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Dr Dimitris Koureas Natural History Museum London Research Data Alliance Biodiversity Information Standards (TDWG) Facilitating biodiversity science through.

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Presentation on theme: "Dr Dimitris Koureas Natural History Museum London Research Data Alliance Biodiversity Information Standards (TDWG) Facilitating biodiversity science through."— Presentation transcript:

1 Dr Dimitris Koureas Natural History Museum London Research Data Alliance Biodiversity Information Standards (TDWG) Facilitating biodiversity science through Virtual Research Environments Challenges and opportunities for the Scratchpads platform

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…)

3 Challenge 1: mobilising data at all scales

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

5 Challenge 3: Synthesising data, e.g. modelling human pressures on biodiversity Projecting Responses of Ecological Diversity In Changing Terrestrial Systems 2M records, 19k sites, 34k spp. Management Practices EcosystemsAgro-systems Small aggregated datasets Species richness in different ecosystems Land-use change Pollution Invasive species Infrastructure 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 Data driven science Open science Efficient infrastructures

7 Data is everywhere and is produced with an ever increasing rate 90% of all science data generated in the last 3 years !

8 Big Data in Taxonomy and Systematics

9 An informaticians view of biodiversity Investigator-focused 'small data‘ Locally generated 'invisible data' 'incidental data' dark (or grey) data 20% 80% Published and discoverable data Dark data more important mainly due to their volume 1 1 Heidorn PB. Library Trends 57:280-299 Dark data lost within 20 years

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

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: 10.1080/14772001003739369

12 VREs sit on the top of e- infrastructures They abstract from available services The role of Virtual Research Environments

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

14 Scratchpads 150,000 taxa 650 Communities 6,500 active users 3.1 million visitors

15 A Scratchpad is a collaborative platform, a gateway to big data In-house data External data & services Open Biodiversity standards and services (e.g. TDWG: DwC) Feed to Harvest

16 User and stakeholder engagement Data preservation & citability Service longevity

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

18 Incentives for mobilising long-tail data User and stakeholder buy-in 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 Biodiversity communities Vocabularies and ontologies Data structure, annotation and storage Effective implementation of Knowledge Organisation Systems 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 Technical maintenance Open source & modular User support Crowdsourcing support activities Maximising support efficiency Three basic pillars for community support Community based sustainability model


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

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

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

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

28 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. Tools for making sense of the big data world are important because…

29 Thank you @DimitrisKoureas

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