View weekly updates and historical trends for 15 Year Mortgage Rate. from United States. Source: Freddie Mac. Track economic data with YCharts analytics.

View daily updates and historical trends for 15 Year Fixed Rate Conforming Mortgage Index. from United States. Source: Optimal Blue. Track economic data w…

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

• This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
• It includes daily stock data with opening and closing prices, highs, lows and trading volume.
• This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
• By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

Columns Description:

1. Date: The trading date of the stock data entry.
2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

Usefulness of Data:

1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

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The yield on US 30 Year Bond Yield rose to 4.76% on December 2, 2025, marking a 0.02 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.06 points and is 0.35 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 30 Year Bond Yield - values, historical data, forecasts and news - updated on December of 2025.

Mortality from all causes (for ages < 1yr all deaths, including where no cause is recorded; for ages >= 1 yr ICD-10 A00-Y99 equivalent to ICD-9 001-E999). To reduce mortality. Legacy unique identifier: P00357

15-Year Fixed Mortgage Rate - Historical chart and current data through 2025.

Chennai Weather Dataset (2009-2024): Comprehensive Climate Records

This dataset offers a detailed view of weather patterns in Chennai, India, spanning from September 9, 2009, to July 29, 2024. It includes 6,488 unique entries, providing a rich source of meteorological data for analysis.

Key features: 1. Temporal data: Date and time stamps, including day, month, and year 2. Wind conditions: Wind speed 3. Temperature: Ranging from 21°C to 38°C 4. Barometric pressure: Measured in millibars (mb) 5. Humidity: Percentage from 0% to 100% 6. Weather descriptions: E.g., "Passing clouds", "Broken clouds", "Drizzle", "Fog"

The dataset is structured with hourly readings, typically in 6-hour intervals (00:00-06:00, 06:00-12:00, 12:00-18:00, 18:00-00:00). This granularity allows for detailed analysis of daily and seasonal weather patterns.

Notable characteristics: - Temperature range captures Chennai's tropical climate - Humidity levels reflect the coastal location's moisture patterns - Varied weather descriptions provide insight into typical conditions

This comprehensive dataset is suitable for various meteorological studies, climate trend analysis, and weather prediction modeling for the Chennai region.

Mortality from intentional self-harm (ICD-10 X60-X84 equivalent to ICD-9 E950-E959). This indicator does not include deaths by injury undetermined. To reduce the number of suicides. Legacy unique identifier: P00541

Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Males data was reported at 68.067 % in Mar 2025. This records a decrease from the previous number of 68.153 % for Feb 2025. Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Males data is updated monthly, averaging 67.871 % from Feb 1978 (Median) to Mar 2025, with 566 observations. The data reached an all-time high of 75.155 % in Feb 1978 and a record low of 63.372 % in May 2020. Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Males data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G048: Labour Force Participation Rate. Employment to Population Ratio refers to the number of employed persons expressed as a percentage of the civilian population aged 15 years and over.

Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Females data was reported at 60.416 % in Mar 2025. This records a decrease from the previous number of 60.465 % for Feb 2025. Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Females data is updated monthly, averaging 51.551 % from Feb 1978 (Median) to Mar 2025, with 566 observations. The data reached an all-time high of 60.524 % in Nov 2024 and a record low of 39.727 % in Jun 1979. Australia Employment to Civilian Population Ratio: Trend: 15 Years and Over: Females data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G048: Labour Force Participation Rate. Employment to Population Ratio refers to the number of employed persons expressed as a percentage of the civilian population aged 15 years and over.

The time trend of APOs in women with SLE over the past 15 years.

🎬 Korean Box Office (2010–April 2025)

This dataset contains detailed records of all movies released in South Korea from January 2010 through April 2025.
Collected from the official Korean Film Council (KOFIC) database, it provides insights into audience behavior, genre trends, revenue performance, and more.

🧾 What’s included?

Each movie entry includes: - 🎞️ Title (translated to English) - 🎬 Director name - 📅 Release date - 🌍 Nation of origin - 🏷️ Genre (standardized in English) - 💸 Total revenue (nationwide and Seoul) - 👥 Audience count (nationwide and Seoul) - 📽️ Movie type (Feature, Short, Omnibus, etc.) - 🏛️ Release type (Theatrical, Online, etc.) - 🔞 Rating (e.g. 15+, 12+, R) - 🎟️ Film classification (Commercial, Indie/Art Film)

📊 Use cases

• Track and visualize genre popularity over time
• Compare audience turnout vs. revenue by region
• Analyze age ratings and their box office impact
• Discover how Korean movie trends evolved from Myeongryang to Parasite

📌 Source

Data collected from KOBIS - Korean Box Office Information System, the official film statistics portal maintained by the Korean Film Council (KOFIC).
Data was manually cleaned, translated, and formatted for global analysis.

⚠️ Notes

• 2025 data is current up to April 30. This is not a full-year dataset.
• Genres and other fields have been translated to English for consistency.
• Future updates may be considered depending on demand.

