As of June 2024, 71 percent of countries worldwide had data privacy legislation in place. Furthermore, nine percent had the legislation drafted. Overall, 15 percent of markets worldwide had no data privacy legislation yet, and five percent have not provided any data on such laws.

The 2024 Road Network File depicts the digital road line coverage for Canada. It contains information such as street arc unique identifier (UID), name, type, direction and address range, as well as class. It also includes province or territory (PR) and census subdivision (CSD) information for each side of a street arc (where applicable). The Road Network File is portrayed in Lambert conformal conic projection (North American Datum of 1983 [NAD83]) and is available as a national file.

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard, which can be accessed via the link below. Data derived from SNOMED codes is used in some measures such as those for birthweight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. Since last month, this publication contains a new Clinical Quality Improvement Metric (CQIM) called CQIMReadmissions. This new metric reports the number of babies born in hospital then discharged home, who were then readmitted to hospital while still under 30 days old. This is supported by five new data quality metrics to ensure we only publish CQIMReadmissions figures where the underlying data is of sufficient completeness and quality. The new data quality metrics are CQIMDQ46 to CQIMDQ50. Further information about this new readmissions metric can found in this publication’s Data Quality Statement. This new data can be found in the Measures file available for download and in the CQIM and CQIM+ pages in the National Maternity Dashboard, and further information on the new metrics can be found in the accompanying Metadata file. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. This dashboard has been updated following the release of CNST Y6 criteria, and can be accessed via the link below. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

This collection of datasets originates from the Statistics Center's service interface, known as Tilastokeskus (Statistics Finland), in Finland. The collection is composed of related data tables, with each table presenting a variety of related data in a structured format of columns and rows. The data in this collection is highly detailed and organized, providing a valuable resource for those seeking to understand specific statistical areas. The datasets in this collection are current as of 2024. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

A listing of hearings completion data by name of individual administrative law judges (ALJ) for all ALJs in the Office of Hearings Operations (OHO). The data includes hearing office name, total dispositions, decisions, allowances, denials, and fully favorable or partially favorable decisions. Report for January 2024.

Punjab Budget 2024-25: Statistical Abstract

This data release contains the U.S. salient statistics and world production data extracted from the STRONTIUM data sheet of the USGS Mineral Commodity Summaries 2024.

During a survey in the United States in 2024, almost **** of respondents stated that their favorite physical activity was exercising outdoors. In comparison, ***** percent of respondents said that playing golf was their favorite way of staying fit.

Statistics and insights on automation trends in US manufacturing, based on a survey of 85,975 leaders.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

Table F is the Expenditure and Income for the Budget Year and Estimated Outturn for the previous Year. It contains –‘Expenditure’ and ‘Income’ Adopted by the Council for the Budget Year; 'Expenditure’ and ‘Income’ Estimated by the Chief Executive for the Budget Year; 'Expenditure’ and ‘Income’ Adopted by the Council for the previous Year; ‘Expenditure’ and ‘Income’ Estimated Outturn for the previous Year. Table F provides a breakdown of the Expenditure to Sub-Service level and Income to Income Source per Council Division contained in Table A.In the published Annual Budget document, Table F is published as a separate table for each Division.Section 1 of Table F contains Expenditure broken down by ‘Division’, ‘Service’ and ‘Sub-Service’. Section 2 of Table F contains Income broken down by ‘Division’, ‘Income Type’ and ‘Income Source’. The data in this dataset is best interpreted by comparison with Table F in the published Annual Budget document which can be found at https://www.sdcc.ie/en/services/our-council/policies-and-plans/budgets-and-spending/annual-budget/Data fields for Table F are as follows –Doc : Table Reference Heading : Indicates sections in the Table - Table F is comprised of two sections : Income and Expenditure. Heading = 1 for all Expenditure records; Heading = 2 for all Income records. Ref : Division Reference Ref_Desc : Division Description Ref1 : Service Reference for all Expenditure records (i.e. Heading = 1) or Income Type for all Income records (i.e. Heading = 2) Ref1_Desc : Service Description for all Expenditure records (i.e. Heading = 1) or Income Type for all Income records (i.e. Heading = 2) Ref2 : Sub-Service Reference for all Expenditure records (i.e. Heading = 1) or Income Source for all Income records (i.e. Heading = 2) Ref2_Desc : Sub-Service Description for all Expenditure records (i.e. Heading = 1) or Income Source for all Income records (i.e. Heading = 2) Adop : Amount Adopted by Council for Budget Year EstCE : Amount Estimated by Chief Executive for Budget Year PY_Adop : Amount Adopted by Council for previous Financial Year PY_Outturn : Amount Estimated Outturn for previous Financial Year

Quarterly data on real estate transfers and constitution of mortgages. The Real Estate Registry statistic has the participation of practically all Spanish Property Registries, that is, 1,103 offices distributed properly throughout the Spanish geography. It reflects quarterly information on real estate transfers and constitution of mortgages.

This data release contains the U.S. salient statistics and world production data extracted from the RHENIUM data sheet of the USGS Mineral Commodity Summaries 2024.

In a 2024 survey of small business leaders and IT professionals in the United States, 58 percent of respondents stated that employee data was their most compromised company data in cyberattacks. Additionally, 53 percent of respondents reported customer or consumer data as the second most impacted company data.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics