Landscape Laboratory Data Infrastructure

A smart data management infrastructure to support long-term research in the context of the Landscape Laboratory.

How does a human-environment system (i.e., a landscape) respond to change?

Many of the challenges facing cities such as Sheffield in mitigating, adapting and developing resilience to Climate Change are linked, within the landscape of the River Don.

• Flood risk downstream, land use upstream.
• Water supply, water consumption.
• Our capacity to withstand drought and extremes of heat and cold.

Current environmental policies and the drive to Net Zero depend on the better understanding of long-term consequences of management decisions, which are highly contextual, require long time periods to assess the validity of modelled impacts.

The Landscape Laboratory concept is a 15-year catchment management infrastructure focused on the Upper Don region with an extraordinary opportunity for applied interdisciplinary research, knowledge exchange and impact. It intends to develop a programme of interdisciplinary, long-term research studies across the Upper Don catchment, which will enable Sheffield Hallam University (SHU), Sheffield and Rotherham Wildlife Trust (SRWT) and partner stakeholders to address system-level problems.

Crucial to this ambition is the foundation of a technological infrastructure for the processing of data relevant to and acquired by projects running within the Landscape Laboratory over a long period of time (10-15 years).

The Landscape Laboratory Data Infrastructure project aims to research, design and develop a smart data management infrastructure to support the mission of the Landscape Laboratory. We will investigate how a coordinated data infrastructure could support the execution and posterior sharing of data produced by ecological, social, cultural and economic/policy projects. This infrastructure must deal with a large range of data generated by different examinations of different components of the landscape system and facilitate the integration and analysis of these data to support land management decision-making.