1. Facilitating biodiversity science through
Virtual Research Environments
Challenges and opportunities for the Scratchpads platform
Firstly, please accept my apologies for not being able to physically attend this workshops. The reason was prior teaching engagements.
What I aim to do within the next 15 minutes is present the context in which VREs have been developed and the challenges that they serve, especially in the biodiversity domain using the example of Scratchpads
Dr Dimitris Koureas
Natural History Museum London
Research Data Alliance
Biodiversity Information Standards (TDWG)

2. The problem: Capturing and integrating biodiversity data
How to we join up these activities?
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
What infrastructures do we need?
(technologies, tools, standards…)
What processes do we need?
(Modelling, workflows…)
What data do we need?
(Genes, localities…)
Studies of biodiversity have many components, broadly they map to one or more of these six different circles on this slide. Topics like Applied Ecology, Conservation Science; Genomics and Evolutionary Biology are at the heart of what we do.
The answers to global urgent societal challenges like biodiversity loss, climate change and sustainability of ecosystem services or invasive alien species can only be provided if we link these topics up, and use the product of these activities as a resource to address issues relevant to science and society.
This means our job is to in part, to identify the kinds of infrastructures we need to achieve this (so for example, where does cloud computing, or High Performance Computing fit on to this diagram). We need to ask ourselves what kinds of processes are needed to achieve this linkage (so what kind of workflows are required, what models do we need). And of course we need to identify and target the data we need to address these questions (what genotypic, phenotypic, environmental and temporal data are required to integrate these areas).
So my talk today is really about the kinds of work we need to address to achieve integration between these topics, and how we might develop these within new consortia as part of the Horizon 2020 funding framework.

3. mobilising data at all scales
Challenge 1:
mobilising data at all scales
If I was giving this talk say five years ago, I’d say the major barrier toward delivering this integration concerned a lack of computing power, storage or maybe getting the right algorithms together. These used to be the limiting factor when we thought about infrastructure integration. Now these limiting factors are different and in my view they are three-fold.
Firstly the major challenge is finding the data. Within our community we are surrounded be a sea of legacy data and many new form of data. There are major digitisation efforts going on

4. linking & aggregating data at different scales
Challenge 2:
linking & aggregating data at different scales
Communities c.50k
(e.g. Scratchpads)
National Efforts c.5M
(e.g. NHM Data Portal)
Linking that data
Date is created and
Global Efforts c.500M
(e.g. GBIF Data Portal)

5. Models to predict how biodiversity responds to human pressures
Challenge 3:
Synthesising data, e.g. modelling human pressures on biodiversity
Management Practices
Ecosystems
Agro-systems
2M records, 19k sites, 34k spp.
Small aggregated datasets
Species richness in different ecosystems
Making use of the data,
PREDICTS - Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - is a collaborative project aiming to use a meta-analytic approach to investigate how local biodiversity typically responds to human pressures such as land-use change, pollution, invasive species and infrastructure, and ultimately improve our ability to predict future biodiversity changes. The PREDICTS project is collecting data from scientists worldwide in order to produce a global database of terrestrial species' responses to human pressures.
Thanks to generous contributions from researchers, and a great deal of hard work by students and staff at the Natural History Museum and UNEP-WCMC, the project now has over 2 million biodiversity records from over 19,000 sites, covering more than 34,000 species.
Looking at species richness in a range of natural, agricultural and land management settings, and then extrapolating these data forward to make predictions on species richness based on predicted changes in patters on land use.
Land-use change
Pollution
Invasive species
Infrastructure
Projecting Responses of Ecological Diversity In Changing Terrestrial Systems
Models to predict how biodiversity responds to human pressures

6. Reaching the long term vision is predicated on
a BIG change in the way we (researchers) work
To actually be able to tackle these global challenges in biodiversity sciences and fulfil the community inspired vision of modelling the biosphere, researchers and scientists in general need to significantly change the way they do science.
Where each step of the scientific lifecycle is subject to huge changes. A paradigm shift in science only similar in scale and impact to the scientific revolution of the Enlightenment period.
Data driven science
Open science
Efficient infrastructures

7. 90% of all science data generated in the last 3 years!
Data is everywhere and is produced with an ever increasing rate
Data are everywhere, they are being produced with unprecedented rates. It only when we realise their volume that we can appreciate the potential impact and opportunities, as well as the size of the challenges that lie ahead!
90% of all science data generated in the last 3 years!

8. Big Data in Taxonomy and Systematics
Even within traditionally isolated research domains like alpha-taxonomy there is a weave of new data that support the publication of c new taxa on a yearly basis!
BUT, producing the data doesn’t actually mean that we can effectively re-use them, preserve them or aggregate them... (next slide)
c new taxa
described every year

9. 80% dark (or grey) data An informaticians view of biodiversity 20%
Investigator-focused 'small data‘
Locally generated 'invisible data'
'incidental data'
Dark data more important mainly due to their volume1
80%
dark
(or grey)
data
Dark data lost within 20 years
In fact 80% of all the data produced in science is estimated that eventually are lost after two decades!
Most of the dark data are produced by…. Forming the main corpus of what we call the long tail data!
Published and
discoverable data
20%
1Heidorn PB. Library Trends 57:

10. Socio-cultural & Technological challenges
To fully embark into the new data-driven scientific era
Socio-cultural & Technological challenges
Socio-cultural: Shift in the modus operandi of doing science
Technical: Mobilisation, standardisation and accessibility

11. Biodiversity informatics landscape
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, Syst. & Biodiv. 2010
doi: /

12. The role of Virtual Research Environments
VREs sit on the top of e-infrastructures
They abstract from available services
Thematic gateways to data and cyberinfrastructures, collaboration platforms, capacity buidling, interdisciplinary research, cross-border collaboration

13. Biodiversity data online
Virtual Research Environment
Enter – Structure – Curate – Link – Share – Publish
Biodiversity data online
8 years of continuing development | 3 major Grants | Industry leading platform

14. Scratchpads 650 Communities 3.1 million visitors 150,000 taxa
6,500 active users
The Scratchpads platform is being developed for the last 7 years under this framework. To provide researchers with the necessary tools to make taxonomy digital, open and linked!
To facilitate the development of virtual research environments 14

15. A Scratchpad is a collaborative platform, a gateway to big data
Harvest
Open Biodiversity
standards and services
(e.g. TDWG: DwC)
Scratchpads are fed by your data. Scratchpads help you structure your data in a way that makes them both human and machine readable. Allows you to contribute to global biodiversity databases and also aggregates all related to your data information from external resources.
Feed to
In-house data
External data & services

16. User and stakeholder engagement
Data preservation & citability
Service longevity

17. User buy-in Incentives for mobilising long-tail research
Share your work and take credit for it
Publication of data to peer-reviewed open access journals
Biodiversity Data Journal – Pensoft
GigaScience - BMC, Scientific Data – NPG & F1000 Research
Pensoft
Writing
Tool
XML
PWT

18. User and stakeholder buy-in
Incentives for mobilising long-tail data
Confidence
Commitment
Longevity
Agility
Adaptability
User monitoring
Marketing
Visibility
Intuitive interface

19. Data structure, annotation and storage
Adhere to ratified community standards
DwC (DwC-A)
Audubon core
Phytogeographical areas
Allocation of persistent identifiers to data objects
- PURL already in place
- Deposition in open repositories

20. Data structure, annotation and storage
Effective implementation of Knowledge Organisation Systems
Biodiversity
communities
Vocabularies and ontologies
The domain is lagging in achieving the optimum use of controlled vocabularies and ontologies

21. Longevity of services is key
Need to look beyond the fragile model of recursive research funding
Shift in the way we think
of e-infrastructures and information resources
Stable/rigid system
Dynamic/open process
Outsource to the
end user community
We need to set up the environment that will enable the community contribution

22. Infrastructure maintenance
Community based sustainability model
Infrastructure maintenance
Technical maintenance
User support
Open source & modular
Crowdsourcing support activities
Maximising support efficiency
Three basic pillars
for community support

24. Key actions to increase interoperability, efficiency and uptake
Minimise infrastructure redundancy
Harmonise user experiences
Open access and open source
Learn from experience across domains

25. Leverage effort and data impact
mobilisation &
generation
Data
curation
Data
publishing
Data
analysis
Seamless virtual research environments that incentivise mobilisation of long tail research

26. Common issues - different approaches
A highly dynamic but fragmented landscape

27. Efficient Networking and collaboration platforms
Biodiversity Data Integration IG
The single largest organisation on research data
Crossdomain | Bottom-up | Multilateral agreement
ca.60 members
European COST Actions
European ESFRI projects
US RC Networks

28. Science is a ‘light’s better’ endeavour in that research effort is
Tools for making sense of the big data world are important because…
Science is a ‘light’s better’ endeavour in that research effort is
not directed at areas where the work is technically infeasible.
Research is directed where real, interpretable results may be
obtained.

29. Thank you http://uk.linkedin.com/in/dkoureas @DimitrisKoureas