In 2014 the Office for National Statistics (ONS) designed the London Business Survey (LBS), on behalf of the London Enterprise Panel and the GLA. In 2021 the GLA requested an alternative to the London Business Survey, to provide a snapshot of businesses in London without the use of a bespoke survey. As such the ONS - London team has compiled various published datasets from multiple sources into a singular workbook. It is important to note that all data in this workbook is already in the public realm and has been compiled into this format for the convenience of users. As part of this project some bespoke adhoc data requests were produced to best match the original questions from the 2014 survey questions (where possible). Data is provided at the London or local authority level where it is published, however there are some cases where data is limited to the national level. Data is provided for the most recent year available. This varies by dataset.

Air Pollution Health Impacts in London Boroughs

Incidence and 95% Confidence intervals

By data.world's Admin [source]

About this dataset

This dataset reveals the long-term health impacts of air pollution in London's boroughs. Home to over 8 million people, London's air pollution is a growing health concern and this study provides invaluable insights into the devastating effects of exposure.

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How to use the dataset

How to Use this Dataset:

This dataset provides detailed analysis of the long-term health impacts of air pollution. It includes estimated cases and costs associated with each borough, as well as projections for each scenario used in modelling the effects. This dataset is useful for learners who want to learn about how various factors, such as population growth or new technologies, may affect future health outcomes related to air pollution in London.

The columns included are ‘Scenario’ (the scenario used), ‘Year’ (the year modelled), ‘Disease’ (the type of disease modelled), ‘AgeGroup’ (the age group of the population modelled) and ‘95% CL’ (confidence level).

To understand these columns further we recommend looking at the original source report. This will provide additional detail about each element considered when modelling.

To get started with analysing this data set we recommend exploring how estimates differ between scenarios and considering which ages benefit most from different interventions proposed by London Environment Strategy for reducing diseases caused by air pollution. Additionally you could look at different diseases separately, or consider disease costs versus number of cases across different age groups and scenarios

Research Ideas

• Analyzing the long-term impact of air pollution on London's NHS and social care system by borough.
• Comparing the health impacts of different scenarios related to air pollution in different years and age groups to inform effective policymaking.
• Modeling how changes in air pollution levels might affect different diseases or health outcomes over time in a particular area or community

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: newham-no2-xlsm-18.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

File: bromley-pm25-xlsm-35.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

Introduction

The 2023 mid-year estimate (MYE) is the current official estimate of the population for local authorities in England and Wales. Estimates are produced annually by the Office for National Statistics (ONS) and the 2023 MYE was published on 15 July 2024.

Comparison to previous MYE data

The previous MYE series (for the period 2012-2020) starts with the 2011 census estimate. Each subsequent year’s population is calculated by adding estimates of births, deaths and migration to the previous year’s population. The 2021 MYE represents a break in this series as it uses the 2021 census as its base.

The ONS revised the 2012-2020 MYE series to bring it in line with the 2021 MYE, so that comparisons could be made between between this series and the previous series. The values plotted on the chart are the revised values of the previously published estimates for 2011 to 2022, together with the estimates for 2023.

Key Points

• London’s mid-2023 population was 8.945 million
• London’s population increased by 76,300 persons compared to the previous mid-year value
• Components of change were as follows:
• 105,100 births and 53,500 deaths (natural change of 51,600)
• Net domestic migration was an outflow of 129,200
• Net international migration was an inflow of 154,100

Population Change

London’s 2023 population was 8,945,310. The first chart below shows the 2023 MYE in the context of previous estimates. There is an uptick after a temporary decrease in population which we attribute to the COVID-19 pandemic.

https://cdn.datapress.cloud/london/img/dataset/763802e7-af17-4b77-995d-44c494fb68af/2025-06-09T20%3A56%3A29/666cd938678c5361c953cb608e532416.webp" width="1152" alt="Embedded Image" />

Components of Change

Births, deaths and migration form the components of population change.

The 2023 MYE value for births was 4% lower than that in 2022, and for deaths 3% higher. The consequent value for natural change (births - deaths) was 10% lower than in 2022.

At -129,000, the value for domestic migration (migration within the UK) was nearly 3% higher than the 2022 value, so still significantly lower than the peak net outflow during the COVID-19 pandemic of -186,000. An outflow of domestic migrants from London is normal and this has been the case each year for the last two decades. This flow is partly because many international in-migrants initially settle in London before moving out to other parts of the UK. The second move in this sequence is counted as a domestic migration.

