Dataset no longer updated: Due to changes in the collection and availability of data on COVID-19, this dataset is no longer updated. Latest information about COVID-19 is available via the UKHSA data dashboard. The UK government publish daily data, updated weekly, on COVID-19 cases, vaccinations, hospital admissions and deaths. This note provides a summary of the key data for London from this release. Data are published through the UK Coronavirus Dashboard, last updated on 23 March 2023. This update contains: Data on the number of cases identified daily through Pillar 1 and Pillar 2 testing at the national, regional and local authority level Data on the number of people who have been vaccinated against COVID-19 Data on the number of COVID-19 patients in Hospital Data on the number of people who have died within 28 days of a COVID-19 diagnosis Data for London and London boroughs and data disaggregated by age group Data on weekly deaths related to COVID-19, published by the Office for National Statistics and NHS, is also available. Key Points On 23 March 2023 the daily number of people tested positive for COVID-19 in London was reported as 2,775 On 23 March 2023 it was newly reported that 94 people in London died within 28 days of a positive COVID-19 test The total number of COVID-19 cases identified in London to date is 3,146,752 comprising 15.2 percent of the England total of 20,714,868 cases In the most recent week of complete data (12 March 2023 - 18 March 2023) 2,951 new cases were identified in London, a rate of 33 cases per 100,000 population. This compares with 2,883 cases and a rate of 32 for the previous week In England as a whole, 29,426 new cases were identified in the most recent week of data, a rate of 52 cases per 100,000 population. This compares with 26,368 cases and a rate of 47 for the previous week Up to and including 22 March 2023 6,452,895 people in London had received the first dose of a COVID-19 vaccine and 6,068,578 had received two doses Up to and including 22 March 2023 4,435,586 people in London had received either a third vaccine dose or a booster dose On 22 March 2023 there were 1,370 COVID-19 patients in London hospitals. This compares with 1,426 patients on 15 March 2023. On 22 March 2023 there were 70 COVID-19 patients in mechanical ventilation beds in London hospitals. This compares with 72 patients on 15 March 2023. Update: From 1st July updates are weekly From Friday 1 July 2022, this page will be updated weekly rather than daily. This change results from a change to the UK government COVID-19 Dashboard which will move to weekly reporting. Weekly updates will be published every Thursday. Daily data up to the most recent available will continue to be added in each weekly update. Data summary Local authority data Demographics Notes on data sources Source: UK Coronavirus Dashboard. For more information see: Coronavirus (COVID-19) in the UK - About the Data. Cases Data UK Health Security Agency (UKHSA) reports new and cumulative cases identified by Pillar 1 and Pillar 2 testing. Pillar 1 testing relates to tests carried out in UKHSA laboratories or NHS Hospitals for those with clinical need, and health and care workers. Pillar 2 testing relates to tests carried out on the wider population in Lighthouse laboratories, public, private, and academic sector laboratories or using lateral flow devices. The cases data is published by day for Countries within the UK, and Regions, Upper Tier Local Authority (UTLA) and Lower Tier Local Authority (LTLA) within England. The data used here is taken from the regional and UTLA level cases data. Notice: Changes to COVID-19 case reporting As of 31 January 2022, UKHSA moved all COVID-19 case reporting in England to use an episode-based definition which includes possible reinfections. Those testing positive beyond 90 days of a previous infection are now counted as a separate infection episode (a possible reinfection episode). Previously people who tested positive for COVID-19 were only counted once in case numbers published on the daily dashboard, at the date of the first infection. Full details of the changes can be found here Changes to COVID-19 testing in England The availability of free COVID-19 tests in England changed on 1 April 2022. Information on who can access free tests has been published by UKHSA. Changes to patient testing in the NHS in England have also been published by NHS England. Deaths data Data on COVID-19 associated deaths in England are produced by UKHSA from multiple sources linked to confirmed case data. Deaths are only included if the deceased had a positive test for COVID-19 and died within 28 days of the first positive test. Postcode of residence for deaths is collected at the time of testing. This is supplemented, where available, with information from ONS mortality records, Health Protection Team reports and NHS Digital Patient Demographic Service records. Full details of the methodology are available in the technical summary of the PHE data series on deaths in people with COVID-19. Hospital admissions data UKHSA publish the daily total number of patients admitted to hospital, patients in hospital and patients in beds which can deliver mechanical ventilation with COVID-19. In England this includes COVID-19 patients being treated in NHS acute hospitals, mental health and learning disability trusts, and independent service providers commissioned by the NHS. Vaccination data UKHSA publish the number of people who have received a COVID-19 vaccination, by day on which the vaccine was administered. Data are reported daily and can be updated for historical dates as vaccinations given are recorded on the relevant system. Therefore, data for recent dates may be incomplete. Vaccinations that were carried out in England are reported in the National Immunisation Management Service which is the system of record for the vaccination programme in England. Only people aged 12 and over who have an NHS number and are currently alive are included. Age is defined as a person's age at 31 August 2021. The data includes counts of vaccinations by age band, dose, region, and local authority. Additional analysis of the vaccine roll out in London can be found here. ONS population estimates The counts of vaccines given has been converted to percentage of the population vaccinated using the ONS 2020 mid-year population estimates. This is a different population estimate to that used on the UK Coronavirus Dashboard for sub-national data. The UK Coronavirus Dashboard uses people aged 16 and over in the National Immunisation Management Service (NIMS), which is based on GP registrations. In more urban areas like London, NIMS is likely to give an overestimate of the population due to increased population mobility increasing the likelihood duplicate or out of date GP records. Due to the differences in population estimates the percentage of the population vaccinated given here will be higher than the figures included for London on the UK Coronavirus Dashboard. Data and Resources phe_deaths_age_london.csv Source: https://coronavirus.data.gov.uk/ phe_deaths_london_boroughs.csv Source: https://coronavirus.data.gov.uk/ phe_vaccines_age_london_boroughs.csv

