The yield on US 10 Year Note Bond Yield eased to 4.22% on July 1, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.23 points and is 0.22 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 July of 2025.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 2-Year Constant Maturity, Quoted on an Investment Basis (DGS2) from 1976-06-01 to 2025-06-27 about 2-year, maturity, Treasury, interest rate, interest, rate, and USA.

The yield on US 2 Year Note Bond Yield rose to 3.73% on July 1, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.22 points and is 1.02 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 July of 2025.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis (DGS1MO) from 2001-07-31 to 2025-06-26 about 1-month, bills, maturity, Treasury, interest rate, interest, rate, and USA.

The yield on 10 Year TIPS Yield rose to 1.96% on June 27, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.17 points and is 0.10 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 10 Year TIPS Yield.

The yield on China 10Y Bond Yield eased to 1.65% on July 1, 2025, marking a 0 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.05 points and is 0.59 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on Germany 10Y Bond Yield eased to 2.51% on June 20, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.14 points, though it remains 0.10 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Germany 10-Year Bond Yield - values, historical data, forecasts and news - updated on June of 2025.

Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States (IRLTLT01USM156N) from Apr 1953 to May 2025 about long-term, 10-year, bonds, yield, government, interest rate, interest, rate, and USA.

The interview protocol aims to gauge their perceptions and responses toward brand activism, with a focus on their interactions with brands endorsing social issues. Within this dataset, interviewees unpack their perspective on Gen Z attributes and evaluate their resonance with prevalent depictions. Central to the interview is the participants' feedback on prominent brand campaigns such as Nike's "JUST DO IT," Gillette's "We Believe," and Libresse/Bodyform's "Viva la Vulva," to name a few. Their analyses unveil perceptions of brand genuineness, the synergy between a brand's image and its advocated social concerns, and the overarching ramifications of brand activism on consumer purchasing decisions. In addition, the dataset broaches essential themes like social credibility, the influence of brand spokespeople, geographical variances in brand activism, and the prospective outcomes for customer fidelity and product pricing. This collection offers an in-depth glimpse into the intricate dynamics between Gen Z and brands during this period of intensified social and political awareness. This dataset comprises qualitative data obtained from interviews with 37 individuals from the Gen Z demographic, predominantly aged between 20-25 years. Of these participants, 53.3% identified as male (n=20), 40% as female (n=15), and 6.7% opted not to specify their gender (n=2). The participants for these interviews were strategically sourced using the snowballing technique between 2021 and 2022. Among them, 33 are international young adults who, at some point within the last 1-2 years, were studying or employed in the Netherlands. It is noteworthy that between October 2021 and February 2022, the Netherlands observed a stringent lockdown, mandating remote work. Consequently, some interviewees, despite affiliations with Dutch organizations, were in their home countries during their respective interviews. The distribution of participants based on their continents of origin, namely North America, Europe, and Asia, and taking into account their place of residence in instances of dual citizenship, is detailed as follows: 1) Europe has the predominant representation with a sum of 20 participants; 2) North America consists of 7 participants, all originating from the USA; 3) Asia comprises 6 participants spread across four countries: Vietnam, Indonesia, Japan, and South Korea. Additionally, 4) South America is denoted by one participant from Bolivia and another participant holding dual citizenship from Argentina but currently residing in the US.

The yield on Canada 2 Year Bond Yield eased to 2.60% on June 27, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.03 points and is 1.40 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 Canada 2Y.

ANALYTICS IN RETAIL: With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival. Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers. Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others. DATA AVAILABILITY:  Retail Data.xlsx o This book has three sheets (Customer, Transaction, Product Heirarchy) o Customer: Customers information including demographics o Transaction: Transactions of customers o Product Heirarchy: Product information (cateogry, sub category etc...) BUSINESS PROBLEM: A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories. Create a report and display the below calculated metrics, reports and inferences. 1. Merge the datasets Customers, Product Hierarchy and Transactions as Customer_Final. Ensure to keep all customers who have done transactions with us and select the join type accordingly. 2. Prepare a summary report for the merged data set. a. Get the column names and their corresponding data types b. Top/Bottom 10 observations c. “Five-number summary” for continuous variables (min, Q1, median, Q3 and max) d. Frequency tables for all the categorical variables 3. Generate histograms for all continuous variables and frequency bars for categorical variables. 4. Calculate the following information using the merged dataset : a. Time period of the available transaction data b. Count of transactions where the total amount of transaction was negative 5. Analyze which product categories are more popular among females vs male customers. 6. Which City code has the maximum customers and what was the percentage of customers from that city? 7. Which store type sells the maximum products by value and by quantity? 8. What was the total amount earned from the "Electronics" and "Clothing" categories from Flagship Stores? 9. What was the total amount earned from "Male" customers under the "Electronics" category? 10. How many customers have more than 10 unique transactions, after removing all transactions which have any negative amounts? 11. For all customers aged between 25 - 35, find out: a. What was the total amount spent for “Electronics” and “Books” product categories? b. What was the total amount spent by these customers between 1st Jan, 2014 to 1st Mar, 2014?

