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

working towards building an infrastructure designed to tag and track scientific collections and all of their derivatives.
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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

Tracking FaceBook relationships …
From “ Facebook Visualizer ”

Can we track relationships for Biological Objects as well?

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.

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

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

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

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

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.

Related Projects that are Grouping
Like terms into Classes
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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

Creating Globally Unique Identifiers (GUIDs)
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Globally unique (mandatory)
Persistent (not mandatory, but very helpful)
Resolvable (not mandatory, but very helpful)
Resolution/Domain http://mycollection.org/specimen/
+
Identifier
JDeckSpecimen1 (A named identifier) http://example.org/urn:lsid:example.org:specimen/
+1-541-914-4739 (Unique, at least for phones)
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

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

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 ”

Georeference1
(BioGeomancer)
Inferred Relationship Chains relatedTo Location1
(Essig Museum) hasSpatialThingGeoreference
Organism1
(Essig Museum) sameAs Organism2
(Smithsonian)
48.198,16.371;crs=wgs84;u=40
Tissue1
(Essig Museum)
Tissue2
(Smithsonian)
Tissue1
(Essig Museum)
Tissue2
(Smithsonian)
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.

Tools in Development
“ Bio-Plugins ”

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)
…
Search Descendents
(By Recent Modification)
BiSciCol API
(Search on Date
And return graph
Of object)

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

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

Example Taxonomic Query
Client Interface:
Search Scientific Name: Aedes increpitus Run
Results:
OccurrenceID1 ( Aedes increpitus Dyar, 1916 )
OccurrenceID3 ( Aedes vittata Theobald, 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

Working with Locations
E.g. Tracking location in space of a moving individual (whales)
IndividualID1 EventID1
EventID2
EventID3
GeoreferenceID
1
GeoreferenceID
2
GeoreferenceID
3

Data Impact Factor – Graph Metrics
Graphs Collectors
Gustav Paulay
(102,000 direct children)
Christopher Meyer
(83,000 direct children)
Craig Moritz
(523 direct children)
[ ] GBIF Relations Graph
[X] Moorea Biocode
[X] SI MSNGR System
[+] Add New Graph
Whats New?
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”)
Events
Biocode10234
(4234 direct children)
Occurrences
MBIO99999
(1024 total descendents)
Expedition21234
(1023 direct children)
IMBL8888888
(723 total descendents)

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

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 1 st , 2 nd , 3 rd , or n th order relations.

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/