The yield on US 10 Year Note Bond Yield rose to 4.12% on December 2, 2025, marking a 0.02 percentage points increase from the previous session. Over the past month, the yield has remained flat, and it is 0.11 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 December of 2025.

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.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis (DGS30) from 1977-02-15 to 2025-11-28 about 30-year, maturity, Treasury, interest rate, interest, rate, and USA.

The yield on US 20 Year Bond Yield rose to 4.73% 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 0.23 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 US 20Y.

The yield on US 2 Year Note Bond Yield eased to 3.54% on December 2, 2025, marking a 0.01 percentage points decrease from the previous session. Over the past month, the yield has fallen by 0.08 points and is 0.65 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 2 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on December of 2025.

Index Time Series for Fidelity® Dividend ETF for Rising Rates. The frequency of the observation is daily. Moving average series are also typically included. The fund normally invests at least 80% of assets in securities included in the underlying index and in depository receipts representing securities included in the underlying index. The underlying index is designed to reflect the performance of stocks of large and mid-capitalization dividend-paying companies that are expected to continue to pay and grow their dividends and have a positive correlation of returns to increasing 10-year U.S. Treasury yields.

The yield on US 8 Week Bill Bond Yield held steady at 3.91% on November 28, 2025. Over the past month, the yield has fallen by 0.04 points and is 0.65 points lower 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 8 Week Bill Yield.

The Global Dataset of Historical Yield (GDHYv1.2+v1.3) offers annual time series data of 0.5-degree grid-cell yield estimates of major crops worldwide for the period 1981-2016. The crops considered in this dataset are maize, rice, wheat and soybean. The unit of yield data is t/ha. The grd-cell yield data were estimated using the satellite-derived crop-specific vegetation index and FAO-reported country yield statistics. Maize and rice have the data for each of two growing seasons (major/secondary). "Winter" and "spring" are used as the growing season categories for wheat. Only "major" growing season is available for soybean. These growing season categories are based on Sacks et al. (2010, doi:10.1111/j.1466-8238.2010.00551.x). The geographic distribution of harvested area changes with time in reality, but we used the time-constant data in 2000 (Monfreda et al., 2008, doi:10.1029/2007GB002947). Many missing values are found in the first (1981) and last (2016) years because grid-cell yields are not estimated for these years when growing season spans two calendar years. The data for the period 1981-2010 are the same with the version 1.2 (doi:10.20783/DIAS.528). For the period 2011-2016, a newly created version 1.3 using the satellite products that are different with earlier versions was alighned to ensure the continuity of yield time series. This version is therefore called "the alighned version v1.2+v1.3".

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Where should we live in the next 10 years? Where should we settle down without relying on public transport? Which city should we move to without fearing losing our homes?

As weather patterns become more unpredictable with aggressive changes in temperatures, I collected some data below to see if there would be a city that could help assess our answers to the prior questions. I am curious to see if cities that typically have great infrastructure for walking, biking or public transit will be better prepared than those that are more typically car centric. Whichever you prefer, we can have a sense on where you might be migrating, and to which areas.

Here's how the data was collected:

1. Rhodium & ProPublica's combined work on counties risk factors against climate change across continental US (excludes Hawaii, Alaska, Puerto Rico and Guam. Washington D.C, is excluded as it does not have a county.)
2. The available Walk Score of major cities that have a population above 100,000 represented. Cities like Delaware's Wilmington or Maine's Portland are not considered as it falls under a small-city definition
3. Maximum temperatures (for select cities): This dataset is collected from the UC Davis Department of Agricultural and Resource by Prof. Aaron Smith. I selected a monthly temporal unit and county spatial unit ranging from 2019 - 2024 July. This dataset is extracted based on the average of highest temperatures in each selected counties. I did not use the overall daily average as it can easily shadow the extremities of temperature fluctuations.

The columns have different rating systems. The counties have all major climate risks expected in the future, while corresponding cities in each county have walking, transit and biking scores to assess livability without cars.

Understanding County Climate Risks The counties were were represented on a 1- 10 scale, based on RCP 8.5 levels. Here are the following explanations (0 = lowest, 10 = highest)

1) Heat: Heat is one of the largest drivers changing the niche of human habitability. Rhodium Group researchers estimate that, between 2040 and 2060 extreme temperatures, many counties will face extremely high temperatures for half a year. The measure shows how many weeks per year will we anticipate temperatures to soar above 95 degrees. (0 = 0 weeks, 10 = 26 weeks).

2) Wet Bulb: Wet bulb temperatures occur when heat meets excessive humidity. This is commonplace across cities that have a urban island heat effects (dense concentration of pavements, less nature, higher chances of absorbing heat). That combination creates wet bulb temperatures, where 82 degrees can feel like southern Alabama on its hottest day, making it dangerous to work outdoors and for children to play school sports. As wet bulb temperatures increase even higher, so will the risk of heat stroke — and even death. The measure shows how many days will a county experience high wet bulb temperatures yearly, from 2040 to 2060. (0 = 0 days, 10 = 70 days)

3) Farm Crop Yield: With rising temperatures, it will become more difficult to grow food. Corn and soy are the most prevalent crops in the U.S. and the basis for livestock feed and other staple foods, and they have critical economic significance. Because of their broad regional spread, they offer the best proxy for predicting how farming will be affected by rising temperatures and changing water supplies. As corn and soy production gets more sensitive to heat than drought, the US will see a huge continental divide between cooler counties now having more ability to produce, while current warmer counties loosing all abilities to produce basic crops. The expected measure shows the percent decline yields from 2040 to 2060 (0 = -20.5% decline, 10 = 92% decline).

4) Sea Level Rise: As sea levels rise, the share of property submerged by high tides increases dramatically, affecting a small sliver of the nation's land but a disproportionate share of its population. The rating measures how much of property in the county will go below high tide from 2040 to 2060 (0 = 0%, 10 = 25%).

5) Very Large Fires: With heat and evermore prevalent drought, the likelihood that very large wildfires (ones that burn over 12,000 acres) will affect U.S. regions increases substantially, particularly in the West, Northwest and the Rocky Mountains. The rating calculates how many average number of large fires will we expect to see per year (0 = N/A, 10 = 2.45) from 2040 to 2071.

6) Economic Damages: Rising energy costs, lower labor productivity, poor crop yields and increasing cr...

This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "Positive effects of public breeding on U.S. rice yields under future climate scenarios". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm.

This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205.

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting

Overview

CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.

* Crops : Wheat & Maize
* Spatial Coverage : Wheat (29 countries), Maize (38).
See CY-Bench paper appendix for the list of countries.
* Temporal Coverage : Varies. See country-specific data

Data

Data format

The benchmark data is organized as a collection of CSV files, with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.

Data content

All data files are provided as .csv.

Folder structure

The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**.
```
CY-Bench
│
└─── maize
│ │
│ └─── AO
│ │ -- crop_calendar_maize_AO.csv
│ │ -- fpar_maize_AO.csv
│ │ -- meteo_maize_AO.csv
│ │ -- ndvi_maize_AO.csv
│ │ -- soil_maize_AO.csv
│ │ -- soil_moisture_maize_AO.csv
│ │ -- yield_maize_AO.csv
│ │
│ └─── AR
│ -- crop_calendar_maize_AR.csv
│ -- fpar_maize_AR.csv
│ -- ...
│
└─── wheat
│ │
│ └─── AR
│ │ -- crop_calendar_wheat_AR.csv
│ │ -- fpar_wheat_AR.csv
│ │ ...
```

