Concentrations of SARS-CoV-2 RNA and physichochemical data on wastewater samples collected from six sites across England and Wales between March and July 2020. Also included are the number of COVID-19 positive tests and COVID-19 related deaths for the same period collated from publicly available records. COVID-19 data relate to the lower tier local authority that the wastewater treatment plant was located within. Full details about this dataset can be found at https://doi.org/10.5285/ce40e62a-21ae-45b9-ba5b-031639a504f7

The Health Survey for England, 2000-2001: Small Area Estimation Teaching Dataset was prepared as a resource for those interested in learning introductory small area estimation techniques. It was first presented as part of a workshop entitled 'Introducing small area estimation techniques and applying them to the Health Survey for England using Stata'. The data are accompanied by a guide that includes a practical case study enabling users to derive estimates of disability for districts in the absence of survey estimates. This is achieved using various models that combine information from ESDS government surveys with other aggregate data that are reliably available for sub-national areas. Analysis is undertaken using Stata statistical software; all relevant syntax is provided in the accompanying '.do' files.

The data files included in this teaching resource contain HSE variables and data from the Census and Mid-year population estimates and projections that were developed originally by the National Statistical agencies, as follows:

• The main data file, 'hse_data.dta', is a reduced version of the HSE for 2000 and 2001. In order to combine data from two years of the HSE in a consistent way some changes have been made to the weights in each year. Additionally, some recoding of the limiting long term illness (LLTI), disability and the age variable has also been undertaken.
• File 'practical_1_task_5_data.dta' contains population counts and model mobility disability rates (estimated during practical 1) distinguishing single year of age and sex for the six case study districts.
• File 'practical_2_data.dta' contains the aggregate data required for Practical 2, including age- and sex-specific rates of LLTI (Census) for six UK case study districts, age- and sex-specific rates of mobility disability for England (HSE), and population counts for the six districts.
• File 'pop_data_practical_3.dta' contains population counts for the six districts (by age, sex and LLTI status) required for practical 3

The original HSEs for 2000 and 2001 are held at the UK Data Archive under SNs 4628 and 4912 respectively. Full details of the recoding of HSE variables and how the aggregate data was produced can be found in the data documentation.

This unrestricted access data collection is freely available to download under an Open Government Licence from the UK Data Service. Note that the files should be unzipped/saved to the C: drive of the computer to be used; all syntax assumes files are saved at this location.

The dataset contains occupancy estimates for 1,535 species of six invertebrate taxa (Apoidea - bees, Syrphidae - hoverflies, Coccinellidae - ladybirds, Arachnida - spiders, Carabidae - carabids and Heteroptera - plant bugs) in regions of high, low and no cropland cover in Great Britain between 1990 and 2019. Occupancy is the proportion of 1km grid cells occupied by a species, as estimated by an occupancy-detection model. The dataset includes 999 samples from the model’s posterior distribution per species:year combination and for each of three regions of high, low and no cropland cover.

About this layerNetwork contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores were based on inputs from CEH Land cover 2007, Met Office average rainfall and 5 meter DTM. The High contribution category boundary was determined by selecting +1 standard deviation of the distribution of the scores.What can you do with the layer?Visualisation: This layer can be used for visualisation online in web maps.Analysis: This layer can be used in dashboards.Download: The data is downloadable.

This dataset contains a collation of marine habitat and species biotope records created during contracts commissioned by Natural England; collected by Defra and associated bodies/agencies; or provided by third parties that have allowed their data to be republished under the Open Government Licence (OGL). The datasets comprises six sub-datasets (feature classes): one point dataset and five polygonal. There are two versions of this dataset available for download: 'Marine Habitats and Species (Open)' and 'Marine Protected Areas Habitats and Species (Open)'. The dataset 'Marine Habitats and Species (Open)' represents all publicly available marine habitats and species (feature) data held by Natural England. The dataset 'Marine Protected Areas Habitats and Species (Open)' is a subset of the habitat and species data, which shows habitats and species data only within the site in which they are legally designated. Both datasets are provided as an ESRI File Geodatabase (GDB) with an ESRI structured layer file (LYR). All dataset geometry has been validated using the ESRI validation method. It has not been validated using the Open Geospatial Consortium (OGC) validation method and therefore may not comply with the OGC specification. These datasets are available under the Open Government Licence (OGL).

Records of crayfish caught in Crayfish traps at six sites across England from October 2013 to November 2015, including counts and site details.

The dataset contains estimates of the projected area of vegetation derived from the analysis of side-on photographs through the vegetation canopy and recorded for survey quadrats at six UK saltmarsh sites. Three of the sites were in Morecambe Bay, North West England and three of the sites were in Essex, South East England, each of these sites consisted of a saltmarsh area and adjacent mudflat area. Each site comprised 22 quadrats in the vegetated area of salt marsh. A calibrated camera was used to photograph through a 600 x 200mm section of vegetation against a red background. Calibrated images were then classified into vegetation and background classes and parameters of vegetation density in the horizontal were computed. This data was collected as part of Coastal Biodiversity and Ecosystem Service Sustainability (CBESS): NE/J015644/1. The project was funded with support from the Biodiversity and Ecosystem Service Sustainability (BESS) programme - is a six-year programme (2011-2017) funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) as part of the UK's Living with Environmental Change (LWEC) programme.

Access Folk is a research project exploring ways to increase and diversify participation in folk singing in England. Like many in the arts, the folk scene is facing hardship because the impacts of covid-19 and the current economic climate are affecting venues, organisers, amateur and professional singers, and audience members alike. These issues, combined with the ageing of many of the scene’s key activists, raises questions about how the folk singing scene in England might develop over the coming decades. At University of Sheffield, a team of academic and community partners are looking into the current problems and testing potential solutions. The five-year project (2022-2027) hopes to prompt action to help increase accessibility to folk singing for more diverse populations in England.This collection of analyses presents a views and experiences gathered through the first phase of Access Folk through a process we call the Consulting Group. The purpose of the Consulting Group was to gather current issues faced by different sectors of society when accessing folk singing in England in order to inform our future research. We began by identifying six areas where it seemed that access to folk singing might be problematic. Informed by the protected characteristics categories from the Equality and Human Rights Commission (2010), and Arts Council England (2021), our six chosen themes are – Belief, Politics & Religion; Disability; Gender & Sexuality; Race & Ethnicity; Age; and Class & Socio-Economic Background. Each group looked at the access issues for their specific area in relation to folk singing participation. We tried to be as inclusive and open as possible so that we can include anything that those taking part in the research consider to be folk singing in England in our definition – otherwise we felt we would be risking policing the boundaries before we even started the research.This submission includes 6 data packs includes summary reports which were commissioned by attendees from the listed Consulting Groups, associated Focus Groups and individual consultation. Reports have been edited as needed and names have been removed where anonymity was requested through consent forms or direct request.These analyses fed into the Accessing Folk Singing in England research report (2022) and Access Folk Podcast series (2023), which involved co-researchers reflecting on the report.The Consulting Group method was approved by the University of Sheffield ethical review process: number 19926.All the available items arising from the project are available in the Access Folk Collection.

Median price of existing residential property sales in England and Wales by property type and for a range of national and subnational geographies, for individual months.

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

The data file contains individual encounter histories of 1,358 adult Sanderlings (Calidris alba) that winter in six different areas, England, France, Portugal, Mauritania, Ghana or Namibia and were observed between 2007-2013 in the main study site, their winter area (between 1 November – 1 March) and/or outside the main study site, during the interval between encounter occasions at the main study sites (during migration in Europe between 15 March and 15 October and at least 2 latitudinal degrees north of the median winter latitude). Encounter histories are coded as ‘LDLD’ (“Live, dead, live, dead”), where L codes for life encounters in the main study site (0: not observed, 1: observed) and D for (life or dead) encounters outside the main study sites (0: not observed, 1: observed dead, 2: observed alive). The encounter history of an individual starts with the first observation in its wintering region in the year after marking. Winter regions are indicated by the last six columns of the data file which are separated by spaces, where a 1 in the first column indicates England, the second column indicates France, the third Portugal, the fourth Mauritania, the fifth Ghana and the sixth Namibia. Further details can be found in the methods section in the manuscript.

British English Phonetic Dataset

  Introduction

This dataset is an extension of Common Voice, from which 6 subsets were selected (Common Voice Corpus 1, Common Voice Corpus 2, Common Voice Corpus 3, Common Voice Corpus 4, Common Voice Corpus 18.0, Common Voice Corpus 19.0). All data containing the England accent from these 6 subsets were extracted and phonetically annotated accordingly.

  Description

Key fields explanation:

sentence: The English sentence… See the full description on the dataset page: https://huggingface.co/datasets/zdm-code/england-phoneme-dataset.

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Year 6 age children (three year average between academic years 2016/17, 2017/18, 2018/19).Although this layer is symbolised based on an overall score for excess weight, the underlying data, including the raw data for Year 6 children, is included in the dataset.ANALYSIS METHODOLOGYEach MSOA was given a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the NUMBER of Year 6 children with excess weight and;B) the PERCENTAGE of Year 6 children with excess weight.An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of Year children with excess weight, compared to other MSOAs. In other words, those are areas where a large number of children have excess weight, and where those children make up a large percentage of the population of that age group, suggesting there is a real issue with childhood obesity in that area that needs addressing.DATA SOURCESNational Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEBased on data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. Data analysed and published by Ribble Rivers Trust © 2021.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Network contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores were based on inputs from CEH Land cover 2007, Met Office average rainfall and 5 meter DTM. The High contribution category boundary was determined by selecting +1 standard deviation of the distribution of the scores.

