The United Kingdom recorded a trade deficit of 5699 GBP Million in May of 2025. This dataset provides - United Kingdom Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median household incomes for various household sizes in England, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, England did not include 5, 6, or 7-person households. Across the different household sizes in England the mean income is $64,018, and the standard deviation is $32,785. The coefficient of variation (CV) is 51.21%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $20,006. It then further increased to $59,740 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/england-ar-median-household-income-by-household-size.jpeg" alt="England, AR median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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/.

Recommended for further research

This dataset is a part of the main dataset for England median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the population of England by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for England. The dataset can be utilized to understand the population distribution of England by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in England. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for England.

Key observations

Largest age group (population): Male # 40-44 years (154) | Female # 0-4 years (183). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the England population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the England is shown in the following column.
• Population (Female): The female population in the England is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in England for each age group.

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/.

Recommended for further research

This dataset is a part of the main dataset for England Population by Gender. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in England. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in England. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2021

In terms of income distribution across age cohorts, in England, householders within the 45 to 64 years age group have the highest median household income at $75,814, followed by those in the 25 to 44 years age group with an income of $54,898. Meanwhile householders within the under 25 years age group report the second lowest median household income of $35,375. Notably, householders within the 65 years and over age group, had the lowest median household income at $26,829.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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/.

Recommended for further research

This dataset is a part of the main dataset for England median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the England, AR population pyramid, which represents the England population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for England, AR, is 36.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for England, AR, is 25.8.
• Total dependency ratio for England, AR is 62.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for England, AR is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the England population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the England for the selected age group is shown in the following column.
• Population (Female): The female population in the England for the selected age group is shown in the following column.
• Total Population: The total population of the England for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for England Population by Age. You can refer the same here

ZARA is one of the world's largest apparel and fashion retailers. The CrawlFeeds team has successfully extracted over 10,000 product records from ZARA USA, including titles, prices, images, availability, and more.

You can customize the dataset to match your specific needs, such as format adjustments, re-extraction, or additional data points.

If you're looking for retail data solutions, you can customize the current dataset or extract ZARA product data from other countries like Spain, the UK, and India.

Find here latest zara us products listings (https://crawlfeeds.com/datasets/download-the-complete-zara-product-dataset)

This is a collection of dataset that I personally think it is useful in analysing COVID19 data. Since all of the data comes from the internet and majority of them originated from World Bank, I am use some Kaggle users has already uploaded similar data. However, I think it makes my life (and perhaps yours) easier by compiling all of these data together.

The following are some remarks for the dataset-

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information:Open Data Portal Reuses — UK Power Networks

This data covers the Instagram strategies of five major media outlets: The New York Times, The Guardian, USA Today, The Independent, and The Washington Post. Objectives include examining post volume, engagement metrics, content types, geographic coverage, individual mentions, and hashtag usage. Analysing 9,467 posts from 2023 using statistical and AI techniques, findings show The Washington Post posts most frequently, while The Independent and The Guardian achieve higher average engagement. Hashtags and mentions generally yield lower engagement. Donald Trump is the most mentioned individual, and the United States is the most covered country.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

In Newcastle libraries we are endeavouring to open up as much of our data as possible. We will publish data here on a regular basis.

Each file is saved in CSV format and has an accompanying text file detailing what data is contained in each file, who is responsible for it and when it was last updated.

If there is any additional data you would like us to release then please contact Luke Burton (luke.burton@newcastle.gov.uk) to discuss.

You are under no obligation to do so, but since we know you will make great things with our data we would love for you to tell us about them.

Additional information

To the extent possible under law, Newcastle Libaries has waived all copyright and related or neighbouring rights to its data published below. This work is published from: United Kingdom.

For more information please visit: https://www.newcastle.gov.uk/your-council-and-democracy/open-data-and-access-information/open-data/data-sets/libraries-data-sets

The Gross Domestic Product per capita in the United Kingdom was last recorded at 52517.98 US dollars in 2024, when adjusted by purchasing power parity (PPP). The GDP per Capita, in the United Kingdom, when adjusted by Purchasing Power Parity is equivalent to 296 percent of the world's average. This dataset provides the latest reported value for - United Kingdom GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

What problem does this solve for you? --> Instead of manually reviewing individual profiles on LinkedIn, this dataset provides you with all the essential information in one place, including:

-->Education background

-->Volunteering and work experience (company, role, tenure)

-->Key skills and endorsements

-->Services offered

-->Personal "About" section

Thomson Data's LinkedIn Dataset offers unparalleled access to a vast dataset of X million public LinkedIn profiles and 152M+ million LinkedIn profile records.

