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Data sharing and integration in the RELU programme: a researchers perspective Piran White.

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Presentation on theme: "Data sharing and integration in the RELU programme: a researchers perspective Piran White."— Presentation transcript:

1 Data sharing and integration in the RELU programme: a researchers 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 ProsCons Efficient and cost- effective Existing data not fit for purpose Time-savingHypothesis-driven v data-driven science New insightsResearch questions increasingly specific Participant welfare (avoids fatigue) Basic data collection, e.g. monitoring, not in vogue with funders

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 Time Space DaysCenturies Global WeeksYearsGenerations Continental National Regional County District Neighbourhood Field Social policy Global ecology Experimental ecology Macroeconomics Conservation biology Microeconomics Physical geography Behavioural ecology Social anthropology Barriers to interdisciplinary sharing: time and space

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 1 Social 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 2 Collaboration 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

11 Selling, not sharing ? ……

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

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) – RELU SEIRA project team (PI: Meg Huby) – ESRC CWES seminars team – RELU and ESRC/NERC for funding

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