Australia invests $AUD1-2B per annum in research data. Like most countries, it wants to get the best return possible on this data. Europe is spending E1.4B on their open data “pilot”. This means the data should be FAIR: findable, accessible, interoperable, and reusable. Part of this is that data should be routinely “published” and available in a “data repository”. But what does this mean? Ross Wilkinson CEO, Australian National Data Service Presented at the 2015 Wiley Publishing Seminar, 5 November, Melbourne, Australia.

1. Research Data Publishing
International and National Trends
Ross Wilkinson
Australian National Data Service
Melbourne, November, 2015
1

2. Outline
 Trends
 The research data assets of Australia
 International trends
 The challenges for the publication process
 The opportunity
 Conclusions
2

3. Some Trends:
 Reproducible Science
 Open Science
 Open Data
 Data Citation
 Data Citation
Bibliometrics
 Data Journals
 Data Repositories
 Trusted Data
Repositories
 FAIR Data
 Funded Fair Data
3

4. What’s going on?
 Data is no longer a by-product of research
 Data is valuable
 Funders and Government want more from their
research investments
 So do research institutional leadership
4

5. 5

6. The Value of Open Data Report
The analysis in the report suggests that the value of
data in Australia’s public research is at least $1.9
billion per annum and possibly up to $6 billion per
annum – at 2012-13 levels of expenditure and
activity.
The report discusses the implications for Australia
including if this value is not realised while
recognising potential costs if its value is to be
effectively leveraged.
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7. 7

8. Australian Research Data Activity
 Data Policy
 Capturing data valuable over long periods in Marine,
Astronomy, Earth Sciences, Ecosystems …for a wide
range of research purposes
 Supporting the storage of data
 Supporting the management of data
 Supporting the enhancement of data
 Building Institutional Research Data Capacity
 Developing data partnerships with industry?
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9. Research Data Policy
 ARC and NHMRC: Treat data as an asset
 Department of Environment: Requirement that
data is open, discoverable, and available
 Department of Education: The Australian Research
Data Infrastructure Strategy provides
recommendations for coherent approach to
research data and research data infrastructure
9

10. Data is Transformative
 Governments are not investing in research data to
make life easier for researchers
 Investments in research data to enable societal
problems to be addressed
 This requires data to be in a form that allows a
wide variety of use
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11. Data as a research output – and more
• Funders are seeing research
data a publishable output
• They expect data to be
managed
• They expect it to be
available for industry,
education, the public and
further research
11
Industry Education
Public Research
Data

12. AURIN – Urban data infrastructure
 How can I increase the value of my suburban
property development?
 How do I make it more “liveable” to attract more
buyers?
 Integrate data from developers, local government,
state government, federal government, mapping
data, roads data, public transport maps….
 Apply University of Melbourne developed
“walkability” index 12

13. How do you develop suburbs that
work for residents, developers and
local government?
 Along the Maribyrnong River, 10 km from Melbourne’s CBD, 128 ha of government land is
ripe for redevelopment
 It could accommodate 3000 dwellings and offices for 3000 people
 Planning a sustainable, liveable community integrated into its urban surrounds demands
information on transport, health services, environment, housing prices, recreation
facilities and more
 This comes from Federal and State government agencies, local councils, utilities and
private companies
 For Maribyrnong, data and 80 tools to manage it are being made available through the
Australian Urban Research Intelligence Network (AURIN) and the Australian National Data
Service (ANDS)
 New tools—such as employment opportunities and walkability—are being added
 Similar projects can facilitate development across Australia’s cities and towns
13

14. Data Value
 Stronger research
 More efficient research
 Stronger partnerships
 More industry engagment – data as a trust builder
14

15. Australian National Data Service:
To make Australia’s research data assets
more valuable for its researchers, research
institutions and the nation
15

16. So we need to transform:
Data that are:
Unmanaged
Disconnected
Invisible
Single use
To Structured Collections that are:
Managed
Connected
Findable
Reusable
so that researchers can easily publish, discover, access
and use research data.
Value

17. Major Open Data Program
 Connecting mining data, to
research techniques, to
industry exploration
 Connecting twitter data to
Jakarta map to analytics for
managing flooding
 Collecting tropical data to
institutional strategy
 Collecting ancient DNA for
forming international
partnerships for new results 17

