This dataset provides historical stock price data for the BSE 500 index over a period of 10 years (31/03/2014 - 01/04/2024). It includes daily information such as opening price, closing price, highest price, and lowest price for each trading day.

The yield on 10 Year TIPS Yield eased to 1.90% on August 1, 2025, marking a 0.07 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.06 points, though it remains 0.14 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 10 Year TIPS Yield.

Replication Package of the paper "From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects"ABSTRACT:Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. Software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache Software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git).We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.You can find the full paper at: https://doi.org/10.1145/3345629.3345639If you use this dataset for your research, please reference the following paper:@inproceedings{Vieira:2019:RBC:3345629.3345639, author = {Vieira, Renan and da Silva, Ant^{o}nio and Rocha, Lincoln and Gomes, Jo~{a}o Paulo}, title = {From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects}, booktitle = {Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering}, series = {PROMISE'19}, year = {2019}, isbn = {978-1-4503-7233-6}, location = {Recife, Brazil}, pages = {80--89}, numpages = {10}, url = {http://doi.acm.org/10.1145/3345629.3345639}, doi = {10.1145/3345629.3345639}, acmid = {3345639}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Bug-Fix Dataset, Mining Software Repositories, Software Traceability}, } P.S: We added a new dataset version (v1.0.1). In this version, we fix the git commit features that track the src and test files. More info can be found in the fix-script.py file.

The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

The yield on US 10 Year Note Bond Yield eased to 4.28% on June 26, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.17 points and is 0.01 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 10 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on June of 2025.

Ten-year comparison (2004-2013) of taxpayer income showing an analysis of the number of filers and the Adjusted Gross Income.

Context

COVID-19 or Corona Virus is on anyone's lips, since it has affected (and still affecting) a lot of aspects in our lives. From when the virus was first considered a pandemic until now, it has driven the markets crazy, having one of the most significant effects on the past years. No one was able to predict this and none of the financial models was prepared for the huge change the market has suffered. This dataset aims to explain the market evolution before and after the COVID-19

Content

Financial historical data from the World Major Indices, including: Shanghai, FTSE MIB, S&P 500, Nasdaq, Dow 30, Euro Stoxx 50, and much more. The dataset contains: OHLC values, the Volume and the Currency.

Note that the dataset has been generated using investpy an open-source Python package to extract financial data from Investing.com, and you can find all the usage information and documentation at: https://github.com/alvarobartt/investpy.

Inspiration

This dataset aims to explain the market evolution before and after the COVID-19 so as to extract conclusions based on just market data or maybe aggregating external data such as news reports, tweets, etc. so feel free to use this dataset and combine it with others so that we, the community, can develop useful kernels so as to analyse and understand this situation and its impacts. So it is also an open call to researchers, data scientists, financial analysts, etc. so to collaborate together in a market study on the impacts of COVID-19.

Acknowledgements

This dataset been created by Álvaro Bartolomé del Canto using investpy so as to retrieve the historical data from Investing.com. Also, the banner image is property of Investing.com since it is an Investing.com Weekly Comic.

The yield on India 10Y Bond Yield eased to 6.37% on August 1, 2025, marking a 0 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.03 points, though it remains 0.53 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. India 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on August of 2025.

Data used for the Meta-Analysis of 46 Career Pathways Impact and data from four large nationally representative longitudinal surveys, as well as licensed data on occupational transitions from online career profiles, to examine workers’ career paths and wages for the Career Trajectories and Occupational Transitions Study.

1. With continued global changes, such as climate change, biodiversity loss and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture-recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture-recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state space definition and between-primary-period movement models on demographic parameter estimation. We demonstrate the implications on a 10-year camera-trap study of tigers in India. (This is the dataset presented here).

2. The results of our simulation study show that movement biases survival estimates in open SCR models when little is...

As of January 1, 2009, Connecticut law mandates that medical providers must conduct annual lead screening (i.e., blood lead testing) for each child 9 to 35 months of age. Furthermore, the law requires that any child between 36-72 months of age who has not been previously tested must also be tested by the child’s medical provider, regardless of risk.

This dataset includes the 10-year prevalence in Connecticut's top five cities.

