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– Formal and informal data

Pros and cons of data sharing

Pros Cons

Efficient and cost-effective

Existing data not fit for purpose

Time-saving Hypothesis-driven v data-driven science

New insights Research questions increasingly specific

Participant welfare (avoids fatigue)

Basic data collection, e.g. monitoring, not in vogue with funders

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

Time

Space

Days Centuries

Global

Weeks Years Generations

Continental

National

Regional

County

District

Neighbourhood

Field

Social policy

Global ecology

Experimentalecology

Macroeconomics

Conservation biology

Microeconomics

Physical geography

Behaviouralecology

Social anthropology

Barriers to interdisciplinary sharing: time and space

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

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)

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)

Data sharing between researchers and policy-makers

• RELU SEIRA project

• Dataset creation

• Huby et al. (2006) J. Ag. Econ. 57, 295-312

www.sei.se/relu

Sharing across different spatial units

www.sei.se/relu

Selling, not sharing ? ……

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

Benefits of qualitative insight …

Collaboration

Incentives

“Let’s not create more legislative bureaucracy, put it [public money] into raising the image of the product [venison] and the image upon the markets”

“Collaboration is the way it should go and I personally think compulsory but because they’re [fallow deer] a roaming species and therefore no one land owner could say – that’s my population.”

“It would probably be a better incentive [financial payments] than to increase the carcass value. Because all that’s going to do is increase the poaching.”

“We weren’t against the [collaboration] principle; we just think the practicalities for our area would be quite difficult”

“If it met all my goals then I was quite happy to go the collaborative route”

“If the collaboration was going to be mutually beneficial and appealing anyway, why would you then need a cash incentive to go into it”

“More money should be available for habitat management, deer fencing, perhaps fencing on roadsides. So, its not just having more money just to shoot more [deer], it needs to be spread over the whole spectrum.”

“You can’t just expect people to agree to a sort of blanket collaboration when you don’t know who you are going to be collaborating with“

Collaboration

Incentives

“Let’s not create more legislative bureaucracy, put it [public money] into raising the image of the product [venison] and the image upon the markets”

“Collaboration is the way it should go and I personally think compulsory but because they’re [fallow deer] a roaming species and therefore no one land owner could say – that’s my population.”

“It would probably be a better incentive [financial payments] than to increase the carcass value. Because all that’s going to do is increase the poaching.”

“We weren’t against the [collaboration] principle; we just think the practicalities for our area would be quite difficult”

“If it met all my goals then I was quite happy to go the collaborative route”

“If the collaboration was going to be mutually beneficial and appealing anyway, why would you then need a cash incentive to go into it”

“More money should be available for habitat management, deer fencing, perhaps fencing on roadsides. So, its not just having more money just to shoot more [deer], it needs to be spread over the whole spectrum.”

“You can’t just expect people to agree to a sort of blanket collaboration when you don’t know who you are going to be collaborating with“

Austin, White et al., in prep.

• Participatory GIS; RELU deer project

• Irvine et al. (2009) J. Appl. Ecol. 46, 344-352

Sharing between researchers and stakeholders

a) original model prediction covered 51% b) improved model prediction covered 77%

of observed stag locations in winter of observed stag locations in winter

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

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

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