Imports from United Kingdom in France increased to 2310 EUR Million in January from 2218 EUR Million in December of 2023. This dataset includes a chart with historical data for France Imports from Uk.

The following dataset provides the results of the TdF social and economic impact survey, conducted during the Tour de France 2014. Please note We are aware of data quality errors in this dataset. This is currently under review. Any personal data collected has been removed and postcode restricted to district area. This is a one off publication and will only be updated to improve the quality of data.

This dataset is about books. It has 22 rows and is filtered where the book subjects is British-France. It features 9 columns including author, publication date, language, and book publisher.

The information relates to the economic boost from the Tour de France 2014, for the City of Bradford Metropolitan District and wider. How various teams worked together seamlessly to provide a service, and the overall impact on tourism, business, cycling and the region.

Exports to United Kingdom in France increased to 3113 EUR Million in January from 2811 EUR Million in December of 2023. This dataset includes a chart with historical data for France Exports to Uk.

Our Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and globally scaled POI data featuring over 164 million verified locations across 220 countries. We have been providing this data to companies worldwide for 30 years.

• Develop mapping and navigation tools and software.
• Identify new areas and locations suitable for business development.
• Analyze the presence of competitors and nearby populations.
• Optimize routes to enhance delivery efficiency.
• Evaluate property values based on nearby infrastructure.
• Support disaster management by identifying high-risk areas.
• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

Dataset

This dataset was created by Lena Baruh

Contents

These Kaggle datasets provide downloaded real estate listings from the French real estate market, capturing data from a leading platform in France (Seloger), reminiscent of the approach taken for the US dataset from Redfin and UK dataset from Zoopla. It encompasses detailed property listings, pricing, and market trends across France, stored in weekly CSV snapshots. The cleaned and merged version of all the snapshots is named as France_clean_unique.csv.

The cleaning process mirrored that of the US dataset, involving removing irrelevant features, normalizing variable names for dataset consistency with USA and UK, and adjusting variable value ranges to get rid of extreme outliers. To augment the dataset's depth, external factors like inflation rates, stock market volatility, and macroeconomic indicators have been integrated, offering a multifaceted perspective on France's real estate market drivers.

For exact column descriptions, see columns for France_clean_unique.csv and my thesis.

Table 2.5 and Section 2.2.1, which I refer to in the column descriptions, can be found in my thesis; see University Library. Click on Online Access->Hlavni prace.

If you want to continue generating datasets yourself, see my Github Repository for code inspiration.

Let me know if you want to see how I got from raw data to France_clean_unique.csv. There are multiple steps, including cleaning Tableau Prep and R, downloading and merging external variables to the dataset, removing duplicates, and renaming some columns.

Through two decades of campaigns delivery and optimization, Expandi has created the most comprehensive GDPR-compliant European database covering SMB, Midmarket, and Enterprise companies. Our data base is enriched with up-to-date technographic, financial and intent data. All our data is updated regularly and includes only active companies, allowing you to reach the most relevant and appropriate customers for your business.

Our available data: • Updated company Firmographic, Financial data (revenues, financial strength, profit/loss), Decision Making Unit structure, and Key decision maker contacts (name, job title, LinkedIn profile). • Multi-language buyer intent data coming from omni-channel interactions and scored by brand and solutions. • Technographic and brand preference data. • Company IP addresses and Device ID mapping and tracking to help you identify unknown online traffic and boost the results of your awareness and branding campaigns.

Target selection criteria: • Region / State-Province • Range employees (starting from 50+) • Range Revenues • Industry / Sub-industry • Financial strength • Decision Making Unit • Technographic data • Intent data solution / Intent data stage

Data delivery: One-off purchase.

Let’s start today to boost your demand generation campaigns and raise awareness of your brand and solutions!

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR07644.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

IntroductionAlthough essential tremor (ET) is considered a common adult movement disorder, evidence on its incidence is still scant. This study aims at estimating ET incidence in two European countries, namely, the UK and France.MethodsIncident cases of ET were identified within the Health Improvement Network (THIN®) database between 1st January 2014 and 31 December 2019. Yearly crude and standardized incidence rates (IR) were estimated across the study period for both countries. Poisson regression models were built to assess temporal trends in IRs and differences between sexes and age classes.ResultsIn total, 4,970 and 4,905 incident cases of ET were identified in the UK and France, respectively. The yearly average crude IR (per 100,000 person-years) was 18.20 (95%CI: 15.09–21.32) in UK and 21.42 (17.83–25.00) in France, whereas standardized ones were 19.51 (18.97–20.01) and 19.50 (18.97-20.05). Regression analyses showed slightly increasing trends in both countries, higher incidence among males, and a significant increase with age. Yearly average IR increased from 3.96 (0.95–6.97) and 5.28 (1.12–9.44) in subjects aged <20 years to 49.27 (26.29–72.24) and 51.52 (30.19–72.86) in those aged >80 year in UK and France.ConclusionsStandardized ET incidence was comparable in the UK and France, showing a slight increase in both countries, reporting a higher value among people aged 60 years and older. This study outlines the need to conduct future studies to estimate the burden of ET in terms of disease control and healthcare resource utilization.

