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Bioschemas presentation at ECCB 2016, The Hague

Bioschemas is an open community initiative that aims to improve data interoperability in the life sciences. It does so by encouraging life scientists to use schema.org mark-up, so that their websites and services contain consistently structured information. This makes it easier to discover, collate and analyse distributed data. The main outcome of Bioschemas is a collection of specifications that provide guidelines to facilitate a more consistent adoption of schema.org mark-up within the life sciences.

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Bioschemas.org
Structured data for Life
Sciences using Schema.org
Niall Beard
Scientific WebTechnologist, University of Manchester
ELIXIR: European infrastructure for
biological information
Data infrastructure for
Europe’s life-science
research:
www.elixir-europe.org
@ELIXIREurope
Data
Interoperability
Tools
Compute
Training
Marine metagenomics
Human data
Crop and forest plants
Rare diseases
• 20 Members
• 1 Observer
ELIXIR Hub based
alongside EMBL-EBI
in Hinxton
• 20 Members
• 1 Observer
FAIR
Findable
Accessible
Interoperable
Reusable
Finding resources – Search engine index
Resource Resource Resource
Finding resources – Catalogues
bio.tools
tess.elixir-uk.org

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Bioschemas presentation at ECCB 2016, The Hague

  • 1. Bioschemas.org Structured data for Life Sciences using Schema.org Niall Beard Scientific WebTechnologist, University of Manchester
  • 2. ELIXIR: European infrastructure for biological information Data infrastructure for Europe’s life-science research: www.elixir-europe.org @ELIXIREurope Data Interoperability Tools Compute Training Marine metagenomics Human data Crop and forest plants Rare diseases • 20 Members • 1 Observer ELIXIR Hub based alongside EMBL-EBI in Hinxton
  • 3. • 20 Members • 1 Observer
  • 5. Finding resources – Search engine index Resource Resource Resource
  • 6. Finding resources – Catalogues bio.tools tess.elixir-uk.org
  • 7. Discover resources by filtering metadata
  • 8. Finding resources – Content Integration platforms Training Resource Training Resource Training Resource Tool Resource Tool Resource Tool Resource bio.tools tess.elixir-uk.org Programmatically aggregated
  • 12. <div itemscope itemtype="http://schema.org/Recipe"> <div itemprop="nutrition” itemscope itemtype="http://schema.org/NutritionInformation"> Nutrition facts: <span itemprop="calories">144 kcal</span>, </div> Ingredients: - <span itemprop="recipeIngredient">800g small new potato</span> - <span itemprop="recipeIngredient">3 shallot</span> . . .
  • 13. <script type="application/ld+json"> { "@context": "http://schema.org", "@type": ”Recipe", "name": ”Potato Salad", “NutritionInformation”: { "calories”: “144 kcal”, "recipeIngredient”: “800g small new potato”, "recipeIngredient”: “3 shallot” . . .
  • 15. Search engine readable = optimized Content Content Content Schema.org Schema.org Schema.org
  • 16. Search engines favour websites containing schema.org in their search results
  • 19. BioSchemas.org minimal, maximal, extensible Training materials Events Organizations Data Standards Software Minimum information for one content type Training materials Events Organizations DataSoftware Standards Common properties among content types
  • 20. More depth to a broad-reach technology Depth DATS Reach
  • 21. Use case 1:TeSS, ELIXIRTraining Portal - Aggregates Life ScienceTraining Materials
  • 22. Large Training Sites • Well-formed APIs • XML Dumps • RSS feeds Medium/Small Sites • No structured data
  • 23. The long tail, collections sets and small science Slide courtesy of Todd Vision, Dryad
  • 25. Biosamples entry (Diabetic mouse strain) Diabetes term EFO_0000400 Experimental Factor Ontology Defined byisAbout Courtesy of Tony Burdett and Simon Jupp Use case 2: Mapping data to ontologies
  • 26. Organization - name MedicalEntity - name - description MedicalCode - codeValue - codingSystem MedicalCode - name - url - alternateName - description - codeValue - codingSystem … CreativeWork - about - name - description - url - datePublished … Data Term Ontology Courtesy of Tony Burdett and Simon Jupp Use case 2: Mapping data to ontologies
  • 27. Use case 3.1: Dataset Markup, Citation • Dataset Citation • Mapping to JATS Journal ArticleTag Suite Data extension* • Metadata for data citationGoogle, Bing, Yahoo, Yandex Training materials Events Organizations DataSoftware Standards *Daniel Mietchen et al , Adapting JATS to support data citation, Journal Article Tag Suite Conference (JATS- Con) Proceedings 2015, Bethesda (MD): National Center for Biotechnology Information 2015.
  • 28. Use case 3.2: Dataset Markup, Samples • Biobank Samples • Limited number of simple key properties • Disease, gender, age and sample type, data available • Cross-walk MIABIS: Minimum Information About BIobank data Sharing Google, Bing, Yahoo, Yandex Training materials Events Organizations DataSoftware Standards Cataloging 400 UK Biobanks
  • 29. Value for content providers • More exposition through search engines and portals • Favoured in search results • Low barrier for adoption • Embedding schema.org in pages can be done with off-the-shelf CMS • Tools for most frameworks and web scripting languages • Longevity of Standard • Standard is open to the wider community and will survive past funding • Less chance of the schema deprecating after implementation
  • 30. Value for content integration platforms • Good benefits to persuade providers to structure their data • Lots of tooling available for parsing structured data • Many open RDFa, JSON-LD, and microdata parses available on GitHub • Wider community engaged in construction • Schema.org is a public forum so not limited to just the people you know • Much more scalable than scraping • Bespoke scripts that gain technical debt when scraping
  • 33. Acknowledgments • TeSS Niall Beard • BioSharing SA Sansone, A Gonzalez-Beltran, P McQuilton, P Rocca- Serra • NIH BD2K bioCADDIE SA Sansone, A Gonzalez-Beltran, Jeff Grethe • Community Premysl Velek • Event Martin Cook • Training materials Aleksandra Nenadic & Gabriella Rustici Organization representatives Group chairs BioSchemas community • ELIXIR Premysl Velek • Pistoia Alliance Richard Holland • GOBLET Terri Attwood • BBMRI Michaela Mayrhofer • Organization Richard Holland & Rafael C Jimenez • Person Niall Beard • Standard A Gonzalez-Beltran & P McQuilton
  • 34. Contributors • Aleksandra Nenadic • Adam Hospital • Gabriella Rustici • Carlos Horro • Martin Cook • Niall Beard • Rafael C Jimenez • Andy Jenkinson • Manuel Corpas • Roberto Preste • Richard Holland • Alejandra Gonzalez-Beltran • Andrew Lonie • CaroleCoble • Peter McQuilton • PremysilVelek • Ian Dunlop • Jef Grethe • MiloThurston • Niklas Blomberg • Isabelle Perseil • Jaap Heringa • Jon Ison • John Hancock • Simon Jupp • John (Jack) D. Van Horn • Ivana Krenkova • Laura Furlong • Morris Swertz • Mateusz Kuzak • Mario Alberich • Mark Thompson • Maria Martin • Mikael Borg • Montserrat González • Norman Morrison • Núria Queralt-Rosinach • Olivier Sallou • Robert Pergl • Pedro Fernandes • Yasset Perez-Riverol • Sarala Wimalaratne • Nick Juty • Jose Luis Ambite • Brane Leskošek • Celia van Gelder • Christa Janko • Christine Staiger • Dan Brickley • Daniel Faria • Dmitry Repchevsky • Daniel Sobral • Daniel Vaughan • Ian Fore • Frederik Coppens • Josep Ll. Gelpi • ChuQiao Gong • Hedi Peterson • Hervé Ménager • Nina Hrtonova • Pierre Larmande • Rob Finn • Renzo Kottmann • Rodrigo Lopez • Sameer Velankar • Sara Light • Carol Shreffler • Silvano Squizzato • Susanna Sansone • Tony Burdett • Terri Attwood • Cath Brooksbank • Hedi Peterson • Luc Deltombe • Michaela Mayrhofer • Philippe Rocca-Serra
  • 35. Upcoming Bioschemas Activities • Biosoftware description using bio.tools and schema.org - NETTAB, 24th October • Bioschemas AGM on 8th -9th November in Rothamsted UK • See: https://goo.gl/hu7uYK • Implementation study proposal being drafted • Develop more content types for life sciences: • Data repository • Dataset • Sample • Phenotype • Protein annotations

