Winter school in research data science research data management - final

Winter School in Research Data Science
Research Data Management I
14-15 June 2018
University of Queensland
© The authors This work is licensed under a
Creative Commons Attribution-NonCommercial
4.0 International License.

Introductions
Natasha Simons
Program Leader, Skills Policy and Resources ANDS, Nectar, RDS
& Industry Fellow, The University of Queensland
Professor Ginny Barbour
Director, Australasian Open Access Strategy Group,
Professor Library and OREI, QUT

Research Data Management
● Introduction to data management
● Data management planning
● Making your data FAIR (Findable, Accessible,
Interoperable and Reusable)
● How to manage your working data
● Managing personal and sensitive data
● Maximizing your research (data) impact
● Bringing it all together

Your experience
● Have you ever used someone else’s
data?
● Have you ever shared your data with
someone else?
● Where (if anywhere) have you
published your data?
● What do you consider to be the
biggest challenge in managing your
data?

Research Data Management:

What is Research Data?
Research data means: data in the form of facts, observations,
images, computer program results, recordings, measurements
or experiences on which an argument, theory, test or
hypothesis, or another research output is based. Data may be
numerical, descriptive, visual or tactile. It may be raw, cleaned
or processed, and may be held in any format or media.
But this is only one definition
of many….
Photo by rawpixel on Unsplash

What is Research Data?
Any definition of research data is likely to depend on the
context in which the question is asked.
http://www.ands.org.au/guides/what-is-research-data
Photo by h heyerlein on Unsplash

What’s Research Data Management?
Research Data Management covers the planning, collecting,
organising, managing, storage, security, backing up,
preserving, and sharing your data. It ensures that research
data are managed according to legal, statutory, ethical and
funding body requirements. Source: UQ LibGuide
Any research will require some level of data management.
Photo by imgix on Unsplash

Why should you care about RDM?
Good data management can:
• Increase the efficiency of your
research
• Help guarantee the quality and
authenticity of your data
• Enable the exposure of your
research outcomes through
collaboration and dissemination
• Provide for the reproducibility of
experimental and computational
outcomes
• Facilitate the validation and
verification of results. Photo by Jaron Nix on Unsplash

More publishers require data
A condition of
publication in a Nature
journal is that authors
are required to make
materials, data, code,
and associated
protocols promptly
available to readers
without undue
qualifications.

More funders require data
“We want the research we fund – like publications, data, software and
materials – to be open and accessible, so it can have the greatest possible
impact” – Wellcome Trust https://wellcome.ac.uk/what-we-do/topics/data-
sharing
NHMRC’s Australian Code for the Responsible Conduct of Research: includes
the proper management and retention of the research data.
Australian Research Council (ARC) application forms (Discovery; Linkage) have
a short section where you are required to provide an outline of your data
management plan.
ANDS Guide: ARC applications – filling in the data management
sectionhttp://www.ands.org.au/guides/arc-guide-to-filling-in-the-dm-section

More government policies on data
The main purpose of the site is to encourage public access to and reuse of
public data. It was created following the Government’s Declaration of
Open Government and as a response to the Government 2.0 Taskforce
Report.

More institutional policies on data
The University of Sydney RDM Policy -
http://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2013/337&RendNum=0 -
and RDM Procedures -
http://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2014/366

More researchers care about data
sharing
Figshare open data survey of researchers 2017:
• 82% aware of open data sets
• 80% willing to reuse open data sets in own
research
• 60% routinely share their data (frequently or
sometimes)
• 21% have never made a data set openly available
• 74% are now curating their data for sharing
• 77% value a data citation the same as an article
Science, Digital (2017): The State of Open Data 2017 Report - Infographic.
figshare.https://doi.org/10.6084/m9.figshare.5519155.v1 pp. 7-11

More researchers are sharing their
data
More than two thirds of Wiley
researchers reported they are
now sharing their data.
Though this varies
geographically and across
research disciplines we are
seeing that more researchers
are sharing their data and
taking efforts to make it
reproducible.
Wiley Global Data Sharing
Infographic June 2017.
https://authorservices.wiley.c
om/author-resources/Journal-
Authors/licensing-open-
access/open-access/data-
sharing.html

Data sharing models
https://vimeo.com/125783029

Why should you share your data?
Read Nature Blog: 10 Reasons to share your data
https://www.natureindex.com/news-blog/ten-
reasons-to-share-your-data
Then class discussion:
● What do think of the arguments put forward here?
● Do you agree? Disagree?
● Have you had a different experience?