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

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.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

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, OH Population Breakdown by Race
• New London, OH Non-Hispanic Population Breakdown by Race
• New London, OH 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 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.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources. This collection contains over a thousand records of cases involving persons of African descent, both enslaved and free. It was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers. If a record of interest is found, and a reproduction of the original record is desired, you may submit a request via e-mail or by contacting the History & Genealogy Unit of the Connecticut State Library at (860) 757-6580. Please include the names of the parties, if known, as well as the box and folder numbers. Reproduction formats and fees available, are as follows: Photocopy: black & white copy, 8 1/2 X 11″ or 11 X 14″ sized paper, 25 cents; 11 X 17″, 50 cents per photocopied page, plus a $3.00 handling fee and first class postage charges. Photocopy: color copy 8 1/2 X 11″ or 11 X 14″ sized paper, $1.00 per photocopied page, 11 X 17″, $1.25 per photocopied page plus a $3.00 handling fee and first class postage charges. Digital images (low or high resolution): PDF, JEG, TIFF, or DNG images, 25 cents per image, plus a $3.00 handling fee. Digital file may be delivered via internet for no additional cost. Pre-payment is not needed as a bill will accompany the finished product, either in the mail with photocopies or with the digital images.

Analysis of ‘London bike sharing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

License

These licence terms and conditions apply to TfL's free transport data service and are based on version 2.0 of the Open Government Licence with specific amendments for Transport for London (the "Licence"). TfL may at any time revise this Licence without notice. It is up to you ("You") to regularly review the Licence, which will be available on this website, in case there are any changes. Your continued use of the transport data feeds You have opted to receive ("Information") after a change has been made to the Licence will be treated as Your acceptance of that change.

Using Information under this Licence TfL grants You a worldwide, royalty-free, perpetual, non-exclusive Licence to use the Information subject to the conditions below (as varied from time to time).

This Licence does not affect Your freedom under fair dealing or fair use or any other copyright or database right exceptions and limitations.

This Licence shall apply from the date of registration and shall continue for the period the Information is provided to You or You breach the Licence.

Rights You are free to:

Copy, publish, distribute and transmit the Information Adapt the Information and Exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in Your own product or application Requirements You must, where You do any of the above:

Acknowledge TfL as the source of the Information by including the following attribution statement 'Powered by TfL Open Data' Acknowledge that this Information contains Ordnance Survey derived data by including the following attribution statement: 'Contains OS data © Crown copyright and database rights 2016' and Geomni UK Map data © and database rights [2019] Ensure our intellectual property rights, including all logos, design rights, patents and trademarks, are protected by following our design and branding guidelines Limit traffic requests up to a maximum of 300 calls per minute per data feed. TfL reserves the right to throttle or limit access to feeds when it is believed the overall service is being degraded by excessive use and Ensure the information You provide on registration is accurate These are important conditions of this Licence and if You fail to comply with them the rights granted to You under this Licence, or any similar licence granted by TfL, will end automatically.