This data release contains eight datasets that represent the entirety of the data collected for a study that examined breeding-bird densities in native mixed-grass prairie from 2003 to 2012 at and adjacent to wind facilities in North Dakota and South Dakota, USA. Data were collected to determine breeding-bird density per 100 hectares (ha) by distance bands from turbines and by excluding habitat that may not be considered suitable as breeding habitat for particular bird species. A subset of the data that included only one year prior to turbine construction and several years post-construction and that lent itself to a before-after-control-impact (BACI) assessment was published as its own data release and paper in 2016 in Conservation Biology by authors J. Shaffer and D. Buhl. The all-inclusive data release described hereafter is of the same basic format but includes all years and all study sites (also referred to as study plots), even those that did not lend themselves to a BACI assessment. The data release contains eight datasets with discrete topics of information, namely on bird occurrence; years of study organized by study site and treatment (that is, impact—indicating post-turbine construction) or control status and pre- or post-treatment status; overall study plot area; plot area by habitat-exclusion areas and by distance bands from turbines; turbine locations; vegetation structural data; locations of survey grid points; and bird codes and associated English common names and scientific names for the list of bird species observed during the study. The ‘bird occurrence’ dataset includes bird species identification, sex, mating-pair status, and geographic locations of individual birds, which were obtained to determine locations of individual birds from nearest turbine location and to ultimately determine bird density per 100 ha. The ‘pre post years’ dataset indicates the years that individual study sites (also known as study plots) within study areas were surveyed, whether the study site was a control or treatment site (thus indicating whether the site was never expected to experience turbine construction or whether the site was expected to experience turbine construction), and whether the study site was considered a pre-treatment or post-treatment year (thus indicating for treatment sites whether the site did not have or did have turbines that particular year). The ‘overall plot area’ dataset provides the overall areal extent of each study plot within which bird and vegetation data were obtained and to aid in determining bird density per 100 ha. The ‘plot area by distance band habitat’ dataset represents a refinement of plot area by categorizing area within up to four habitat-exclusion variables and by four distance bands in concurrent rings from turbines (that is, 0–100 meters [m], 100–200 m, 200–300 m, and greater than 300 m from turbines); these data allow one to calculate bird density per 100 ha by distance band and with exclusion of habitat in which the bird species would not be expected to be occupying. The ‘turbine location’ dataset indicates geographic location of individual turbines, which was obtained in order to determine distance from individual bird locations to nearest turbine location. The ‘vegetation’ dataset contains measurements that characterize average vegetation structural measurements and life form and was collected to determine if there were differences in vegetation structure between control and treatment sites and pre-treatment and post-treatment years. The ‘study survey grid point’ contains geographic location of individual grid points by study site, which indicates the exact location of each study site. The ‘bird codes and names’ dataset indicates the four-letter bird codes and the English common and scientific names that they represent and also provides a list of bird species observed during the study.

This is a data set of 10-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1990. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al.(2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre.

Series of Incident data and summary statistics reports produced which provide statistical information on incidents by type, year, geographical location, and others. The data provided is that from the Hazardous Materials Incident Report Form 5800.1

About this dataset

Context

The dataset tabulates the Ten Sleep population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Ten Sleep. The dataset can be utilized to understand the population distribution of Ten Sleep by age. For example, using this dataset, we can identify the largest age group in Ten Sleep.

Key observations

The largest age group in Ten Sleep, WY was for the group of age 60 to 64 years years with a population of 31 (14.49%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Ten Sleep, WY was the 25 to 29 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Ten Sleep is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Ten Sleep total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Ten Sleep Population by Age. You can refer the same here

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This data is downloaded from the official Bombay Stock Exchange Website (BSE). This file contains the last 10 years of Historical Stock Price (By Security & Period) Security Name - Nestle India Ltd. Period - Daily Start Date - 2nd January 2012 End Date - 21st April 2022. This is one of the Best datasets for Regression Supervised Machine Learning. You can Perform SImple as well as Multiple Linear Regression on this Dataset.

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on corporate actions data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally and over 20K+ from Asia, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data

In the short term the Infrastructure Plan will help industry, with its own planning and workforce management, and inform decision-making across all levels of government. Over the longer term, the Infrastructure Plan sets direction for planning and delivering infrastructure in the Northern Territory. All statistics referred to in the Infrastructure Plan are based on 2015–16 unless otherwise stated.

Ocean circulation model hindcast of Kiritimati (Republic of Kiribati) developed by the SIROCCO team for SPC.

Simulations performed using SYMPHONIE for the period 2010-2019 with an horizontal resolution of 150 m.

The model is forced at the boundaries by a larger scale SYMPHONIE model at 1 km resolution, itself forced at the boundaries by the Mercator global ocean analysis (https://doi.org/10.48670/moi-00016). Wind forcing is provided by the ECMWF ERA5 reanalysis (https://doi.org/10.24381/cds.adbb2d47). The 150 m domain is forced at the boundaries with tidal harmonics provided by the FES2014 global ocean tide atlas (https://doi.org/10.5194/os-17-615-2021).

This product has not been validated against in situ data. PCCOS/SPC does not warrant or suggest that this data is fit for any particular purpose.