18. Data Opportunities – and threats
 Data sharing is great for trust development
 Data openness challenges traditional business
models
 Data partners can be anywhere – EU is investing
€1.4B in open data to drive jobs and innovation
 Research data environment in Australia is world
leading
18

19. Back to some Trends:
 Reproducible Science
 Open Science
 Open Data
 Data Citation
 Data Citation
Bibliometrics
 Data Journals
 Data Repositories
 Trusted Data
Repositories
 FAIR Data
 Funded Fair Data
19

20. 20
From G. Boulton

21.  Royal Society
publishes “Science as
an open enterprise” –
written by Geoffrey
Boulton
 Influential in EU/UK
21

22. EU Open Data “Pilot”
 1.4B Euros as part of H2020
 80% take up
22

23. Data citation
 Data that is used
should be cited – just
as other work is cited
 Provides appropriate
credit
 Enables reproduction
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 DataCite provides
reliability
 Agreed basic
information: Creator
(Publication year), Title,
Publisher, Identifier
 Suitably formatted DOI

24. Data citation works with..
 ORCID – for people
 Crossref – for papers
 Fundref – for funders
 IGSN – for specimens
 …
 Can we measure the
value? Bibliometricians
arise!
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 Connection is key
 And the connections
should be machine
operable
 Research is more
valuable if it is more
connected

25. Data Journals
 Geoscience Data
Journal (Wiley)
 Scientific Data (Nature)
 Journal of Open
Archaeology
Data (Ubiquity)
 Biodiversity Data
Journal (Pensoft)
 A means of describing
the data – its formation,
properties, usage
 Enables recognition of a
contribution
 Enhances usage of the
data
 Enables “traditional”
bibliometrics
25

26. Data Repositories:
Provide:
Data storage
Metadata storage
Data access methods
Data management
software
But also:
Integrated approach to
content and metadata
Policies, processes,
services, and people
Overall commitment to
the stewardship of
digital materials
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27. Trusted data repositories
 Need for reliable data
 Trusted repositories:
 Trusted Repositories Audit & Certification (TRAC) -ISO 16363
 Data Seal of Approval e.g. Pacific and Regional Archive for
Digital Sources in Endangered Cultures (PARADISEC)
 Often required by publishers
 May be increasingly required (and funded) by
research funders
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28. FAIR Data – (FORCE 11)
To be Findable:
(meta)data are assigned a globally unique
and eternally persistent identifier.
data are described with rich metadata.
 (meta)data are registered or indexed in a
searchable resource.
metadata specify the data identifier.
To be Accessible:
 (meta)data are retrievable by their identifier
the protocol is open, free, and universally
implementable
the protocol allows for an authentication and
authorization procedure, where necessary.
metadata are accessible, even when the data
are no longer available.
To be Interoperable:
(meta)data use a formal, accessible, shared,
and broadly applicable language for
knowledge representation
(meta)data use vocabularies that follow FAIR
principles.
 (meta)data include qualified references to
other (meta)data.
To be Re-usable:
meta(data) have a plurality of accurate and
relevant attributes.
(meta)data are released with a clear and
accessible data usage license
(meta)data are associated with
their provenance.
 (meta)data meet domain-relevant
community standards.
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29. Funded FAIR data
 All of the data that support a research finding
should be FAIR
 It should be stored in a trusted repository
 It should be funded
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30. 30

31. The Opportunity
 Fully integrated publication of all outputs of a
scholarly endeavour with rich connection
 FAIR data in a trusted repository
 Fully explorable scholarly journals
 Researchers get much better exposure of their
research
 The outcomes are defensible
 New research and partners become available
31

32. Conclusions
 Research data is valuable
 It should be expected that the data underpinning
findings are available for scrutiny
 Far greater value is available, especially if it is
findable, accessible, interoperable and reusable
 This is helped if data is published
32

33. 33This work is licensed under a Creative Commons Attribution 3.0 Australia License
ANDS is supported by the Australian Government through the National Collaborative
Research Infrastructure Strategy (NCRIS).
Thank you!