Key messages
• Any definition of research data is likely to depend on the
context in which the question is asked.
• Any research will require some level of data management.
• Good data management can increase the efficiency of
your research and enable the exposure of your research
outcomes through collaboration and dissemination
• More publishers, funders, governments and institutions
require data management and sharing
• More researchers care about data sharing and are sharing
their own data
• Not all data can be shared. Data can be open, shared or
closed.

Research Data Management:

Data Management Planning
How to get there from here
IMT
Sue Cook| Data Librarian

22 |Presentation title | Presenter name
your data
FAIR
data

“A Data Management Plan (DMP) typically outlines what research
data will be created during the course of a research project and how
it will be created, plans for sharing and preserving the data and any
restrictions that may need to be applied.”
http://www.ands.org.au/working-with-data/data-management/data-management-plans
Planning in order to get there
23 |
http://www.ands.org.au/guides/data-management-plans

• What 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
• And …
– Much more
Just some of the sorts of RDM decisions that need to be
planned:
24 |

25 |
https://www.nature.com/articles/d41586-018-03065-z

Example funder policies
• Australian funding example- ARC
“Researchers must outline briefly in their Proposal how they plan to manage
research data arising from a Project” Section A11.5.2
• International examples:
• National Science Foundation
“Proposals submitted … must include a supplementary document of no more
than two pages labeled "Data Management Plan".”
• Wellcome Trust
“Data sharing plans should address seven key questions as clearly and
concisely as possible, as noted in the Trust's Guidance for researchers:
Developing a data management and sharing plan”
26 |

• Australian http://projects.ands.org.au/policy.php
• International http://www.ands.org.au/working-with-data/data-
management/data-management-plans#International-2
• Discipline specific http://www.ands.org.au/working-with-data/data-
management/data-management-plans#Discipline_specific_DMPs-3
• Published
https://riojournal.com/browse_journal_articles.php?form_name=filter_
articles&sortby=0&journal_id=17&search_in_=0&section_type%5B%5D
=231
Example DMPs to look at
27 |

1. Constraints and obligations
2. Access
3. Description
4. Processes
5. Storage and compute
https://confluence.csiro.au/display/RDM/Research+Data+Planner
Example: CSIRO Research Data Planner
28 |

• Research Data Management Organiser RDMO
https://rdmorganiser.github.io/en/
• DMPonline https://dmponline.dcc.ac.uk/
• DMP Tool https://dmptool.org/
• DMPonline/DMPtool https://github.com/DMPRoadmap
• Protypes:
• Data Stewardship Wizard https://dmp.fairdata.solutions/
• Institutional example:
• Research Data Manager (UQRDM)
https://research.uq.edu.au/project/research-data-manager-uqrdm
Tools Available
29 |
}

Final word: DMP future directions
• Live DMPs
• maDMPs
• Exposing DMPs
• Standards for DMPs
• Force11 FAIR DMPs group
30 |

DMP Common Standards - Outputs
• Common data model for machine-actionable DMPs
• to model information from standard DMPs
• NOT a template
• NOT a questionnaire
• modular design
– core set of elements
– domain specific extensions
• Reference implementations
• ready to use models
– JSON, XML, RDF, etc.
• Guidelines for adoption of the common data model
• requirements for supporting systems
• pilot studies
www.rd-alliance.org - @resdatall
Status: Recognised & Endorsed

