TraitBank is the structured data service of the Encyclopedia of Life. Launched in 2014, it currently hosts 9 million data records for 1.7 million taxa, including trait records (eg: cell size, life history traits) and other attributes including administrative ones (eg: IUCN status, type specimen repository). Marine datasets include verbal localities from WoRMS, habitat categories from AlgaeBase, water temperature ranges based on known occurrence records from OBIS, and literature derived datasets including cell masses of phytoplankton and tissue mineralization types of algae and invertebrates. Hosted records include all available metadata, including detailed attribution, url of data source if online; organism information including sex and life stage; date, locality and method information for field studies, and any other fields provided by the source. TraitBank is not a repository. Most hosted records are deposited with a scholarly publication, or an institutional or aggregator database. Presence in TraitBank makes individual records findable by EOL search (http://eol.org/ data_search) or web search engine. Search results on EOL are available by CSV download and records are available to semantic web applications via a JSON-LD web service, including all metadata. Fresh Data is a data search service in development primarily for the Citizen Science community, funded by NSF. Interested occurrence data providers will register to be indexed. Their data will be deposited at GBIF, using the IPT, if possible, and in TraitBank otherwise (eg: presence/absence or abundance data, if GBIF cannot accommodate them). Searchers can query the index for recent records by time, location and taxonomic group. Registered researchers will also be able to save and publish their data queries, which will alert them if new data appears matching their criteria, and alert the data provider that their data was delivered to a subscriber.

1. Data dissemination tools for organism
attributes and new data records
EOL: data retrieval and use 101
Jen Hammock1, Katja Schulz1, Jorrit Poelen3
1- Smithsonian’s National Museum of Natural History
2- Global Biotic Interactions

2. preview
 TraitBank: organism attribute data
 How can I get at it?
 Example: tissue mineralization biogeography
 Overview of available datasets
 Ecological interactions data
 Fresh Data- recent observations (sneak peek)

3. Global Access to Knowledge About Life on Earth
http://eol.org

4. Global Access to Knowledge About Life on Earthhttp://eol.org/data_search

11. Web service
http://www.eol.org/info/traitbank_api
eg: http://eol.org/api/traits/204729

12. Example: tissue mineralization
biogeography
www.iobis.org

13. Example: tissue mineralization
biogeography

14. Example: tissue mineralization
biogeography
• 70,106 hits out of 130,160 OBIS records
• 3659 out of 7943 taxa

15. Example: tissue mineralization
biogeography

16. Example: tissue mineralization
biogeography
Also available: crystal structure, etc.
• Aragonite
• Calcite
• Apatite
• etc.

17. Example: tissue mineralization
biogeography
• Occurrence Data: OBIS (2015). Global biodiversity indices from the Ocean
Biogeographic Information System. Intergovernmental Oceanographic
Commission of UNESCO. Web. http://www.iobis.org (consulted on
2015/05/11)
• Mineralization types from 145 literature sources
• Trait Propagation from higher taxa according to WoRMS and AlgaeBase

18. Marine data summary
Large datasets
 Environmental data ranges- lat, long, depth, temperature, water chemistry (OBIS
and World Ocean Atlas)
 Geographic distribution keywords (marinespecies.org & marineregions.org)
 Habitat keywords textmined from EOL articles
 Ecological associations data (GloBI, aggregates from many sources, eg: GoMexSI)
 Tissue mineralization
Other interesting datasets
 Copepod life history and size
 Phytoplankton cell mass, volume, shape
 PolyTraits- polychaete life history, physical description
 Mollusk shell dimensions
See all datasets

19. GloBI: Global Biotic
Interactions
Jorrit Poelen

20. http://www.globalbioticinteractions.org/browse/

21. http://www.globalbioticinteractions.org/browse/

22. http://www.globalbioticinteractions.org/browse/

23. https://github.com/jhpoelen/eol-globi-data/wiki

24. Fresh Data
(Hammock, Poelen)

25. Robertson & Van Tassell D. & J., CC-BY-NC-SA
OBIS (2015). Global biodiversity indices from
the Ocean Biogeographic Information System.
Intergovernmental Oceanographic Commission
of UNESCO. Web. http://www.iobis.org
(consulted on 2015/05/18)

29. problem: so many places
records might appear
 Citizen reporting platforms
 NaturaLista/iNaturalist
 iSpot
 Reef Life Survey
 Small, localized monitoring projects or research projects
 Social media and random places on the internet??!!!??
 Museums and other natural history collections
 Government agency surveys

30. Fresh Data- a community search index
 Query the index for occurrence data (powered by GBIF +…)
 Available to anyone
 Geographical selection
 Taxonomic filter
 Save query
 Initially only available to registered curators
 Provide a sentence or two describing your interest in the data
 Set email notification preferences (daily/weekly/etc.)
 Anonymous or contactable?
 Receive notifications when fresh data appears
 Data providers will be notified that their data was sent to
someone

31. Engaging the public in scientific
research
What we know from recent studies of citizen science:
 Data fit for use? This is a reasonable expectation, though it
will not necessarily take less effort to train and manage
volunteers as it would take to gather the data yourself.
 Added benefit: inspired scientific literacy. *Not* necessarily
taught by the citizen science project. Participants are
motivated to go out and acquire more information.
Danielsen, F., P.M. Jensen, N.D. Burgess, R. Altamirano, P.A. Alviola, H. Andrianandrasana, J.S. Brashares, A.C. Burton, et
al. 2014. A Multi-Country Assessment of Tropical Resource Monitoring by Local Communities. BioScience, 64:236-251. [pdf]
Fortson, Lucy, Karen Masters, Robert Nichol, E. M. Edmondson, C. Lintott, J. Raddick, and J. Wallin. "Galaxy Zoo."
Advances in machine learning and data mining for astronomy (2012): 213-236.

35. also…. ask Jen!
Thanks
This work was supported by:
 The Smithsonian’s National Museum of Natural History
 The Sloan Foundation
 David M. Rubenstein
Jen: hammockj@si.edu