The yield on India 10Y Bond Yield eased to 6.37% on July 1, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.09 points, though it remains 0.64 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 July of 2025.

High-frequency observations of surface water at fine spatial scales are critical to effectively manage aquatic habitat, flood risk and water quality. We developed inundation algorithms for Sentinel-1 and Sentinel-2 across 12 sites within the conterminous United States (CONUS) covering >536,000 km2 and representing diverse hydrologic and vegetation landscapes. These algorithms were trained on data from 13,412 points spread throughout the 12 sites. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables not only from Sentinel-1 and Sentinel-2, but also variables derived from topographic and weather datasets. The Sentinel-1 model was developed distinct from the Sentinel-2 model to enable the two time series to be integrated into a single high-frequency time series, while open water and vegetated water were both mapped to retain mixed pixel inundation. Results were validated against 7,200 visually inspected points derived from WorldView and PlanetScope imagery. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for Sentinel-1 and 3.1% and 0.5% for Sentinel-2, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. Sentinel-2 showed higher accuracy (10.7% omission and 7.9% commission error) relative to Sentinel-1 (28.4% omission and 16.0% commission error). Our results demonstrated that Sentinel-1 and Sentinel-2 time series can be integrated to improve the temporal resolution when mapping open and vegetated waters, although sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for subpixel, vegetated water compared with open water.

The yield on France 10Y Bond Yield rose to 3.29% on June 30, 2025, marking a 0.03 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.09 points, though it remains 0.01 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. France 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.

National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.

The specific raster datasets in this publication include:

Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.

Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.

Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.

Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.

Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.

Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.

Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.

Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S.

This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. There are two companion data publications that are part of the WRC 2.0 data update: one that includes datasets of wildfire hazard and risk for populated areas of the nation, where housing units are currently present (Jaffe et al. 2024, https://doi.org/10.2737/RDS-2020-0060-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030).

The Twentieth Century Reanalysis Project, produced by the Earth System Research Laboratory Physical Sciences Division from NOAA and the University of Colorado Cooperative Institute for Research in Environmental Sciences, is an effort to produce a global reanalysis dataset spanning a portion of the nineteenth century and the entire twentieth century (1851 - near present), assimilating only surface observations of synoptic pressure. Boundary conditions of pentad sea surface temperature and monthly sea ice concentration and time-varying solar, volcanic, and carbon dioxide radiative forcings are prescribed. Products include 6-hourly ensemble mean and spread analysis fields on a 2 by 2 degree global latitude-longitude grid, and 3 and 6-hourly ensemble mean and spread forecast (first guess) fields on a global Gaussian T62 grid. Fields are accessible in yearly time series (1 file per parameter) and monthly synoptic time (all parameters per synoptic hour) files. This dataset provides the first estimates of global tropospheric variability spanning 1851 to 2012 at six-hourly resolution. Fields from 1851 to 1860 are a first attempt at this period and will be improved in future versions. Fields from 1861 to 2011 are most relevant for climate and weather studies. 20th Century Reanalysis Version 2c uses the same model as version 2 with new sea ice boundary conditions from the COBE-SST2 (Hirahara et al. 2014), new pentad Simple Ocean Data Assimilation with sparse input (SODAsi.2, Giese et al. 2015) sea surface temperature fields from through 2012, Daily High-Resolution-Blended Analyses for Sea Surface Temperature starting with 2013, and additional observations from ISPD version 3.2.9.

A low pressure bias in marine pressures from the US Maury Collection (Woodruff et al. 2005, Wallbrink et al. 2009, ) appears to have affected the resultant 20CR version 2c mass-related fields (e.g., pressure, geopotential height) from 1851 to about 1865. Please see opportunities for improvement [https://www.esrl.noaa.gov/psd/data/gridded/20thC_ReanV2c/opportunities.html] for additional information.

The Twentieth Century Reanalysis Project version 2c used resources of the National Energy Research Scientific Computing Center managed by Lawrence Berkeley National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Version 2c is a contribution to the international Atmospheric Circulation Reconstructions over the Earth initiative. Support for the Twentieth Century Reanalysis Project is provided by the U.S. Department of Energy Office of Science (BER) and the NOAA Climate Program Office MAPP program.

The yield on Australia 10Y Bond Yield rose to 4.16% on June 30, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.11 points and is 0.22 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Australia 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on June of 2025.

cloudsen12

A dataset about clouds from Sentinel-2 CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land… See the full description on the dataset page: https://huggingface.co/datasets/jfloresf/mlstac-demo.

The yield on Indonesia 10Y Bond Yield eased to 6.62% on June 30, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.21 points and is 0.45 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Indonesia 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on June of 2025.

The yield on Japan 10Y Bond Yield eased to 1.40% on July 1, 2025, marking a 0.04 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.11 points, though it remains 0.31 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Japan 10 Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.