CEEDS: The Centre for Energy Epidemiology Data Service
The Centre for Energy Epidemiology (CEE) recognises several significant challenges relating to data and end use energy demand research, which include:
- The lack of access to good quality, high resolution energy data of the statistical quality that most other disciplines would consider a pre-requisite for the pursuit of good science and robust conclusions.
- A limited capacity to analyse, organise or archive data, despite significant sums of money invested to collect data through individual projects.
- No basis for systematic reviews of research findings, and little basis for project-by-project learning, have resulted in limited impact of data on the policy process.
- Poor access to data makes it difficult to establish and maintain benchmarks for performance or to ground models. Practitioners have been left without usable guidelines, and policymakers without the tools to devise and evaluate policy.
- End Use Energy Demand research is hampered by a lack of good quality data, either because it doesn’t exist or it is difficult or impossible to access.
CEEDS aims to address both issues by proposing the generation of new, high-quality datasets (e.g. a UK Energy Longitudinal Survey) and facilitating sharing of existing datasets.
The CEEDS Solution
The Centre for Energy Epidemiology Data Service (CEEDS) aims to address these challenges by developing a holistic solution that will meet the needs of the research community across the energy demand sector.
- The primary function of CEEDS is not to manage data but to manage and share data knowledge.
- CEEDS is mostly concerned with end use energy demand data (including behavioural and social science data), while seeking to build partnerships with data resources in other disciplines.
- As there are many existing sources of aggregated energy statistics, CEEDS will primarily focus on micro-data at appropriate levels of granularity to facilitate meaningful study of end use energy demand behaviours.
- CEEDAR - a Data Asset Register logging relevant datasets with accompanying information and metadata (where available).
- Conceptual Data Model – We aim to develop a conceptual or semantic model that maps relevant data variables and their relationships
- Data Knowledge – Sharing data knowledge (e.g. best practices) is as important as sharing data itself. CEEDS will investigate enabling solutions such as data knowledge networks and open data journals.
- Data Advocacy – in a broad (across the sector) and specific (negotiating access to high value datasets) sense.
- Smart data – recognizing the potential for smart meter / smart grid data to be game changing for energy demand research.
- External data resources – CEEDS aims to collaborate with and encourage the utilization of the many valuable data resources that already exist or are being developed.
- CEE Research Data Management – CEE recognizes that it must first ensure that its own research projects conform to best practice in Research Data Management (RDM) and will develop relevant materials and protocols to facilitate this.
The current version of the CEE Data Asset Register (12-2015) can be downloaded here in Excel
Please contact , CEE Data Manager, if you have any questions, comments or suggestions for datasets to be added to CEEDAR.