The webinar on data management plans (DMPs) conducted by the Australian National Data Service discusses the significance of DMPs in research, detailing their purposes, requirements, and the roles they play within research institutions. It emphasizes the evolving nature of DMP tools from basic documents to more integrated and machine-readable systems that align with funding bodies' requirements and improve researcher efficiency. The session included discussions on motivations for using DMPs and a call for audience participation through questions and feedback.

1. Webinar: Data Management Plans
16 February 2017
Natasha Simons
Kathryn Unsworth

2. The Australian National Data Service (ANDS) makes
Australia’s research data assets more valuable for
researchers, research institutions and the nation.

3. Getting involved
#andsdata
Please use the Chat
box for questions
and comments

4. Data Management Plans
ands.org.au

5. Todays webinar in 3 parts
1. Talks:
• DMPs – overview + intro to v2.0 (Natasha)
• DMP Birds of a Feather session at eResearch Australasia
(Kathryn)
• DMPs at The University of Melbourne (Nick Smale)
2. Open Mike time
3. DMP Community of Practice in Australia.
There will be a pop up survey for your ideas after you leave the
webinar - please don’t ignore it!
We’ll relay questions and tweets during the
webinar, so please Chat pod & Tweet as
things occur to you

6. Natasha Simons
DMP webinar
Senior Data Management Specialist
16 February 2017

7. What are Research DMPs?
A research data management plan is a document that describes:
• What research data will be created
• What policies will apply to the data
• Who will own and have access to the data
• What data management practices will be used
• What facilities and equipment will be required
• Who will be responsible for each of these activities.
http://www.ands.org.au/guides/data-management-plans
http://www.ands.org.au/partners-and-communities/23-
research-data-things/all23/thing-15

8. Why DMPs?
The carrot: improvements to efficiency,
protection, quality and exposure.
The stick: basic data management is
required by the Australian Code for the
Responsible Conduct of Research; some
funders mandate DMPs e.g. NSF.
Institutional reasons: keep a register of data creators
and their needs (planning); reduce risk associated with
unorganised data collection etc.

9. Added incentives for researchers
University of Colorado Boulder
2015 DMP competition – see
winners at
https://data.colorado.edu/cudmp
guidance
At Curtin University, data management plans are mandatory if a
researcher:​​
• Requires human or animal research ethics approval
• Is an HDR (Higher Degree by Research) student
• Would like access to the R: drive
See
http://libguides.library.curtin.edu.au/c.php?g=202401&p=133310
8

10. DMP tools

11. Next gen DMP Tools?

12. “apples” DMPs
• PDF or word doc
• Not connected to any system
• Complete at start of project
• Outcome not measured
• Not machine-readable
• Private

13. “apples + peanut butter” DMPs
• “DMP 2.0”
• Public
• Measurable
• Connected to at least one system
• Machine readable
• Consistent with FAIR principles
• Flexible living document
+ = Yum!
http://web.stanford.edu/~vcs/talks/RDA-DMP-2016-STODDEN.pdf

14. DMP BoF – eResearch Australasia 2016 –
report back
DMPs: Aligning use to
motivations and intended
outcomes

15. Abstract (extract)
DMPs are increasingly seen as a key component of data infrastructure. Fundamentally,
DMPs are a mechanism for researchers to state their intentions on how they plan to manage
their data across the data lifecycle. However, the agents and motivations driving DMP use
differ, providing us with a number of use cases to examine and interrogate. DMP use is now
driven by multiple agents: funding bodies to encourage data sharing, researchers themselves
as a project management tool, and institutions to increase researcher efficiency, for business
intelligence or as a connection point for systems integration.
Questions posed to presenters and the audience included:
• Why implement a DMP tool?
• Does DMP use align with an agent’s motivations and more importantly with
intended outcomes?
• What are the expected outcomes?
• Enterprise-level DMP tools (one-size-fits-all) – what is their place in the
landscape?
• Is ‘best practice’ for researchers the aim or a hoped for by-product?

16. NCSU
DMP Service by Cross, Davis
NSF
Further Guidance in 2017-2018
Note: Machine-Readable DMPs
Research Ideas and Outcomes (RIO) journal
DMP for Moore Investigator in Data Driven Discovery Grant
Public Access Plans
CENDI (Federal Agencies Public Access Programs)
SPARC (Federal Agencies Data Sharing Requirements)
Chris Erdmann - Chief
Strategist for Research
Collaboration (NCSU)

17. Goal
Helping a research group to reach, document and communicate
data management decisions
Live document
Metadata reuse from existing systems
Metadata reuse to other systems
Guided questions
Minimum mandatory questions
Conditional questions
Supporting and supported by data management governance and policy
procedures
1
7
Sue Cook - Data Librarian,
Research Data Support Team
(CSIRO)

18. Working
Party
Issues
Policy &
Procedure
Online
Form
System
Integration
Libbie Blanchard –
Senior librarian,
Research (CQU)

19. Old UoM DMP template New UoM DMP template*
Forms
Questions
Words
Guidance
Mandatory
Purpose
* New UoM DMP template produced by Peter Neish, Nick Smale, Helen
Philip-Tchepikov, Benjamin Cleary, members of the UoM DMP working
group
2
90
11,689 word 40 page
document
✓
(but little evidence of researchers using it)
?
3494
-
-
-
-
-
#1 aim of helping the individual
researcher for project management
Nick Smale –
Project Officer,
OREI (UoM)

20. DMP use cases
• Funding bodies or institutions requiring researchers to complete DMPs to
encourage the sharing of better managed data – improve return on investment
• Measurable outcome: Researchers who do DMPs more likely to share data
• Institutions requiring researchers to complete DMPs to effect change in researcher
behaviour and practices - educative tool
• Measurable outcome: Researchers who do DMPs more efficient and productive
• Institutions requiring researchers to complete DMPs to garner information about
what data are being created/collected/compiled during the course of research -
business intelligence tool
• Measurable outcome: Institutions actively using DMP data for business purposes
• Researchers complete DMPs (or data management plans/planning) as part of their
routine research project design and planning - project management
• Measurable outcome: Projects that have DMPs are more efficient and better capitalised

25. Senior Data Management Specialist
Natasha.simons@ands.org.au
NATASHA SIMONS
With the exception of third party images or where otherwise indicated, this work
is licensed under the Creative Commons 4.0 International Attribution Licence.
ANDS is supported by the Australian
Government through the National Collaborative
Research Infrastructure Strategy Program.
Monash University leads the partnership with
the Australian National University and CSIRO.
KATHRYN UNSWORTH
Data Librarian – Institutional
Engagement
Kathryn.unsworth@ands.org.au