Data on the performance of the United Kingdom's (UK) research base compared to seven other research-intensive countries (Canada, China, France, Germany, Italy, Japan, and US) and, where data are available, with the EU27, the Organisation for Economic Co-operation and Development (OECD) member countries' groups, and three other fast growing nations (Brazil, Russia and India). Investment and performance in the national research system is tracked in an international setting, combining bibliometric and other research measure across different international indicators, presenting a multifaceted view of the UK's comparative performance in research as well as the trends that may affects its position.

This dataset replaces Volume 1 of the Immigration Statistics: before entry. Before travelling to the UK, a person may be required to apply for and be granted an entry clearance visa, depending on their nationality, purpose of visit and intended length of stay. On arrival at UK ports, or UK border controls in France and Belgium (often termed ‘juxtaposed’ controls), all individuals have to satisfy a Border Force officer that they have the right to enter the country before being admitted to the UK. The data in this section include dependants and exclude those visiting the UK or in transit, unless stated otherwise.

Unlock the power of 164M+ verified locations across 220+ countries with high-precision geospatial data. Featuring 50+ enriched attributes including coordinates, building type, and geometry. Our AI-powered dataset ensures unmatched accuracy through advanced deduplication and enrichment. With 30+ years of industry expertise, we deliver trusted, customizable data solutions for mapping, navigation, urban planning, and marketing, empowering smarter decision-making and strategic growth.

Key use cases of Geospatial data have helped our customers in several areas:

1. Gain a Competitive Edge with Smarter Mapping : Use geospatial data to analyse competitors, identify high-traffic zones, and optimize locations for maximum impact.
2. Enhance Navigation & Location-Based Engagement : Improve turn-by-turn navigation, EV charging station discovery, and real-time travel insights for seamless customer experiences.
3. Find High-Value Locations for Business Growth : Leverage geospatial intelligence to select profitable retail sites, franchise locations, and warehouses with precision.
4. Streamline Deliveries & Address Validation : Improve shipping accuracy, reduce failed deliveries, and optimize courier routes for better customer satisfaction.
5. Drive Smarter Decisions with Spatial Analysis : Utilize location intelligence for disaster risk assessment, public health campaigns, and agricultural planning.

This release replaces the previous annual and quarterly publications Control of Immigration Statistics and the annual British Citizenship, following a public consultation. Each topic now has its own entry, links to these related reports can be found under the "additional links" section. The statistics in this topic briefing cover: applications, issues and refusals of entry clearance visas; and non-asylum passengers initially refused entry at ports. Prior to travel to the United Kingdom, a person may be required to apply for and be issued with an entry clearance visa, depending on their nationality, purpose of visit and intended length of stay. On arrival at UK ports or a juxtaposed control in France or Belgium, before being admitted to the country, all individuals have to satisfy a Border Force Officer that they have the right to enter the country. From Q2 2014 this dataset has been split into two sections: Vol 1 is now the Immigration Statistics: Sponsorship data tables, and Vol 2, 3 and 4 are now the Immigration Statistics: Entry clearance visas granted outside the UK

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Time series data for the statistic Imports_United_Kingdom_from_France. Indicator Definition:Goods, Value of Imports, Cost, Insurance, Freight (CIF), US DollarsThe indicator "Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars" stands at 3.81 Billion as of 5/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 11.67 percent compared to the value the year prior.The 1 year change in percent is 11.67.The 3 year change in percent is 3.47.The 5 year change in percent is 127.43.The 10 year change in percent is 35.04.The Serie's long term average value is 2.71 Billion. It's latest available value, on 5/31/2025, is 40.59 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 8/31/1993, to it's latest available value, on 5/31/2025, is +236.58%.The Serie's change in percent from it's maximum value, on 5/31/2023, to it's latest available value, on 5/31/2025, is -18.95%.

hello, this is France, Germany and London stock index daily historical daily data. who can use various historical analysis for investment or show direction of economy.

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AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot