1. Jonathan Yu, Environmental Informatics, CSIRO Land and Water
Hacking Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers
Jonathan Yu, Environmental Informatics, CSIRO Land and Water
Acknowledgements to OzNome team at CSIRO (Ben Leighton, Simon Cox, Paul Box, David Lemon, Hendra Wijaya, Ramneek Singh, Ashley Sommer, Andrew Freebairn, Peter Fitch, Matt Stenson, Sally Tetreault-Campbell, Andy Reeson, Todd Sanderson)

2. Data science and modelling
Cloud computing
Data
Linked Data resources …
Repositories, web services, infrastructure, formats…
(ontologies, vocabs, semantics)
HPC, *aaS
Data science and modelling
Containerisation, collab tech
Access to compute anywhere, anytime, at scale
Access to data at increasing volume, variety, velocity,
Capturing increasing amount of precise info models, definitions for all scientific concepts
Reproducible, repeatable, collaborative, shareable, mobile orchestration of workflows
Increasingly powerful, shareable, environments for access and use of data for science
We live in an exciting time
computing wise…

3. Dealing with data silos Making sense of the data
Data challenges
Dealing with data silos
Unconnected, unknown, unmanaged
Improving data access
and connectivity
Making sense of the data
Understanding and using data in context.
Integrating it with other datasets.
Data access and connectivity
Majority of data points are not yet connected today, and companies often do not have the right platforms – curate, publish, aggregate, analyse
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

4. Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

5. Access and use any data precisely, Using native tools,
Users
Access and use any data precisely,
Using native tools,
Run any-everytime,
From any-everywhere,
Reproducible,
Shareable/linkable/citeable,
Collaborative,
“Just works” …
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

6. Concrete metrics of use Dialogue with users
Providers/producers
Concrete metrics of use
Dialogue with users
Shareable/linkable/citeable
Collaborative
Easy to share data …
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

7. Leverage existing resources Meet users where they are now
Show the possible with modern computing and Linked Data approaches
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

8. Hacking to link across existing data
Aim is to network existing information infrastructures with the knowledge network. Leverage what is there but with the principle that everything has a “Cool” identifier.
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

9. Link to tools people already useC
Link to tools people already use
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

10. Link up communities that already exist
Aim is to network existing information infrastructures with the knowledge network. Leverage what is there but with the principle that everything has a “Cool” identifier.
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

11. Typical researcher user experience…
Spatial features
Web application
Data users
3rd parties or new researchers
X data 2005-
Policy
Reports
Data Providers
e.g Govt’ agencies, research groups
X definitions
General public C
File download
Research
Modelling environments
X historical
Current AWRA data ecosystem - does that resonate with you?
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

12. Register Integrate Transform Analyse Synthesize Spatial features
Deliver
Data users
Spatial features
Discover
Access
Understand
Transform
Integrate
Critical mass
Easy
3rd parties or new researchers
Reports
Data Providers
e.g Govt’ agencies, research groups
Policy
Domain
Definitions
General public C
Research
Register outputs
Modelling environments
Data
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

13. Bootstrapping existing resources minting identifiers and APIs…
Data users
Any spatial feature
Knowledge Network
3rd parties or new researchers
Definition Service
Reports
Data Providers
e.g Govt’ agencies, research groups
Policy
Data broker APIs
General public
Any definition C
Research
Any gridded data
Modelling environments
Bootstrapping existing resources minting identifiers and APIs…
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

14. CSIRO Knowledge Network
URIs/HTTP Identifiers for everything…
Indexing:
70,000+ datasets
170,000+ geo-features
AusGovt organisations list (1220 organisations, ABNs next) …
Possible extensions:
LinkedIn, ORCID, twitter, etc.
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

15. View of analytics on a data provider’s knowledge repository
Link to feature here:
Data visualisation of
key themes in
Knowledge repository
Analysis of Top 100 themes in knowledge repository
Social analytics about knowledge people love
Social analytics about knowledge people are viewing
Knowledge network | Jonathan Yu

16. Example
PID for spatial thing:
Rpub doc:
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

17. Jupyter NB: Github link
Example
PID for data collection:
Jupyter NB: Github link
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

18. Example
Cookie-cut spatial region (using PID) from continental scale dataset via web service
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

19. Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

20. Data science and modelling
Cloud computing
Linked Data resources
Data …
It just works! … …
Containerisation, collab tech
Data science and modelling
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

21. Challenges Making it intuitive and easy
Need more “Linked Data” tools/libraries and examples at user end of ecosystem (e.g. Python,
Need more resources to bootstrap
Conventions/protocols about the content
Base ingredients to work with
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

22. ABS Statistical Areas Level 2
ABS Greater Capital Cities Classification
SA2
GCCSA
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

23. Common convention for encoding spatial things
Original
Common convention for encoding spatial things
skos:prefLabel “Australian Capital Territory”;
skos:notation “801” ^^ abs:SA4_Code ;
skos:notation “8ACTE” ^^ abs:GCC_Code ;
skos:exactMatch “8” ^^ abs:STE_CODE16 .
RDF/SKOS
prefLabel: Australian Capital Territory”
notation: “801”
related: “abs-gcc:8ACTE”
exactMatch: “abs-ste:8”
SKOS inspired KVP
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

24. What do these keywords mean? 5-star vocabulary terms?
Precision vs. existence
How trusted is this?
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

25. Build on existing resources Meet users where they are now
Hack Show the possible with modern computing and Linked Data approaches
Bootstrapping via identifiers for things so we can demonstrate value
Better conventions for content
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers

26. Thanks! Land and Water Jonathan Yu Research scientist
Land and Water
Jonathan Yu
Research scientist t
e jonathan.yu [at] csiro.au
Weaving an integrated environmental data ecosystem using Linked Data and resource identifiers