Exemptions This Licence does not:

Transfer any intellectual property rights in the Information to You or any third party Include personal data in the Information Provide any rights to use the Information after this Licence has ended Provide any rights to use any other intellectual property rights, including patents, trade marks, and design rights or permit You to: Use data from the Oyster, Congestion Charging and Santander Cycles websites to populate or update any other software or database or Use any automated system, software or process to extract content and/or data, including trawling, data mining and screen scraping in relation to the Oyster, Congestion Charging and Santander Cycles websites, except where expressly permitted under a written licence agreement with TfL. These are important conditions of this Licence and, if You fail to comply with them, the rights granted to You under this Licence, or any similar licence granted by TfL, will end automatically.

Non-endorsement This Licence does not grant You any right to use the Information in a way that suggests any official status or that TfL endorses You or Your use of the Information.

Context

The purpose is to try predict the future bike shares.

Content

The data is acquired from 3 sources:
- Https://cycling.data.tfl.gov.uk/ 'Contains OS data © Crown copyright and database rights 2016' and Geomni UK Map data © and database rights [2019] 'Powered by TfL Open Data'
- freemeteo.com - weather data
- https://www.gov.uk/bank-holidays
From 1/1/2015 to 31/12/2016

The data from cycling dataset is grouped by "Start time", this represent the count of new bike shares grouped by hour. The long duration shares are not taken in the count.

Metadata:

"timestamp" - timestamp field for grouping the data
"cnt" - the count of a new bike shares
"t1" - real temperature in C
"t2" - temperature in C "feels like"
"hum" - humidity in percentage
"wind_speed" - wind speed in km/h
"weather_code" - category of the weather
"is_holiday" - boolean field - 1 holiday / 0 non holiday
"is_weekend" - boolean field - 1 if the day is weekend
"season" - category field meteorological seasons: 0-spring ; 1-summer; 2-fall; 3-winter.

"weathe_code" category description:
1 = Clear ; mostly clear but have some values with haze/fog/patches of fog/ fog in vicinity
2 = scattered clouds / few clouds
3 = Broken clouds
4 = Cloudy
7 = Rain/ light Rain shower/ Light rain
10 = rain with thunderstorm
26 = snowfall
94 = Freezing Fog

--- Original source retains full ownership of the source dataset ---

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.

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.

Dataset last updated: 8th January 2025

This dataset provides indicative areas of biodiversity hotspots in Greater London, identified by research and data analysis using methods derived from the Greater London Authority’s (GLA) “Planning for Biodiversity?” report (2016).

The dataset has been created by Greenspace Information for Greater London CIC (GiGL). GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to enable informed discussion and decision-making in policy and practice. The dataset is based on GiGL partnership data which are continuously updated.

The underlying data for the dataset may have been subject to changes since the current version was modelled. Subsequent versions will provide updated information from the GiGL database annually. The dataset is a coarse-resolution presentation of high-resolution data. To access data at their original resolution, please contact GiGL or visit www.gigl.org.uk for more information.

Research for this dataset has been assisted by London and South East England Local Records Centres (LaSER) and the London Boroughs Biodiversity Forum (LBBF), and is based on advice provided by the Open Data Institute (ODI).

Description

To meet Policy G6 D of The London Plan (2021), the capital’s spatial development strategy, " Development proposals should manage impacts on biodiversity and aim to secure net biodiversity gain. This should be informed by the best available ecological information and addressed from the start of the development process".

The Biodiversity Hotspots for Planning (BHP) dataset provides developers, homeowners and LPAs an indication of areas, where data are available, that have potential impacts on biodiversity and are likely to be relevant to local planning decisions by applying biodiversity criteria developed by GiGL, based on the original “Planning for Biodiversity?” research. ‘Hotspot’ areas indicate a detected presence of sensitive biodiversity that could potentially be affected by development. Original records can be accessed from GiGL to assist the decision-making process.

N.B. 1: Areas without these biodiversity indicator records may still have undetected biodiversity so should also be considered for biodiversity potential on a case-by-case basis.

N.B. 2: The dataset is purely indicative and an ecological data search report must still be commissioned as evidence for planning applications. See here for help on this.

Specification

The GIS file shows London as 100m hexagon tiles. Each tile is scored for the known presence of protected species, sites and habitats impact areas based on the impact buffer size as specified in the criteria table below, giving a cumulative score range of 0 to 3. Tiles are considered a hotspot where impact areas overlap the tile by more than 10%.

https://cdn.datapress.cloud/london/img/dataset/54117e0c-098e-4c3d-ae1d-82e6cc01b3f8/_import/LDS_GiGL_BHP_CriteriaTable.JPG" alt="LDS_GiGL_BHP_CriteriaTable.JPG" />

Tiles with a score of 0 indicate that there are currently no known protected species, sites or habitats impact areas present in that area based on the criteria table, which excludes some protected species. Tiles with a score of 3 indicate the presence of impact areas for all three categories. Intermediate scores indicate the presence of impact areas for one or more of the categories without specifying which are present. The scores can be used in a thematic map to colour the tiles and visually indicate areas with greater presence of impact areas. A sample thematic map is provided.

The dataset will be updated annually using the latest protected species, sites and habitats data available to GiGL at time of creation. Please give GiGL appropriate credit when using, adapting or sharing the dataset following the guidance below:

In-text citation: GiGL, [dataset creation date]

Reference: "Biodiversity Hotspots for Planning" Greenspace Information for Greater London CIC, [dataset creation date]

Where data is used in maps: Map displays GiGL data [dataset creation date] </blockq

The Centre for Longitudinal Studies (CLS) and the MRC Unit for Lifelong Health and Ageing (LHA) have carried out two online surveys of the participants of five national longitudinal cohort studies which have collected insights into the lives of study participants including their physical and mental health and wellbeing, family and relationships, education, work, and finances during the coronavirus pandemic. The Wave 1 Survey was carried out at the height of lockdown restrictions in May 2020 and focussed mainly on how participants’ lives had changed from just before the outbreak of the pandemic in March 2020 until then. The Wave 2 survey was conducted in September/October 2020 and focussed on the period between the easing of restrictions in June through the summer into the autumn. A third wave of the survey was conducted in early 2021.

In addition, CLS study members who had participated in any of the three COVID-19 Surveys were invited to provide a finger-prick blood sample to be analysed for COVID-19 antibodies. Those who agreed were sent a blood sample collection kit and were asked to post back the sample to a laboratory for analysis. The antibody test results and initial short survey responses are included in a single dataset, the COVID-19 Antibody Testing in the National Child Development Study, 1970 British Cohort Study, Next Steps and Millennium Cohort Study, 2021 (SN 8823).

The CLS studies are:

• Millennium Cohort Study (born 2000-02) both cohort members and parents (MCS)
• Next Steps (born 1989-90) (NS)
• 1970 British Cohort Study (BCS70)
• 1958 National Child Development Study (NCDS).

The LHA study is:

• MRC National Survey of Health and Development, 1946 British birth cohort (NSHD)

The content of the MCS, NS, BCS70 and NCDS COVID-19 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The COVID-19 Survey in Five National Longitudinal Cohort Studies: Millennium Cohort Study, Next Steps, 1970 British Cohort Study and 1958 National Child Development Study, 2020-2021 contains the data from waves 1, 2 and 3 for the 4 cohort studies. The data from all four CLS cohorts are included in the same dataset, one for each wave.

The COVID-19 Survey data for the 1946 birth cohort study (NSHD) run by the LHA is held under "https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8732" style="background-color: rgb(255, 255, 255);">SN 8732 and available under Special Licence access conditions.

Latest edition information
For the fourth edition (June 2022), the following minor corrections have been made to the wave 3 data:

• corrections to a small number of cases where CW3_GROW and CW3_GROWB were incorrectly calculated
• recoded values and reformatted the code list for CW3_COVIDVAC as the original value of 3 was removed from the final version of the survey
  

Context

The idea is to have a very simple time series dataset to be used for experiments with easy but effective visualizations on actual data. It is amazing how much a single graph can comunicate syntehetically a lot of information.

Content

The dataset was downloaded from the National Centers for Environmental Information (NCEI), the data is in the public domain and can be used freely. If interested in generating a similar dataset from another station you can start from the Search Tool select Daily Summaries, the time range of interest, search for Cities and in the Search Term put the city you're looking for. When selected you need to add to Cart like an order but there is no charge for ordering data from Climate Data Online as explained in their FAQs.

Acknowledgements

Thanks to National Centers for Environmental Information for collecting and making available for free meteorological data from many stations all over the world. In case using the same dataset or generating a new one from NCEI you need to cite the origin.

Inspiration

Mostly to see how many different effective visualizations can be generated from a very simple dataset.