There has been a marked change in immigration since 2021. This can be attributed to the end of free movement for EU nationals, easing of travel restrictions following the COVID 19 pandemic, and the war in Ukraine. At over 150,000, the 2023 MYE value for London’s net international migration was more than 18% higher than 2022, and represents a considerable increase from 78,000 in 2021.

https://cdn.datapress.cloud/london/img/dataset/763802e7-af17-4b77-995d-44c494fb68af/2025-06-09T20%3A56%3A29/cb537d44954e11f7f7b7e2189ae74629.webp" width="1152" alt="Embedded Image" />

Age structure of the population

https://cdn.datapress.cloud/london/img/dataset/763802e7-af17-4b77-995d-44c494fb68af/2025-06-09T20%3A56%3A29/6d4cf55b96888dbc3aacfc1de5c664ec.webp" width="1152" alt="Embedded Image" />

Future Updates

The release of the next mid-year estimates is expected in July 2025.

The full ONS mid-year population estimates release and back series can be found on the ONS website: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates.

For information relating to London’s population see the demography pages of the London Datastore: https://data.london.gov.uk/demography/ or email demography@london.gov.uk.

An in-depth review of the available evidence for population change in London since the start of the coronavirus pandemic has been produced by GLA Demography: https://data.london.gov.uk/dataset/population-change-in-london-during-the-pandemic.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

About this dataset

Context

The dataset tabulates the London population by race and ethnicity. The dataset can be utilized to understand the racial distribution of London.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• London, AR Population Breakdown by Race
• London, AR Non-Hispanic Population Breakdown by Race
• London, AR Hispanic or Latino Population Distribution by Their Ancestries

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on the sales and purchases, and exporting and importing behaviour of London businesses corresponding with Section 5 of the London Business Survey 2014: Main Findings report. Information is provided on: The value of goods and service traded London businesses with sales to London, the rest of the UK, elsewhere in Europe and the rest of the world London businesses with purchases from London, the rest of the UK, elsewhere in Europe and the rest of the world The average proportion of sales to, and purchases from, specified locations As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

London Property Listings Dataset

Description

This dataset expands upon the original London Property Listings by including additional attributes to facilitate deeper analysis of rental properties in London. It is ideal for research and projects related to real estate trends, price categorization, and area-wise analysis in one of the world's busiest markets.

Dataset Features

• Price: Monthly rental price in GBP.
• Property Type: Classification of the property (e.g., Apartment, Flat).
• Bedrooms: Number of bedrooms in the property.
• Bathrooms: Number of bathrooms.
• Size: Property size in square feet (where available).
• Postcode: Postal code of the property location.
• Area: General area or neighborhood information.
• Price_Category: Categorization of prices into predefined ranges (e.g., Low, Medium, High).
• Area_Avg_Price: Average price of properties within the same area.

Potential Use Cases

• Price Analysis: Study how property attributes impact rental prices.
• Price Prediction Models: Utilize the dataset for machine learning tasks like regression and classification.
• Regional Insights: Compare rental trends across different neighborhoods.
• Categorical Analysis: Investigate trends within predefined price categories.

Data Summary

• Total Records: 29,537
• Total Attributes: 9
• Data Completeness: No missing values. All columns are fully populated.

Attribution

This dataset was prepared and uploaded by Mehmet Emre Sezer. It is intended for educational and non-commercial use.

About this dataset

Context

The dataset tabulates the New London population by race and ethnicity. The dataset can be utilized to understand the racial distribution of New London.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• New London, IA Population Breakdown by Race
• New London, IA Non-Hispanic Population Breakdown by Race
• New London, IA Hispanic or Latino Population Distribution by Their Ancestries

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014.

This dataset contains information on London businesses’ awareness and experience of business support available to SMEs corresponding with Section 6 of the London Business Survey 2014: Main Findings report.

Information is provided on:

• The sources of external advice used by London businesses

• The topics on which external advice is sought by London businesses

• Business awareness and use of incubator, accelerator and co-working spaces

As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms.

The results are presented by enterprise size band and industry sector.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on London as a business location and the factors affecting businesses corresponding with Section 2 of the London Business Survey 2014: Main Findings report. Information is provided on: How businesses rate London as a place to do business for a range of factors including: transport infrastructure, digital and communications infrastructure, affordable workspace, the cost of housing and staff with the appropriate skills The expected impact on the business of leaving the EU (but not the single market) How Greater London compares to cities outside the UK on availability of work visas for non–European employees Satisfaction with local facilities including parks, sporting and recreational facilities, cultural activities, local amenities, and the safety and cleanliness of the local environment Factors affecting businesses including the costs of energy and materials, taxes and business rates, government regulations, travel infrastructure, IT/connectivity As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

UCLA Consortium for Neuropsychiatric Phenomics LA5c Study

Preprocessed data described in

Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research 2017, 6:1262 https://doi.org/10.12688/f1000research.11964.2

are available at https://legacy.openfmri.org/dataset/ds000030/ and via Amazon Web Services S3 protocol at: s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/

Subjects / Participants

The participants.tsv file contains subject IDs with demographic informations as well as an inventory of the scans that are included for each subject.