Australasian DMP Interest Group
● Formed early 2017
● Facilitated by ANDS
● Seeks to answer questions about Data Management Plans
● Brings the Australian and New Zealand community together to discuss DMP
tools and approaches
● Links into international DMP developments, in particular through involvement
in the Research Data Alliance DMP IG
● Meets every 2 months online
● Held a workshop at the eResearch Australasia conference
http://www.ands.org.au/partners-and-communities/ands-communities/dmps-interest-group

https://www.youtube.com/watch?v=1GYMB8QdT60

FAIR
● Findable
● Accessible
● Interoperable
● Reusable

Setting the Scene...
https://goo.gl/cAzYpj

Open Data/Data sharing - FAIR precursors
Blogs.nature.com. (2018). [online] Available at: http://blogs.nature.com/naturejobs/files/2017/06/mat1.jpg [Accessed 23 Mar. 2018].

The FAIR Principles
Wilkinson, M., Dumontier, M., Aalbersberg, I., Appleton, G., Axton, M., & Baak, A. et al. (2018). The FAIR Guiding Principles for scientific data
management and stewardship. Scientific Data. Retrieved 27 March 2018, from https://www.nature.com/articles/sdata201618
Commons.wikimedia.org. (2016). File:FAIR data principles.jpg - Wikimedia Commons. [online] Available at:
https://commons.wikimedia.org/wiki/File:FAIR_data_principles.jpg [Accessed 10 Apr. 2018].
“As open as possible, as closed as necessary”

A tool to assess how FAIR data are
https://www.ands-nectar-rds.org.au/fair-tool

Find a dataset that relates to your work using one of the following resources, and
assess it against the principles using the ANDS FAIR tool https://www.ands-nectar-
rds.org.au/fair-tool
Research Data Australia - https://researchdata.ands.org.au/
CSIRO Data Access Portal - https://data.csiro.au/
DataCite - https://search.datacite.org/
Discipline-specific repositories https://www.re3data.org/browse/by-subject/
A repository of your choosing.
Presentation title | Presenter name40 |
Activity - FAIR data assessment

F is for Findable
To be Findable:
F1. (meta)data are assigned a globally unique and eternally
persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable
resource.
F4. metadata specify the data identifier.
http://www.uniprot.org/uniprot/P98161

What is Metadata?
Ontotext.com. (2018). [online] Available at: https://ontotext.com/wp-content/uploads/2017/02/Metadata_01-768x384.png [Accessed 23 Mar. 2018].
“Metadata, you see, is really a love note – it might be to
yourself, but in fact it’s a love note to the person after you, or
the machine after you, where you’ve saved someone that
amount of time to find something by telling them what this
thing is.”
Cit. Jason Scott’s Weblog

Metadata types
Ontotext.com. (2018). [online] Available at: https://ontotext.com/wp-content/uploads/2017/02/Types-of-Metadata_03-768x384.png [Accessed 23 Mar. 2018].
● Metadata standards

Metadata standards
Fairsharing.org. (2018). FAIRsharing. [online] Available at: https://fairsharing.org/educational/# [Accessed 28 Mar. 2018].
http://www.dcc.ac.uk/resources/subject-areas/biology

Findable - metadata standards
Examples:
• Dublin Core http://dublincore.org/
• Darwin Core
• ANZLIC
• Marine Community Profile
• VO
https://www.ands.org.au/working-with-data/metadata

Resources on Findable
● DOI/Handle minting
● Metadata Standards Directories
● Research Data Australia
● Re3Data
● Thing 4: Data Discovery
● Thing 8: Citation metrics for data
● Thing 11: What's my metadata schema?
● www.ands.org.au/fair

A is for Accessible
TO BE ACCESSIBLE:
A1 (meta)data are retrievable by their identifier using a
standardized communications protocol.
A1.1 the protocol is open, free, and universally
implementable.
A1.2 the protocol allows for an authentication and
authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no longer
available.

Resources on Accessible
● Med.data materials
● Australian Data Access conditions
● ANDS Sensitive data materials
● Discovering data services
● Data Services Interest Group
● Thing 10: Sharing sensitive data
● Thing 19: APIs and apps
● www.ands.org.au/fair

Persistent Identifiers - The problem...

What are Persistent Identifiers?
● A persistent identifier (PiD) is a long–lasting reference to a
digital resource
● Usually has two parts:
○ A unique identifier (ensures the provenance of a
digital resource)
○ Location for the resource over time (ensures that the
identifier resolves to the correct location)

Digital Object Identifiers (DOIs)
● DOIs can be created for data sets and associated outputs
(e.g. literature, workflows, algorithms, software etc) - DOIs
for data are equivalent with DOIs for other scholarly
publications
● DOIs enable accurate data citation and bibliometrics (both
metrics and altmetrics)
● Resolvable DOIs provide easy online access to research
data for discovery, attribution and reuse
● DOIs are a persistent identifier and as such carry
expectations of curation, persistent access and rich
metadata

Why use PiDs?
● PiDs play a key role in the discoverability, accessibility and
reproducibility of research
● Persistent identifiers solve the problem of the persistence
of cited resource, particularly in the scholarly literature
● Some persistent identifiers (e.g. DOIs), have an added
value in discoverability, making digital objects findable and
reusable in multiple scholarly resources

Findable
Global, persistent ID
Metadata
Searchable

Accessible (metadata)
• Metadata is valuable in itself,
when planning research, especially
replication studies.
• But it doesn’t replace the original
data.
https://retractionwatch.com/2016/12/02/stolen-data-
prompts-science-flag-debated-study-fish-plastics/
“the theft of the computer on which the raw data for
the paper were stored. These data were not backed
up on any other device nor deposited in an
appropriate repository.”

Globally unique & persistent identifiers (PIDs)
• Book > International Standard
Book Number (ISBN)
• Research article / data / software > Digital Object Identifier (DOI)
Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, et al. The
FAIR Guiding Principles for scientific data management and stewardship. Sci Data.
2016;3: 160018 doi: 10.1038/sdata.2016.18
• Person > Open Researcher &
Contributor ID (ORCID)

Activity - ORCID profile
ORCID has recently emerged as the preferred identifier for people by a range of
Australian universities, funders and publishers worldwide. Choose from two activities:
Option 1 - Don’t have an ORCID?
1. Follow steps 1 and 2 at https://orcid.org/
2. When you’re done, add your ORCID to your email signature, LinkedIn profile, and
any other places (e.g. blog)
3. Send your new ORCID number to a colleague and ask for some feedback on your
profile
Option 2 - Already have an ORCID?
Read https://orcid.org/blog/2015/07/23/six-things-do-now-you've-got-orcid-id and
take a moment to update your profile.

Accessible
Standard access
protocols (e.g. https,
sftp, Web API)
Explicit
access
conditions

Discovery Mechanisms
Pixabay.com. (2018). Free Image on Pixabay - Needle, Hay, Needle In A Haystack. [online] Available at: https://pixabay.com/en/needle-hay-needle-in-a-haystack-1419606/
[Accessed 4 Apr. 2018].
● Google
● Ask a colleague
● Find link to data in a journal article
● Data journals
● Database registries e.g. re3data
● Open data portals e.g. data.gov
● Institutional repositories
● Data / Discipline repositories e.g. Dryad
● Project website
● Library catalogues, databases
● Data discovery aggregators like Research Data Australia
Where do you look for Data...

Research Data Australia
https://researchdata.ands.org.au/

Bio-related Repositories
● https://www.nature.com/sdata/policies/repositories#life
● Gene Expression Omnibus:
https://www.ncbi.nlm.nih.gov/geo/
● Array Express: https://www.ebi.ac.uk/arrayexpress/
● ELIXIR Deposition Databases for Biomolecular Data:
https://www.elixir-europe.org/platforms/data/elixir-
deposition-databases

Interoperable
To be interoperable the data will need to use community
agreed formats, language and vocabularies. The
metadata will also need to use a community agreed
standards and vocabularies, and contain links to related
information using identifiers.

