Annual estimates of the number of entries/exits and interchanges at each station in Great Britain. These estimates are based primarily on ticket sales and are produced by Steer on behalf of ORR. For details on how these statistics are compiled, please see our Estimates of station usage quality and methodology report. We have also produced a list of frequently asked questions. For any information on revisions, please see our Revisions log.
The amount of data generated from the world around us has reached levels that were previously unimaginable. Meanwhile, uses of data-enabled technologies promise benefits, from improving healthcare and treatment discovery, to better managing critical infrastructure such as transport and energy.
British Gas Download Usage Data
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These new applications can make a great contribution to human flourishing but to realise these benefits, societies must navigate significant choices and dilemmas: they must consider who reaps the most benefit from capturing, analysing and acting on different types of data, and who bears the most risk.
Over the past year the British Academy and the Royal Society have brought together leading academics, industry leaders, civil society and data and technology specialists to better understand the needs of a 21st century data governance system:
Under the Data Access Agreement, service providers in one country may respond to qualifying, lawful orders for electronic data issued by the other country, without fear of running afoul of restrictions on cross-border disclosures. The Data Access Agreement fosters more timely and efficient access to electronic data required in fast-moving investigations through the use of orders covered by the Agreement. This will greatly enhance the ability of the United States and the United Kingdom to prevent, detect, investigate, and prosecute serious crime, including terrorism, transnational organized crime, and child exploitation, among others.
The Data Access Agreement sets out numerous requirements that must be met for U.S. or UK authorities to invoke the Agreement. For example, orders submitted by U.S. authorities must not target persons located in the UK and must relate to a serious crime. Similarly, orders submitted by UK authorities must not target U.S. persons or persons located in the United States and must relate to a serious crime. U.S. and UK authorities must also abide by agreed requirements, limitations and conditions when obtaining and using data obtained under the Data Access Agreement.
Among its various functions as U.S. Designated Authority, OIA has created a CLOUD team to review and certify orders that comply with the Agreement on behalf of federal, state, local, and territorial authorities located in the United States, transmit certified orders directly to UK service providers, and arrange for the return of responsive data to the requesting authorities.
This release has now been discontinued. Following a short period of research, we have identified that similar data can be found the annual Technology Tracker published by the Office for Communications (OFCOM)
The survey data for this release were collected by the Labour Force survey (LFS) between January and March 2020. This straddles the period before and after the COVID outbreak in March 2020 so it is not possible to use the 2020 data to indicate changes in internet usage as a result of the pandemic.
The survey data for this release are collected by the Labour Force Survey (LFS). There are indications that participation in the LFS was affected by the coronavirus (COVID-19) pandemic, from early March 2020.
As the survey data for this release were collected in January to March 2020, there could be some impact from the pandemic on the survey results. The larger than usual growth in internet use by adults aged 75 years and over, could be partly related to the effects of the beginning of the pandemic and we therefore advise that results for this age group are treated with a degree of caution.
From September 2023, these statistics include a second rail usage time series which excludes Elizabeth Line service (and other relevant services that have been replaced by the Elizabeth line) from both the travel week and its equivalent baseline week in 2019. This allows for a more meaningful like-for-like comparison of rail demand across the period because the effects of the Elizabeth Line on rail demand are removed. More information can be found in the methodology document.
Survey estimates in this release are based on the Crime Survey for England and Wales (CSEW). While this is the first comparable survey data with pre-coronavirus pandemic data from the year ending March 2020, they are not National Statistics and caution must be taken when using these data. The CSEW statistics presented in this release are based on nine months of data collection between October 2021 and June 2022, rather than the normal 12-month interview period, and on a lower response rate, which may affect the quality of the estimates.
Ecstasy: Prevalence of last year ecstasy use was at its lowest level since data were first collected; 0.7% of adults aged 16 to 59 years and 1.1% of adults aged 16 to 24 years had reporting taking this drug in the last year; a 47% decrease for those aged 16 to 59 years and a 72% decrease for those aged 16 to 24 years compared with the year ending March 2020.
Drug misuse data included in this release are sourced from the Crime Survey for England and Wales (CSEW). Our User guide to crime statistics for England and Wales provides detailed information about the crime survey.
While this is the first comparable survey data with pre-coronavirus (COVID-19) pandemic data, they are not National Statistics and caution must be taken when using these data. The CSEW statistics presented in this release are based on nine months of data collection between October 2021 and June 2022, rather than the normal 12-month interview period and are based on a lower response rate, which may affect the quality of the estimates. Significance testing has been conducted to compare estimates for the year ending June 2022 with year ending March 2020, year ending March 2012 (ten-year comparison) and year ending December 1995 (earliest data available) to understand trends over time where possible. Other comparison years have been used where these data are not available.
Estimates within this publication are based on data from the CSEW self-completion module. The upper age limit for respondents eligible for the self-completion module was removed from 2019 to 2020, but remains in place for questions on drug use because of the low prevalence of drug use in those aged 60 years and over.
In addition, CSEW estimates in this release should be interpreted with caution because of the shorter nine-month data collection period and lower survey response rates. Similarly, some tables typically published as part of this release have not been produced because of quality concerns or disclosure constraints.
User guide to crime statistics for England and Wales Methodology | Released 4 November 2021Ā Quarterly statistics on crime levels and trends in England and Wales. This user guide contains detailed information on the datasets used to compile crime statistics published by Office for National Statistics (ONS).
United Kingdom drug situation 2019: Focal Point annual reportĀ Bulletin | 31 March 2021Ā Annual report and data tables from the UK Focal Point on Drugs on the national prevalence, impact, prevention, and treatment of drug use.
Percentage of items in the norms data subset (y-axis) according to the number of valid word association responses for the item (x-axis). All 182 items in the norms subset have at least 100 valid responses.
Here we provide a new set of UK-based dominance norms from spoken ambiguous words, based primarily on previously-collected published word association data. We include the raw data, meaning codes/definitions for each item, item summary statistics, and sound files ( ). The item summary data may be useful for researchers who wish to, for instance, select stimuli based on meaning dominance criteria (e.g. biased vs. balanced word sets), match stimuli sets for dominance, or use meaning dominance as a predictor/covariate in their analyses.
We also provide an automated coding script for coding new word association responses based on the coded data. This script may help to address one of the main limitations of the word association method, which is the time-consuming nature of the response coding (Armstrong et al., 2012). Our testing shows that around 80% of new word association responses can be automatically coded using this script and so can greatly reduce the time taken to code new responses, while also reducing the number of human errors.
We note that the dominance estimates reported here are not directly comparable to those reported in other norming studies. Each of the aforementioned published norms differs from our dataset in multiple ways, making it difficult to pin-point specific reasons for differences in results. While a comprehensive comparison between this data and previously-published norms is beyond the scope of this report, here we will remind readers of the key differences between this dataset and existing norms, along with some examples.
The differences between our norms and those that have been previously published raise a number of important questions for future research. For instance, it would be useful to investigate the effects of modality, dialect and other factors on the preferred interpretations of ambiguous words in order to understand whether these factors primarily affect a small set of unique items, such as the examples given here, or if there are perhaps subtle but reliable differences that affect a much broader set of words. Determining the effects of these factors is made more difficult because of the difference in data collection methods (word association and eDom), both of which may systematically under- or over-estimate meaning dominance. In order to answer these questions, the effects of each factor must be examined separately from the norming method, and the norming methods must also be compared against other dominance indicators such as meaning frequencies from corpus studies and predictive validity in task performance (Armstrong et al., 2012, 2016). ff782bc1db
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