virtual research environments supporting biodiversity research: needs & priorities for horizon...
DESCRIPTION
Presented at the 10th e-Concertation Meeting in Track 4 (Digital environments for collaboration), Brussels, Belgium 6-7 March 2013.TRANSCRIPT
Virtual Research Environments supporting biodiversity research
Needs & priorities for Horizon 2020
Vince SmithViBRANT Coordinator
Natural History Museum, [email protected]
ViBRANTVirtual Biodiversity
6-7th March 201310th e-Concertation Meeting, BrusselsTrack 4: Digital environments for collaboration
Virtual Biodiversity ResearchViBRANT
The problem
Science is global• It needs global standards• Global workflows• Cooperation of global players
Science is carried out “locally”• By local scientists• Being part of local infrastructures• Having local funders
2 of 10
Virtual Biodiversity ResearchViBRANT
The problem applied to biodiversity science
• Inventory the Earth’s species• Document their relationships• “Publish” & apply these data
Goal…
• 1.8 M described spp. (10M names)• 300M pages (over last 250 years)• 1.5-3B specimens
Data set…
People…• 4-6,000 scientists• 30-40,000 “pro-amateurs”• Many more citizen scientists?
Linking grand challenges to local research activities
3 of 10
Virtual Biodiversity ResearchViBRANT
Linking “local” to “global” problems
537 Scratchpads Communities
by 7,291 active registered users
covering 18,790 taxa
in 511,192 pages
• Hosted VRE (Scratchpads) for biodiversity• Ecosystem of user communities• Communities self-assemble• We CANNOT predict their research
questions• We CAN predict their data & service needs • Support data management & workflows
User engagement though a VRE
“Success defined through engagement”
4 of 10
Virtual Biodiversity ResearchViBRANT
A modular, flexible VRE ArchitectureA cloud of niche services plugged into a core VRE
Service based Virtual Research Environment
Core/Pluggable servicesExternal service
External service External service
External service
External service
5 of 10
Virtual Biodiversity ResearchViBRANT
A modular, flexible VRE ArchitectureA cloud of niche services plugged into a core VRE
Service based Virtual Research Environment
Data publishing
Data modelling
Phylogenetic analysis
Citizen science
Species Identification
Research Support• Data management• Visualisation• Workflows
Management Support• Communication• Task assignment• Planning
6 of 10
Virtual Biodiversity ResearchViBRANT
Data workflowsServices into and out of the VRE
7 of 10
Virtual Biodiversity ResearchViBRANT
Incentives to encourage uptake & use
Data paper assembled from Scratchpad database
XML submission, peer review & marked-up publication by Pensoft
5-step workflow for selecting data, adding metadata & previewing
New Biodiversity Data Journal(worldwide coverage)
PD
FH
TM
LX
ML
Citation, usage statistics, data aggregation and data/paper publishing
doi:10.3897/zookeys.50.539
8 of 10
Virtual Biodiversity ResearchViBRANT
Challenges to VRE activities in H2020…
Generic VRE issues to address:• Sustaining & enhancing VRE core
activities• Trusting external services• Persistence of data & services• Effective embedding and training of
user community• Agile development to meet
changing user needs• Delivering immediate user benefits• Success defined through usage
(not always cutting edge, must be useful, simple & easy to use)
• Interoperability, not just about data standards
• Global use and benefits (not just within the EU)
Data management themes: • Integration with long term data
repositories• Persistent identification & data citation• Open access, data publishing & Open
Science• Data and metadata standards,
controlled vocabularies• Automated data extraction from text
and other resources• Automated processing to link related
data (including Linked Open Data)• Crowd-sourcing expertise• Mobile technologies to enable early
data capture• Capacity building to expand data
capture
9 of 10
Virtual Biodiversity ResearchViBRANT
2014-2020: Biodiversity data capture needs…
Contact: Vince Smith ([email protected])
10 of 10
Aspect of biodiversity Type of data Capabilities Landscape/ecosystem Imagery Satellites, Drones, LIDAR Species distribution (including community composition)
Point observation Specimens, Survey/monitoring, Citizen science, Camera traps, Acoustic monitoring, Ecogenomics, Satellite/drone imagery, Literature, Traditional Knowledge, Regional species lists, Image recognition
Species abundance Shape files Expert judgement, Niche modelling, Models including interactions
Measurement Survey/monitoring, Citizen science, Ecogenomics, Satellite/drone imagery, Literature, Traditional Knowledge
Species Traits and functions
Specimens, Literature, Genomics, Traditional Knowledge, Crowd- sourced data
Identification Specimens, Literature, Barcode sequences, Diagnostic keys, Crowd- sourced expertise
Interactions Specimens, Text-mined Literature, Genomics, Traditional Knowledge, Crowd-sourced data, Isotopes
Phenology Specimens, Literature, Genomics, Diagnostic keys, Traditional Knowledge, Crowd-sourced data
Threat status Red list, Statutory authorities, Automated change detection