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Introduction

This UK English Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native UK English speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

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Comprehensive dataset containing 198 verified Mobile network operator businesses in United Kingdom with complete contact information, ratings, reviews, and location data.

Introduction This dataset shows an anonymised list of live, committed, import-related projects within UK Power Networks' licence areas. This includes demand-only projects that are 5,000 kilovolt-amperes (kVA) and above, as well as battery energy storage systems (BESS).

This list has been determined using internal systems UK Power Networks uses to manage all committed projects in the process of connecting to our network. To protect the identity of the sites, entries have been anonymised and only the licence area, the grid supply point the project is connecting at (or under), rounded requested import capacity, and application date have been provided.

Methodological Approach Live, committed demand projects are identified through desktop exercises using UK Power Networks' internal customer relationship management system and extracted.

The projects are then filtered to only show projects where The required import capacity is more than or equal to 5,000kVAThe required export capacity is 0MVA.

These project entries are then cross-referenced with other sources to verify its status. Any discrepancies are manually reviewed and kept/omitted as appropriate.To protect the identity of the demand projects the required import capacity is rounded, and the project names are anonymised by providing an arbitrary sequential number.

Quality Control Statement The dataset is primarily built upon internal data, relating to live demand projects in UK Power Networks' licence areas. Information about battery energy storage systems are taken from existing datasets relating to Appendix G information UK Power Networks manages.Data have been checked with both automatic and manual validation methods.

Assurance Statement The dataset is generated through a manual process, conducted by the Distribution System Operator's Regional Development Team. The dataset will be reviewed monthly to assess any changes, and to determine if any updates to the methodology are necessary. This process ensures that the dataset remains relevant and reflective of the live large demand projects UK Power Networks is working on. There are sufficient projects per licence area to assure anonymity of projects.While all reasonable efforts have been made to ensure the accuracy of the information provided in this dataset, neither the licensee nor any of its directors or employees is under any liability for any errors, or for any misstatement on which a user of the data seeks to rely. Please view our Terms and Conditions for more information.The data provided constitutes UK Power Networks’ provisional view of the status at this GSP at the date of publication and is for general information only.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/For prospective customers considering a connection to our network, we provide pre-application support on our website to make the connection journey as smooth as possible: Pre-application support and advice | UK Power NetworksWe also offer an "Ask the Expert" service, designed for some of your more complex connection questions that go beyond our FAQs. You can request an "Ask the Expert" surgery session, where our specialists can provide more specific technical guidance: Ask the Expert | UK Power NetworksTo view this data please register and login.

IntroductionUK 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 Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). 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 33kV 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 Grid 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 - Grid 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 NetworksTo view this data please register and login.

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...

The Network Model digitally represents England’s Strategic Road Network. The model contains critical information about our road’s location, names, lanes and widths. The Network Model was derived from Ordnance Survey (OS) Highways data and enriched with internal datasets. It reflects National Highways roads that are open for traffic and have been validated against our Operational Highway Boundary (RedLine). To ensure the model remains accurate, we have implemented processes to track changes across the network. However, if you have noticed any inaccuracies in the data, please report it here. (https://survey123.arcgis.com/share/283607b463dc4db985f9bc28e9e90b9d?portalUrl=https://highways-england.cloud.esriuk.com/portal) This form is to be used to report data issues only. In this initial release, speed limit and smart motorway information has been removed pending data validation. For maintenance issues on the network please report here. (https://report.nationalhighways.co.uk/) For non-emergency incidents please contact our Customer Contact Centre on 0300 123 5000. The data is published under an Open Government Licence.

UK email data is increasing your sales pipeline and improving your market position. Reach the UK market effectively with our premium UK email data. This comprehensive resource provides access to a vast network of contacts. As a result, you can increase your brand awareness and drive sales. Moreover, our data is regularly updated and verified. Therefore, you can improve your marketing ROI. Consequently, you can target specific demographics and industries. Furthermore, this valuable resource allows you to connect with key decision-makers. Finally, List to Data offers this powerful dataset to fuel your business growth in the UK. UK consumer email list provides a comprehensive solution for reaching a vast consumer market, maximizing your marketing impact and ROI. Access the UK market with our premium UK consumer email list. This comprehensive resource provides access to a vast network of potential customers. As a result, you can increase your brand visibility and drive sales. Moreover, our data is regularly updated and verified. Therefore, you can improve your marketing ROI. Consequently, you can target specific demographics and regions. Furthermore, this valuable resource allows you to connect with key decision-makers. Finally, List to Data offers this powerful dataset to fuel your business growth in the UK. UK business email list is a premium database for connecting with professionals in the UK. This resource includes verified leads to ensure your campaigns are impactful. Additionally, it is designed to optimize your outreach efforts. Moreover, the directory is updated regularly to reflect the latest market trends. Furthermore, it offers a cost-effective way to expand your business reach. As a result, you can improve engagement and drive sales. In addition, this library of contacts is perfect for B2B and B2C campaigns. Finally, rely on List To Data to provide a dataset that enhances your marketing strategy and grows your business.

IntroductionThis dataset shows the utilisation band of our secondary sites across our three networks, over 1 April 2024 to 31 March 2025. This dataset is a redacted version of an annual regulatory submission to Ofgem. The data consists of a single utilisation value over a year for each of UK Power Networks secondary substation. This dataset is redacted in that any secondary sites with five or less customers will not be included to protect privacy. This dataset is a mixture of actual utilisation data and analytics based on transformer profile. The utilisation bands do not indicate contractually committed capacity e.g. capacity on our network may be unused now but expected to be used by a connection in a future year, recent acceptances which will be covered in next year's data.If you notice any errors, please let us know by feedbacking against the record in the table tab.

Methodological Approach UK Power Networks worked with other DNOs and the Energy Networks Association (ENA), to agree a methodology to calculating transformer utilisation.

It considered the following:

Transformer rating; Current peak loading; Year demand growth; Year ahead peak demand; and Utilisation calculation.

Quality Control Statement This dataset is provided "as is".

Assurance Statement The Open Data Team and DSO Network Strategy Team worked to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/To view this data please register and login.

Tudor Networks of Power - Correspondence Network Dataset

Ruth Ahnert, Sebastian E. Ahnert, Jose Cree, and Lotte Fikkers

© 2023. This work is licensed under a CC BY-NC-SA 4.0 license. If using this dataset, please cite:

• R. Ahnert, S E. Ahnert, "Tudor Networks of Power", Oxford University Press, 2023.

• R. Ahnert, S. E. Ahnert, J. Cree, & L. Fikkers, "Tudor Networks of Power - correspondence network dataset". Apollo - University of Cambridge Repository (2023). https://doi.org/10.17863/CAM.99562

The data is released under a Creative Commons BY-NC-SA 4.0 license, which: - requires attribution - permits distribution, remixing, adaptation, or building upon this data as long as the modified material is licensed under identical terms - only permits non-commercial uses of the work

This data contains a temporal, directed edgelist representing (to the best of our knowledge) all items of correspondence in the Tudor State Papers (1509-1603), which are the official government records of the Tudor period in England. The data covers State Papers Domestic and Foreign.

The dataset was created by first extracting the relevant XML metadata of the State Papers Online resource developed by Gale Cengage. We would like to acknowledge the help and support that Gale Cengage provided for our research. The XML metadata closely corresponds to the State Papers Calendars of the 19th century. These contain many ambiguities regarding the identities of people and places, resulting in an extensive effort on our part to disambiguate and de-duplicate person identities and places of writing. The details of this process can be found in our book (see citation above).

