overview

Introduction to data

management plan requirements

Data Management Plan Checklist

Review good and bad data

management plan excerpts

Introduction to data

management plans

Follow guidelines provided by

granting agency, directorate, and

solicitation

Keep the plan clear, complete, and

concise

Refer back to the project

proposal, if necessary

Recheck requirements for changes

Data Management Checklist1. What type of data are being produced and what are the file

formats?

2. How much data are being produced, and at what growth rate?

Will the data change?

3. How long should the data be retained?

4. What directory and file naming conventions will be used?

5. Do you need data identifiers?

6. Are there tools and software needed to render the data?

7. Who will be responsible for data management?

8. Are there privacy, legal, ethical, or security

requirements?

9. Does the funder require a data sharing policy, data

management plan, or other information?

10. Are the data properly described (metadata) and the overall

project documented?

11. How will you store and backup the data?

12. Do you need to publish the data in a repository?

Data types & file formats

What types of data

file formats have

you encountered?

Data types & file formats

Match data types to file formats

Favor open source and widely used formats

Consider data repository requirements

Quantity of data A

LOT?

A little

data

Or…

Retention of data

Time

Directory and file naming

conventions

Avoid special characters ("/ \ : * ?

" < > [ ] & $)

Use underscores, not spaces

Avoid names longer than 25 characters

Use consistent versioning

identification (DM_Guide_v03)

Use the ISO 6801 standards for date

formats (YYYY-MM-DD)

Use names that describe the content

Directory and file naming

conventions

“…the PIs, senior personnel, technician and

students on the project will convene a

dedicated data management meeting. At this

time, the PIs will set out naming,

processing and storage conventions for all

data collected at the experimental and

observational sites…training will be

reiterated at a yearly data management and

analysis meeting to remind participants of

the conventions and train any new

participants.”

From Elsa Cleland's proposal The influence of plant functional types on ecosystem

responses to altered rainfall. Available at http://idi.ucsd.edu/data-

curation/examples.html

Data identifiers

“Message error 404” by Roberto Zingales,

https://www.flickr.com/photos/filicudi/2891898817 (CC BY 2.0)

Rendering data

By Images courtesy of http://abstrusegoose.com/ under a Creative Commons license via

Wikimedia Commons, http://www.ccc.uga.edu/summer/programs/comic2.png (CC BY-SA 3.0)

In 30

years,

how will

you

access

your

data?

Who is

responsible?

From “Lease”, by

Randall Monroe

http://xkcd.com/616/

(CC BY-NC 2.5)

Privacy, legal, ethical, or

security requirements

“Speak no evil, See no evil, Hear no evil” by Rose Davies,

https://www.flickr.com/photos/rosedavies/110850792/ (CC BY 2.0 )

Publishing, Preserving, & Rights

Determine where data

will be preserved and

shared after the

conclusion of a project

Outline the rights

associated with the

data

“Cat #24 - Mummy Cat” by Marty Omnitarian,

https://www.flickr.com/photos/omnitarian/4300610111/ (CC BY-NC-ND 2.0)

Funder requirements

Funder

guidelines

can be very

simple or

very complex

NSF Basic DMP Requirements1. the types of data, samples, physical collections, software,

curriculum materials, and other materials to be produced in the

course of the project;

2. the standards to be used for data and metadata format and content

(where existing standards are absent or deemed inadequate, this

should be documented along with any proposed solutions or remedies);

3. policies for access and sharing including provisions for appropriate

protection of privacy, confidentiality, security, intellectual

property, or other rights or requirements;

4. policies and provisions for re-use, re-distribution, and the

production of derivatives; and

5. plans for archiving data, samples, and other research products, and

for preservation of access to them.

From the Grant Proposal Guide

(http://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/gpg_2.jsp)

Description

&

Documentation

U.S. National Archives and Records Administration [Public domain], via Wikimedia Commons, http://commons.wikimedia.org/wiki/File%3ADon't_kill_your_reputation%2C_organize_your_information_-_NARA_-_518156.jpg

Storage & backup

Maintain 3 copies of data--

one remotely

Storage & backup

Where do you store and

back up your data?

Storage & backup

Storage

OptionThe Good The Bad

Personal

computer/laptopConvenient for active data Lost/stolen; fail; responsible for backups

Network/departmen

t drivesAutomatic backup & security Access/capacity limitations

External devices Low cost; portable; easy use Lost/stolen; fail

Holland Computing

CenterAutomatic backup & security Cost for storage

Box Global access; collaboration Security/privacy limitations

Physical (e.g.

notebook)Convenient; tangible Manual backup

Data management plan excerpts

All sample data will be collected and

organized using [Specialty Software

Name]. The files will contain information

about sample characteristics and the

conditions under which these

characteristics were measured.

Approximately 1-2 Gb of data will be

generated.

What’s wrong with this example?

Data management plan excerpts

All files will be stored on the

PI’s secure computer. All

laboratory notebooks will be

stored in the PI’s office.

What’s wrong with this example?

Data management plan excerpts

Data will be available to

anyone who desires access to

our data. When possible, data

will be made available online.

What’s wrong with this example?

Data management plan excerpts

This DMP covers the data which

will be This study will only

collect non-sensitive data. No

personal identifiers will be

recorded or retained by the

researchers in any form.

What’s right with this example?

Data management plan excerpts

The project will leverage existing

metadata standards currently stored in

Ecological Metadata Language (EML)

format. We chose EML format for our

metadata since it allows integration with

existing NutNet data housed in the

Knowledge Network for Biocomplexity (KNB)

data repository.

What’s right with this example?

Questions?

Resources & References

Basics of Data Management:

http://unl.libguides.com/datamanagement

UNL Libraries Data Management Services:

http://libraries.unl.edu/data-management

Example NSF DMPs from UC San Diego:

http://idi.ucsd.edu/data-

curation/examples.html

NISO Training Thursday • February 26, 2015

Questions?All questions will be posted with presenter answers on

the NISO website following the webinar:

http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/

NISO Training Thursday Crafting a Scientific Data Management Plan

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