Track real-time 10 Year Treasury Rate yields and explore historical trends from year start to today. View interactive yield curve data with YCharts.

The yield on US 10 Year Note Bond Yield eased to 4.04% on October 10, 2025, marking a 0.11 percentage points decrease from the previous session. Over the past month, the yield has edged up by 0.01 points, though it remains 0.07 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 October of 2025.

View market daily updates and historical trends for 10 Year Treasury Rate. from United States. Source: Federal Reserve. Track economic data with YCharts a…

This is an Annual Periodic Site Status Report (PSSR) that includes four quarterly groundwater monitoring/sampling events, two in-situ chemical oxidation (ISCO) injection events, and one confirmation soil borings and soil sampling event.bThe Thomas O. Price Service Center (TOPSC) is located at 4004 South Park Avenue, Tucson, Arizona, near the southwest corner of Park Avenue and Ajo Way. TOPSC formerly contained 23 underground storage tanks (USTs) and associated fuel dispensing equipment/piping that were the sources of multiple petroleum releases. In June 1989, diesel fuel was observed seeping from concrete joints at the north end of the dispenser islands, and evidence of petroleum releases were reported to ADEQ on June 14, 1989. All USTs were taken out of service by November 1992. Since the initial discovery, the site has been investigated and remediated under ADEQ’s LUST program. TOPSC appeared to be the source of Liquid-Phase Hydrocarbon (LPH) and dissolved petroleum hydrocarbons in the shallow groundwater zone that extended northeasterly from TOPSC to COT Fire Station #10 (TFS-10), located north of Ajo Way. However, LPH discovered in monitor well WR-220A exhibited characteristics that were inconsistent with TOPSC fuel, and it was determined by COT and ADEQ not to be associated with the TOPSC LUST site (Accutest, 2013). Monitor and remediation wells at the site are either screened across the shallow or deep-zone perched aquifers. The shallow zone was defined in the original Corrective Action Plan (CAP), 1994 as the uppermost groundwater unit beneath the Site with a depth-to-water of approximately 90 to 115 feet below ground surface (ft bgs). The deep zone was defined in the CAP as the second groundwater-bearing zone encountered beneath the Site with a depth-to-water of approximately 114 to 145 ft bgs. The deep-zone perched aquifer appears to be isolated from the underlying regional aquifer by 40 to 50 feet. Site remedial goals have been the removal of LPH from groundwater associated with the shallow perched aquifer and removal of residual hydrocarbons from the vadose zone. The LPH plume was previously located east of the TFS-10 property. Based on the latest collected data, LPH may have been eliminated through implementation of the 2021 and/or 2022 ISCO injection events; however, additional monitoring is needed for long-term verification. Past remedial activities included direct LPH recovery by hand bailing, pumping, and air sparging with soil vapor extraction (AS/SVE). Skimmer pumps were phased out and replaced with AS/SVE technology; no skimmer pumps remained by June 2013. Figure 2 depicts locations of TOPSC, TFS-10, and the location of monitor and recovery wells in and around the Site.

In June 2025, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by February 2026. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial markets.

View market daily updates and historical trends for 10-2 Year Treasury Yield Spread. from United States. Source: Department of the Treasury. Track economi…

View a 10-year yield estimated from the average yields of a variety of Treasury securities with different maturities derived from the Treasury yield curve.

Records of Seattle Fire Department (SFD) permits related to decommissioning of a residential heating oil tank, permit code 6103. A record with incomplete tank info indicates that the required follow-up report has not been received by SFD. Please note that SFD records begin in 1996 when state requirement was introduced. Decommissioning of a residential heating oil tank might have occurred prior to 1996, in which SFD will not have a record. Commercial UST records can be requested through City Public Records Request Center at http://www.seattle.gov/public-records/public-records-request-center

After to as low as low as **** percent in July 2020, in the wake of the coronavirus outbreak, the yield on 10-year U.S treasury bonds increased considerably. As of June 2025, it reached **** percent.

Graph and download economic data for 10-Year Treasury Constant Maturity Minus Federal Funds Rate (T10YFF) from 1962-01-02 to 2025-10-09 about yield curve, spread, 10-year, maturity, Treasury, federal, interest rate, interest, rate, and USA.

