Presented by V. Smith at the Biodiversity Informatics Horizons conference, Sapienza – Università di Roma, Rome, Italy. 3-6 Sept. 2013.

1. The biodiversity
informatics landscape:
a systematics perspective
Vince Smith
Biodiversity Informatics Horizons
Rome, 3-6 Sept 2013

2. Overview
1.
Background – the biodiversity informatics domain
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2.
Social challenges
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3.
Mobilizing existing data (metadata, literature, collections)
New forms of data ([meta]genomics & observatories)
Synthetic challenges
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5.
Openness
Collaboration and communities
Standards, identifiers & protocols
(Big) data challenges
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4.
The problem (i.e. why are we here)
Representations of the domain (data, infrastructures, projects…)
Toward an integrated view (strategy)
Data Aggregation & linking
Visualisation
Modeling
Next steps (data infrastructures & funding)
•
Lessons learned: new informatics opportunities in H2020

3. 1. Background

4. The problem – integrating biodiversity research
How to we join up these activities?
What infrastructures do we need?
(technologies, tools, standards…)
What processes do we need?
(Modelling, workflows…)
What data do we need?
(Genes, localities…)
How do we use this as a tool?
Species conservation & protected areas
Impacts of human development
Biodiversity & human health
Impacts of climate change
Food, farming & biofuels
Invasive alien species

5. Natural History – the foundation
Darwin’s “tangled bank”…
"It is interesting to contemplate a tangled
bank, clothed with many plants of many
kinds, …, so different from each other, and
dependent upon each other in so complex a
manner, have all been produced by laws acting
around us.”
C. Darwin "On the Origin of Species”, 1859
Systematics, a foundational “law”

6. Ecological interactions

7. A granular understanding of biodiversity
Genes
Individuals Populations Species
Interactions
AB C D E F
GCGC
GTAC
CTAG
GenBank
i
ii
iii
iv
v
vi
1
2
1
2
3
Local populations
A
B
C
D
E
F
Global
biodiversity
-+++++
+-+++
+++
+
+
Biological
networks

8. An informaticians view of biodiversity
GenBank
MorphBank
Interactions
Geospatial
Census
Genotype
Phenotype
Biotic
Interactions
Environment
Human Effects
IUCN
Pop. data
Niche & Pop.
Ecology
TreeBase
Biodiversity
Loss
GBIF
Phylogenetic
Trees
IPNI, Zoobank
Taxonomy
AquaMaps
Geographic
Dsitributions
Extent of Occurrence
Range Maps
Conservation &
management
AquaMaps
Forecasts of
Change
Data
Products
Systems
Key problems
• Landscape is complex, fragmented & hard to navigate
• Many audiences (policy makers, scientists, amateurs, citizen scientists)
• Many scales (global solutions to local problems)
Figure adapted from
Peterson et al 2010

9. A project centric view of biodiversity
Scan / Mark/up
PLAZI
Inotaxa
BHL
eFloras
CDM
GNA (NameBank)
Phylogenetic
Tree of Life
TreeBase
CIPRES
Descriptive /
classification
EoL
Scratchpads
CATE
MorphoBank
Wikipedia
Molecular
Databases
NCBI/EMBL/DDBJ
CBoL
Barcode of Life
Initiative
Bibliographic
IPNI
Google Scholar
Connotea
ViTaL
ISI
Institutional
EMu (=MOA)
Recorder
uBio
TDWG
Checklists
Identification
Key2Nature
IdentifyLife
Inter-Institutional
Synthesis
BCI
BioCASE
GeoCASE
MaNIS
PESI:
ERMS
Fauna Europea
Euro+Med Plantbase
ORBIS
WORMS
Flora Europea
Nomenclators
Index Fungorum
ZooBank
IPNI
(Kew/AUS/Harvard)
ING
AFD/APC/APUI
NZOR
CoL (Sp2000& ITIS)
ZooRecord
LifeWatch
GBIF
Biodiversity
ALA
CONABIO
CRIA (Brazil)
IUCN
SEEK
OPAL
DAISIE
iNaturalist
A snapshot from 2009, “the dance of the initiatives”