Editor's Notes

  1. Working group developing several schemas in training, events, software, samples data, standards, ontologies Open to new people joining in existing or proposing new schemas
  2. managing and safeguarding the increasing volume of data being enables to access vital data, tools, standards, compute and training services for their research.
  3. Growing network. 4 new this year Estimates there are 500,000 life-science researchers in Europe Heterogenous and distributed network of resources – How can we make them discoverable and interoperable
  4. Published 15 March 2016 It’s the law to show this slide. Most interested in Findable, with a dash of interoperable Content providers want more visibility. More people using their content, bigger remit for further funding, more community engagement.
  5. Search engines index words Query words match resources Good if you know what you’re looking for Algorithms to guess what a page is about
  6. Another way is through portals unifies the silod information across application boundaries Training and events Standards databases and policies And tools and data services
  7. Access to discoverability features – faceted filtering – By ontology terms like topic, audience etc Some level of curation – looking at automatic ontology classification More bespoke to our communities needs
  8. TeSS and bio.tools are automatically aggregated Most up to date content by programatically parsing resources regularly
  9. Needed a publicly accessible schema to adopt. Very indepth but with a minimum model to be used at different depths
  10. Collection of schemas can be used to describe online objects
  11. Schema.org very lightweight
  12. BioCADDIE is the Rosetta Stone ELIXIR-NL FAIRifier DCAT+ coverage Indexing and harvesting: OAI-PMH COTS
  13. Easy to integrate large repositories but difficult for the large number of small sites
  14. Lightweight linked data Specify what ontology term data relates to Specify what ontology an ontology term relates to Scrape these sites and build knowledge graphs about ontologies
  15. DCIP BioCADDIE-Force11 Data Citation Implementation Project It’s all about the metadata and knowledge graph 3rd of web pages Builds on a shared core and data structure Simple embedding in web pages and CMS Widespread tooling, harvesters and indexing Search engines and Integration tools
  16. DCIP BioCADDIE-Force11 Data Citation Implementation Project It’s all about the metadata and knowledge graph 3rd of web pages Builds on a shared core and data structure Simple embedding in web pages and CMS Widespread tooling, harvesters and indexing Search engines and Integration tools
  17. A considerably more scalable solution to HTML scraping that is easy to adopt for content providers and easy to integrate for content integration platforms
  18. Empirical assessment of existing data types