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Data sharing and integration in the RELU programme: a researcher’s perspective

Data sharing and integration in the RELU programme: a researcher’s perspective. Piran White. Types of data sharing in RELU. Policy-makers / agencies – researchers Researchers – researchers Within projects Between projects Sometimes interdisciplinary Researchers – stakeholders

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Data sharing and integration in the RELU programme: a researcher’s perspective

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  1. Data sharing and integration in the RELU programme:a researcher’s perspective Piran White

  2. Types of data sharing in RELU • Policy-makers / agencies – researchers • Researchers – researchers • Within projects • Between projects • Sometimes interdisciplinary • Researchers – stakeholders • Formal and informal data

  3. Pros and cons of data sharing

  4. Barriers to data sharing • Intellectual barriers • The wrong sort of data • Changing nature of science, e.g. rise of the Ecosystem Approach • Technical barriers • Different units • Time and space - spatial data • Ecological/environmental v socio-economic - grid v administrative areas • Different formats • Qualitative – quantitative

  5. Global ecology Macroeconomics Physical geography Microeconomics Conservation biology Social policy Social anthropology Experimental ecology Behavioural ecology Barriers to interdisciplinary sharing: time and space Global Continental National Regional County Space District Neighbourhood Field Days Weeks Years Generations Centuries Time

  6. Barriers to data sharing • Social barriers • Different cultures • Different personalities • Political barriers • Access restrictions • Practical barriers • Poor data management • time; priorities • quality of metadata • Cost

  7. RELU project 1Social and environmental inequalities • Deprivation as key social indicator • Links between socio-economic and environmental degradation • Inequality relationships with social problems • Are environmental inequalities also important? • What evidence is there for social and environmental injustice? • Mapping social and environmental inequalities • Participatory research with the public • Small project team (3 CoIs, one institution)

  8. RELU project 2Collaboration in deer management • Conflicts around deer • RTAs, conservation damage, income, employment, tourism, agriculture and forest damage • Inefficiencies of management • Collaboration as a means of enhancing efficiency at landscape level • What are the barriers to collaboration? • How can they be overcome? • Ecological, economic, social and political research • Large project team (11 CoIs, 6 institutions)

  9. Data sharing between researchers and policy-makers • RELU SEIRA project • Dataset creation • Huby et al. (2006) J. Ag. Econ. 57, 295-312

  10. Sharing across different spatial units www.sei.se/relu

  11. Selling, not sharing ? …… www.sei.se/relu

  12. Data sharing between researchers • RELU deer project: choice experiments • Economic and social data (quantitative/qualitative) • Quantitative analysis ….

  13. Benefits of qualitative insight … Austin, White et al., in prep.

  14. Sharing between researchers and stakeholders • Participatory GIS; RELU deer project • Irvine et al. (2009) J. Appl. Ecol. 46, 344-352

  15. Barriers to data sharing?Reflections from the two RELU projects • Size of project team • Inequalities team collected all data as a team and hence all ‘owned’ it • Number of institutions involved • Sometimes difficulty within the same institutions • Different types of institutions • Research institutes v universities? • Different types of stakeholders • Public v landowners/stalkers – financial interests • Different types of researchers • Personalities rather than cultural academic differences

  16. Data sharing in the future • Natural sciences • Automated sensor networks • Traditional form of data but at massive volume and high resolution • Social sciences • Larger volumes of data but in new forms • Internet-based social media • Changing philosophy • Open source, instant access • New approaches to publication: ESM, PLoS journals • Re-defining researcher-stakeholder interactions, e.g. blogs • Stakeholder interaction – mash-ups • Itself generating new data, e.g. Twitter social network analysis

  17. Acknowledgements • RELU deer project team (PI: Justin Irvine) • www.macaulay.ac.uk/relu/ • RELU SEIRA project team (PI: Meg Huby) • www.sei.se/relu/ • ESRC CWES seminars team • http://www.york.ac.uk/res/cwes/ • RELU and ESRC/NERC for funding

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