10. The strategic view: community informatics challenges
GBIF GBIC Report
(Coming soon)
EU Biodiversity Strategy
(2011)
Biodiv. Inf. Challenges
(2013)
Grand Challenges for Biodiversity Informatics
(integrating activities for H2020)

11. 2. Social challenges
- Openness
- Collaboration and communities
- Standards, identifiers & links

12. Openness in biodiversity informatics
“A piece of data or content is open if anyone is free to use, reuse, and redistribute it subject, at most, to the requirement to attribute and/or share-alike.” http://opendefinition.org/
• Sharing data is a foundation
for our activities
• Normal practice in some
communities (molecular)
• Mandated by some funders
& governments
Many kinds of openness:
• Open Access
• Open Data
• Open Science
• Open Source
E. Archambault et. al., Proportion of Open Access Peer-Reviewed Papers at the
European and World Levels--2004-2011, June 2013, Science-Metrix Inc.
“One-half of all papers are now freely available
within a year or two of publication”

13. Openness in biodiversity informatics
“A piece of data or content is open if anyone is free to use, reuse, and redistribute it subject, at most, to the requirement to attribute and/or share-alike.” http://opendefinition.org/
• Sharing data is a foundation
for our activities
• Normal practice in some
communities (molecular)
• Mandated by some funders
& governments
Many kinds of openness:
• Open Access
• Open Data
• Open Science
• Open Source
Incentivise through credit via citation (e.g. BDJ)
Need to continue to incentivise openness

14. What are Scratchpads? (http://scratchpads.eu)
Collaboration & communities
Making taxonomy a team sport
e.g., Scratchpad Virtual Research Communities
Taxa
Projects
544 Scratchpad Communities
by
6,644 active registered users
covering
91,631 taxa
in 535,317 pages.
Regions
Societies
In total more than
1,300,000 visitors
81 paper citations in 2012
Our infrastructures need to facilitate collaboration

15. Standards, identifiers & protocols
Facilitating data sharing across communities
A foundation for integration
Key requirements:
• Need to be inclusive, practical & extensible
• Readable by humans & machines
• Widely used
Good examples:
• Darwin Core
• CrossRef & DataCite DOIs
• ORCHID Author identifiers
Gaps / Problems
• Reuse & persistence of identifiers
• Vocabularies & ontologies (time consuming / little reward)
Potential solutions
• Build them into our credit systems
• Show sematic reasoning potential (LOD & RDF demonstrators)
Standards can’t be developed in isolation – they must be used

16. 3. (Big) data challenges
- Mobilising existing data
- New forms of data

17. Mobilising existing data
Collections, literature & metadata
How can we quickly, efficiently and cost
effectively mobilise biological data at scale?
Collections
• 1.5-3B specimens in collections worldwide
• Fragments efforts / heterogeneity of process
• Needs ambition (NHM: 20M in 5 yrs.) & coord.
Literature
• >300M pages of biodiversity literature
• BHL (41M pp.) an example of what can be done
• Needs a sustainability & article metadata
NHM
Digitisation
BHL
literature
Metadata registries
• Data about data (cheaper & scalable)
• e.g. bibliographic data, dataset portals
Informatics challenges
• Storage & persistence
• Automation & annotation
• Incentives to digitise & fitness for use
Bibliography of Life
(RefFinder & RefBank)

18. Mobilising & managing new forms of data
Metagenomics & ecological observatories
These new data types do not depend on
traditional taxonomy & systematics
New Molecular approaches
• Molecular detection & monitoring of organisms is routine
• Metagenomics (env. sequencing) commonplace
• Becoming the 1° route to understanding biodiversity
3-4 June 2013, NHM
Ecological observatories
• Automated biodiversity detection
• Remote sensing (e.g. satellite & acoustic data, drones, camera traps)
• Monitoring conspicuous, rare or invasive spp. (algal blooms, palms)
• Monitoring human activity
Informatics challenges
• Very large quantities of data (2.5-10TB per researcher per yr.)
• Doesn’t map well to existing data infrastructures
• Challenge current networking & storage capacity
• Digital and physical collections become equally important?
22 July, 2013

19. 4. Synthetic challenges
- Data aggregation & linking
- Visualisation
- Modeling

20. Aggregation & linking
Portals bringing together distributed & diverse forms of data
Giving consistent and comprehensive access
to all biological data
eMonocot
Several approaches, with different advantages
• Tightly coupled to a few data sources
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(e.g. eMonocot, CDM)
• Loosely coupled to many sources
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•
(e.g. BioNames, Wikipedia)
Hybrid forms (e.g. Canadensys, EOL, GBIF)
Selective & accurate but hard to scale
(276k taxa, 8k images, 13 keys & 3 phylogenies)
Informatics challenges
• Portals are hard to sustain
• New methods of data discovery & access
• Create new windows (views) on content
• New data structures, new types of database
BioNames
Scalable but less accurate
(3M taxon names, 93k phylogenies & 28k articles)

21. Visualisation
Visually synthesizing large, linked biodiversity datasets
Making biodiversity data accessible &
understandable
Research opportunities
• Tools integration (e.g. GeoCat, CartoDB)
• Span multiple audiences
Outreach opportunities
• Visually compelling story telling
• Crowdsourcing tools (e.g. Notes From Nature)
Exploiting new technologies
• Touch screens
• Mobile
• Location awareness
Informatics challenges
• Very specific to individual use cases
• Sustainability issues
NHM specimen records
http://data.nhm.ac.uk/globe/

22. Modeling the biosphere: a (the) 30 year goal?
Reasoning across large, linked biodiversity datasets
A clear, singular, long-term vision, which
biodiversity data can contribute too
Conceptually has many potential uses
• Identifying trends
• Explaining patterns
• Making predictions
• Real time alerts
- when data contradicts current knowledge
• The ultimate policy tool
Major informatics challenges
• Technical very difficult (many years off)
• Needs effective prototypes & platforms
• Some first steps e.g. OBOE, LEFT
Nature 2013, doi:10.1038/493295a

23. 5. Next steps

24. Lessons learned: new opportunities in H2020
PATHWAYS TO INTEGRATION
(by addressing these social, data & synthetic challenges)
• Break out of the discipline, technical &
project centric activities (it is
unsustainable, inefficient & bad for science)
• Integrate & build on exiting programmes
where possible (LifeWatch is a potential umbrella
for these activities)
• Bridge the disconnect between
informaticians & users (make the users
informaticians & in informaticians users)
• Our products well suited to address these
challenges
• Use H2020 as a mechanism to achieve
integration
How do we join up these activities?

25. QUESTIONS

26. Possible biodiversity informatics design principles*
= experience from 7-years with the Scratchpads
= lessons for infrastructures in H2020?
1. Start with needs - focus on real user needs (not just the ‘official process’)
2. Do less - if someone else is doing it, link to it or use it
3. Design with data - prototype and test with real users on the live website
4. Do the hard work to make it simple - let the computer take the strain
5. Iterate. Then iterate again. - iteration reduces risk & is more sustainable
6. Build for inclusion – it’s easier in the long run
7. Understand context - we are designing for people, not a screen or a brand
8. Build digital services, not websites - there is life beyond the website
9. Be consistent, not uniform - every circumstance is different
10. Make things open: it makes things better - it’s more sustainable
*https://www.gov.uk/designprinciples

27. Mobilising existing data: how to prioritise
CONTENT
FUN
LEARNING
OUTREACH
Digitise a few things & invest in
depth, description & promotion
A LITTLE
A LOT
Digitise lots of things, put little effort
into description & promotion
AGGREGATION
COLECTIONS
MANAGEMENT
METADATA
DATA MINING
RESEARCH
Nick Poole, UK Collections Trust

28. Collaboration & communities
Making taxonomy a team sport
Average dates when increasing numbers of taxonomists were involved in describing species
CONE SNAILS
BIRDS
MAMMALS
AMPHIBIANS
SPIDERS
PLANTS
Joppa et al, 2011
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Very few recent single author papers
Most (fundable) science is cross-disciplinary
Need to incentivise data curation & annotation
Need mechanisms to share annotations
Our infrastructures need to facilitate collaboration