Example : CSV data content for maize in country X

```
X
└─── crop_calendar_maize_X.csv
│ -- crop_name (name of the crop)
│ -- adm_id (unique identifier for a subnational unit)
│ -- sos (start of crop season)
│ -- eos (end of crop season)
│
└─── fpar_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- fpar
│
└─── meteo_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)

│ -- tmin (minimum temperature)
│ -- tmax (maximum temperature)
│ -- prec (precipitation)
│ -- rad (radiation)
│ -- tavg (average temperature)
│ -- et0 (evapotranspiration)
│ -- cwb (crop water balance)
│
└─── ndvi_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- ndvi
│
└─── soil_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- awc (available water capacity)
│ -- bulk_density
│ -- drainage_class
│
└─── soil_moisture_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- ssm (surface soil moisture)
│ -- rsm ()
│
└─── yield_maize_X.csv
│ -- crop_name
│ -- country_code
│ -- adm_id
│ -- harvest_year
│ -- yield
│ -- harvest_area
│ -- production

Data access

The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library

License and citation

We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.

Monthly and long-term United States Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Perennial grasses like switchgrass (Panicum virgatum) and miscanthus (Miscanthus × giganteus) are expected to supply a substantial amount of the United States bioeconomy’s feedstock demand. However, uncertainties around patterns of long-term yields challenge the viability of this potential. To resolve long-term yield patterns, we analyzed over 200 plantings of switchgrass and miscanthus across Michigan and Wisconsin USA measured over 5 to 15 years. During a yield-building phase, peak yields occurred within 4 – 5 years after planting, followed by a 6 – 7 year yield-decline phase in which switchgrass and miscanthus lost 30 – 47% and 14 – 40% of peak yields, respectively. Added nitrogen increased peak-yields by 10 – 20% and attenuated the yield decline by 20 – 50%. A farm-to-gate economic analysis suggests replanting switchgrass and miscanthus 5 and 9 years following their peak yields optimize profit over a 30-year time horizon. This conserved long-term yield pattern has implications for c..., Switchgrass and M. × giganteus long-term Yield data for this analysis was collected from were collected from multiple long-term experiments along Michigan and Wisconsin, USA. The 12 different experimental sites were planted through 2007 – 2013 and provided a wide range of conditions experimental conditions, plot sizes, and N fertilization rates. Given the multiple planting years our long-term yield dataset included different experiment lengths; 16 plots with up to 5 years of data, 86 plots with up to 9 years of data, 60 plots with up to 10 – 12 years of data, 32 plot with up to 13 years of data, and 20 plots with up to 15 years of data at the moment of analysis. , , # Switchgrass and Miscanthus Long-term Yield Dataset

https://doi.org/10.5061/dryad.6m905qg9x

Description of the data and file structure

The file contains Switchgrass and M. × giganteus long-term yield data were collected from multiple long-term experiments along Michigan and Wisconsin, USA. The 12 different experimental sites were planted through 2007 – 2013 and provided a wide range of conditions experimental conditions, plot sizes, and N fertilization rates. Given the multiple planting years our long-term yield dataset included different experiment lengths; 16 plots with up to 5 years of data, 86 plots with up to 9 years of data, 60 plots with up to 10 – 12 years of data, 32 plot with up to 13 years of data, and 20 plots with up to 15 years of data at the moment of analysis. These multiple planting years also allowed us to compare yields of different ages grown under the same growing seasons to better separate the age and growing seas...,

The yield on US 3 Month Bill Bond Yield held steady at 3.78% on December 2, 2025. Over the past month, the yield has fallen by 0.10 points and is 0.68 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 3 Month Bill Yield - values, historical data, forecasts and news - updated on December of 2025.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

Total-Yield-That-Is-Dividend-Plus-Net-Buyback-Yield Time Series for JPMorgan Chase & Co. JPMorgan Chase & Co. operates as a financial services company worldwide. It operates through three segments: Consumer & Community Banking, Commercial & Investment Bank, and Asset & Wealth Management. The company offers deposit, investment and lending products, cash management, and payments and services; mortgage origination and servicing activities; residential mortgages and home equity loans; and credit cards, auto loans, leases, and travel services to consumers and small businesses through bank branches, ATMs, and digital and telephone banking. It also provides investment banking products and services, including corporate strategy and structure advisory, and equity and debt market capital-raising services, as well as loan origination and syndication; payments; and cash and derivative instruments, risk management solutions, prime brokerage, and research, as well as offers securities services, including custody, fund services, liquidity, and trading services, and data solutions products. In addition, the company provides financial solutions, including lending, payments, investment banking, and asset management to small and midsized companies, local governments, nonprofit clients, and municipalities, as well as commercial real estate clients. Further, it offers multi-asset investment management solutions in equities, fixed income, alternatives, and money market funds to institutional clients and retail investors; and retirement products and services, brokerage, custody, estate planning, lending, deposits, and investment management products to high net worth clients. The company was founded in 1799 and is headquartered in New York, New York.

TBI Hospitalizations/Deaths

A 2001-2010 Study of U.S. Rates

By Health [source]

About this dataset

This dataset examines the troubling national rise in traumatic brain injury (TBI)-related emergency department (ED) visits, hospitalizations and deaths over the past decade. While TBI-related ED visits make up a large share of this increase, rates of hospitalizations related to TBI remain relatively stable. The total combined rate of all three categories steadily increased from 521.0 per 100,000 people in 2001 to 823.7 per 100,000 people in 2010 – an alarming 57% rise that demands our attention and rapid solutions in order to reverse this trend. Not only is the sudden spike concerning but so too is the slightly decreasing rates for TBI-related deaths which dropped from 18.5 per 100,000 to 17.1 per 100,000 over this time period despite overall numbers continuing to climb upwards with no sign of slowing down soon. Have a look at this dataset and explore what we can do together to work towards a healthier future free of needless fatalities caused by preventable injuries such as those related to TBIs

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Take a look at the Total column – it combines all 3 types of hospitalization numbers (Emergency Department Visits, Hospitalizations and Deaths) together into one figure per year. This makes it easy to see what the overall rate over time has been.

• The Emergency Department Visits, Hospitalizations and Deaths columns can be used individually as well – view them separately on their own scales so you can better compare them against each other year by year.

• Use filtering tools or visualizations tools if you’d like to dive deeper into each figure separately in order to pinpoint trends or changes in any particular subcategory more closely.

• The data is displayed historically; however, use math operations such as averaging or percentage increases/decreases across different years if you’d like analyze trends over time more broadly

Research Ideas

• To compare the rate of TBI-related hospitalizations, ED visits and deaths between states/countries/age groups.
• To create a visual representation (i.e., an infographic) to track TBI-related hospitalization, ED visit and death rates over the past decade in order to inform public health initiatives.
• To study the effect of investments made in prevention programs on the rate of TBI-related hospitalizations, ED visits and deaths in different regions or cities over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Rates_of_TBI-related_Emergency_Department_Visits_Hospitalizations_and_Deaths_United_States_2001_2010.csv | Column name | Description | |:--------------------------------|:------------------------------------------------------------------------------------------------| | Year | Year of the data point. (Integer) | | Emergency Department Visits | Number of TBI-related emergency department visits per 100,000 people. (Float) | | Hospitalizations | Number of TBI-related hospitalizations per 100,000 people. (Float) | | Deaths | Number of TBI-related deaths per 100,000 people. (Float) | | Total | Total number of TBI-related ED visits, hospitalizations, and deaths per 100,000 people. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

The yield on 5 Year TIPS Yield rose to 1.36% on December 1, 2025, marking a 0.07 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.03 points, though it remains 0.40 points lower 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 5 Year TIPS Yield.

Marsh migration potential in the Chesapeake Bay (CB) salt marshes is calculated in terms of available migration area for each marsh unit defined by Ackerman and others (2022). The space available for landward migration is based on the NOAA marsh migration predictions under 2.0 feet of local sea-level rise (SLR). The migration space is further divided by National Hydrography Dataset (NHD) Plus catchments before assigning related catchment polygons to each marsh unit. The migration rates are then calculated using present day estimates at the prescribed rate of SLR, which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). Through scientific efforts, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Chesapeake Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Marsh migration is one of the natural responses to SLR. References: Ackerman, K.V., Defne, Z., and Ganju, N.K., 2022, Geospatial characterization of salt marshes in Chesapeake Bay: U.S. Geological Survey data release, https://doi.org/10.5066/P997EJYB. Sweet, W.V., Hamlington, B.D., Kopp, R.E., Weaver, C.P., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K.D., and Zuzak, C., 2022, Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp.