BiSciCol: Tracking Biodiversity Objects to Brokering Standards “ Or, Gustav ’ s Big Problem ”

1. John Deck, University of California, Berkeley Brian Stucky, University of Colorado, Boulder Lukasz Ziemba, University of Florida, Gaineseville Nico Cellinese, University of Florida, Gainesville Rob Guralnick, University of Colorado, Boulder BiSciCol Team Reed Beaman, Nico Cellinese, Jonathan Coddington, Neil Davies, John Deck, Rob Guralnick, Bryan P. Heidorn, Chris Meyer, Tom Orrell, Rich Pyle, Kate Rachwal, Brian Stucky, Rob Whitton, Lukasz Ziemba BiSciCol: Tracking Biodiversity Objects to Brokering Standards“Or, Gustav’s Big Problem”

2. Biological Science Collections Tracker working towards building an infrastructure designed to tag and track scientific collections and all of their derivatives. • National Science Foundation funded 2010 – 2014 • Partners are University of Florida at Gaineseville, University of Colorado at Boulder, Bishop Museum, University of California at Berkeley, Smithsonian Institution, University of Arizona at Tucson • Relies on globally unique identifiers (GUIDs) to track objects • Implements a Linked Data approach • Provides support for the Global Names Architecture

3. Tracking FaceBook relationships … From “Facebook Visualizer”

4. Can we track relationships for Biological Objects as well?

5. Why? Here is Gustav’s Problem…. Lots of Data …. Generates … (Prefers to collect stuff) Due to project requirements and integration needs, Gustav is left navigating a plethora of redundant and disconnected distributed Databases. Lots of effort to track objects And their derivatives.

6. Can we borrow from Facebook and social networking to help solve Gustav’s Problem?

7. A Biological Relationship Graph … Functions Class Filter Infer Relationships Across providers Specimens X X Tissues Taxonomic Type Filter Sequences X

8. Moorea Biocode Example: Tracking biological material from field collection through analysis, across multiple systems Taxon Taxon (Taxon) Taxon*n (Key) Blast Key Blast*n (Biocode Event) (metagenomic Sequencing) (CAMERA Gut Sample Event) (Essig Museum Specimen) (Genbank Sequence) (Smithsonian Tissue)

9. How do we Track Biological Objects and their Relations Across Distributed, Heterogeneous systems?

10. Tracking Biological Object Relationships Group like terms into classes. In Darwin Core, for example we have the following “groups of terms”: Events, Locations, Occurrences, GeologicalContext, Identification, Taxon. Assign Identifiers. Use globally unique, resolvable, persistent identifiers for each class or term. Link Identifiers using Relationship Terms. For example, “This object is related to that object.” Put this data on the Web.

11. Related Projects that are Grouping Like terms into Classes • Darwin-SW (http://code.google.com/p/darwin-sw/) Building an ontology of Darwin Core Terms to make it possible to describe biodiversity resources on the web. • Gene Ontology (http://www.geneontology.org/) Standardizing the representation of gene and gene product attributes across species and databases. • ENVO (http://environmentontology.org/) Annotating the environment for any biological sample. • OBO Foundry (http://www.obofoundry.org/) A suite of orthogonal interoperable reference ontologies in the biomedical domain

12. Creating Globally Unique Identifiers (GUIDs) • Globally unique (mandatory) • Persistent (not mandatory, but very helpful) • Resolvable (not mandatory, but very helpful) + Resolution/Domain Identifier http://mycollection.org/specimen/ JDeckSpecimen1 (A named identifier) +1-541-914-4739 (Unique, at least for phones) http://example.org/urn:lsid:example.org:specimen/ 7217D220-836A-11DF-8395-0800200C9A66 (opaque) Examples: http://mycollection.org/specimen/JDeckSpecimen1 http://mycollection.org/specimen/uuid=7217D220-836A-11DF-8395-0800200C9A66 http://example.org/urn:lsid:example.org:specimen/7217D220-836A-11DF-8395-0800200C9A66

13. Linking Identifiers Using Relationship Terms An RDF Statement: Object Subject OR relatedTo (Transitive): Predicate Predicate relatedTo relatedTo GUID1 <-> GUID2 GUID2 <-> GUID3 GUID1 <-> GUID3 GUID2 GUID2 GUID1 GUID1 GUID3 A Simple BiSciCol Graph (graph=set of RDF Statements): relatedTo relatedTo GUID1 GUID3 GUID2 Date a a Date Event Tissue a Date “2011-06-20” “2011-05-01” Specimen “2011-06-01”

14. Getting the most out of your data: Inferring Object Relationships Facebook Inferencing: “Let us sell you, to others (or vice-versa)” BiSciCol Inferencing: “What relationships exist that haven’t been explicitly expressed”

15. Inferred Relationship Chains Georeference1 (BioGeomancer) relatedTo Location1 (Essig Museum) inferred relatedTo hasSpatialThingGeoreference Organism1 (Essig Museum) sameAs Organism2 (Smithsonian) 48.198,16.371;crs=wgs84;u=40 relatedTo relatedTo inferred inferred Tissue1 (Essig Museum) Tissue2 (Smithsonian) Tissue2 (Smithsonian) Tissue1 (Essig Museum) Even though Tissue #2 is not directly related to Location1, we can Still infer its relationship through Organism1 and Organism2 being the same as each other.

16. Tools in Development “Bio-Plugins”

17. Update Mechanisms Gustav’s Watchlist: GP12345-3939-33939 (Occurrence) BE99999-3939-3dd39 (Event) GP12346-3939-33II3 (Occurrence) GP12dd6-3939-3xxxI (Tissue) GP9999-xkx9d-dkdkd (Occurrence) … BiSciCol API (Search on Date And return graph Of object) Search Descendents (By Recent Modification) Updates

18. Genomic Rosetta Stone Uses GUIDs, classed data, and links to tie Organismal data to Genomic Data.

19. “Triplifier”linking biological objects Darwin Core Archive BiSciCol “Triplifier” Create links from Native data formats Mysql KEMU Mysql

20. Example Taxonomic Query Client Interface: Search Scientific Name: Aedes increpitus Run Results: OccurrenceID1 (AedesincrepitusDyar, 1916 ) OccurrenceID3 (AedesvittataTheobald, 1903) Taxon SERVICE (ITIS / GNUB) http://lsid.itis.gov/urn:lsid:itis.gov:itis_tsn:126314 http://lsid.itis.gov/urn:lsid:itis.gov:itis_tsn:126317 http://gnub.org/8E19F1DC-74BA-47D4-A505-6498414B4CCE BISCICOL SERVICE LOOKUP: dwc:IdentificationID1 :relatedTo http://lsid.itis.gov/urn:lsid:itis.gov:itis_tsn:126314 dwc:IdentificationID1 :relatedTo dwc:OccurrenceID1 dwc:IdentificationID2 :relatedTo http://lsid.itis.gov/urn:lsid:itis.gov:itis_tsn:126317 dwc:IdentificationID2 :relatedTo dwc:OccurrenceID3

21. Working with Locations E.g. Tracking location in space of a moving individual (whales) GeoreferenceID1 IndividualID1 EventID1 GeoreferenceID2 EventID2 GeoreferenceID3 EventID3

22. Data Impact Factor – Graph Metrics Whats New? Occurrences Events Collectors Graphs Occurrence:MBIO1234 (“2011-10-18 09:10:00”) DNA Extraction:Extrac9999 (“2011-10-18 09:00:00”) Sequence:s1113939999 (“2011-10-18 08:00:00”) Occurrence:MBIO1235 (“2011-10-17 00:00:00”) Photo:P123456 (“2011-10-17 00:00:00”) [ ] GBIF Relations Graph [X] Moorea Biocode [X] SI MSNGR System [+] Add New Graph Gustav Paulay (102,000 direct children) MBIO99999 (1024 total descendents) Biocode10234 (4234 direct children) Craig Moritz (523 direct children) IMBL8888888 (723 total descendents) Expedition21234 (1023 direct children) Christopher Meyer (83,000 direct children)

23. Web Interface (Demonstration Wed. 2pm at BiSciCol Meeting)

24. Summary All objects are re-usable in the semantic web. We only need to express an identifier once and then it can be linked by anything else (either directly or indirectly) By using sameAs relations it is possible to infer relations for data that was not previously expressed. Queries are easily federated – possibility to create global graphs and ask questions against heterogeneous databases. Graph based databases can help us understand the relevance of individual objects. For example, indicate the number of relations a particular object has for 1st, 2nd, 3rd, or nth order relations.

25. How to Get Involved “Create stable identifiers, link them to other stable identifiers, and put them on the web.” http://biscicol.blogspot.com/ http://code.google.com/p/biscicol/