Interoperable
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles.
I3. (meta)data include qualified references to other
(meta)data.

Metadata use a formal,
accessible and shared language
Ontologies
An ontology is a formal, explicit specification of a shared
conceptualisation (Studer 1998)
Formal: Machine readable
Explicit specification: It explicitly defines concept, relations,
attributes and constraints
Shared: It is accepted by a group
Conceptualisation: An abstract model of a phenomenon

Examples of Ontologies
● Gene Ontology Consortium (GO)
● The Sequence Ontology (SO)
● The Generic Model Organism Project (GMOD)
● Ontology for Biomedical Investigation

Controlled Vocabularies
● A controlled vocabulary
reflects agreement on
terminology used to label
concepts.
● When research
communities agree to use
common language for the
concepts in datasets, then
the discovery, linking,
understanding and reuse
of research data are
improved.
http://www.ands.org.au/online-services/research-vocabularies-australia

Metadata use vocabularies that
follow FAIR principles
The controlled vocabulary used to describe
data sets needs to be documented and
resolvable using globally unique and
persistent identifiers. This documentation
needs to be easily findable and accessible by
anyone who uses the data set.

Research Vocabularies Australia
https://vocabs.ands.org.au/

Interoperable
• Formats (open standards)
• Data types (defined and used
consistently)
• Transfer (exchange between
systems)
• Rules (schemas, vocabularies)
• Linked data models and cross-
referencing

Reusable
Reusable data should maintain its initial richness. For
example, it should not be diminished for the purpose of
explaining the findings in one particular publication. It
needs a clear machine readable licence and provenance
information on how the data was formed. It should also
have discipline-specific data and metadata standards to
give it rich contextual information that will allow for reuse.
From: https://www.ands.org.au/working-with-data/fairdata

Reusable
R1.1. (meta)data are released with a clear and accessible data
usage license.
R1.2 (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community
standards.

License
If data is not licensed no-one else can use it. In Australia,
no licence is regarded as the same as 'all rights
reserved', confining any reuse to very limited
circumstances.
From: http://www.ands.org.au/working-with-
data/publishing-and-reusing-data/licensing-for-reuse
https://www.youtube.com/embed/SHR1EJ0kQ3g?rel=0
[?]

Creative Commons
Because:
● Title “Creative Commons 10th
Birthday Celebration San Francisco”
● Author “tvol” – linked to his profile
page
● Source “Creative Commons 10th
Birthday Celebration San Francisco”
– linked to original Flickr page
● License “CC BY 2.0” – linked to
license deed
How you attribute authors of the CC
works will depend on whether you modify
the content, if you create a derivative, if
there are multiple sources, etc.
Source: https://creativecommons.org/use-remix/get-permission/
“Creative Commons 10th Birthday
Celebration San Francisco” by tvol is licensed
under CC BY 2.0

CC-BY SA
You are free to:
● Share — copy and redistribute the material in any medium or format
● Adapt — remix, transform, and build upon the material for any purpose, even
commercially.
Under the following terms:
● Attribution — You must give appropriate credit, provide a link to the license, and
indicate if changes were made. You may do so in any reasonable manner, but not in
any way that suggests the licensor endorses you or your use.
● ShareAlike — If you remix, transform, or build upon the material, you must distribute
your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that
legally restrict others from doing anything the license permits.

CC Australia - License explainer

Are you being FAIR to the future you?
In 5 years time will my research data be:
• Findable – A top draw of USB drives and sticks isn’t always a good data
archive
• Accessible – My new desktop doesn't have a DVD drive or what was the
password on that encrypted data drive?
• Interoperable – Wonder where I put my old copy of that software that
compiles this binary data file?
• Reusable – How accurate was that sensor network I used to gather these
observations? Am I allowed to reuse this data?
FAIR - Working Data | John Morrissey79 |

FAIR Working Data
Findable by whom? How? Minimum viable metadata?
• Standardized naming conventions for folders and files
• Consider using Readme.txt files to describe content? Maybe you could
include metadata.txt or metadata.json files embedded in folders
• Think about what persistent identifiers are useful in your project.
• Do you need a basic registry to manage metadata?