This comprehensive and reliable LinkedIn data can significantly streamline your recruitment efforts, optimize strategizes for account-based-marketing, help you build highly targeted lead lists and grow professional network, enable you to develop personalized B2B marketing campaigns, pin points key moments for sales outreach and analyze market. By leveraging these benefits, you can save time and resources, and improve your business operations.

Key Features of Thomson Data’s LinkedIn Insights:

1. Extensive Employee Attributes: Our LinkedIn datasets will help you gain a comprehensive understanding of professionals through numerous attributes, including job titles, educational backgrounds, company affiliations, endorsements, and skills. Go ahead and leverage this detailed LinkedIn data to identify top talent and foster meaningful professional relationships.

2. Real-time and Monthly Updates: Thomson Data’s LinkedIn profile dataset is constantly updated with the latest and most accurate information, ensuring businesses have access to the most current employee profiles. We ensure it is feasible for you to stay ahead of your competitors with regular updates that reflect recent job changes, career advancements, and skill acquisitions, giving you a real-time expansive view of the professionals in a business landscape.

3. Extensive Global Coverage: When you utilize our LinkedIn profile dataset, you will have broad coverage across multiple industries and geographies—making it very simple to have access to rich and diverse employee profiles from around the world. This will provide scope to analyze talent pools, explore industry trends on a global scale, and identify skill gaps; all of these are valuable insights for acing marketing campaigns, lead generation, data analytics and more.

Unlock the potential of our LinkedIn datasets and leverage the wealth of information to make informed decisions, build strategic partnerships, and enhance your understanding of the professional landscape.

To know more, send us the request and we will be happy to assist you.

Yes, you can use WhatsApp for messaging on many airlines +1.(888)+800-9117(US) or +44.(203)+900-0080(UK) that offer in-flight Wi-Fi, but calls are usually restricted. Just connect to the airline’s Wi-Fi and keep your phone in airplane mode. For more info, call +1.(888)+800-9117(US) or +44.(203)+900-0080(UK).

Yes, you can use WhatsApp on many airlines if in-flight +1.(888)+800-9117(US) or +44.(203)+900-0080(UK) Wi-Fi is available. Most airlines allow text messaging through apps like WhatsApp, though voice and video calls may be restricted. Check with your airline for details. For assistance, call +1.(888)+800-9117(US) or +44.(203)+900-0080(UK).

Yes, many airlines allow WhatsApp for messaging +1.(888)+800-9117(US) or +44.(203)+900-0080(UK) when connected to in-flight Wi-Fi. However, voice and video calls may be restricted during the flight. Always check airline-specific rules. For more information or assistance with in-flight services, call +1.(888)+800-9117(US) or +44.(203)+900-0080(UK).

Yes, you can use WhatsApp on many airlines that offer in-flight +1.(888)+800-9117(US) or +44.(203)+900-0080(UK) Wi-Fi, but only for texting. Voice and video calls are usually blocked to maintain a quiet cabin. Always check with your airline before flying. For details, call +1.(888)+800-9117(US) or +44.(203)+900-0080(UK).