The dataset contains:

• 'letter_edgelist.tsv' - Directed temporal edge list of letters
• 'people_labels.tsv' - Key for the person IDs used in letter_edgelist.tsv
• 'place_labels.tsv' - Key for the place IDs used in letter_edgelist.tsv
• 'people_metadata.tsv' - Additional metadata and URIs for a subset of people
• 'places_metadata.tsv' - Geolocations and metadata for a large subset of places

Both the code and more extensive datasets that give context to the data curation process, the network analysis methods, and quantitative results in the book can be found at https://github.com/tudor-networks-of-power/code.

Ofcom's annual reports on the UK’s fixed broadband coverage, mobile and wifi network coverage, digital television, digital radio and internet infrastructure. Please see our terms of use for our licence conditions, and please refer to the Ofcom website to see the pdf 'About this data' documents. High quality, widespread communications, fixed and mobile, are an engine of our economy and the pulse of our society. They are not nice-to-haves, but essential enablers of our working and social lives. As businesses and consumers drive an ever-increasing demand for communications, the infrastructure that serves them must keep pace with their demands and needs. One of Ofcom’s roles is to make sure that the UK has the communications infrastructure it requires. This means making services available where people live and work; call connections being clear and robust; and data being down- and up-loaded at speeds that deliver a good experience. Ofcom is also responsible for providing clear, accurate, easy-to-use information. This equips businesses and consumers to make informed decisions about the services that can serve them best. The Connected Nations Report (previously called the Infrastructure Report) charts the UK’s evolving communications infrastructure, and our progress towards becoming genuinely connected nations. Where we can, we have made the data underlying the 2018 report available for download. Please see our terms of use for our licence conditions. If you have any questions or feedback on the data we have made available, please contact us at open.data@ofcom.org.uk. The 2019 Media Nations data can be found on the Ofcom website in an interactive format.

IntroductionUK 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. 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 across our license areas. 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 132 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 132kV 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 of 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 Statement

Creating 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)To view this data please register and login.

Trustpilot Reviews 123k

Welcome to the Trustpilot Reviews 123k Dataset, a collection of user feedback gathered from Trustpilot UK. This dataset encompasses a total of 123,181 reviews spanning 1,680 companies across 22 categories. The reviews were methodically collected over a brief period between 19th December 2024 and 7th January 2025, providing a snapshot of customer sentiments during this timeframe. This dataset is intended for researchers and practitioners interested in natural language processing, sentiment analysis, and text generation.

Categories

• Animals & Pets
• Beauty & Well-being
• Business Services
• Construction & Manufacturing
• Education & Training
• Electronics & Technology
• Events & Entertainment
• Food, Beverages & Tobacco
• Health & Medical
• Hobbies & Crafts
• Home & Garden
• Home Services
• Legal Services & Government
• Media & Publishing
• Money & Insurance
• Public & Local Services
• Restaurants & Bars
• Shopping & Fashion
• Sports
• Travel & Vacation
• Utilities
• Vehicles & Transportation

Columns

• category: The primary Trustpilot category assigned to the company.
• company: The URL of the company being reviewed.
• description: A brief description of the company.
• title: The title of the review.
• review: The text of the review.
• stars: The reviewer's rating of the company, on a scale from 1 to 5.

Language

The reviews in this dataset were sourced from the UK-based Trustpilot website and as a result, they are predominantly in English. It is worth noting that the language conventions and spellings used in the reviews are largely consistent with British English, rather than American English. This may be relevant for models or applications that are sensitive to regional language variations.

Important Notices:

Potentially Sensitive Content

The reviews in this dataset were sourced from various companies, some of which offer products or services that may be subject to age restrictions or other regulations in regions and countries outside of the UK.

Reviewer Discretion Advised

Please be aware that the reviews may contain references to sensitive or regulated topics, including (but not limited to):

• Vaping and tobacco-related products
• Alcoholic beverages and related food products
• Online gambling and lottery platforms
• Toy, model and BB guns
• Products containing CBD

In an effort to preserve the diversity and representativeness of the dataset, we opted not to filter out reviews containing these references. Users are advised to exercise discretion when working with this dataset and to comply with applicable laws and regulations in their region.

Sample usage

For a demonstration of how this dataset can be utilized to fine-tune a DistilBERT model for review sentiment analysis, please refer to the Kaggle notebook available here: Fine-tune DistilBERT on TP123k (Kaggle).

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

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 33kVto 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. This dataset provides half-hourly current and power flow data across these named transformers, in our South Eastern region, 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.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. To find which transformer you are looking for, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Monthly Dataset, which describes by month what transformers 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: 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 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. Where the primary transformer has 5 or fewer customers, we redact the dataset.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 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 Statement Creating 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 information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: 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 NetworksTo view this data please register and login.

UK Social Contact Network

Age, Gender, and Household Characteristics

By [source]

About this dataset

This dataset contains a detailed overview of the 2013 Van Hoek Social Contact Network Study in the UK. With this dataset, we have a unique opportunity to see how age, gender, and household size impact our social contact networks. From looking at the day of week contact was made and how often family contacted each other, to understanding the socio-economic backgrounds of participants and ethnicities represented - this data provides us with an interesting look into how our social connections are shaped. By diving deeper into these variables, we can gain valuable insight into our current culture's trends regarding who we interact with on a daily basis

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How to use the dataset

In order to use this dataset effectively, it is important to familiarize oneself with all of the columns included: dayofweek (the day of week on which contact was made), Contact.Day.if.different.from.allocated.day (the day of week on which contact was made if different from allocated day), day (day of month when contact was made), month (month when Contact was made), FTM (frequency of contact with family members), Socia_economic (social economic status participant belongs to), Noe_of _siblings (number of siblings participant has) ,Ethnicity(ethnicity details) part_age_detail(age detail).

Once you become familiar with all columns included in this dataset you can begin to identify relationships between these demographic factors and how different social contacts were enacted among them by examining how frequently households interacted as well as what age/gender/ethnic composition within each household look like at different times during a given month or year period; or even see what variables have an influence over who is contacted more often than not within a household setting or across multiple households- all depending on your need for specific insights from your research perspective!

Research Ideas

• Analyzing seasonal Social Contact trends: Using the month and dayofweek features, we could analyse how contact tends to vary across different seasons (e.g., more contact during summer months).

• Predicting Participants' Age Group: With the age detail, gender, and soci_economic features, predictive models can be built in order to estimate the age range of participants from given socio-demographic information.

• Evaluating Cyber Bullying and Online Social Networking Trends: Studying the self-reported frequency of contact between family members (FTM), researchers can evaluate cyber bullying trends in various communities as well as measure changes in social network size over time with respect to a given demographic group such as gender or ethnicity

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2013_VanHoek_UK_sday.csv | Column name | Description | |:------------------------------------------------|:-------------------------------------------------------------| | dayofweek | The primary day contact was made. (String) | | Contact.Day.if.different.from.allocated.day | Any days when contact deviated from allocated days. (String) | | day | The day of the month contact was made. (Integer) | | year | The year contact was made. (Integer) |

File: 2013_VanHoek_UK_participant_extra.csv | Column name | Description | |:--------------------|:--------------------------------------------------------| | FTM | Frequency of contact with family members. (Numeric) | | Socia_economic | Socioeconomic status of each participant. (Categorical) | | No.of.siblings | Number of siblings for each participant. (Numeric) | | Ethnicity | Ethnicity for each participant. (Categorica...

The provided dataset contains financial and operational metrics spanning from January to September 2020 for a company operating in the UK. It reflects key aspects like revenue, expenses, profit, customer count, transactions, stock price, market sentiment, loan approval rate, employee count, and marketing spend.