This dataset includes the locations and species information for over 1,100,000 of London's public realm trees. It also includes additional information such as size and age for some of these trees. These are predominantly street trees and trees in parks and open spaces, but the dataset also includes some trees found in school grounds and on publicly maintained housing land.

The data does not represent the entirety of the capital's urban forest - the London iTree report estimated that there are over eight million trees in London, which includes trees in woodlands, parks, streets, private gardens and more. The data includes tree inventory data from 30 of London's 32 boroughs, the City of London, Transport for London, the Royal Parks, the London Legacy Development Corporation (which manages Queen Elizabeth Olympic Park) and Quintain (which manages Wembley Park).

As recognised in the London Urban Forest Plan (LUFP), collating data about London's urban forest is challenging due to the number of landowners and managers involved, as well as the limited resources available. Both the original LUFP and the associated 2025 update committed to undertaking regular updates to this map, and also, over time, to collating a London-wide inventory of publicly owned and managed trees, in line with emerging national standards.

The data is used on the London Public Realm Tree Map.

Notes on the data:

• This map was first published in 2016, based on data collected in 2014-15. A first update was issued in 2021, based on data collected in 2019-20. The latest update was released in May 2025, based on data collected in 2024-25. Where boroughs did not provide updated data in 2024-25, the 2019-20 data has been retained (e.g. Bromley). The data supplied for this map is a direct extract from London’s public realm tree managers’ individual tree inventories at that time. Trees may have been surveyed some time before it came to be shared for this map, therefore some records may be several years old.
• The map includes data from 30 of the London boroughs, the City of London and Transport for London (TfL), the Royal Parks, the London Legacy Development Corporation (which manages Queen Elizabeth Olympic Park) and Quintain (which manages Wembley Park).
• Data has not been received to date from three boroughs. We aim to add any additional data received to the Datastore and online map.
• Some large gaps on the map are due to large green spaces such as woodlands, reservoirs, or airports, as well as those boroughs where data is missing.
• The data received varies significantly by borough. Some boroughs have only included trees on highways, whilst others have included trees on housing land, in schools or in parks. In some cases trees were recorded only when work was carried out.
• There is not a consistent or agreed format for collecting or recording tree data across London. This presents a challenge in collating data across multiple boroughs. Many boroughs collect a range of information about their trees (e.g. age, height). However this varied by borough in terms of the information collected and categorisations used so we were not able to standardise this information.
• Tree species data has been standardised to allow for simplified common name to be used as filters display on the tree webmap (e. g. “Pear”). These are the common names for the 24 types of tree appearing most frequently in the data (which encompass 90% of all trees), with the remaining trees categorised as “Other”. We have also left the species name as provided by the borough in the data.
• The ‘tree ID’ number has been added to the data to help map it. This number is not linked to borough’s tree management systems.

Warning: Large file size may result in a long download time

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3350264%2Fd8e67354ac359542555de37891fedf75%2F3E6REX4SHBAJFINHHNZSSD7YAA.jpg?generation=1664643658765570&alt=media" alt="">

Context

An ongoing outbreak of monkeypox, a viral disease, was confirmed in May 2022. The initial cluster of cases was found in the United Kingdom, where the first case was detected in London on 6 May 2022 in a patient with a recent travel history from Nigeria.

Content

This is a SYNTHETIC dataset generated based on a study published by thebmj: Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: descriptive case series.

Dataset consists of a CSV which have a record of 25,000 Patients with their corresponding features and a target variable indicating if the patient has monkeypox or not. Features: Patient_ID, Systemic Illness, Rectal Pain, Sore Throat, Penile Oedema, Oral Lesions, Solitary Lesion, Swollen Tonsils, HIV Infection, and Sexually Transmitted Infection Target Variable: MonkeyPox The dataset currently contains boolean and categorical features and in future, we might add more data and features to help you identify the patients of Monkey-Pox.

