Dr Jon Barry (Cefas)
Dr Evalyne Muiruri (Cefas)
Dr Naomi Greenwood (Cefas/UEA)
Cefas and the wider monitoring community is increasingly looking to fulfil monitoring objectives using a combination of data sources. For example: Cefas has a unique dataset of 130 years of sea temperature data, containing over 10 million records from 17 data systems; data collected for seafloor litter monitoring performed with a variety of different fishing gears; eutrophication monitoring collated from devices installed on board vessels, deployed on automated systems or estimated from satellite imagery.
This project will use publicly available datasets. The available data are described in the publications below. The objective of this project is to combine data from different sources in a way that results in statistically unbiased estimates that can be used to assess spatial and temporal trends.
Two-stage analytic methods will be developed: firstly, a common dataset-specific model is developed and secondly, meta-analytic methods are used to pool the model parameters across datasets .
For the sea temperatures, the aim is to evaluate changes over time but making sure that these changes are real and not functions of the assessment methods used. With eutrophication, the question is whether and how to combine results measured using different methods. With litter, the main challenge is to create spatially and temporally coherent estimates of sea floor litter (principally plastic) levels without confounding by the different trawl methods used to collect the data.
The research will start with sea temperatures and then move to other areas if needed.
The student will be based in UEA’s School of Computing Sciences, where there is considerable expertise in Big Data analysis. They will also spend a significant amount of time at Cefas in Lowestoft, where the datasets are curated and where their co-supervisors are based.
The student will receive advanced training in statistics, Big Data and environmental sciences. They would also receive help and guidance on the environmental and ecological aspects of the work from scientists at Cefas.
Applicants should have a minimum 2:1 Bachelor degree in statistics, mathematics or another numerical discipline, and strong computing skills.