Why do we need to develop a better data science framework?

Because this will create a step-change in the ability of the research community and others to understand, predict and manage the environment.

UK-SCAPE is creating a collaborative data science framework (DSF) for combining data, models and analytics tools from across the environmental science community so that many research questions can be addressed, new data products created and platforms for decision support developed.

We are developing the infrastructure and tools needed to realise the potential afforded by advances in monitoring and modelling of the environment. Such advances have created the opportunity to observe, simulate and forecast our environment at unprecedented scales of space, time and complexity. However, to do so requires use of innovative computing infrastructures, methods and analytical tools to overcome challenges such as:

  • Providing access to quality assured data in real-time
  • Sharing models, analytical methods and tools
  • Handling environmental “big data” analytics
  • Creating collaborative analytical environments
  • Quantifying uncertainty across scales
  • Repeatability and reusability of analysis results

By collaboration across environmental data providers, environmental and data scientists, computer specialists, statisticians and data users, we will provide the ability to marshal a wide range of data, models, analytical and informatics tools to enable researchers and stakeholders to access, exploit and enhance them as a harmonised research resource.

The aim of the DSF is to facilitate the creation of and access to new and innovative data products, development of new models and analytical methods, and provision of cloud-based collaborative research environments. Hence, adding value to our underpinning monitoring.

We are developing the framework using scientific case studies which will answer specific science questions.

Our approach will ensure that data, information and forecasting capabilities are accessible, timely and efficient.

Data Access and APIs

Providing timely access to quality-assured data in appropriate formats and through standards-based services open for use by the community.

Data Analysis and Methods

Developing approaches that enable unified, integrated analyses of data streams and model outputs to produce novel outputs at appropriate temporal and spatial scales with quantified uncertainty.

Data Portals

User friendly interfaces to visualise, interrogate and access data products through online web-based portals.


Provision of internet-based, collaborative research environments, to enable a co-design approach in developing new analytics capabilities for a range of complex environmental research questions.


Co-design is seen as crucial part of the data science approach and we aim to bring together the computation, analytical and environmental researchers needed to successfully deliver this data science framework.

Reports and other outputs