Dataset Derivatives (/derivatives)

The /derivaties folder contains summary information that reflects the data and its contents:

1. Final_Scan_Count.pdf - Plot showing the over all scan inclusion, for quick reference.
2. parameter_plots/ - Folder contains many scan parameters plotted over time. Plot symbols are color coded by imaging site. Intended to provide a general sense of protocol adherence throughout the study. Individual parameters scan be found in the scan .json sidecar file. A single file containing the combined data from all of the imaging .json sidecars if provided in parameter_plots/MR_Scan_Parameters.tsv file.
3. physio_plots/ - Folder contains a plot of the physiological recording trace for the Breath Hold and Resting State functional scans. For the BHT, the instructional cue timings are represented by shaded background.
4. event_plots/ - Simple plots of the function task events files. The x-axis is always time (onset), and the y-axis can be task-specific. Also intended as a quick reference or summary.
5. mriqcp/ - Output of the current version (as of 27 Jan 2016) of MRIQCP (MRI Quality Control Protocol: https://github.com/poldracklab/mriqc). Included are numeric results of anatomical and functional protocols as well as single subject results plotted against group distribution.
6. data_browser/ - a rudimentary data visualization for MRIQP (see: http://wtriplett.github.io/ds030/)

Scan-specific Notes

All scan files were converted from scanner DICOM files using dcm2niix (0c9e5c8 from https://github.com/neurolabusc/dcm2niix.git). Extra DICOM metadata elements were extracted using GDCM (http://gdcm.sourceforge.net/wiki/index.php/Main_Page) and combined to form each scan's .json sidecar.

Note regarding scan and task timing: In most cases, the trigger time was provided in the task data file and has been transferred into the TaskParameter section of each scans *_bold.json file. If the trigger time is available, a correction was performed to the onset times to account for trigger delay. The uncompensated onset times are included in the onset_NoTriggerAdjust column. There will be an 8 second discrepancy between the compensated and uncompensated that accounts for pre-scans (4 TRs) performed by the scanner. In the cases where the trigger time is not available, the output of (TotalScanTime - nVols*RepetitionTime) may provide an estimate of pre-scan time.

T1w Anatomical

Defacing was performed using freesurfer mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface)

Bischoff-Grethe, Amanda et al. "A Technique for the Deidentification of Structural Brain MR Images." Human brain mapping 28.9 (2007): 892–903. PMC. Web. 27 Jan. 2016.

PAMenc / PAMret

The larger amount of missing PAM scans is due to a task design change early in the study. It was decided that data collected before the design change would be excluded.

Stop Signal

The Stop Signal task consisted of both a training task (no MRI) and the in-scanner fMRI task. The data from the training run is included in each subject's beh folder with the task name "stopsignaltraining".

Known Issues:

Some of the T1-weighted images included within this dataset (around 20%) show an aliasing artifact potentially generated by a headset. The artifact renders as a ghost that may overlap the cortex through one or both temporal lobes. A list of participants showing the artifact has been added to the dataset.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on London as a business location and the factors affecting businesses corresponding with Section 2 of the London Business Survey 2014: Main Findings report. Information is provided on: How businesses rate London as a place to do business for a range of factors including: transport infrastructure, digital and communications infrastructure, affordable workspace, the cost of housing and staff with the appropriate skills The expected impact on the business of leaving the EU (but not the single market) How Greater London compares to cities outside the UK on availability of work visas for non–European employees Satisfaction with local facilities including parks, sporting and recreational facilities, cultural activities, local amenities, and the safety and cleanliness of the local environment Factors affecting businesses including the costs of energy and materials, taxes and business rates, government regulations, travel infrastructure, IT/connectivity As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on London’s private sector workforce and recruitment by London businesses corresponding with Section 3 of the London Business Survey 2014: Main Findings report. Information is provided on: The number of employees working in London businesses by gender The change in the number of employees compared to 12 months ago, and the outlook for the next 12 months Reasons for a rise or fall in the number of employees Recruitment, including whether London businesses have recruited via Job Centre Plus (JCP), and the perceived suitability of these candidates For statistics on the number of full-time and part-time employees working in London, the ONS’s Business Register and Employment Survey (BRES)is the recommended official source. As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (1 hour in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 48420 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the UK population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on the sales and purchases, and exporting and importing behaviour of London businesses corresponding with Section 5 of the London Business Survey 2014: Main Findings report. Information is provided on: The value of goods and service traded London businesses with sales to London, the rest of the UK, elsewhere in Europe and the rest of the world London businesses with purchases from London, the rest of the UK, elsewhere in Europe and the rest of the world The average proportion of sales to, and purchases from, specified locations As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

The 2014 London Business Survey (LBS) is an innovative survey designed by the Office for National Statistics, on behalf of the London Enterprise Panel and the GLA. The survey collected information from a representative sample of private sector businesses in London in May-July 2014. This dataset contains information on innovation activity and investments corresponding with Section 7 of the London Business Survey 2014: Main Findings report. Information is provided on: * London businesses which are innovation active, by the type of innovation including product, process, marketing and organisational innovations * Investment in innovation by businesses by type of investment, including Research and Development (R&D), the purchase of licensing of patents and existing knowledge, the training of staff for innovative activities, and the acquisition of machinery, equipment and software As with any survey, the 2014 LBS is based on a sample and as such is subject to variability in the results. Care should therefore be taken in interpreting the survey findings. For all estimates, lower and upper limits of 95% confidence intervals are provided in the data files to assist with interpretation. The LBS results represent the population of business units in London. A business unit is defined as a site/workplace, which may also be a head office if the head office is in London. It will be the whole business in the case of businesses which only have one site, or part of the business in the case of multi-site firms. The results are presented by enterprise size band and industry sector.

The COVID Symptom Tracker (https://covid.joinzoe.com/) mobile application was designed by doctors and scientists at King's College London, Guys and St Thomas’ Hospitals working in partnership with ZOE Global Ltd – a health science company.

This research is led by Dr Tim Spector, professor of genetic epidemiology at King’s College London and director of TwinsUK a scientific study of 15,000 identical and non-identical twins, which has been running for nearly three decades.

The dataset schema includes:

Demographic Information (Year of Birth, Gender, Height, Weight, Postcode) Health Screening Questions (Activity, Heart Disease, Diabetes, Lung Disease, Smoking Status, Kidney Disease, Chemotherapy, Immunosuppressants, Corticosteroids, Blood Pressure Medications, Previous COVID, COVID Symptoms, Needs Help, Housebound Problems, Help Availability, Mobility Aid) COVID Testing Conducted How You Feel? Symptom Description Location Information (Home, Hospital, Back From Hospital) Treatment Received The data is hosted within the SAIL Databank, a trusted research environment facilitating remote access to health, social care, and administrative data for various national organisations.

The process for requesting access to the data is dependent on your use case. SAIL is currently expediting all requests that feed directly into the response to the COVID-19 national emergency, and therefore requests from NHS or Government institutions, or organisations working alongside such care providers and policymakers to feed intelligence directly back into the national response, are being expedited with a ~48-hour governance turnaround for such applications once made. Please make enquiries using the link at the bottom of the page which will go the SAIL Databank team, or to Chris Orton at c.orton@swansea.ac.uk

SAIL is welcoming requests from other organisations and for longer-term academic study on the dataset, but please note if this is not directly relevant to the emergency research being carried out which directly interfaces with national responding agencies, there may be an access delay whilst priority use cases are serviced.

Please note: the CVST dataset in SAIL has not been updated since 01/11/2023.

This dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.

This data shows the number of people registered with Councils with Social Services Responsibilities (CSSRs) as being deaf or hard of hearing by age group.

Age groups are: 0-17, 18-64, 65-74, 75 and over.

Numbers are rounded to nearest five.

The data are compiled from the triennial return SSDA 910 which is submitted to The Information Centre (The IC).

People who are registered as deaf or hard of hearing that are also blind or partially sighted are recorded on the Register of Blind and Partially Sighted Persons (SSDA 902 form), unless stated these are excluded from this report. Data on these by category of disability is available here:

http://data.london.gov.uk/dataset/number-registered-blind-and-partially-sighted-people-additional-disability-categor

and by age here:

http://data.london.gov.uk/dataset/number-registered-blind-and-partially-sighted-people-age-group-borough

All ages total includes some cases where the age was not known. Therefore the age groups may not add to the total. Regional totals are estimated to take account of missing data.

