Net-a-Porter web scraped data

  About the website

Net-a-Porter operates in the highly competitive e-commerce industry in EMEA, particularly in the United Kingdom. This vibrant industry is characterized by a dynamic digital environment encompassing various segments, notably fashion, luxury goods, and retail. These segments continue to witness significant growth, driven by advancements in technology and changing consumer behaviors. Net-a-Porter, being a premium online luxury… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Net.a.Porter.Product.prices.United.Kingdom.

My Theresa web scraped data

  About the website

My Theresa is a major player in the Ecommerce industry in EMEA, with a strong presence in the United Kingdom. The online luxury Fashion business has experienced significant growth over the years, driven by advancements in technology, rising disposable income, increased internet penetration, and changing consumer preferences. Ecommerce is ever-evolving; with an amalgamation of technological disruption, competitive dynamics, and… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/My.Theresa.Product.prices.United.Kingdom.

Blickers web scraped data

  About the website

The Blickers operates in the eCommerce industry in the EMEA region, predominantly in the United Kingdom. This industrys key essence revolves around buying and selling goods or services using the internet and transferring money and data to carry out transactions. It has dramatically redefined many consumers shopping habits in the UK. The dataset observed involves eCommerce product-list page (PLP) data for Blickers operations in… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Blickers.Product.prices.United.Kingdom.

Price Paid Data tracks the residential property sales in England and Wales that are lodged with HM Land Registry for registration.

Our price paid data tracks the residential property sales in England and Wales that are lodged with us for registration. The dataset is a reliable source of house price information and consists of more than 24 million definitive records dating back to January 1995. For more information on this dataset and what it does and doesn't include, visit https://www.gov.uk/about-the-price-paid-data

Choose from three options to select the data that best meets your requirements:

• monthly file: contains a single monthly file of the transactions received in the period from the first to the last day of the corresponding month, including any changes or deletions to previously downloaded data. The data is updated monthly and the average size of this file is 11 MB.

• single file: contains all the up to date data from 1995 to the current date. The data is updated monthly and the average size of this file is 2.86 GB.

• yearly files: contains annual files of up to date data, ranging from 1995 to the current date. Unlike the monthly files described above, yearly files are collated on the date of the transaction/deed date rather than the date that the information was lodged with HM Land Registry. The data is updated monthly and the sizes of these files range from 87 MB to 222 MB. If you are having trouble downloading any of the year files in full, they are also available as two smaller, evenly split CSV files.

We try to make sure that our public data is accurate, but cannot guarantee that it is free from errors or fit for your purpose or use. Reports are based on data collected at the time a property transaction is registered with us and will not necessarily be up to date with the most recent information. For more information, read https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#when-using-or-publishing-our-price-paid-data

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

Celine web scraped data

  About the website

The Ecommerce industry in the EMEA region, particularly in the United Kingdom, has shown a significant growth rate in recent years, becoming a pivotal element in the regions economic landscape. The rise of digital technologies and change in consumer behavior has further accelerated this upward trend. In this digital marketplace, Celine, a well-known high-end fashion brand, has maintained its mark. The dataset at-hand encompasses… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Celine.Product.prices.United.Kingdom.

Saint Laurent web scraped data

  About the website

Saint Laurent operates within the luxury fashion industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the United Kingdom. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards Ecommerce sales, owing to customers’ increasing preference for online… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Saint.Laurent.Product.prices.United.Kingdom.

The aim is to forecast the chief components of inflation (such as changes in fuel prices, food prices and prices of durable goods) for the USA, UK and South Africa, and to test whether the weighted sum of the component forecasts gives a more accurate overall forecast for inflation, than simply forecasting overall inflation itself. In the long run, the ratios of these prices to the overall consumer price index have altered because of technological changes and globalization, among other factors. For example, the prices of internationally traded consumer goods have fallen relative to prices of services. By building separate models for the components, the long-run information in the data and specific economic features likely to drive each component can be exploited. These models will test for asymmetries, such as the tendency of petrol prices to respond faster to rises than to falls in oil prices. The models should help better understand the causes of overall inflation through understanding the inflation trends of the underlying sectors. Modelling the components separately should also highlight where interest rate policy could be effective, and where other policies such as competition policy or price regulation might have complementary benefits.

This dataset provides monthly, quarterly and annual average regular or premium unleaded gasoline pump prices, taxes and ex-tax pump prices in Canada, USA, France, Germany, Britain and Japan, all converted to Canadian cents per litre. To view charts and current fuel price data you can also visit the motor fuel prices page. *[USA]: United States of America

(Banking And Systemic Crises)

prepared by (Mohamed Abd Al-mgyd)

https://github.com/1145267383/Systemic-And-Banking-Crises

• Web APP > https://crises.herokuapp.com/

Dataset

A)20160923_global_crisis_data:

https://www.hbs.edu/behavioral-finance-and-financial-stability/data/Pages/global.aspx

This data was collected over many years by Carmen Reinhart (with her coauthors Ken Rogoff, Christoph Trebesch, and Vincent Reinhart). This data contains the banking crises of 70 countries, from 1800 AD to 2016 AD, with a total of 15,190 records and 16 variables. But the data stabilized after cleaning and adjusting to 8642 records and 17 variables.

B)Label_Country: This data contains a description of the country whether it's Developing or Developed .

Variable: Description:

1-Case: ID Number for Country.

2-Cc3: ID String for Country.

3-Country : Name Country.

4-Year: The date from 1800 to 2016.

5-Banking_Crisis: Banking problems can often be traced to a decrease the value of banks' assets.

A) due to a collapse in real estate prices or When the bank asset values decrease substantially . B) if a government stops paying its obligations, this can trigger a sharp decline in value of bonds.

6-Systemic_Crisis : when many banks in a country are in serious solvency or liquidity problems at the same time—either:

A) because there are all hits by the same outside shock. B) or because failure in one bank or a group of banks spreads to other banks in the system.

7-Gold_Standard: The Country have crisis in Gold Standard.

8-Exch_Usd: Exch local currency in USD, Except exch USD currency in GBP.

9-Domestic_Debt_In_Default: The Country have domestic debt in default.

10-Sovereign_External_Debt_1: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom and post-1975 defaults on Official External Creditors.

11-Sovereign_External_Debt_2: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom but includes post-1975 defaults on Official External Creditors.

12-Gdp_Weighted_Default:GDP Weighted Default for country.

13-Inflation: Annual percentages of average consumer prices.

14-Independence: Independence for country.

15-Currency_Crises: The Country have crisis in Currency.

16-Inflation_Crises: The Country have crisis in Inflation.

17-Level_Country: The description of the country whether it's Developing or Developed.

Inflation Rate in the United Kingdom remained unchanged at 3.80 percent in August. This dataset provides - United Kingdom Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gasoline Prices in the United Kingdom increased to 1.82 USD/Liter in September from 1.81 USD/Liter in August of 2025. This dataset provides the latest reported value for - United Kingdom Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These datasets contain aggregated expenditure and demographic variables, that are derived from the Family Expenditure Survey (GN 33057), the Expenditure and Food Survey/Living Costs and Food Survey (GN 33334), the General Household Survey (GN 33090) and the Health Survey for England (GN 33261). These files can be used to replicate the results in the paper Banks, J., Blundell, R., Levell, P. and Smith, J. "Life-Cycle Consumption Patterns at Older Ages in the US and the UK: Can Medical Expenditures Explain the Difference?", AEJ: Economic Policy (August, 2019) (see related resources). This proposal sets out a major new programme of research that will lead to significant scientific progress and policy impact. Building on the expertise developed at the Centre and at IFS, we will use the developments in econometric techniques and data availability, including linked survey and administrative data, to push our research agenda in exciting new directions. The focus of the work will be on: a) Consumers and markets. We will use insights from behavioural economics and robust methods to understand within-household behaviour and we will explore the relationships between government policy, firm behaviour and outcomes for consumers. This work has the potential to transform our understanding of the effects of policy interventions that either change the relative prices of the goods consumers buy (e.g. taxes on alcohol, green levies, sugar taxes) or try to change consumers' preferences (e.g. through information campaigns or restrictions on advertising). b) Inequality, risk and insurance. Understanding the determinants of inequality is central to our agenda. We will focus on understanding inequality across the life cycle and across and within generations. We will explore the role of housing, of insurance and of market and non-market mechanisms in managing risk and uncertainty. The availability of new administrative data linked to existing surveys will allow us to examine the dynamics of inequality and the impact of alternative policies. In particular, we will focus on the role of wealth and bequests in generating within-cohort inequality among the younger generations and we will investigate how uncertainty is resolved over the life cycle and how this affects the degree of insurance provided by taxes and benefits at different ages. c) Public finances and taxation. Focusing on high earners and multinational companies, we will use newly-available data to throw new light on risks to the public finances in the UK from these vital but increasingly risky sources of revenue. We will also develop a programme of work that focuses on the particular issues facing tax design in middle-income countries. d) Evolution of human capital over the life cycle. We aim to make major strides in understanding the process of formation of human capital from the early years to young adulthood, how human capital is rewarded in the labour market, how it is linked to labour supply and productivity, and how the evolution of health and well-being interacts with labour supply and other outcomes in later years. These issues are intricately related and we envisage a joined-up programme of work that will provide new answers to some of the most important questions currently facing policymakers. How do people make decisions over savings, nutrition, education and labour supply and how can government influence those decisions? What is driving increased levels of income inequality and how might interventions in education and through the tax and welfare system ameliorate them, and at what cost? How should governments respond to the pressures on corporate and individual tax revenues created by increasing globalisation? What drives decisions over pension savings, health behaviours and retirement decisions and how should governments design policy in the face of an ageing population? In answering these questions, we will make use of the unique expertise and data resources brought together at the Centre. Crucially, our intention is also to take a consistent approach in which we will model the determinants of individual decisions over the life course and the interactions between economic actors; we will model behavioural 'biases' and market frictions; we will use a combination of available data, randomised controlled trials and structural modelling to understand not just the effect of policies but also what drives that effect and hence what might be the effect of other policies; and we will develop new data and measurement tools. Derived dataset using data collected from household surveys of the UK population. The LCFS collects detailed data on household expenditure which we were able to use to separate out spending into different categories for comparison with spending in the United States (as measured in the Consumer Expenditure Survey). The HSE and GHS were chosen as they have household level data on self-reported health which we were able to compare across different cohorts and also with measures from similar surveys in the US.

Prada web scraped data

  About the website

Prada operates in the luxury fashion industry within the EMEA region, emphasizing particularly in the United Kingdom. The luxury fashion industry in the UK has a considerable potential market for Prada since it is well-known for its affluent consumer base. This industry has witnessed significant growth due to the rise in online shopping, thereby boosting the Luxury Ecommerce market segment. Our dataset displays Ecommerce… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Prada.Product.prices.United.Kingdom.

Abstract copyright UK Data Service and data collection copyright owner. The OECD Banking statistics database includes data from 1979 to 2009 on classification of bank assets and liabilities, income statement and balance sheet and structure of the financial system for OECD countries. The OECD have discontinued this dataset, so no further updates will be made. The OECD Banking Statistics are presented in the following tables (some tables will include missing data): Classification of bank assets and liabilities This dataset provides the composition of bank assets and liabilities of residents and non-residents denominated in domestic and foreign currencies based on financial statements of banks in each OECD member country and Russia. Data are reported at current prices in millions of national currency and in millions of Euros for OECD countries. The data covers the years starting from 2005 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and Russian Federation. Income statement and balance sheet This comparative tables comprises statistics on country’s financial profiles by presenting their respective extensive income statements, balance sheets and capital adequacy by banking group that can be further analyzed by type of financial institution such as commercial banks, savings banks co-operative banks and other monetary institutions. This dataset provides information on income statements, balance sheets and capital adequacy by banking group. Data are reported at current prices in millions of national currency. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. Structure of the financial system This dataset provides information on the overall structure of the financial system per country by type of institution and their components: Central banks, other monetary institutions, other financial institutions and insurance institutions. Data relate to number of institutions, number of branches, number of employees, total assets and liabilities and total financial assets. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. These data were first provided by the UK Data Service in December 2014. Main Topics: • Banking • Financial statement • Financial structure • Financial system • Monetary institutions • Monetary system 1979 2009 ACCOUNTING ASSETS Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan BANKS Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi CURRENCIES Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Rep... Chad Channel Islands Chile China Colombia Comoros Congo Costa Rica Croatia Cuba Curacao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Economic conditions... Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Europe European Union Coun... FINANCIAL INSTITUTIONS Faroe Islands Finland France Gabon Gambia Georgia Germany October 1990 Ghana Gibraltar Greece Grenada Guatemala Guinea Guinea Bissau Honduras Hong Kong Hungary INSURANCE Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan Latvia Lebanon Lesotho Liberia Lithuania Luxembourg MONETARY ECONOMICS Macao Macedonia Madagascar Malawi Malaysia Mali Malta Mauritania Mauritius Mexico Moldova Montenegro Morocco Mozambique Multi nation Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Romania Russia Rwanda Saint Lucia Saint Martin Saint Vincent Saotome Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea Spain Sri Lanka Sudan Surinam Swaziland Switzerland Tajikistan Tanzania Thailand Togo Trinidad and Tobago Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands USA Zambia Zimbabwe

Crude Oil rose to 62.42 USD/Bbl on October 8, 2025, up 1.12% from the previous day. Over the past month, Crude Oil's price has fallen 0.33%, and is down 14.77% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on October of 2025.