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A simple model for large-scale data
mobilization across a diverse
organisation
Nicky Nicolson, RBG Kew
@nickynicolson
Biodiversity Information Standards (TDWG) annual meeting
Nairobi, Kenya / 28th September – 1 October 2015
“Biodiversity Knowledge Graph”
Linked Open Data
Principle #4: "Refer to other things using their
HTTP URI-based names when publishing data on
the Web"
How?
Services philosophy (1)
... Put the services and tools in the hands of
researchers
Services philosophy (2)
... Use general tools, focus on unique problems
http://bit.ly/k-names-service
Connecting name data to other resources
Schinus longifolius var. paraguariensis
(Hassler) F. Barkley
Taxonomic status of 229196-2?
229196-2
Synonym
IPNI Reconciliation Service
IPNI Reconciliation Service
IPNI Reconciliation Service
We’ve converted a name to an identifier
Schinus longifolius var. paraguariensis
(Hassler) F. Barkley
229196-2
Now we can use that identifier to add in
more data…
Connecting name data to other resources
Schinus longifolius var. paraguariensis
(Hassler) F. Barkley
Taxonomic status of 229196-2?
229196-2
Synonym
Different kinds of research…
http://bit.ly/plant-species-gendergap
Thanks to:
• Biodiversity Informatics team (Abigail Barker,
Matt Blissett, James Crowe, John Iacona, Rob
Turner, Alecs Gueder)
• Plant & fungal name curation team (Christine
Barker / Irina Belyaeva / Katherine Challis /
Rafael Govaerts / Paul Kirk / Heather Lindon /
Emma Williams)
• Data improvement team (Anna Lynch, Rachel
Witherow, Malin Rivers, Esther Wainwright-Deri)
@nickynicolson / n.nicolson@kew.org
http://bit.ly/k-names-service
http://github.com/RBGKew
Biodiversity Information Standards (TDWG) annual meeting
Nairobi, Kenya / 28th September – 1 October 2015

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Tdwg 2015-nicolson-kew-mobilisation

Hinweis der Redaktion

  1. This shows the kinds of data elements that Kew has collected and how they interlink to form a “knowledge graph”
  2. Fieldwork
  3. …carried out in a particular geographical region…
  4. … collects physical material…
  5. … accessioned into multiple specialist collections (e.g. herbarium, DNA bank, seed & living collections)
  6. Duplicate specimens are shared with other organisations
  7. Individual researchers, teams, and organisations are represented as agents
  8. One key activity is for researchers to label specimens with determinations
  9. A determination is a link between a specimen and the concept that it represents
  10. Concepts fit into classifications
  11. The core of the concept is the name, which has a special link to a specimen via type citation
  12. Names and classifications are published in scientific literature, accessed via bibliographic citations
  13. Concepts can be mapped to management classifications (for reporting purposes) and to phylogenies
  14. Finally – once we have recognised species, we can assert facts about them – e.g. their physical characteristics, traits, distributions and uses
  15. In summary: elements about the physical specimens
  16. … Elements which use those physical specimens to define and name species
  17. Assertions about species
  18. Here, we show elements which are shared with other scientific and / or academic domains: geographic localities, people/teams/organisations and scholarly literature
  19. If we want this rich graph of data, how do we build it?
  20. Deb’s talk : what’s an API?
  21. A walkthrough of matching up a dataset containing names to some Kew data resources Match the names against IPNI, get an identifier, ask other resources what they know about that identifier. (i.e. names matching isolated into one place)
  22. Reconciliation service configured to run against an IPNIN dataset. Can be configured to expose any tabular dataset or result-set from a relational DB. Data first transformed – using a set of rules defined in configuration – then matched. Transformations handle things like gender agreement: "-us" on one side and "-a" on the other transform to the same form. Transformers can also handle authorship: "F. Barkley" and "F.A.Barkley“ are probably the same.
  23. A user can explore the service using a web interface
  24. This shows the results of the query.
  25. Use open refine for large volumes of data – load into Open Refine, identify the column of scientific names that you want to “reconcile” (send to the service), choose “Reconcile” “Start Reconciling” on the column of name data
  26. Select the IPNI service
  27. The data are sent to the service (via JSON over HTTP), and IPNI ids (with hyperlinks to IPNI) are brought back.
  28. The ID can be extracted and held in its own column.
  29. Choose “Add columns from TPL…”
  30. TPL gives us a list of “properties” it knows names have. I’ve chosen “taxonomic status”, and there’s a preview on the right.
  31. A minute later, and an extra column is added with the status from TPL. Fourth from the top is a synonym, but this real dataset shouldn’t have had any synonyms in.
  32. Users of the service include research & development staff at many institutes – largely without support from Kew (using Open Refine user support material)
  33. Linking the data like this enables us to do different kinds of research