The yield on US 10 Year Note Bond Yield eased to 4.11% on October 8, 2025, marking a 0.03 percentage points decrease from the previous session. Over the past month, the yield has edged up by 0.02 points and is 0.04 points higher 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 October of 2025.

The yield on US 30 Year Bond Yield rose to 4.73% on October 8, 2025, marking a 0.01 percentage points increase from the previous session. Over the past month, the yield has remained flat, and it is 0.39 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 October of 2025.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 10 feet (305 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 20 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 10-ft scenario represented here, the modeled water level for a 20-day frequency is 374 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. As an alternative to the SLR scenarios in other data layers that we provide, our project also provides the ability to select specific amounts of SLR in increments of one foot, independent of any particular scenario. This information can be used if guidance for a project requires planning for a specific amount of SLR rather than a time horizon. The present layer models a sea level rise of 10 feet (305 cm). We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 10-ft scenario represented here, the modeled water level for a 50-day frequency is 369 cm. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

The yield on 10 Year TIPS Yield eased to 1.77% on October 7, 2025, marking a 0.04 percentage points decrease from the previous session. Over the past month, the yield has edged up by 0.08 points, 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.

About this dataset

Context

The dataset tabulates the Rising Sun 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 Rising Sun. The dataset can be utilized to understand the population distribution of Rising Sun by age. For example, using this dataset, we can identify the largest age group in Rising Sun.

Key observations

The largest age group in Rising Sun, MD was for the group of age 10 to 14 years years with a population of 272 (9.87%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Rising Sun, MD was the 80 to 84 years years with a population of 42 (1.52%). 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 Rising Sun is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Rising Sun 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 Rising Sun Population by Age. You can refer the same here

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

United States ACM 10 Year Treasury Term Premium data was reported at 0.615 % in Apr 2025. This records an increase from the previous number of 0.391 % for Mar 2025. United States ACM 10 Year Treasury Term Premium data is updated monthly, averaging 1.465 % from Jun 1961 (Median) to Apr 2025, with 767 observations. The data reached an all-time high of 5.141 % in May 1984 and a record low of -1.317 % in Jul 2020. United States ACM 10 Year Treasury Term Premium data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.M025: Treasury Term Premia.

About this dataset

Context

The dataset tabulates the United States population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of United States across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of United States was 340.11 million, a 0.98% increase year-by-year from 2023. Previously, in 2023, United States population was 336.81 million, an increase of 0.83% compared to a population of 334.02 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of United States increased by 57.95 million. In this period, the peak population was 340.11 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the United States is shown in this column.
• Year on Year Change: This column displays the change in United States population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 United States Population by Year. You can refer the same here

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The yield on US 3 Year Note Bond Yield held steady at 3.58% on October 8, 2025. Over the past month, the yield has edged up by 0.06 points, though it remains 0.35 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 3 Year Note Yield - values, historical data, forecasts and news - updated on October of 2025.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

Inflation Rate in the United States increased to 2.90 percent in August from 2.70 percent in July of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal, state, and local sources. First, a decision tree-based dataset is built that describes the fabric or integrity of the coastal landscape and includes landcover, elevation, slope, long-term (>150 years) shoreline change trends, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (e.g., flooding, wave power, and probability of storm overwash) and persistent hazards (e.g., relative sea-level rise rate, short-term (about 30 years) shoreline erosion rate, and storm recurrence interval). The fabric dataset is then merged with the coastal hazards databases and a training dataset made up of hundreds of polygons is generated from the merged dataset to support a supervised learning classification. Results from this pilot study are location-specific at 10-meter resolution and are made up of four raster datasets that include (1) quantitative and qualitative information used to determine the resistance of the landscape to change, (2 & 3) the potential coastal hazards that act on it, (4) the machine learning output, or Coastal Change Likelihood (CCL), based on the cumulative effects of both fabric and hazards, and (5) an estimate of the hazard type (event or persistent) that is the likely to influence coastal change. Final outcomes are intended to be used as a first order planning tool to determine which areas of the coast may be more likely to change in response to future potential coastal hazards, and to examine elements and drivers that make change in a location more likely.

Summarizes the U.S. government's total outstanding debt at the end of each fiscal year from 1789 to the current year.