Acknowledgements

https://www.bmj.com/content/378/bmj-2022-072410
https://www.bmj.com/company/newsroom/study-finds-important-differences-in-monkeypox-symptoms-between-current-and-previous-outbreaks/

This dataset provides a list of surnames that are reliably Irish and that can be used for identifying textual references to Irish individuals in the London area and surrounding countryside within striking distance of the capital. This classification of the Irish necessarily includes the Irish-born and their descendants. The dataset has been validated for use on records up to the middle of the nineteenth century, and should only be used in cases in which a few mis-classifications of individuals would not undermine the results of the work, such as large-scale analyses. These data were created through an analysis of the 1841 Census of England and Wales, and validated against the Middlesex Criminal Registers (National Archives HO 26) and the Vagrant Lives Dataset (Crymble, Adam et al. (2014). Vagrant Lives: 14,789 Vagrants Processed by Middlesex County, 1777-1786. Zenodo. 10.5281/zenodo.13103). The sample was derived from the records of the Hundred of Ossulstone, which included much of rural and urban Middlesex, excluding the City of London and Westminster. The analysis was based upon a study of 278,949 adult males. Full details of the methodology for how this dataset was created can be found in the following article, and anyone intending to use this dataset for scholarly research is strongly encouraged to read it so that they understand the strengths and limits of this resource:

Adam Crymble, 'A Comparative Approach to Identifying the Irish in Long Eighteenth Century London', _Historical Methods: A Journal of Quantitative and Interdisciplinary History_, vol. 48, no. 3 (2015): 141-152.

The data here provided includes all 283 names listed in Appendix I of the above paper, but also an additional 209 spelling variations of those root surnames, for a total of 492 names.

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

The dataset consists of semi-structured interviews with governance stakeholders in London: representatives of London boroughs, the Greater London Authority, business organisations and non-governmental organisations. The interviews focused on: how the governance system in London works, how individual actors responded to the Covid-19 pandemic, how the pandemic influenced Londoners’ local socio-economic status, and how individual actors envision an ideal London governance.This project responds to three global challenges: unequal urbanisation, growing complexity of the governance systems and a crisis of trust in democracy. More than half of the world population currently live in cities and this share is expected to increase. Modern urban areas are highly unequal, with vast shares of the population living in poverty and struggling to access city-services (Tonkiss 2018). The growing complexity of governance systems leads to an increased number of non-state actors who are not held democratically accountable and whose actions are difficult to control or regulate (Jervis 1997). Finally, along dropping trust in democratic governance, there is rising support for populism and growing acceptance for authoritarian practices (Foa and Mounk 2016). Due to the global impact and interconnected nature of these challenges, any social-scientific response to these problems cannot treat them separately. Existing interdisciplinary research tends to focus on solutions to some of these challenges, often without acknowledging its broader impact. For example, research on governing complex urban polities is focused on top-down and technocratic tools, which contributes to the deepening of the democratic crisis and further inequality (e.g. Kubler and Lefevre 2017). In turn, democratic and participatory solutions to urban inequality often rely on bottom-up communities and face-to-face decision-making, while ignoring the wider complexity of urban decision-making. Finally, research on the possibilities of democratic control over the complex governance ignores the questions of inequality it can produce. As a result, to respond to these three challenges, new, more integrated solutions are necessary. I am applying for ESRC Fellowship to propose such an integrated solution. My project 'Coping with Complexity and Urban Inequality: Dilemmas of Democratic Mega-city Governance' investigates strategies of urban governance, in conditions of complexity, to realise democratic ideals and lead to more equal urban societies. It draws on my previous PhD research in political theory, complements it with an empirical case-study of constraints of democratic governance in London in order to produce a monograph. Big mega-cities, such as London, due to their global population share and projected continued growth will be vital actors in an integrated global response to the crisis of trust to democratic governance, managing the growing complexity of governance and to responding to consequences of unequal urbanisation. London is a critical case study, due to its inequalities, complex governance consisting of local, national and intra-national bodies and its democratic political system. This project employs a cross-disciplinary, innovative research design that is rooted in applied political philosophy and qualitative case studies. Employment of applied political philosophy facilitates analysis of theoretical possibilities of democratic, equalising urban governance. Further, it facilitates re-shaping of the philosophical concepts according to the constraints of real-life circumstances (in London). Application of qualitative methods - a series of interviews with urban practitioners, urban NGO's, city council members, inhabitants' associations - provides information on constraints and opportunities for the development of democratic, equalising governance in London. This analysis constitutes a basis for policy-recommendation applicable beyond the specific case studies. Further, a planned workshop engages urban practitioners, urban NGO's, representatives of inhabitants' urban council and selected neighbourhood association. To identify solutions to given urban problems, the workshops will apply a management tool called Three Horizons' Framework (Sharpe 2016). Such mixed-methods research design combining political philosophy and qualitative methods provides a basis for philosophically informed urban policy and constitutes a methodological innovation. Semi-structured interviews with elite participants.