🧑‍💻 Created by: Minjeong (민정)
📍 Digital Reputation Analyst | Data Storyteller

Korea, #boxoffice, #movies, #film, #cinema

jats:titleABSTRACT/jats:titlejats:pThere are now a rich variety of genomic and genotypic resources available to wheat researchers and breeders. However, the generation of high-quality and field-relevant phenotyping data which is required to capture the complexities of gene x environment interactions remains a major bottleneck. Historical datasets from national variety performance trials (NVPT) provide sufficient dimensions, in terms of numbers of years and locations, to examine phenotypic trends and study gene x environment interactions. Using NVPT for winter wheat varieties grown in the UK between 2002 – 2017, we examined temporal trends for eight traits related to yield, adaptation, and grain quality performance. We show a non-stationary linear trend for yield, grain protein content, HFN and days to ripening. Our data also show high environmental stability for yield, grain protein content and specific weight in UK winter wheat varieties and high environmental sensitivity for Hagberg Falling Number. Using the historical NVPT data in a genome-wide association analysis, we uncovered a significant marker-trait association peak on wheat chromosome 6A spanning the jats:italicNAM-A1/jats:italic gene that have been previously associated with early senescence. Together our results show the value of utilizing the data routinely collected during variety evaluation process for examining breeding progress and the genetic architecture of important traits./jats:p

Emergency admissions to hospital for neuroses. The purpose of this indicator is to help monitor NHS success in prevention and treatment of neuroses outside hospital. In 2014, NHS England set a target to reduce total emergency admissions by 3.5%, ‘as a clear indicator of the effectiveness of local health and care services in working better together to support people’s health and independence in the community’. Emergency admissions to hospital can be avoided if local systems are put in place firstly to identify those at risk prior to attendance and target primary care services, and secondly to identify those emergency department attendees better cared for outside of hospital and provide a safe route into more appropriate community care. The next release date for this indicator is to be confirmed. Legacy unique identifier: P02179

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

View market daily updates and historical trends for 15-Year Eurozone Central Government Bond Par Yield Curve. Source: Eurostat. Track economic data with Y…

BackgroundPrevious studies examining trends in sleep loss among adolescents have mainly focused on single countriy and region. This study aims to analyze temporal trends in the prevalence of anxiety-induced sleep loss among adolescents from 29 countries in five regions.MethodsThis study used data from the Global School-based Student Health Survey 2003–2018, which surveyed 215,380 adolescents from 29 countries with at least two cross-sectional surveys per country. The weighted country-specific prevalence of anxiety-induced sleep loss and trends across the survey years were evaluated. Random- or fixed-effects meta-analyses were used to calculate pooled prevalence and temporal trends across 29 countries.ResultsTemporal variations in anxiety-induced sleep loss across countries were identified. Increasing (Suriname, Vanuatu, and Myanmar), decreasing (Namibia, Jamaica, the Philippines, Samoa, and Indonesia), and stable (all other countries) trends in anxiety-induced sleep loss were noted. The pooled weighted prevalence of anxiety-induced sleep loss was 11.35 and 10.67% in the first and last surveys, respectively. There was no meaningful change in the propensity to have anxiety-related sleep disorders over time, with the reduction and OR of these two surveys being 0.54 (−0.53–1.61) and 0.98 (0.88–1.10). For subgroup analyses, no significant differences in pooled anxiety-induced sleep loss trends were seen between the two surveys for different sexes, regions, incomes, survey years in the first wave, survey periods, or number of surveys.ConclusionTrends in the prevalence of anxiety-induced sleep loss in adolescents varied significantly across different countries. Generally, a stable trend was observed in 21 of the 29 countries surveyed. Our study provides data that can aid policymakers in establishing country-specific strategies for reducing anxiety-induced sleep loss in adolescents.

BackgroundStudies of secular trends in mental unhealth indicate that adolescents in the Nordic countries, especially girls, have an increased reported prevalence of mental health problems the last decades. This increase needs to be seen in the light of the adolescents' assessments of their perceived overall health.ObjectiveTo investigate whether a person-centered approach to research can enhance understanding of changes over time in the distribution of mental health problems among Swedish adolescents.MethodA dual-factor approach was used to study changes over time in mental health profiles among nationally representative 15-year-old adolescent samples from Sweden. Cluster analyses of subjective health symptoms (psychological and somatic) and perceived overall health from the Swedish Health Behavior in School-aged Children (HBSC) surveys of 2002, 2006, 2010, 2014, and 2018 were used to identify these mental health profiles (n = 9,007).ResultsFour mental health profiles were identified by a cluster analysis which combined all five data collections—Perceived good health, Perceived poor health, High psychosomatic symptoms, and Poor mental health. There were no significant differences in the distributions of these four mental health profiles between the survey years 2002 and 2010, but substantial changes took place between 2010 and 2018. Here, particularly the High psychosomatic symptoms profile increased among both boys and girls. The Perceived good health profile decreased among both boys and girls, and the Perceived poor health profile decreased among girls. The profile involving the most pronounced mental health problems, the Poor mental health profile (perceived poor health, high psychosomatic problems), was stable from 2002 to 2018 among both boys and girls.ConclusionThe study shows the added value of using person-centered analyses to describe differences in mental health indicators between cohorts of adolescents over longer periods of time. In contrast to the long-term increase in mental health problems seen in many countries, this Swedish study did not find an increase among young persons, both boys and girls, with the poorest mental health, the Poor mental health profile. Rather, the most substantial increase over the survey years, predominantly between 2010 and 2018, was found among the 15-year-olds with High psychosomatic symptoms only.

Yearly citation counts for the publication titled "Diagnosis of struma peritonei 15 years after rupture of mature teratoma".