Date

Description: 

In this webinar, Ag will describe the JASMIN data analysis facility for environmental science. JASMIN provides a unique infrastructure supporting large-scale data analysis, collaboration on big scientific data sets, access to Petabytes of environmental data and a range of computing and storage solutions. Ag will present an overview of JASMIN alongside some real user stories that demonstrate its capabilities.

Slides for this talk can be found here

Speaker: 

Ag Stephens is the Head of Partnerships at the Centre for Environmental Data Analysis (CEDA). Ag has 20 years of experience in atmospheric and earth observation data management and software development. Ag manages the development of the UK Climate Projections (UKCP18) User Interface and data services and is the lead developer for the CEDA Web Processing Services (WPS). The latter are deployed for CEDA as well as supporting the delivery of large climate model datasets, such as CMIP6, to the Copernicus Climate Change Service (C3S). Working on a long-term secondment at the Met Office, he works closely with colleagues in NERC and the Met Office to explore and foster data-related collaborations.

This event is part of the 'data management and analytical tools for environmental science' webinar series

The NERC Constructing a Digital Environment programme runs an active webinar activity. Held every three weeks, these aim to develop the digitally enabled environment to benefit scientists, policymakers, businesses, communities and individuals. The webinars are arranged into ‘series’, drawing together presentations following similar themes. 

The fourth webinar series, led by the NERC Environmental Data Service (NERC EDS) focusses on research data management and the analytical tools available to support researchers in the environmental sciences. This series showcases the services provided by the EDS and illustrates how it supports the open data agenda. The series will describe how tools developed by the EDS enable interoperability, support large-scale data analysis and facilitate multi- and trans-disciplinary research.