Intro to Programming with python - TEMP Combined

Programming with Python with the virtual desktop set-up

Open Research Case Study

Whilst the importance of quantifying the impacts of detrimental air quality remains a global priority for both researchers and policy makers, transparent methodologies that support the collection and manipulation of such data are currently lacking. In support of the Britain Breathing citizen science project, aiming to investigate the possible interactions between meteorological or air quality events and seasonal allergy symptoms, we have built a comprehensive data-set, and a web application: ‘Mine the Gaps’, which present daily air quality, pollen and weather readings from the Automatic Urban and Rural Network (AURN) and Met Office monitoring stations in the years 2016 to 2019 inclusive, for the United Kingdom.  Measurement time series are rarely fully complete so we have used machine learning techniques to fill in gaps in these records to ensure as good coverage as possible. To address sparse regional coverage, we propose a simple baseline method called concentric regions. ‘Mine the Gaps’ can be used for graphically exploring and comparing the imputed dataset and the regional estimations. The application code is designed to be reusable and flexible so it can be used to interrogate other geographical datasets. 

UKRI Data Sharing Primer - Script Guide

FAIR Access to Environment Data (RSE role)

Facilitating access to data using open sustainable software development methods is an important part of the RSE role. Working on a recent Turing Institute-funded project, RSEs at the University of Manchester created an open-source environmental data-set and tool-set that have been published in the Nature Scientific Data journal. Working on the Digital Solutions Hub, we will be continuing this work, converting a broad range of hub requirements into a set of tools that allow FAIR (Findable, Accessible, Interoperable, Reusable) access to existing and future datasets.

Data and Software Sustainability

NERC DS Hub RSEs

Intro to Programming with python - online SWB

Mine-the-gaps_and_REs

Whilst the importance of quantifying the impacts of detrimental air quality remains a global priority for both researchers and policy makers, transparent methodologies that support the collection and manipulation of such data are currently lacking. In support of the Britain Breathing citizen science project, aiming to investigate the possible interactions between meteorological or air quality events and seasonal allergy symptoms, we have built a comprehensive data-set, and a web application: ‘Mine the Gaps’, which present daily air quality, pollen and weather readings from the Automatic Urban and Rural Network (AURN) and Met Office monitoring stations in the years 2016 to 2019 inclusive, for the United Kingdom.  Measurement time series are rarely fully complete so we have used machine learning techniques to fill in gaps in these records to ensure as good coverage as possible. To address sparse regional coverage, we propose a simple baseline method called concentric regions. ‘Mine the Gaps’ can be used for graphically exploring and comparing the imputed dataset and the regional estimations. The application code is designed to be reusable and flexible so it can be used to interrogate other geographical datasets. 

Git-workflow-and-sharing

Environmental Intelligence REs and mine-the-gaps

Whilst the importance of quantifying the impacts of detrimental air quality remains a global priority for both researchers and policy makers, transparent methodologies that support the collection and manipulation of such data are currently lacking. In support of the Britain Breathing citizen science project, aiming to investigate the possible interactions between meteorological or air quality events and seasonal allergy symptoms, we have built a comprehensive data-set, and a web application: ‘Mine the Gaps’, which present daily air quality, pollen and weather readings from the Automatic Urban and Rural Network (AURN) and Met Office monitoring stations in the years 2016 to 2019 inclusive, for the United Kingdom.  Measurement time series are rarely fully complete so we have used machine learning techniques to fill in gaps in these records to ensure as good coverage as possible. To address sparse regional coverage, we propose a simple baseline method called concentric regions. ‘Mine the Gaps’ can be used for graphically exploring and comparing the imputed dataset and the regional estimations. The application code is designed to be reusable and flexible so it can be used to interrogate other geographical datasets. 

TTI-git-workflow-RSE-work

Environmental Intelligence POSTER

Whilst the importance of quantifying the impacts of detrimental air quality remains a global priority for both researchers and policy makers, transparent methodologies that support the collection and manipulation of such data are currently lacking. To support research investigating the inter-play between common gaseous and particulate pollutants with meteorology and biological particles, we present a comprehensive data-set containing daily air quality, pollen and weather readings from the Automatic Urban and Rural Network (AURN) and Met Office monitoring stations in the years 2016 to 2019 inclusive, for the United Kingdom. We describe the data sources, how the data has been cleaned, and how we dealt with missing values and sparse regional coverage. The resulting dataset, which integrates supplementary regional data with other relevant variables, including urban-ness and altitude, is designed to maximise its utility to those using air quality data in their research. Alongside the data we provide the tools used for collecting, cleaning and estimation, anticipating that others may want not just to use our data as is, but also extend and modify the approach for their own research. In addition, we introduce our 'Mine the Gaps' web application, providing an interesting and graphical demonstration of how this dataset can be utilised.

Intro to Programming with python - virtual-desktop

Programming with Python with the virtual desktop set-up

Programming with python - advanced

University of Manchester Research-IT: Advanced programming with python course

Introduction to Programming with python