1. Responsible Citizenship of the World of Science
Using Persistent, Unique Identifiers for Samples
Wim Hugo
SAEON, ICSU-WDS

4. Too Large and Complex to be Useful to Science…
The Complete Web: every piece of information at a physical network node is potentially in multiple relationships with every other. This enormous graph is many times larger than the physical internet (1) and is not practically useful for science.
Formal Meta-Data: very few links are formally specified, eliminating almost all of the potential links between pieces of information to favour only a very rigid collection.
(1) Fensel, D. and van Harmelen, F. (2007). Unifying Reasoning and Search to Web Scale, IEEE Computer Society, /07. f

6. The LOD “Cloud”
2011

7. The LOD “Cloud”
April 2014

9. Credibility of Science
Access to original and complete data sets for reproducibility
Re-usability declines with time
Availability declines with age

10. Rationale
Reduction in Complexity of the Semantic Web
Citability and Incentivisation
Re-usability (Interoperability)
Reproducability
Discoverability

11. Solution: Considerations

12. Charles Babbage ( )

13. Sir Robert Peel ( )
The British Parliament, after investing £ 20,000 in the Difference Engine project, was treated to a demo.
£ 2,400,000 in 2017
“Can you set the machine to calculate the time at which it will be of some use???”

14. Information Technology …

15. Systems Engineering Technology Drivers Patterns Use Cases
Design Considerations and Architecture
Other (Science) Drivers
Solution(s)
Implementation

16. Persistent Identifiers and Science
The Fabric of Science
The Process of Science
The Language of Science

17. Persistent Identifiers and Science
The Fabric of Science
The Process of Science
The Language of Science
Mostly Captured in Metadata
Mostly Captured in Metadata
Mostly Captured in Data

18. The Fabric of Science ICSU-WDS Knowledge Network
Scholarly Publications
(CrossRef?)
TDRs
(WDS, DSA, DataCite*)
Samples and Events
People
(ORCID, …)
RDI Outputs/ Online Resources
Coverage
(Temporal, Spatial, Topic)
Data Citations
(DataCite)
Institutions
(GRID,
ISRI)
Projects
Use, Caveats, Lineage,
Provenance, Methods
Initiatives
Licenses
(Creative Commons)
Networks
Platforms, Instruments, Deployments, Sites, …
* Including re3data, DataBib
Funders
(?)
Exists
Started
Not Now
WDS
ICSU-WDS Knowledge Network

19. The Process of Science ICSU-WDS Knowledge Network Sample, Specimen,
Member
“Standard Variables”
Standard Transformation
Real World/
Events
Processing
Observations,
Media
“Analysis Ready Data”
Analysis and Workflows
“Publication Ready Output”
ICSU-WDS Knowledge Network

20. The Language of Scientific Data
Unstructured
Normalised
Graph
Ontology (Resolution uncertainty)
Time
Vocabularies
(“stable”)
Spatial/ Location
Coverage
(Temporal, Spatial, Topic)
Registries
(“unstable”)
Dimensions
(External Entities)
Topic
Variable
Non-Standardised
“Controlled” Variables

21. Framework: Elements of a Solution
Domain-Specific or Community-Specific PIDs
Governance
Process
Framework
Technical Guidance
Conceptual
Framework and Metamodels
End Users and Systems
Certification

22. Design Consideration #1
Precision, Vocabularies, PIDs, and LOD
Precision is Critical in Formal, Structured Data if used for dimensions
Precision is desirable for other data
Graph edges have a weight distribution … assertion
Creativity is impossible if precision is perfect 
Guidance on required on best practices
Value judgment on usability/ trust
Some progress in this symposium
Technical Guidance

23. Design Consideration #2
Single Point of Resolution
Option 1 – voluntary/ community indexing
Option 2 – dedicated resolver
Option 3 – ‘publication’ metadata
Agreement on minimum index metadata
Governance and sustainability
No progress thus far
Governance | Certification

24. Option 1 - Hybrid Graph Solution:
“Scholix”
Scholarly Publications
(CrossRef?)
TDRs
(WDS, DSA, DataCite*)
Samples and Events
People
(ORCID, …)
RDI Outputs/ Online Resources
Coverage
(Temporal, Spatial, Topic)
Data Citations
(DataCite)
Institutions
(GRID,
ISRI…)
Projects
Use, Caveats, Lineage, Methods
Initiatives
Licenses
(Creative Commons)
Networks
Platforms, Instruments, Deployments, Sites, …
* Including re3data, DataBib
Funders
(?)
Exists
Started
Not Now
WDS

25. The Fabric of Science ICSU-WDS Knowledge Network
Scholarly Publications
(CrossRef?)
TDRs
(WDS, DSA, DataCite*)
Samples and Events
People
(ORCID, …)
RDI Outputs/ Online Resources
Coverage
(Temporal, Spatial, Topic)
Data Citations
(DataCite)
Institutions
(GRID,
ISRI, …)
Projects
Use, Caveats, Lineage, Methods
Initiatives
Licenses
(Creative Commons)
Networks
Platforms, Instruments, Deployments, Sites, …
* Including re3data, DataBib
Funders
(?)
Exists
Started
Not Now
WDS
ICSU-WDS Knowledge Network

26. Design Consideration #3
Single Point(s) of Failure
Persistent Identifiers are now fundamental to research data infrastructure and cannot be allowed to fail. Yet may of these services are community-driven, poorly funded, and sometimes rely on voluntary contributions to function
Identify critical elements of RDI and fund this reliably and into the long term
Certify services and manage to maturity
Some progress in this symposium
Governance | Certification

27. Design Consideration #4
Conceptual Models and Semantics
Conceptual Model – “Because it is a sample”
Conceptual Model – “Because of the domain”
Conceptual Model – “Because of the owner”
Perspective from Statistics
Develop a Conceptual Model that allows Protocol-specific, Organisation-Specific, and Domain-specific metadata
Influences resolution and higher-level sample metadata
Hopefully constructed from existing metadata standards without major modification
Good progress in this symposium
Conceptual/ Metamodel

28. A Bit of DataCite Schema
RelatedIdentifier
Suggestion to add sample-related vocabulary
Supplementary Materials
Suggestion to add ‘Sample Metadata’ as a link
Keyword/ Subject Element
Infinitely extensible

29. Design Consideration #5
Definition of Critical Dimensions for Data Families
Sample Identifier is Often Required and Must Become Common Practice in Datasets
Vocabulary or PID for each Dimension
Develop a Conceptual Model for each Data Family and Discipline/ Standard Variable
Influences metadata and data content standards
No progress thus far
Best Practice/ Technical Guidance

30. Generic Dimensions of Data
Sample S
Spatial Coverage
XYZ
Temporal Coverage: T
Topic or Semantic/ Ontological Coverage
D: Demographic
P: Phenomenon
mostly physical, chemical, or other contextual data
B: Biological
Tx: Species and Taxonomy (with some extensions)
Al: Allele/ Genome/ Phylogenetic
Ch: Characteristics, Traits, and, and Life Stages
Each unique combination of these, supported by a vocabularies/ ontology is a generic data family
Continuous or Near-Continuous: Uppercase
Discrete or dispersed: Lowercase
Best Practice/ Technical Guidance

31. Some Generic Data Families and Crosswalk Requirements
Typical Dimensions/ Content
Typical Infrastructure
Typical Syntax/ Schema
Object
xyz, t, P/C, S
DDI
“Sparse”
NetCDF
XYZ, T, P, S
OPeNDAP
Multi-dimensional
S-DB
Traditional Spatial
XYz, t, P, S
WxS
O&M
Signals
XYZ, T, P/ B, S
SensorThings
General Structured, Media, Objects
Object
xyz, t, P/ B, S
CSV, PDF, ZIP
GBIF Index
XYz, t, Tx, S
Species Occurrence, …
DwC
GenBank
XYZ, T, Al, S
Genetic
FTP/ ASN.1
Now Implementing: ✪ Array Databases/ Virtual Cubes for Everything
WCS

32. Simple or Core Information Model
Genes and Alleles
Species and Taxons
Sampling Event
Spatial and Temporal Coverage
Life Stages, Traits and Characters
Physical Phenomena
Best Practice/ Technical Guidance

33. Example: Taxon Abundance, Presence and Absence
Genes and Alleles
Relationship
Species and Taxons
Sampling Event
Spatial and Temporal Coverage
Life Stages, Traits and Characters
Physical Phenomena
Best Practice/ Technical Guidance

34. Example: Phylogenetic Data
Genes and Alleles
Relationship
Species and Taxons
Sampling Event
Spatial and Temporal Coverage
Life Stages, Traits and Characters
Physical Phenomena
Best Practice/ Technical Guidance

35. Example: Morphology Best Practice/ Technical Guidance
Genes and Alleles
Relationship
Species and Taxons
Sampling Event
Spatial and Temporal Coverage
Life Stages, Traits and Characters
Physical Phenomena
Best Practice/ Technical Guidance

36. Example: Biome Definition, Ecosystem Services
Genes and Alleles
Relationship
Species and Taxons
Sampling Event
Spatial and Temporal Coverage
Life Stages, Traits and Characters
Physical Phenomena
Best Practice/ Technical Guidance

37. Design Consideration #6
Identifiers for the Fabric of Science
Certification of the Process of Science
PIDs for All Important Agents/ Objects
Repositories for Objects and Artifacts
Registries and Name Services
World Data System/ DSA considering extension beyond data
Community Assessments –
Rating-driven (TripAdvisor, …)
Comment-driven (GitHub, ...)
Good progress in this symposium
Certification

38. Design Consideration #7
Solution Granularity
Too specialised: reduced utility and much duplication
Too generalised: all communities miss critical aspects
Two-tiered solution?
Some progress in this symposium
Certification

39. “Granularity” of a Solution
Universalise?
Generalise
Specialise
“change the code”
“change the configuration”
“change the reference model”

40. “Granularity” of a Solution

41. Actions Who takes this forward? RDA Interest Group
WG: Sample – Conceptual Model/ Metadata
WG: Develop Two-tiered Solution?
WG: Criteria for Trusted Name Services and Registries
WG: Best Practices in Respect of Sample Management
ICSU-WDS/ DSA/ Institutions (IGSN, Pangaea, DataCite, …)
Develop criteria for trusted sample repositories
Implement certification infrastructure
Implement Scholix Generalisation – institution required

42. “Metadata is a tax in the Data World
“Metadata is a tax in the Data World. R They may not like it, but responsible citizens pay their taxes. Citizens cannot expect services and infrastructure to be built on their behalf if they do not pay tax.”

43. ?