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How e-infrastructure can contribute to Linked Germplasm Data
This document discusses how e-infrastructure can help link germplasm data. It describes the need for e-infrastructure to overcome issues like data silos and lack of interoperability. The agINFRA approach provides services like metadata aggregation, vocabulary publishing, and APIs that can transform and expose germplasm descriptions as linked data. This will help link germplasm databases and make the data more discoverable. Next steps include developing recommendations for publishing germplasm data and deploying transformers and APIs on the agINFRA platform.
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How e-infrastructure can contribute to Linked Germplasm Data
- 1. How e-‐infrastructure can contribute to Linked Germplasm data Giannis Stoitsis, Agro-‐Know stoitsis@agroknow.gr e-‐conference on Germplasm Data Interoperability
- 2. Contents Why we need e-‐infrastructure What e-‐infrastructure can provide The agINFRA approach agINFRA powered services for Germplasm data • What is next • • • •
- 3. WHY WE NEED E-‐INFRASTRUCTURE
- 4. agricultural data • publicaKons, thesis, reports, other grey literature • educaKonal material and content, courseware • primary data, such as measurements & observaKons – structured, e.g. datasets as tables – digiKzed, e.g. images, videos • secondary data, such as processed elaboraKons – e.g. dendrograms, pie charts, models • provenance informaKon, incl. authors, their organizaKons and projects • experimental protocols & methods • social data, tags, raKngs, etc. • …
- 5. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. educators’ view
- 6. • stats • gene banks • gis data • blogs, • journals • open archives • raw data researchers’ • technologies view • learning objects • ………..
- 7. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. practioners’ view
- 8. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ………..
- 9. LD for educationaldata/resource sharing Overview we sKll have data silos Approaches for LD in educational data sharing • Many metadata standards (e.g. DC, IEEE APIs and data (http://www.meducator.net) On the-fly/automated integration of heterogeneous LOM, Dw, local schemas) • Diversity of web interfaces (e.g. REST, OAI-‐PMH, SOAP, SPI, SQI) • Different exchange format (e.g. XML, RDF, JSON) • Fragmented use of t cataloging (http://linkedup-project.eu) Dataset (transformation and)exonomies … and not here … We are still here … ?
- 10. we need ontologies published online and aligned • • • • • • • stats gene banks blogs, journals open archives raw data learning objects
- 11. we need tools to share data
- 12. we need tools to semanKcally annotate data
- 13. and for all this we need
- 15. data infrastructure for agriculture • aim is: promo&ng data sharing and consump&on related to any research ac&vity aimed at improving produc&vity and quality of crops ICT for compu&ng, connec&vity, storage, instrumenta&on
- 16. what researchers need in agINFRA … only a browser and internet connecKon
- 17.
- 18.
- 19. what can be hosted and executed on agINFRA • Data storage & management tools – APIs for content disseminaKon in large networks • Processing & visualisaKon tools • Metadata aggregaKon infra • Search engines and apps for insKtuKons or communiKes • Environments for running experiments e.g. comparing different content recommendaKon algorithms
- 20.
- 21. HOW AGINFRA CAN SOLVE DATA INTEROPERABILITY PROBLEMS
- 22. WORKFLOW FOR METADATA AGGREGATION
- 23. metadata aggregaKons • concerns viewing merged collecAons of metadata records from different sources • useful: when access to specific supersets or subsets of networked collecAons – records actually stored at aggregator – or queries distributed at virtually aggregated collecKons 23
- 24. typically look like this 24 Ternier et al., 2010
- 25. metadata aggregaKon tools More than a harvester: q Valida&on Service q Repository So4ware q Registry Service q Harvester Powered by 25
- 26. a metadata aggregaKon workflow that can be ported on agINFRA HarvesKng ValidaKng Transforming OAI target -‐ XMLs Storing and indexing TriplificaKon
- 27. TOOLS FOR PUBLISHING AND LINKING VOCABULARIES
- 29. AGRICULTURAL DATA DISCOVERY SERVICE/PORTAL OVER THE CLOUD
- 30. agricultural data discovery modules for open source CMS hIp://www.youtube.com/watch?v=OYlxWlyag04&feature=youtu.be
- 31. LINKING GERMPLASM DATABASES AND EXPOSING DESCRIPTIONS AS LINKED DATA
- 32. agINFRA contribuKon in germplasm data interoperability • Define recommendaKons for describing germplasm data • Define mappings between different metadata formats • Provide APIs for transformaKon – triplificaKon of germplasm descripKons
- 33. mapping between different metadata formats powered by agINFRA
- 34. publishing germplasm data as linked data in agINFRA services
- 35. next steps in the context of agINFRA • Develop the recommendaKons for publishing germplasm data • Deploy transformers and make them available in agINFRA • Deploy API for triplificaKon
- 36. thank you! stoitsis@agroknow.gr www.agroknow.gr www.aginfra.eu