London, as a part of the UK, likely shares these trends but could have its specific nuances due to being a distinct economic hub within the country. In this period:

1. Financial Performance: The company's revenue fluctuates throughout the months, peaking at £65,090 in June and dipping to £35,184 in July. Despite varying expenses, profits generally stay positive, showcasing resilience in managing costs against revenue. London, being a financial center, might witness higher revenue or fluctuations due to specific industries concentrated there.

2. Customer Engagement: Customer metrics show variation. Customer count ranges from 131 to 426, with transactions varying from 57 to 188. This indicates fluctuations in customer activity, potentially influenced by market trends, seasonal patterns, or even regional events.

3. Stock Performance: Stock prices show fluctuation, hitting a high of 138.53 and a low of 78.79. Market sentiment, indicating public confidence, also fluctuates, potentially influencing stock prices. London's stock market might reflect similar volatility but could be influenced by the performance of prominent companies headquartered there.

4. Business Operations: Loan approval rates stay relatively stable between 70% to 97%, indicating a consistent approach to risk management. Employee count remains somewhat constant, which could signify stable operations without significant expansion or downsizing.

5. Marketing and Growth: The company's marketing spend varies, suggesting a willingness to adapt strategies based on performance or seasonal demands. London might have higher marketing expenditures due to the competitive market and the need to stand out amidst numerous businesses.

6. Economic Impact: Economic factors affecting the UK market—Brexit discussions, global economic shifts, or even local policies—might influence these metrics. London, as a financial center, could be more sensitive to global economic changes, impacting revenue, market sentiment, and stock prices more profoundly.

7. Covid-19 Influence: Given the timeframe (2020), the dataset might reflect the initial impact of the COVID-19 pandemic. The varying metrics could illustrate the company's adaptation strategies in response to changing consumer behaviors and economic uncertainties.

In London specifically, these trends might amplify due to its prominence in finance, trade, and services. The city's diverse industries and international connections might lead to more pronounced fluctuations in financial indicators like stock prices and market sentiment. Moreover, its position as a global economic hub might expose businesses to unique challenges and opportunities, potentially reflected in the provided dataset.

Understanding London's specific dynamics within the UK would require deeper analysis, considering sector-specific influences, competitive landscape, and regional economic factors. Nevertheless, this dataset offers insights into the company's adaptability and performance within the broader context of the UK's economic landscape.

Introduction This dataset presents import aggregated consumption data from Smart Meter customers at the secondary substation level, along with the count of smart meters contributing to the aggregated half-hourly values. It includes both Active Energy Import and Reactive Energy Import readings.The sample comprises 10,000 records drawn from aggregated smart meter data across our three operational regions: Eastern Power Networks (EPN), London Power Networks (LPN), and South Eastern Power Networks (SPN).

Methodological ApproachPrimary consumption data for Active Energy Import is aggregated based on the number of active devices reporting during each half-hour period. If a device is unreachable during a given interval, its data is excluded from the aggregation to maintain accuracy.Quality Control Statement This dataset is being shared to provide an early preview of the type of data we intend to publish from all smart meters within our regions. We are conducting monthly validation checks to enhance data quality prior to releasing the full dataset. Users are advised to exercise caution when interpreting or utilizing this preliminary data.

Assurance Statement The Smart Metering Team has reviewed the dataset to ensure consistency and accuracy in the presented data. Other Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal Glossary Download dataset information: Metadata (JSON)To view this data please register and login.

Support to providers that the council has contracts with. In addition to financial support the Council has developed a voluntary workforce scheme to support the care homes using volunteers sourced from both local authority staff and individuals from the voluntary and community sector. Several volunteers are currently working in the homes, with a number subsequently taking up full time employment as care workers. The City Council also operates a 24/7 Integrated Crisis Response Service to facilitate hospital discharges and community step up. If needed, this team can be redirected to support failing care homes as part of the Council’s emergency response service, in addition to the Council’s Reablement Service*(from Fee uplifts in dataset) The Council has paid fee uplifts to cover the additional operating costs of self-funder residents in care homes as well as those clients who are fully or part funded by Adult Social Care