We are pleased to announce the launch of the new Centre of Excellence in Environmental Data Science (CEEDS) seminar series! This seminar series aims to enable staff across Lancaster University and the UK Centre for Ecology & Hydrology to engage with our activities; providing an opportunity to learn more about our themes, to encourage greater interest across both organisations, and ultimately to spark new and exciting collaborations within the CEEDS space! During the summer term, we will kick off this series with 3 seminars.
20 May 2020, 2:00-3:30pm
Visions of a Data Driven Environmental Science
In this seminar, we ask four experts in CEEDS to present their visions of a data-driven future, drawing on the different methodological themes we cover; data acquisition, infrastructure, data science methods, and decision-making under uncertainty. The format will be 4 x 10 minute presentations followed by a panel discussion. Each speaker will present their particular vision for the future drawing on examples from their own work. This focus on 'vision' will be an excellent way of launching our seminar series and we look forward to an exciting and stimulating event.
- Matt Fry: Machine learning for quality control of sensor data.
- Amber Leeson: Data related challenges in glaciology.
- Michael Hollaway: DataLabs: Collaborative platform for environmental data science.
- Bran Knowles: Why Trust matters.
For details of how to join the seminar, please see:
3 June 2020, 2:00-3:30pm
Eleanor Blyth (convenor) will focus on quantifying soil moisture at the national scale. Observations at large (satellite) and small (site-based COSMOS UK) scales combined with large-scale models (1km grid across mainland Britain) will be discussed to better understand this challenge.
17 June 2020, 2:00-3:30pm
Robert Dunford (convenor) will be looking broadly at the topic of model coupling as a means to address interdependencies between different aspects of the environment. To what extent can complicated environmental questions be better assessed by integrating models? What are the advantages, the challenges, and the limits of joined-up modelling?