Petroleum Stocks: MG: FMG: RFM: PADD 3 data was reported at 0.000 Barrel th in 20 Jul 2018. This stayed constant from the previous number of 0.000 Barrel th for 13 Jul 2018. Petroleum Stocks: MG: FMG: RFM: PADD 3 data is updated weekly, averaging 707.000 Barrel th from May 1993 (Median) to 20 Jul 2018, with 1315 observations. The data reached an all-time high of 11,894.000 Barrel th in 09 Nov 2001 and a record low of 0.000 Barrel th in 20 Jul 2018. Petroleum Stocks: MG: FMG: RFM: PADD 3 data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB029: Petroleum Stocks: Weekly Report.

FluWatch is Canada's national surveillance system that monitors the spread of flu and flu-like illnesses on an on-going basis. Activity Level surveillance is a component of FluWatch that provides an overall assessment of the intensity and geographical spread of laboratory-confirmed influenza cases, influenza-like-illness (ILI) and reported outbreaks for a given surveillance region. Activity Levels are assigned and reported by Provincial and Territorial Ministries of Health. A surveillance region can be classified under one of the four following categories: no activity, sporadic, localized or widespread. For a description of the categories, see the data dictionary resource. For more information on flu activity in Canada, see the FluWatch report. (https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance/weekly-influenza-reports.html) Note: The reported activity levels are a reflection of the surveillance data available to FluWatch at the time of production. Delays in reporting of data may cause data to change retrospectively.

The Comprehensive Annual Financial Reports are presented in three main sections; the Introductory Section, the Financial Section, and the Statistical Section. The Introductory Section includes a financial overview, discussion of Iowa's economy and an organizational chart for State government. The Financial Section includes the state auditor's report, management's discussion and analysis, audited basic financial statements and notes thereto, and the underlying combining and individual fund financial statements and supporting schedules. The Statistical Section sets forth selected unaudited economic, financial trend and demographic information for the state on a multi-year basis. Reports for multiple fiscal years are available.

United States Off Within 2 Weeks: sa: All Residential: South Bend, IN data was reported at 25.629 % in Jul 2020. This records an increase from the previous number of 25.534 % for Jun 2020. United States Off Within 2 Weeks: sa: All Residential: South Bend, IN data is updated monthly, averaging 19.068 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 39.189 % in May 2020 and a record low of -9.971 % in Feb 2012. United States Off Within 2 Weeks: sa: All Residential: South Bend, IN data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

Weekly Economic Calendar shows future release dates of key economic data and publications used by NSW Treasury for monitoring and analysis.

United States SBP: OS: Last Week Operating Revenue: 125,001-200,000 data was reported at 7.800 % in 04 Oct 2020. This records an increase from the previous number of 7.000 % for 27 Sep 2020. United States SBP: OS: Last Week Operating Revenue: 125,001-200,000 data is updated weekly, averaging 5.850 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 8.700 % in 16 Aug 2020 and a record low of 0.300 % in 03 May 2020. United States SBP: OS: Last Week Operating Revenue: 125,001-200,000 data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).

Data Entry Service Market Outlook

The global data entry service market size is poised to experience significant growth, with the market expected to rise from USD 2.5 billion in 2023 to USD 4.8 billion by 2032, achieving a Compound Annual Growth Rate (CAGR) of 7.5% over the forecast period. This growth can be attributed to several factors including the increasing adoption of digital technologies, the rising demand for data accuracy and integrity, and the need for businesses to manage vast amounts of data efficiently.

One of the key growth factors driving the data entry service market is the rapid digital transformation across various industries. As businesses continue to digitize their operations, the volume of data generated has increased exponentially. This data needs to be accurately entered, processed, and managed to derive meaningful insights. The demand for data entry services has surged as companies seek to outsource these non-core activities, enabling them to focus on their primary business operations. Additionally, the widespread adoption of cloud-based solutions and big data analytics has further fueled the demand for efficient data management services.

Another significant driver of market growth is the increasing need for data accuracy and integrity. Inaccurate or incomplete data can lead to poor decision-making, financial losses, and a decrease in operational efficiency. Organizations are increasingly recognizing the importance of maintaining high-quality data and are investing in data entry services to ensure that their databases are accurate, up-to-date, and reliable. This is particularly crucial for industries such as healthcare, BFSI, and retail, where precise data is essential for regulatory compliance, customer relationship management, and operational efficiency.

The cost-effectiveness of outsourcing data entry services is also contributing to market growth. By outsourcing these tasks to specialized service providers, organizations can save on labor costs, reduce operational expenses, and improve productivity. Service providers often have access to advanced tools and technologies, as well as skilled professionals who can perform data entry tasks more efficiently and accurately. This not only leads to cost savings but also allows businesses to reallocate resources to more strategic activities, driving overall growth.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the data entry service market during the forecast period. This can be attributed to the region's strong IT infrastructure, the presence of numerous outsourcing service providers, and the growing adoption of digital technologies across various industries. North America and Europe are also significant markets, driven by the high demand for data management services in sectors such as healthcare, BFSI, and retail. The Middle East & Africa and Latin America are anticipated to experience steady growth, supported by increasing investments in digital infrastructure and the rising awareness of the benefits of data entry services.

Service Type Analysis

The data entry service market can be segmented into various service types, including online data entry, offline data entry, data processing, data conversion, data cleansing, and others. Each of these service types plays a crucial role in ensuring the accuracy, integrity, and usability of data. Online data entry services involve entering data directly into an online system or database, which is essential for real-time data management and accessibility. This service type is particularly popular in industries such as e-commerce, where timely and accurate data entry is critical for inventory management and customer service.

Offline data entry services, on the other hand, involve entering data into offline systems or databases, which are later synchronized with online systems. This service type is often used in industries where internet connectivity may be unreliable or where data security is a primary concern. Offline data entry is also essential for processing historical data or data that is collected through physical forms and documents. The demand for offline data entry services is driven by the need for accurate and timely data entry in sectors such as manufacturing, government, and healthcare.

Data processing services involve the manipulation, transformation, and analysis of raw data to produce meaningful information. This includes tasks such as data validation, data sorting, data aggregation, and data analysis. Data processing is a critical componen

Euro Area's main stock market index, the EU50, rose to 5428 points on June 6, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 3.78% and is up 7.45% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on June of 2025.

United States Avg Weekly Earnings: RT: Building Material & Supplies Dealer data was reported at 549.430 USD in May 2018. This records an increase from the previous number of 544.360 USD for Apr 2018. United States Avg Weekly Earnings: RT: Building Material & Supplies Dealer data is updated monthly, averaging 575.750 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 617.380 USD in Apr 2011 and a record low of 529.760 USD in Mar 2017. United States Avg Weekly Earnings: RT: Building Material & Supplies Dealer data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Where Will CLPT Stock Be in 16 Weeks?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for 4-Week Moving Average of Continued Claims (Insured Unemployment) from 1967-01-28 to 2025-05-10 about moving average, continued claims, 1-month, insurance, average, unemployment, and USA.

This document contains citywide crime incident reports from the Baltimore Police Department for the City of Baltimore for the week ending 04/26/2025. For any questions related to this data set, please contact the Baltimore Police Department at Contact Baltimore Police Department.

If you have any questions or need to report an issue for this dataset, please use the following feedback form to submit your response, and the Open Baltimore support team will contact you.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

JMACU Stock Forecast: A Speculative Trend For The Next 4 Weeks

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

United States Avg Weekly Earnings: sa: Mfg: DG: Motor Vehicle Electric Equip data was reported at 953.570 USD in May 2018. This records an increase from the previous number of 940.660 USD for Apr 2018. United States Avg Weekly Earnings: sa: Mfg: DG: Motor Vehicle Electric Equip data is updated monthly, averaging 888.020 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 953.570 USD in May 2018 and a record low of 759.140 USD in Oct 2007. United States Avg Weekly Earnings: sa: Mfg: DG: Motor Vehicle Electric Equip data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G033: Current Employment Statistics Survey: Average Weekly and Hourly Earnings: Seasonally Adjusted.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Martin. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Martin, the median income for all workers aged 15 years and older, regardless of work hours, was $28,750 for males and $41,250 for females.

Contrary to expectations, women in Martin, women, regardless of work hours, earn a higher income than men, earning 1.43 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Martin, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $55,179 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Martin was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Martin median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Sinclair. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Sinclair, while the Census reported a median income of $49,375 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the town of Sinclair was not possible.

- Full-time workers, aged 15 years and older: In Sinclair, among full-time, year-round workers aged 15 years and older, males earned a median income of $90,865, while females earned $48,750, leading to a 46% gender pay gap among full-time workers. This illustrates that women earn 54 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sinclair median household income by race. You can refer the same here

Browse 10-Year Treasury Note Wednesday Weekly Options - Week 3 (WY3) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Agg Weekly Payrolls: sa: Index: Mfg: Chemical data was reported at 155.200 2007=100 in Apr 2025. This records an increase from the previous number of 153.700 2007=100 for Mar 2025. Agg Weekly Payrolls: sa: Index: Mfg: Chemical data is updated monthly, averaging 108.250 2007=100 from Mar 2006 (Median) to Apr 2025, with 230 observations. The data reached an all-time high of 156.600 2007=100 in Feb 2025 and a record low of 91.500 2007=100 in Sep 2009. Agg Weekly Payrolls: sa: Index: Mfg: Chemical data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Aggregate Weekly Payroll Index: Seasonally Adjusted.

FOCUS Reporting System FOCUSRPT is an enhanced reporting system used to create reports. Insight is a comprehensive, enterprise-wide data warehouse with advanced reporting and business intelligence capabilities.

United States SBP: TW: Cash on Hand will Currently Cover: 1-2 Business Weeks data was reported at 12.100 % in 04 Oct 2020. This records an increase from the previous number of 10.400 % for 27 Sep 2020. United States SBP: TW: Cash on Hand will Currently Cover: 1-2 Business Weeks data is updated weekly, averaging 10.800 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 16.600 % in 26 Apr 2020 and a record low of 9.600 % in 13 Sep 2020. United States SBP: TW: Cash on Hand will Currently Cover: 1-2 Business Weeks data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).