FAIR Working Data
Accessible by: Whom? How? What?
• How will you manage identity and access control?
• Shared storage resources – where?
• Will you use simple storage or a higher level platform like a shared eLab
notebook or database?
• What categories of data will you hold/share and which data assets need
to be kept long term?

FAIR Working Data
Interoperable:
• What are the key standards currently applied to the projects domain/s?
• Are my data producing assets standards compliant? Do they need to
be? What do I have to do to convert my data assets to the correct
format?
• Do we have a set of vocabularies we want to use within our project?
Where are they?
• Who can help me with my standards compliance work? (Librarians? IT
Specialists? Information Management Specialists?)

FAIR Working Data
Reusable:
• Agree on a licencing framework before the project starts producing data
• What data assets need to be preserved long term?
• What data assets will we publish?
• Where will we publish?
• Who has contributed to the data asset and how will they be represented when the data
published
• Who will manage the long-term data archive?

What are personal and sensitive data?
Privacy Act 1988
Personal
information
Sensitive
information
Health
information
Sensitive data
“data that can be used to
identify an individual,
species, object, process or
location that introduces a
risk of discrimination,
harm or unwanted
attention.”
Guide to Publishing and Sharing Sensitive Data
http://www.ands.org.au/guides/sensitivedata

Why it matters
http://www.abc.net.au/news/2018-03-18/cambridge-analytica-suspended-by-
facebook/9560272
https://www.smh.com.au/national/guilty-health-department-breached-privacy-
laws-publishing-data-of-2-5m-people-20180329-p4z6wf.html

Ethics
• Informed consent
• A key principle of ethics is avoid harm
• Can be achieved by removing/minimising sensitivity
• De-identifying data if possible (and the meaning is not
lost in the process)
• Conditions around access to data (mediated access, 5
safes)
• Ethics committee approval needs to cover consent and access
conditions
• See also ANDS’ medical webinar series
http://www.ands.org.au/working-with-data/sensitive-data/medical-and-
health/webinars-health-and-medical

Informed consent for data sharing
1. Avoid precluding data de-identification,
publication and sharing
2. State possibility of future data publication
3. State conditions of access
4. Document consent with collected data to inform
subsequent users
Example wording available in ANDS Guide to
Publishing and Sharing Sensitive Data

Identifiable*
Re-identifiable*
Non-identifiable*
De-identification/
Anonymisation
No specific individual can be identified
Possible to re-identify an individual
Identity of an individual can be
reasonably ascertained
http://www.ands.org.au/working-with-
data/sensitive-data/de-identifying-data
* Terms from National Statement on Ethical Conduct
in Human Research 2007 (Updated May 2015)
Data de-identification

What about sharing data that can’t be
de-identified?
healthtalkaustralia.org
Informed consent /
mediated access

Mediated access
• The metadata is openly available but the data is
not
• Access mediated through
• The researcher
• The research team
• The repository
• A data access committee

Resources for medical and health data
ands.org.au/working-with-data/sensitive-data
ands.org.au/medical
Publishing and sharing
sensitive data Guide
Data sharing considerations for Human
Research Ethics Committees Guide
De-identification Guide

Activity
Look at the consent forms
• UK Data Archive sample consent form
• Global Alliance for Genomics and Health consent
tools (focus on Section C)
• Health Science Alliance Biobank Consent
• https://www.icpsr.umich.edu/files/ICPSR/access/
dataprep.pdf (bottom of page 13)
Discuss between groups some of the good and bad
points of the consent form you examined.

What is impact?
● Public
○ Saving lives
○ Protecting the environment
and wildlife
○ Supporting the economy
○ Influencing public policy
○ Educational
● Private
○ For you, your collaborators
or university

Principles for maximizing your research
(data) impact
Highest impact will come from data that are:
● High quality
● Well described (FAIR)
● Citable
● Linked
● Open?

How to maximise impact of data
● Think early about what you are trying to achieve with
your data
○ Will it be used for a single or multiple research
projects?
○ Will you want to share/collaborate beyond your
current colleagues
○ How will it relate to publications?
○ Could it have wider (including commercial) impact?

Impact - global, specific purpose
“The arguments for sharing data, and the consequences of
not doing so, have been thrown into stark relief by the Ebola
and Zika outbreaks.
In the context of a public health emergency of international
concern, there is an imperative on all parties to make any
information available that might have value in combating the
crisis.”
Wellcome Trust 2016, reissued 2018

Impact - global, wide purpose
“CERN Open Data
provides content
for both
education and
research.
We aim to
support high-
school students
and teachers as
well as university
students and
professors.”

Impact - regional, social
“Data on Western Australian
children’s health, learning,
development and social
characteristics will be
mapped using geospatial
technology so that
community leaders and
service providers can identify
the priority issues for their
children.”

Impact - regional, economic
“This project is
delivering new
genetic knowledge
directly assisting the
breeding of better
mungbean varieties
for Australian
growers.”

Impact through collaboration
The PetaJakarta Data Sharing
project:
“aims to promote national
and international research
collaboration through the
sharing of data related to the
response of the city of Jakarta
to flooding during the
2014/2015 monsoon season.
Watch: the video
Info: http://www.petajakarta.org/banjir/en/

Impact through publication
● Many journals now require data sharing
● Initially begun because of issues around reproducibility
● Now researchers are using to maximise impact of the work
39% of researchers:
Sharing data
“Increases the impact
and visibility of my
research”

Policies may vary, even within a publisher
https://www.springernature.com/gp/authors/research-data-policy/data-policy-types/12327096

Maximising impact & retaining control at
publication
Data Availability: Data are available
from the Ecosounds Acoustic
Workbench. There are 1200 links
provided in the supporting information
"S4 File: Sample minutes", that provide
access to the data used in this
research. The audio files can be
accessed through the following links.
The project URL for the Ecosounds
Acoustics Workbench is
https://www.ecosounds.org/projects/
1029/sites/1192 and
https://www.ecosounds.org/projects/
1029/sites/1193
Additionally, our data is backed up on
QUT’s own HPC storage.

Appropriate
sharing to
maximise impact
Full access
to some data
Moderated access
to other data

Impact through data sharing is not new

The nuts and bolts of maximising impact
● High quality data and metadata
○ Persistent identifiers
● Consistent mechanisms for citing
● Thing 7: Data citation for access & attribution

Maximising impact: Cite it right
Noble, T., Williams, B., & Mundree, S. (2017). Next generation
mungbean SNP markers (Version 1). Queensland University of
Technology. https://doi.org/10.4225/09/59b8a393e44f9
● author/s
● year of publication
● title
● publisher (for data, this is often the archive where it is housed)
● edition or version
● access information (a URL, DOI or other persistent identifier)

Cite it right
https://citation.crosscite.org

Key messages
● Maximising impact starts when you start collecting data
● Think about what impact means for you
● How will you share your data
● There are tools available to maximise impact, especially to
get the citation right

Bringing it all Together
● How do you feel about Research Data
Management now?
● Are there areas where you feel you need
more information?
● Do you know what impact you want to
get from your data?
● What tips have you got for others?

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, Nectar and RDS are supported by the Australian Government through the National Collaborative Research
Infrastructure Strategy Program (NCRIS).
Thank you
natasha.simons@ands.org.au
@n_simons
ginny.barbour@qut.edu.au
@ginnybarbour

Winter school in research data science research data management - final

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Winter school in research data science research data management - final