Dash ("-") means a local authority was unable to submit details on the number of people registered as being deaf and hard of hearing.

Download from NHS website

Under the direction of University College London (UCL), this international, multidisciplinary project assessed the feasibility of using non-destructive digital imaging technology to make texts visible in images of papyrus in Ancient Egyptian mummy case cartonnages for open research and analysis. Our pilot project has led to an understanding of which imaging modalities are worth pursuing in future research projects. The massive finding of papyri in Egypt between the end of the 19th and the beginning of the 20th century has dramatically increased our knowledge of the ancient world. The recovering of new texts has brought to light classical and biblical literature, and everyday writing of people that have changed the way we interpret antiquity. Papyri were and still are found in two main ways: in situ, i.e. where they were left by the ancients, or recycled for fabricating other objects such as mummy masks and coverings, book binding and other kinds of what scholars broadly define as 'cartonnage.' Papyri were also used sometimes to stuffing animal mummies. In the past, the awareness that such ancient objects could be filled with manuscripts has led papyrologists to destroy cartonnage, mummy masks and other material for retrieving their contents. With the passing of decades, specialists' recognition of the problems connected with such practice has increased, and new, less invasive techniques have been developed in order to avoid the destruction of important historical evidence. The decision to eventually dismount cartonnage involves careful evaluations of the pros and cons and of the methods to be followed. Besides papyrologists, conservators and other specialists, the practice of dissolving cartonnage in order to retrieve papyri has been employed by dealers and collectors seeing the opportunity to multiply their earnings or simply looking for manuscripts without recognizing the issues involved with the destruction of ancient artefacts. In these cases, the damage produced to our cultural heritage is even greater since little if any attention to the methods employed and to the recording of the process is paid. The application of advanced imaging techniques has the potential to dramatically improve our study of papyri encapsulated in ancient artefacts and will potentially solve the problem of invasive, destructive approaches to the remains of our ancient past. This exploratory, pilot project, working with a range of international partners and collections between November 2015 and December 2017, tested the feasibility of non-destructive imaging of multi-layered Papyrus found in Egyptian mummy cartonnages. Our research has shown that no current single imaging technique can identify both iron and carbon based inks at depths within cartonnage. If we are to detect and ultimately read text within cartonnage, a multimodal imaging approach is required, but this will necessarily be limited by cost, access to imaging systems, and the portability of both the system and the cartonnage. We are currently in the process of publishing lessons-learned on findings and imaging methodologies for further research, including on affordances and limitations of specific imaging approaches, and how they can be used in tandem to recover extant text within layers of cartonnage. This data is hosted by UCL Research Data Repository for public access and use. All images are licensed for use under Creative Commons 0 1.0 Universal License.

This data set comprises a core content set of digital images, analytical data and technical reports on the imaging and analysis of mummy mask cartonnage and modern surrogates. These are intended for access by researchers, scholars, students and the general public. The data set contains the following folders organized by imaging method:

Documentation.7z contains documentation, metadata, photographs and reports for each modality (151MB). Data_FiberOpticReflectanceSpectroscopy.7z is Fiber Optic Reflectance Spectroscopy Data from testing conducted by Equipoise Imaging (30MB) Data_OpticalCoherenceTomography.7z is Optical Coherence Tomography Data from imaging conducted in the Duke University Eye Center and Department of Biomedical Engineering. (619MB) Data_Terahertz.7z is Terahertz Data from experimental imaging at the University of Western Australia (1MB) Data_Xray.7z contains XRF data from the SLAC Stanford Synchrotron Radiation Lightsource in California and "Micro-CT ALS Berkeley" data from the Lawrence Livermore National Laboratory Advanced Light Source in California. (21.3GB). ImageData_RBT.7z - Multispectral imaging data from RB Toth Associates at Duke University and University of California at Berkeley, with processed images of US and UCL images. (31 GB.) UCBsn_LC.7z - Data from multispectral imaging at the University of California at Berkeley s.n. cartonnage fragment by the Library of Congress before and after x-ray of the fragment for damage assessment (2.1GB) UCL_Digital_Humanities.7z - Data from multispectral imaging of the UCL Phantom surrogates and Petrie Museum cartonnage UC806037i in the UCL Centre for Digital Humanities, London. (22.6GB) UManchester_JohnRylands: Data from multispectral imaging of both sides of cartonnage Greek P458 P458 at the University of Manchester John Rylands Library. (5.5GB)

README files with more specific information are included with the data set from each imaging modality. This data was first shared online in July 2017. It was moved to its current location and assigned a doi in November 2022.