Pairwise estimates of FST between samples of O. marina from six regions in the UK; *indicates significant difference between population pairs (P<0.05).

SUMMARYThe area (in hectares) of publicly accessible blue- and green-space per 1000 population within each Middle Layer Super Output Area (MSOA).This dataset was produced to identify how much green/blue space (areas with greenery and/or inland water) people have to opportunity to experience within each MSOA. This includes land that the public can directly access and land they are able to walk/cycle/etc. immediately adjacent to.The area of accessible green/blue space, as a percentage of the total area of the MSOA, is also given.ANALYSIS METHODOLOGYThe following were identified as ‘accessible’ blue and green spaces:A) CRoW Open Access LandB) Doorstep GreensC) Open Greenspace (features described as a ‘play space’, ‘playing field’ or ‘public park or garden’)D) Local Nature ReservesE) Millennium GreensF) National Nature ReservesG) ‘Green’ and ‘blue’ land types – inland water, tidal water, woodland, foreshore, countryside/fields – and Open Greenspace types not identified in Point C that are immediately adjacent to*:G1) Coastal Path RoutesG2) National Cycle Network (traffic-free routes only)G3) National Forest Estate recreation routesG4) National TrailsG5) Path networks within built up areas (OS MasterMap Highways Network Paths)G6) Public Rights of Way*Features G1-6 were buffered by 20 m. All land described in Point G that fell within those 20 m buffers was extracted. Of those areas, any land that was >3m away from features G1-6 in its entirety was assumed to have non-green/blue features between the public path/route/trail and it, and was therefore removed.Population statistics for each MSOA were combined with the statistics re. the area of accessible green/blue space, to calculate the area of accessible green-blue space per 1000 population.LIMITATIONS1. Access to beaches and the sea could not be factored into the analysis, and should be considered when interpreting the results for MSOAs on the coastline.2. This dataset highlights were there are opportunities for the public to experience green/blue space. It does not (and could not) determine the level of accessibility for users with differing levels of mobility.3. Public Right of Way (PRoW) data was not available for the whole of England. While some gaps in the data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. As such, this dataset should be viewed in combination with the ‘Area of accessible green and blue space per 1000 population (England): Missing data’ dataset in ArcGIS Online or, if using the data in desktop GIS, the ‘NoProwData’ field should be consulted. The area of accessible green/blue space in those areas could be slightly under represented in this dataset. TO BE VIEWED IN COMBINATION WITH:Area of accessible green and blue space per 1000 population (England): Missing dataDATA SOURCESCoastal Path Routes; CRoW Act 2000 - Access Layer; Doorstep Greens: Local Nature Reserves; Millennium Greens; National Nature Reserves; National Trails: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0. Available from the Natural England Open Data Geoportal.OS Open Greenspace; OS VectorMap® District: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.OS MasterMap Highways Network Paths: Contains Ordnance Survey data © Crown copyright and database right 2021. National Cycle Network © Sustrans 2021, licensed under the Open Government Licence v3.0.National Forest Estate Recreation Routes: © Forestry Commission 2016.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Public Rights of Way: Copyright of various local authorities.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Produced using data: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.; © Sustrans 2021, licensed under the Open Government Licence v3.0.; © Forestry Commission 2016.; © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

London had the highest unemployment rate among regions of the United Kingdom in the third quarter of 2025 at *** percent, while for the UK as a whole, the unemployment rate was **** percent. Six other regions also had an unemployment rate higher than the national average, while Northern Ireland had the lowest unemployment rate in this time period, at *** percent. Labor market recovery after COVID-19 After reaching historically low levels of unemployment in 2019, there was a noticeable spike in the UK unemployment rate in the aftermath of the COVID-19 pandemic. After peaking at ****percent in late 2020, the unemployment rate declined throughout 2021 and 2022. High levels of job vacancies, resignations, and staff shortages in 2022, were all indicative of a very tight labor market that year, but all these measures have started to point in the direction of a slightly looser labor market. UK's regional economic divide While the North of England has some of the country’s largest cities, the sheer size and economic power of London is much larger than the UK's other urban agglomerations. Partly, due to the size of London, the United Kingdom is one of Europe’s most centralized counties, and there is a clear divide between the economic prospects of north and south England. In 2022, for example, the gross domestic product per head in London was ****** British pounds, far higher than the UK average of *******pounds, and significantly larger than North East England, the region with the lowest GDP per head at *******pounds.