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ViBRANT
Virtual Biodiversity

A decadal view of biodiversity informatics:
challenges and priorities Alex Hardisty, Dave Roberts, and the
biodiversity informatics community*

* 80 people took part in the open debate that led to this paper

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PROGRAMME -infrastructure

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“We are drowning in information, while starving for wisdom. The world henceforth will
be run by synthesizers, people able to put together the right information at the right
time, think critically about it, and make important choices” E. O. Wilson, Harvard


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“We are drowning in information, while starving for wisdom. The world henceforth will
be run by synthesizers, people able to put together the right information at the right
time, think critically about it, and make important choices” E. O. Wilson, Harvard

Time to
model all
life on
Earth.
Purves et. al. (2013) Nature,
493: 295-297


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The Grand Challenge for Biodiversity Informatics
An infrastructure to allow the available data to be brought
into a coordinated coupled modelling environment, capable
of addressing questions relating to our use of the natural
environment, that captures the variety, distinctiveness and
complexity of all life on Earth
To achieve it we need:
To build user confidence
Integrative flexible e-Science environments
Predictive models across multiple scales, coupled

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1. Open Data should be normal practice;

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2. Data encoding should
allow analysis across
multiple scales;

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2. Data encoding should
allow analysis across
multiple scales;

3. Infrastructure projects
should devote significant
resources to market the
service they develop;

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Didimosphenia geminata didymo Echinella geminata

AAAAAGCTCGTAGTTGGATTTGTGATG Gomphonema vulgare Bréb.
GAATTTGAATACTTTTAAAGTGTTCTAG
AAACTGTCATCCGTGGGTGGAATTTGT
TTGGCATTAGGTTGTCAGRCAGAGGATG
CCTATMCTTTACTGTGAAAAAATCAGTG Didymosphenia
CGTTCAAAGCAGACTTACGTCGATGAA geminata (Lyngbye)
TGTATTAGCATGGAA
TGTATTAG
TATTAG
TATT
TT Schmidt 1899

Rock Snot
D. geminata
didymo Didymosphenia
geminata (Lyngbye)
Didymosphenia
geminata

Courtesy of
‘Linking Global Names and Pro-iBiosphere’, 2013, D. J. Patterson
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4. A list of taxon names
Names as strings of characters…
Actinobacillus actimomycetemcomitans Difficulties with Latinized Names
Actinobacillus actimycetemcomitans
Actinobacillus actinmycetemcomitans Transcription errors
Actinobacillus actinomicetemcomitans
Actinobacillus actinomy
Actinobacillus actinomyce Actinobacillus actinomycetemcommitans
Actinobacillus actinomycemcomitans Actinobacillus actinomycetemocimitans
Actinobacillus actinomyceremcomitans Actinobacillus actinomycetencomitans
Actinobacillus actinomycetam Actinobacillus actinomycetum
Actinobacillus actinomycetamcomitans Actinobacillus actinomyctemcomitans
Actinobacillus actinomycetecomitans Actinobacillus actinomyectomcomitans
Actinobacillus actinomycetemcmitans Actinobacillus actinomyetemcomitans
Actinobacillus actinomycetemcomintans Actinobacillus actinonmycetemcomitans
Actinobacillus actinomycetemcomitance Actinobacillus actionomycetemcomitans
Actinobacillus actinomycetemcomitans Actinobacillus actynomicetemcomitans
Actinobacillus actinomycetemcomitants Actinobacillus antinomycetemcomitans

Nomenclator provides correct spelling. Indexing infrastructure resolves to it.

Courtesy of Dave Remsen, GBIF
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5. Persistent
Actinobacillus actinomycetemcmitans
Actinobacillus actinomycetemcomintans
Actinobacillus actinomyetemcomitans
Actinobacillus actinonmycetemcomitans Identifiers


DOI: 10.4289/0013-8797.115.1.75

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5. Persistent
Actinobacillus actinomycetemcmitans
Actinobacillus actinomycetemcomintans
Actinobacillus actinomyetemcomitans
Actinobacillus actinonmycetemcomitans Identifiers


DOI: 10.4289/0013-8797.115.1.75
6. Author
identifiers

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7. 3rd party authentication

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Atopobium minutum 8. Classification Bank
Sphaerobacter
thermophilus
strain TH3
Actinomycetes: the antibiotic factories
Bifidobacteriaceae

Actinomycetaceae

Arthrobacteriaceae,
Cellomonadaceae,
Microbacteriaceae,
Dermatophilaceae
Insertion element and realtives
in 23S rRNA Propionibacteriaceae

Nocardioidaceae

Frankiaceae

Corynebacteriaceae,
Mycobacteriaceae,
Nocardiaceae
and realtives

Actinoplanaceae

Pseudonocardiaceae

Streptomycetaceae,
Streptosporangiaceae
and relatives

Embley & Stackebrandt (1994) Bregey’s Manual, 2nd Edition (2012)

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9. Accepted names
Dynamic Annual
Home Edition Checklist

Welcome to the Catalogue of Life website: gateway to our
Overview database of the world's known species of animals, plants, fungi
About the Catalogue of Life and micro-organisms
Contributors & partners
Contact us
I . P . N . I
User Guide
Explore »
Getting started
Versions of the Catalogue
Contributing your data
This Dynamic Edition is a constantly evolving version of the Catalogue of
Life.
Glossary 'The most comprehensive and
Now tracking 70% of species known to science authoritative global index of
Additional Services species currently available, the
Downloads
Advanced services
1,315,754 species Catalogue of Life consists of a
single integrated checklist and
taxonomic hierarchy for all the
Latest on Twitter world's species.'
Catalogue of Life
catalogueoflife

catalogueoflife Catalogue of Life, Annual Checklist »
11th March 2013 is now online at
catalogueoflife.org/col
6 days ago · reply · retweet · favorite The Annual Checklist is a snapshot of the entire
Catalogue of Life: a fixed imprint.
catalogueoflife Catalogue of Life,
08th February 2013 is now online at
catalogueoflife.org/col Why two versions?
37 days ago· reply · retweet · favorite

Join the conversation

© 2013, Species 2000 at University of Reading | Disclaimer Design: Chris Turnbull | Content: Simon Thornton-Wood

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10. Tools to make LOD

Humans are good at interpreting this:
Implicit semantics
o
“Compound 2a melted at 119 C”

Machines need this:
Explicit semantics CML Schema
<cml:molecule ref=“2a”>
<cml:property> Molecules in CML/InChl
<cml:scalar dictRef=“prop:mpt”
units=“units:celsius” propertyDictionary
dataType=“xds:float” unitsDictionary
>119</cml:scalar>
</cml:property> W3CSchema
</cml:molecule>

4 namespaces, 3 dictionaries

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Most textbooks will tell you that, in 1610,
Galileo Galilei became the first person to
observe Saturn's rings.

But what did he really see?

GBIF/GBIC – 2-4 Jul 2012 – Copenhagen, © 2012, R. J. Robbins
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This?

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Or this?

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The generation of
important new
insights while
handicapped with
limited technology,
indirect
measurement, and
fuzzy data is the
mark of scientific
greatness.

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11. Data fit for purpose
Data are received at face-value,
examined and tested. If the
user is satisfied, then the data
will be applied.

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12. Observational data infrastructure
http://www.earthobservations.org/geobon.shtml

http://www.eubon.eu

http://www.teamnetwork.org

http://mooreabiocode.org/

Moorea Biocode Project

http://www.ilternet.edu
http://www.neoninc.org Agriculture Climate Forest Invasion Urban
Systems Change Management Biology Ecosystems

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To build user confidence

Thus far, all projects share a common problem of keeping services
running after project funding ended
New models are needed
To create translational pipelines to industry adoption
To encourage institutional adoption for care and maintenance
For recognition of contribution other than through publication of
academic papers
Stronger marketing and outreach
Invest more in up-skilling and hand-holding

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Integrative flexible e-Science environments
Using standardised building blocks and workflows
Interoperable components
With access to data from multiple sources
Recognise different kinds of VRE
General-purpose / specialised / single scientific objective
- cf. chemistry laboratory vs forensics lab vs HIV vaccine lab
- BioVeL / AquaMaps and iMarine / CarbonWaterCloud
Must generate immediate benefit for users
Science driven, with scientists as active participants in creation of
infrastructure
Functions people find useful: simple and intuitive
Technology invisible (disappears into background)

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Predictive models across multiple scales
A new framework of methods, techniques, standards to bring about
interoperability of data and models across different biological scales
From Genetic through species and ecosystem to landscape
Learn from Virtual Physiological Human and from Numerical weather
prediction and climatology Edwards (2010). A Vast Machine
“General Ecological Models” Purves et al. (2013). doi:10.1038/493295a
Evolvable to incorporate new scientific insights
Re-analysis models
Making data we have global
Implies ‘inversion’ of existing infrastructure

‘inversion’ of existing infrastructure is about re-examining every element of data we have to
re-construct the past biodiversity, as a guide and calibrator of models that can predict the future

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Section 1: The fundamental backbone (getting the basics right)
1. Why are names important? 9. How to balance professional and non-professional
2. How are names organised? contributions
3. Which is the right name? 10. Engagement of users
4. What is the name of that organism? 11. Who's who?
5. Can biodiversity studies be done without names? 12. User identification
6. Biodiversity data beyond names 13. How do we ensure the right metadata are created
7. To link resources we need identifiers at the point of data generation?
8. Centralised or networked services? 14. Sustaining the physical infrastructure
Section 2: The next steps
15. Data Sharing 19. Beyond Sharing and Re-use: the problem of scale
16. Why do we need vocabularies and ontologies? 20. How reliable are the data?
17. How would Knowledge Organising Systems help? 21. What will the physical infrastructure look like?
18. How easy is it to integrate data?
Section 3: New tools
22. How much of the legacy collections can be digitised? 27. How do you aggregate the data you need?
23. How to generate more targeted and reliable data? 28. How complete are the data?
24. What role do mobile devices play? 29. How can we encourage virtual research
25. How do you find the data you need? environments?
26. How do you extract the data you need? 30. What can you do with your data in the future?
Section 4: The human interface
31. How can we give users confidence?
32. Who owns what?
33. What benefits come to contributors?

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Thank you for your attention.
Any questions
Alex Hardisty <hardistyAR@cardiff.ac.uk>
Dave Roberts <dmr@nomencurator.org>

Hardisty et al. (2013) A decadal view of
biodiversity informatics: challenges and priorities.
BMC Ecology (in press)

http://www.biovel.eu http://vbrant.eu
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