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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, [email protected]
Jenny Thoegersen, Data Curation
Librarian, University of
Nebraska-Lincoln Libraries,
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
Thank you for joining us today.
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We look forward to hearing from you!
THANK YOU