Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

The USD/MXN exchange rate rose to 18.7437 on July 14, 2025, up 0.60% from the previous session. Over the past month, the Mexican Peso has strengthened 0.88%, but it's down by 5.73% over the last 12 months. Mexican Peso - values, historical data, forecasts and news - updated on July of 2025.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Problem Statement:

Key Factors Influencing Demand: Demand for bus journeys is influenced by a range of factors, including holiday calendars, wedding seasons, long weekends, school vacations, and exam schedules. Additionally, regional holidays can affect different areas differently, and day-of-week effects further shape booking patterns. However, not all holidays significantly impact demand, making it a complex and non-trivial problem to model.

The Hackathon Challenge: In this hackathon, your task is to develop a model that accurately forecasts demand for bus journeys. We will provide historical booking data from our platform, including the following:

Seats booked: Historical demand data. Date of journey: The actual travel date. Date of issue: The date when the ticket was booked. Search data: The number of users searching for a particular journey date on a given booking date. Your goal:

Predict the demand (total number of seats booked) for each journey at the route level, 15 days before the actual date of journey (doj). Example: For a route from Source City "A" to Destination City "B" with a date of journey (doj) on 30-Jan-2025, you need to predict the final seat count for this route on 16-Jan-2025, which is exactly 15 days prior to the journey date.

Train Variable->Description

doj (Date of Journey)->The date on which the bus journey is scheduled to take place.

srcid (Source City ID)->Unique identifier for the source city of the journey.

destid (Destination City ID)->Unique identifier for the destination city of the journey.

final_seatcount->Total number of seats booked at the end of the journey date (Target Variable).

Test route_key->Unique identifier for each row in the test set.

doj (Date of Journey)->The date on which the bus journey is scheduled to take place.

srcid (Source City ID)->Unique identifier for the source city of the journey.

destid (Destination City ID)->Unique identifier for the destination city of the journey

Transactions doj (Date of Journey)->The date on which the bus journey is scheduled to take place.

doi (Date of Issue)->The date when the ticket was booked.

dbd (Days Before Departure)->The number of days remaining until the journey date from the date of issue, for a given srcid, destid, doi and doj combination.

srcid (Source City ID)->Unique identifier for the source city of the journey.

destid (Destination City ID)->Unique identifier for the destination city of the journey.

srcid_region->The region (state) where the source city is located.

destid_region->The region (state) where the destination city is located.

srcid_tier->The tier classification of the source city (e.g., Tier 1, Tier 2).

destid_tier->The tier classification of the destination city (e.g., Tier 1, Tier 2).

cumsum_seatcount->This represents the cumulative number of seats sold till date.

cumsum_searchcount->This will represent the cumulative number of searches till date.

The source of the data in this table are municipalities and CBS offers them as a service as open data.

Statistics Netherlands (CBS) receives data from municipalities as part of the Information for Third Parties (Iv3) reports. The data in the table have not been edited by Statistics Netherlands. This type of data is also referred to as 'unprocessed data'. CBS bears no responsibility for the quality of the data. The data in Statistics Netherlands' own publications do not have to be traced back one-on-one to the data in this table.

The table contains raw Iv3 data from all reporting types of one reporting year. The types of reports are the budget, the four quarters and the annual accounts. If a municipality has not provided Iv3 data for a report type, then this municipality is included in the table, but each cell has the value '.', in the sense of missing. This is particularly the case for the quarterly accounts of municipalities with fewer than 20 thousand inhabitants (size classes 6.7 and 8), as they are not obliged to supply them to Statistics Netherlands.

The codes used in the table for the categories on the one hand and the task fields and balance sheet items on the other hand, as well as their meaning, are derived from the 'Decree on the budget and accountability of provinces and municipalities' (BBV) of the Ministry of the Interior and Kingdom Relations. The BBV contains, among other things, the regulations for the deliveries of Iv3 data to CBS.

For each type of report, all reports received so far are published at the same time at two points in time. The reason for placing the data a second time is that CBS gives municipalities the opportunity to provide an improved Iv3 dataset. The data that is placed the first time has the value '1st placement' in the topic 'Place'. The data that is placed the second time has the value '2nd placement'.

Data available from: 2025.

Status of figures The figures in this table are final upon publication (i.e., subject to exceptions, once published data are no longer updated).

Changes as of 2 December 2024: None, this is a new table. Figures for the first allocation of the 2025 budget are included.

When will there be new figures? The time of publication of new figures for a type of report depends on the deadline for submission to Statistics Netherlands that applies to the type of report in question. For budgets for year j, the deadline for submission is 14 November in the year preceding the budget year (j-1). For quarterly data for the first, second and third quarters of year j, this is one month after the end of the quarter. For submission of the fourth quarter of year j, a deadline of 14 February in the year following the reporting year (j+1) applies. Finally, for the annual accounts for year j, this date is 14 July in the year following the reporting year (j+1). All reports received for a report type are published at the same time. This publication happens twice. The first time is 10 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than 5 days after the deadline for submission. The second time is 70 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than two months after the deadline for submission. The second allocation of the budget will (possibly) take place in phases: the figures of municipal reclassifications that were missing due to postponement in the regular second placement will be published by the end of May at the latest. The distinction between the first and the second placement can be seen in the subject.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2023 are final. The figures for 2024 and 2025 are provisional.

Changes as of 24 June 2025: The figures for the first quarter of 2025 are available. Figures for 2023 and 2024 have been adjusted due to updated information. The figures for 2023 are final. In the context of the revision policy of National accounts, the dividend tax has been adjusted as of the fourth quarter of 2006. The revised registration aligns more closely with the accrual principle of ESA 2010.

Changes as of 10 April 2025: Due to an error made while processing the data, the initial preliminary figures for the government financial balance sheet in 2024 were calculated incorrectly. This causes a downward revision in other accounts payable.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

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This dataset contains counts of offences recorded by the NT Police, categorised by offence type, time period (month), location and (for assault offences) alcohol and domestic violence involvement. Certain types of offences show strong seasonal impacts and numbers show considerable monthly variation, particularly at the regional level. Since implementation of the SerPro data system in November 2023, it has been identified that entry of the data used for crime statistics generally happens later in the investigation process when compared to the previous PROMIS system. This means that monthly data takes longer to settle and may take several months to reflect the actual numbers of offences recorded by police. For this reason, the monthly crime statistics should be reviewed with caution and will be marked as provisional until data collection is substantially complete There has been a break in the crime statistics time series following November 2023, due to the implementation of SerPro. This means that the statistics from December 2023 onwards should not be compared directly to earlier statistics.

This table presents a number of key figures of the sector accounts. These main indicators provide the most important information on the total economy and on the main institutional sectors of the economy: non-financial corporations, financial corporations, general government, households including non-profit institutions serving households and the rest of the world.

Data available from: Annual figures from 1995. Quarterly figures from first quarter 1999.

Status of the figures: Annual figures from 1995 up to and including 2023 are final. Quarterly data from 2023 are provisional.

Changes as of June 24th, 2025: Data on the first quarter of 2025 have been added. Following revision policy, 2023 and 2024 data are updated, and time series of the sector accounts are revised (annual revision).

Adjustment as of April 14th 2025: Quarterly and annualy data of general government debt (EMU) of 2024 were incorrectly hidden in the last version of this table. This has been adjusted in this version.

Adjustment as of April 10th 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. We refer to the Government Finance Statistics for the current figures. Links to the Government Finance Statistics could be found in paragraph 3. Until the publication end of June the Sector accounts therefore diverge from the Government Finance Statistics.

When will new figures be published? Annual figures: The first annual data are published 85 day after the end of the reporting year as the sum of the four quarters of the year. Subsequently provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Furthermore the sector accounts are annually revised for all reporting periods. These data are published each year in June. Quarterly figures: The first quarterly estimate is available 85 days after the end of each reporting quarter. The first quarter may be revised in September, the second quarter in December. Should further quarterly information become available thereafter, the estimates for the first three quarters may be revised in March. If (new) annual figures become available in June, the quarterly figures will be revised again to bring them in line with the annual figures. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest annual and quarterly figures.

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Note: Due to the RMS change for CPS, this data set stops on 6/2/2024. For records beginning on 6/3/2024, please see the dataset at this link: https://data.cincinnati-oh.gov/safety/Reported-Crime-STARS-Category-Offenses-/7aqy-xrv9/about_data

The combined data will be available by 3/10/2025 at the linke above.

Data Description: This data represents reported Crime Incidents in the City of Cincinnati. Incidents are the records, of reported crimes, collated by an agency for management. Incidents are typically housed in a Records Management System (RMS) that stores agency-wide data about law enforcement operations. This does not include police calls for service, arrest information, final case determination, or any other incident outcome data.

Data Creation: The Cincinnati Police Department's (CPD) records crime incidents in the City through Records Management System (RMS) that stores agency-wide data about law enforcement operations.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/8eaa-xrvz

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.

This table presents health(care) expenditure used by residents of the Netherlands in a year. Health care is delineated according to the international definition of the System of Health Accounts. The figures are thus internationally comparable with Eurostat, OECD and WHO publications. All healthcare activities count, regardless of whether they take place inside or outside the healthcare sector. The figures are derived from the care accounts, which include more activities such as youth care, welfare work, social services and child care. The care accounts also include exports (expenditure on and by non-residents).

Data available from: 2021

Status of the figures: The figures for 2024 are provisional. The figures for 2021-2023 are revised provisional.

Changes as of 27 May 2025: The provisional figures for 2024 have been added and the provisional figures for 2021-2023 have been adjusted to revised provisional figures.

When will new figures be published? At the end of 2025 revised figures for 2021-2024 will be published.

A. SUMMARY This dataset represents all San Francisco (SF) residents who have received a vaccine for certain respiratory viruses that circulate more heavily in the fall and winter months. All vaccines given to SF residents are included, even if they received their vaccination elsewhere in California. The data are broken down by demographic and geographical stratifications.

COVID-19: This dataset represents all SF residents who are considered up to date on their COVID-19 vaccine. A person is up to date if they have received at least one dose of the 2024–2025 COVID-19 vaccine. The specific up-to-date criteria can be found on the California Department of Public Health (CDPH) website.

(Note: As of November 2024, this dataset only contains data regarding COVID-19 vaccinations. This documentation will be updated as other seasonal vaccination data is added).

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in SF is from the California Immunization Registry (CAIR2), run by CDPH. The information on individuals’ city of residence, age, race, and ethnicity are also recorded in CAIR and are self-reported at the time of vaccine administration.

In order to estimate the percent of San Franciscans vaccinated, we provide the 2018-2022 American Community Survey (ACS) population estimates for each demographic group and analysis neighborhood.

C. UPDATE PROCESS Updated daily via automated process.

D. HOW TO USE THIS DATASET SF population estimates for race/ethnicity and age groups can be found in a https://data.sfgov.org/Economy-and-Community/SF-COVID-19-reporting-demographics-population-esti/cedd-86uf">view based on the San Francisco Population and Demographic Census dataset. SF population estimates for analysis neighborhoods can be found in a view based on the San Francisco Population and Geography Census dataset. Both of these views use population estimates from the 2018-2022 5-year ACS.

Before analysis, you must filter the dataset to the desired stratification of data using the “vaccine_type” and "demographic_group" columns. For example, filtering “vaccine_type” to “COVID-19” will allow you to only look at rows corresponding to COVID-19 vaccinations. Filtering “demographic_subgroup” to “Analysis Neighborhood” will allow you to only look at rows corresponding to SF neighborhoods. You can then calculate the percentages of those up to date with their COVID-19 vaccinations by neighborhood. The “vaccine_subtype” field provides information about the current vaccine product being tracked in this dataset.

E. CHANGE LOG