Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

This table shows the price development of newly built and existing dwellings purchased by households. Aside from the price indices, Statistics Netherlands also publishes figures on the number, average purchase price and total sum of the purchase prices of the sold dwellings.

Data available from: 1st quarter 2015

Status of the figures: The figures in this table that are associated with existing homes (PBK) are final. The figures in this table that are associated with new dwellings (PNK) are one period provisional. The figures in this table that are associated with the number of sold dwellings and the average purchase price and related to newly built dwellings and total figures are four periods provisional.

Changes as of 8th of April 2025: The figures for the 4th quarter and the year of 2024 have been added and the 3rd quarter of 2024 are now final.

Changes as of 27th of June 2024: The figures regarding new-build owner-occupied homes for the period 2023 have been corrected as a result of the application of a new methodology.

When will new figures be published? New figures are published in July 2025.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

Steel rose to 2,962 CNY/T on June 27, 2025, up 0.44% from the previous day. Over the past month, Steel's price has fallen 2.02%, and is down 10.27% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Steel - values, historical data, forecasts and news - updated on June of 2025.

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Monthly average retail prices for gasoline and fuel oil for Canada, selected provincial cities, Whitehorse and Yellowknife. Prices are presented for the current month and previous four months. Includes fuel type and the price in cents per litre.

Statistics Netherlands collects monthly data on imports and exports of goods. In this table on imports and exports of goods the change of ownership of the goods is decisive, not whether they crossed the Dutch border. The table comprises index figures and changes in terms of percentage of total imports and exports of goods, broken down by value, price and volume. The indices are based on 2015=100. The changes in terms of percentage are compared with the same period in the previous year. Data available from: 1995 January Status of the figures: Data from 1995 up to and including 2020 are final. Data over 2021, 2022, 2023 and 2024 are provisional. Since this table has been discontinued, provisional data will not become final. Changes as of July 17th 2024: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. For further information see section 3. When will new figures be published? Not applicable anymore.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Gasoline fell to 2.06 USD/Gal on June 30, 2025, down 0.90% from the previous day. Over the past month, Gasoline's price has fallen 0.23%, and is down 19.84% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on June of 2025.

Interactive chart of historical daily coffee prices back to 1969. The price shown is in U.S. Dollars per pound.

Construction Output Price Indices (OPIs) from January 2014 to December 2024, UK. Summary.

📌 Context of the Dataset

The Healthcare Ransomware Dataset was created to simulate real-world cyberattacks in the healthcare industry. Hospitals, clinics, and research labs have become prime targets for ransomware due to their reliance on real-time patient data and legacy IT infrastructure. This dataset provides insight into attack patterns, recovery times, and cybersecurity practices across different healthcare organizations.

Why is this important?

Ransomware attacks on healthcare organizations can shut down entire hospitals, delay treatments, and put lives at risk. Understanding how different healthcare organizations respond to attacks can help develop better security strategies. The dataset allows cybersecurity analysts, data scientists, and researchers to study patterns in ransomware incidents and explore predictive modeling for risk mitigation.

📌 Sources and Research Inspiration This simulated dataset was inspired by real-world cybersecurity reports and built using insights from official sources, including:

1️⃣ IBM Cost of a Data Breach Report (2024)

The healthcare sector had the highest average cost of data breaches ($10.93 million per incident). On average, organizations recovered only 64.8% of their data after paying ransom. Healthcare breaches took 277 days on average to detect and contain.

2️⃣ Sophos State of Ransomware in Healthcare (2024)

67% of healthcare organizations were hit by ransomware in 2024, an increase from 60% in 2023. 66% of backup compromise attempts succeeded, making data recovery significantly more difficult. The most common attack vectors included exploited vulnerabilities (34%) and compromised credentials (34%).

3️⃣ Health & Human Services (HHS) Cybersecurity Reports

Ransomware incidents in healthcare have doubled since 2016. Organizations that fail to monitor threats frequently experience higher infection rates.

4️⃣ Cybersecurity & Infrastructure Security Agency (CISA) Alerts

Identified phishing, unpatched software, and exposed RDP ports as top ransomware entry points. Only 13% of healthcare organizations monitor cyber threats more than once per day, increasing the risk of undetected attacks.

5️⃣ Emsisoft 2020 Report on Ransomware in Healthcare

The number of ransomware attacks in healthcare increased by 278% between 2018 and 2023. 560 healthcare facilities were affected in a single year, disrupting patient care and emergency services.

📌 Why is This a Simulated Dataset?

This dataset does not contain real patient data or actual ransomware cases. Instead, it was built using probabilistic modeling and structured randomness based on industry benchmarks and cybersecurity reports.

How It Was Created:

1️⃣ Defining the Dataset Structure

The dataset was designed to simulate realistic attack patterns in healthcare, using actual ransomware case studies as inspiration.

Columns were selected based on what real-world cybersecurity teams track, such as: Attack methods (phishing, RDP exploits, credential theft). Infection rates, recovery time, and backup compromise rates. Organization type (hospitals, clinics, research labs) and monitoring frequency.

2️⃣ Generating Realistic Data Using ChatGPT & Python

ChatGPT assisted in defining relationships between attack factors, ensuring that key cybersecurity concepts were accurately reflected. Python’s NumPy and Pandas libraries were used to introduce randomized attack simulations based on real-world statistics. Data was validated against industry research to ensure it aligns with actual ransomware attack trends.

3️⃣ Ensuring Logical Relationships Between Data Points

Hospitals take longer to recover due to larger infrastructure and compliance requirements. Organizations that track more cyber threats recover faster because they detect attacks earlier. Backup security significantly impacts recovery time, reflecting the real-world risk of backup encryption attacks.