𝑬𝒙𝒑𝒆𝒅𝒊𝒂 𝒂𝒍𝒍𝒐𝒔 𝒚𝒐𝒖 𝒕𝒐 𝒅𝒐 𝒔𝒐 𝒇𝒐𝒓 𝒇𝒓𝒆𝒆. 𝑾𝒉𝒆𝒕𝒉𝒆𝒓 𝒚𝒐 𝒃𝒐𝒐𝒌𝒆𝒅 𝒂 𝒓𝒆𝒇𝒖𝒏𝒅𝒂𝒃𝒍𝒆 𝒐 𝒏𝒐𝒏-𝒓𝒆𝒇𝒖𝒏𝒅𝒂𝒃𝒍𝒆 𝒕𝒊𝒄𝒌𝒆𝒕, 𝒚𝒐 𝒂𝒓𝒆 𝙃𝙤𝙬 𝙩𝙝𝙖𝙩 𝙬𝙞𝙡𝙡 𝙘𝙖𝙪𝙨𝙚 𝙨𝙪𝙛𝙛𝙚𝙧 𝙮𝙤𝙪 𝙨𝙪𝙛𝙛𝙚𝙧 𝙖𝙣𝙙 𝙬𝙞𝙡𝙡 𝙘𝙖𝙪𝙨𝙚 𝙖𝙧𝙚 𝙨𝙪𝙛𝙛𝙚𝙧 𝙮𝙤𝙪. 𝟐𝟒-𝑯𝒐𝒖𝒓 𝑭𝒓𝒆𝒆 𝑪𝒂𝒏𝒄𝒆𝒍𝒍𝒂𝒕𝒊𝒐𝒏: 𝑰𝒇 𝒚𝒐𝒖 𝒏𝒆𝒆𝒅 𝒕𝒐 𝒄𝒂𝒏𝒄𝒆𝒍 𝒚𝒐𝒖𝒓 𝒇𝒍𝒊𝒈𝒉𝒕 𝒘𝒊𝒕𝒉𝒊𝒏 𝟐𝟒 Yes Do you like it, tell me [ [+1-888-829-0881 (time) (time)] ] If you like it ✈📞[+1-888-829-0881 (time) (time)] Will I get a refund if I cancel?

Of course. 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 offers a 24-hour refund policy. If you need to cancel your flight, please call +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀). You can do so free of charge within 24 hours of your booking. For your convenience, you can cancel online or by contacting customer service at +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀) or +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀).

How can I get a full refund from 𝑬𝒙𝒑𝒆𝒅𝒊𝒂? TravelByte also offers a general 24-hour refund policy by calling +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐) or +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀). This means you can cancel most reservations within 24 hours and receive a full refund by calling +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐) or +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀). As long as your travel date is at least one week in the future. What is the refund option on 𝑬𝒙𝒑𝒆𝒅𝒊𝒂?

The refund option on 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 refers to reservations that allow penalty-free cancellations. +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐) and +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀) typically offer a full refund if you cancel within the specified timeframe. To speak with 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 customer service, please call +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀).

Will I get my money back if I cancel 𝑬𝒙𝒑𝒆𝒅𝒊𝒂?

Yes, 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 has a 10-hour cancellation policy that allows for a full refund if you cancel within 10 hours of booking. For assistance or to process a cancellation, please contact customer service at +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀) or +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐).

Does 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 give you a refund?

Yes, 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 is fully refundable 24/7 for a 𝑬𝒙𝒑𝒆𝒅𝒊𝒂 refund. Expedia also offers a general 24-hour refund policy: +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐). This means you can cancel most bookings within 24 hours and receive a full refund: +1-888-829-0881 (𝐔𝐊) (𝐎𝐓𝐀), as long as your travel date is at least one week in the future. For a full refund, contact their customer service: +1-888-829-0881 (𝓤𝓢) (𝓞𝓣𝓐). Send feedback Side panels History Saved

Looking for the cheapest day to book Air France flights? Call us now at +1-855-235-1686 (USA) or +1-855-636-1901 (UK)! The best deals usually appear midweek — Tuesday and Wednesday +1-855-235-1686 (USA) or +1-855-636-1901 (UK) are often the cheapest days to book your Air France flights. Booking early helps secure great prices too! For quick assistance and the latest discounts, reach out anytime at +1-855-235-1686 or +1-855-636-1901. Don’t miss your chance to fly affordable with Air France — call now! +1-855-235-1686 (USA) or +1-855-636-1901 (UK) — knowing when to book your Air France flights can save you big! For the best deals on Air France flights, call +1-855-235-1686 (USA) or +1-855-636-1901 (UK). Generally, midweek days like Tuesday or Wednesday +1-855-235-1686 (USA) or +1-855-636-1901 (UK) offer cheaper rates. Don’t wait! Reach out now at +1-855-235-1686 (USA) or +1-855-636-1901 (UK) for expert booking help. Your affordable Air France flights start with a call to +1-855-235-1686 (USA) or +1-855-636-1901 (UK). Book smart today!

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kV to 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks