This document discusses the agINFRA project's efforts to enhance interoperability between agricultural data sources by developing a linked data framework for germplasm data. The agINFRA Germplasm Working Group aims to identify relevant standards, analyze existing schemas and vocabularies, and propose recommendations for exposing germplasm resources as linked open data. Key outcomes include a dossier of germplasm information and engagement with stakeholders. The proposed methodology involves defining a base schema, publishing local classifications as linked data, and linking data from different sources using common vocabularies. Implementation plans include publishing germplasm vocabularies and phenotypic data in 2014.

1. Global RDF Descriptors for
Germplasm Data
Vassilis Protonotarios
Agricultural Biotechnologist, PhD
Agro-Know, Greece
RDA 3° Plenary Meeting, Dublin, Ireland
Agricultural Data Interoperability Group Meeting

2. Background

3. Connecting the pieces
agINFRA Germplasm
Working Group
Agricultural Data
Interoperability IG
Germplasm Data
Analysis
Agricultural linked
data layer

4. The agINFRA project
• A project funded under the FP7 program of EC
• Consortium with expertise on
– Technology / infrastructures
– Data / data management
Combined to facilitate agricultural data sharing
More info at:
www.aginfra.eu

5. The agINFRA project
• Aims to enhance the interoperability between
the agricultural data sources
– Data sharing by
• Metadata aggregation & linking data
• Design and deploy the linked ag-data framework
– Methodology for linking data
– Provide the infrastructure needed
• Both cloud- and grid-based services
• Tools, APIs etc.

6. agINFRA major data types
agINFRA
Bibliographic
Agri Statistics
& Economics
Educational
Germplasm
Soil data
Profiles
Raw data
Other?

7. Focusing on germplasm
Local
Databases
National
Databases
Aggregators
GENESYS
EURISCO
GBIF
Italian
Italian
University
Italian research
center
Chinese
Chinese
research center
Data flow

8. Focusing on germplasm
Local
Databases
National
Databases
Aggregators
GENESYS
EURISCO
Italian
Italian
University
Italian research
center
Chinese
Chinese
research center

9. The issue ?
• Heterogeneity!
– Data types
– Data formats
– Data management workflows
– Standards used
– Metadata exposure options
– ….
• Lack of connectivity with other data sources

10. The agINFRA Germplasm Working
Group

11. The Germplasm Working Group
• Created in the context of the agINFRA project
• Initially included agINFRA stakeholders
– now expanded to host all stakeholders
• The group is NOT a group of experts on
germplasm data!

12. The scope of the agINFRA
Germplasm WG
• Enable/enhance interoperability between
germplasm databases
– By developing the services for
• exchanging their data and
• delivering their data to other partners
• Focusing on three actions:
1. Identify
2. Organize & Reuse
3. Propose

13. agINFRA Germplasm WG objectives
• IDENTIFY: collect all information related to germplasm
data
– People/groups
– Namespaces (metadata, KOS)
– Standards
– Workflows
– Events
• ORGANIZE & REUSE: engage all stakeholders & available
resources, analyze existing standards , facilitate
collaboration
• PROPOSE: linked data framework to connect data
sources
• facilitate data sharing between germplasm data sources

14. Germplasm related information
data
management
workflows
metadata
schemas
Working
groups in
germplasm
Events
(for connecting
stakeholders)
KOS
(ontologies,
thesauri,
vocabularies
etc.)
Data exposure
capabilities

15. Germplasm related information
data
management
workflows
metadata
schemas
Working
groups in
germplasm
Events
(for connecting
stakeholders)
KOS
(ontologies,
thesauri,
vocabularies
etc.)
Data exposure
capabilities

16. The Germplasm WG wiki
• Central point of reference
• Freely accessible (no login required)
http://wiki.aginfra.eu/index.php/Germplasm_Working_Group

17. Key outcomes of the group (1)
Dossier on Germplasm Information:
– Major programs
– Major information systems and services
– agINFRA germplasm data sources (CGRIS & CRA)
– Core standards for germplasm information
– Plant nomenclature, taxonomies and ontologies
– Plant genomic resources
– Related references and links
• Freely available from the Germplasm Group wiki

18. Key outcomes of the group (2)

19. Key outcomes of the group (3)
• Speakers from key players in the biodiversity
data field
– GBIF, EURISCO, GENESYS, CGIAR, EGFAR, CRA etc.
• Aimed to provide the basis for the linked
germplasm data framework

20. Existing work

21. DwC-G KOSs
• Germplasm Term Vocabulary
• A vocabulary of terms for describing and annotating
germplasm information resources
– http://purl.org/germplasm/germplasmTerm#TERM
• Germplasm Type vocabulary
• List of controlled values for some of the germplasm terms
– http://purl.org/germplasm/germplasmType#TYPE
• Germplasm ontology
• to digitize and provide persistent identifiers for the terms
contained within the PGR Descriptors publications
– http://purl.org/germplasm/ontology

22. DwC-G linked data

23. DwC-SW
• An ontology using Darwin Core terms to make it possible to
describe biodiversity resources in the Semantic Web.
https://code.google.com/p/darwin-sw

24. Bioversity Crop Descriptors
• Crop Descriptors
– Provide an international format and a universally understood
language for plant genetic resources data.
– They are targeted at farmers, curators, breeders, scientists
and users and facilitate the exchange and use of resources.
– Information includes such details as the plant's height,
flowering patterns and ancestral history.
• FAO/Bioversity Multi-crop Passport Descriptors (MCPD)
– Originally published in 2001
– widely used as the international standard to facilitate
germplasm passport information exchange.
– Now expanded to include emerging documentation needs,
this new version resulted from consultation with more than
300 scientists from 187 institutions in 87 countries.

25. Wheat descriptors
• Descriptors for wheat and Aegilops (1978)
• Descriptors for wheat (Revised) (1985)
– Not available as Linked Data

26. Methodology: towards the RDF
germplasm descriptors

27. Linked Data vocabularies
• Metadata vocabularies: Metadata sets, metadata element
sets
– they provide metadata elements to describe individual pieces of
information in the data sets.
– Example: Dublin Core is a vocabulary that prescribes the
property dc:date for the publishing date of a document.
• Value vocabularies (KOS): Controlled vocabularies,
authority data
– they provide sets of values for (some of) the metadata
elements.
– Example: AGROVOC provides a set of values for agricultural
topics that can be used as values for the dc:subject property.

28. LOD guidelines (Berners Lee, 2006)
1.“Use URIs as names for things”
– concepts / values in value vocabularies and classes and properties in description vocabularies, as well
as the vocabularies themselves, have to be identified by URIs.
2.“Use HTTP URIs so that people can look up those names”
– the URIs for concept / values, classes and properties, as well as vocabularies, have to be resolved as
HTTP URLs.
3.“When someone looks up a URI, provide useful information”
– the URLs for concepts, classes and properties, as well as vocabularies, have to return an HTML page
with useful information when requested by browsers, or RDF when requested by RDF software;
besides, vocabularies should be available for querying behind a SPARQL endpoint.
4.“Include links to other URIs, so that more things can be
discovered”
– the URIs of concepts, classes and properties should whenever possible be linked to URIs in other
vocabularies, for instance as close match of another concept or sub-class of another class.

29. Proposed methodology
1. Analyze metadata schemas & KOSs used to
describe germplasm resources
2. Define attributes & vocabularies that can be
used to expose germplasm resources in linked
data format.
3. Provide a set of recommendations for the
exposure of germplasm resources as linked data
4. Embed the recommendations in the data
infrastructure of agINFRA
– to allow the exposure of germplasm resources as
LOD.

30. The next steps

31. Application of the linked agricultural
data framework in germplasm
1. Definition of base schema
– Darwin Core for Germplasm to be used as base
schema
• Already available in SKOS
• Vocabularies published as linked data
– Germplasm Vocabularies
• Germplasm Term Vocabulary
• Germplasm Type Vocabulary
– Germplasm ontology
2. Publication of local classifications / lists for
germplasm as LOD KOSs
– if possible use DwC Types directly

32. Application of the linked agricultural
data framework in germplasm
3. Linking of terms in new KOSs to terms in existing
KOSs
– e.g. DwC Types, AGROVOC
4. Link CAAS and CRA germplasm records using
scientific name > AGROVOC
5. Collaboration with technical partners
– technical specifications on how to write procedures that extract the
relevant data from the database and "triplify" them (i.e. both serialize
them as RDF and use URIs instead of just strings whenever possible, also
linking to AGROVOC URIs when possible).

33. …and more next steps (optional)
• Update the existing analysis with new data
• Collect new user requirements
• (re)define the mappings between metadata
schemas and KOSs (if needed)
• Fine-tune the linked data approach

34. Time plan

35. Time plan
• June 2014: Germplasm vocabularies
– Metadata model: Darwin Core SW + DwC-G as the
reference
• Publish local classifications / lists for germplasm as LOD
KOSs (if possible use DwC Types directly)
• Link terms in new KOSs to terms in existing KOSs (e.g.
DwC Types, AGROVOC)
• Germplasm phenotypic values / classifications linked to
Phenotypic Ontology terms?

36. Time plan
• August 2014: Germplasm RDF
– Expose some RDF output and API access for
germplasm datasets (basic DwC RDF, essentially
basic passport descriptors).
– Mandatory data for interlinking: scientific name
OR AGROVOC term

37. Time plan
• October 2014: Consuming data from agINFRA
services and components
– Link CGRIS and CRA germplasm records using
scientific name > AGROVOC

38. Source: http://verastic.com/social/why-do-people-not-say-thank-you.html
vprot@agroknow.gr