View market daily updates and historical trends for 30-10 Year Treasury Yield Spread. from United States. Source: Department of the Treasury. Track econom…

Index Time Series for ProShares Ultra 7-10 Year Treasury. The frequency of the observation is daily. Moving average series are also typically included. The fund invests in financial instruments that ProShare Advisors believes, in combination, should produce daily returns consistent with the Daily Target. The index is designed to measure the performance of U.S. dollar denominated sovereign debt publicly issued by the U.S. government. The fund is non-diversified.

United States - Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity was 4.13% in October of 2025, according to the United States Federal Reserve. Historically, United States - Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity reached a record high of 15.84 in September of 1981 and a record low of 0.52 in August of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity - last updated from the United States Federal Reserve on October of 2025.

As of July 22, 2025, the yield for a ten-year U.S. government bond was 4.38 percent, while the yield for a two-year bond was 3.88 percent. This represents an inverted yield curve, whereby bonds of longer maturities provide a lower yield, reflecting investors' expectations for a decline in long-term interest rates. Hence, making long-term debt holders open to more risk under the uncertainty around the condition of financial markets in the future. That markets are uncertain can be seen by considering both the short-term fluctuations, and the long-term downward trend, of the yields of U.S. government bonds from 2006 to 2021, before the treasury yield curve increased again significantly in the following years. What are government bonds? Government bonds, otherwise called ‘sovereign’ or ‘treasury’ bonds, are financial instruments used by governments to raise money for government spending. Investors give the government a certain amount of money (the ‘face value’), to be repaid at a specified time in the future (the ‘maturity date’). In addition, the government makes regular periodic interest payments (called ‘coupon payments’). Once initially issued, government bonds are tradable on financial markets, meaning their value can fluctuate over time (even though the underlying face value and coupon payments remain the same). Investors are attracted to government bonds as, provided the country in question has a stable economy and political system, they are a very safe investment. Accordingly, in periods of economic turmoil, investors may be willing to accept a negative overall return in order to have a safe haven for their money. For example, once the market value is compared to the total received from remaining interest payments and the face value, investors have been willing to accept a negative return on two-year German government bonds between 2014 and 2021. Conversely, if the underlying economy and political structures are weak, investors demand a higher return to compensate for the higher risk they take on. Consequently, the return on bonds in emerging markets like Brazil are consistently higher than that of the United States (and other developed economies). Inverted yield curves When investors are worried about the financial future, it can lead to what is called an ‘inverted yield curve’. An inverted yield curve is where investors pay more for short term bonds than long term, indicating they do not have confidence in long-term financial conditions. Historically, the yield curve has historically inverted before each of the last five U.S. recessions. The last U.S. yield curve inversion occurred at several brief points in 2019 – a trend which continued until the Federal Reserve cut interest rates several times over that year. However, the ultimate trigger for the next recession was the unpredicted, exogenous shock of the global coronavirus (COVID-19) pandemic, showing how such informal indicators may be grounded just as much in coincidence as causation.

The yield on US 30 Year Bond Yield eased to 4.62% on October 10, 2025, marking a 0.10 percentage points decrease from the previous session. Over the past month, the yield has fallen by 0.04 points, though it remains 0.21 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.

SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network

Version 2.3, September 2020

Created by

Mark Cartwright (1,2,3), Jason Cramer (1), Ana Elisa Mendez Mendez (1), Yu Wang (1), Ho-Hsiang Wu (1), Vincent Lostanlen (1,2,4), Magdalena Fuentes (1), Graham Dove (2), Charlie Mydlarz (1,2), Justin Salamon (5), Oded Nov (6), Juan Pablo Bello (1,2,3)

Music and Audio Research Lab, New York University

Center for Urban Science and Progress, New York University

Department of Computer Science and Engineering, New York University

Cornell Lab of Ornithology

Adobe Research

Department of Technology Management and Innovation, New York University

Publication

If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:

Cartwright, M., Cramer, J., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. [pdf]

Description

SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for realistic urban noise monitoring. The audio was recorded from the SONYC acoustic sensor network. Volunteers on the Zooniverse citizen science platform tagged the presence of 23 classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into 8 coarse-grained classes. The recordings are split into three sets: training, validation, and test. The training and validation sets are disjoint with respect to the sensor from which each recording came, and the test set is displaced in time. For increased reliability, three volunteers annotated each recording. In addition, members of the SONYC team subsequently created a subset of verified, ground-truth tags using a two-stage annotation procedure in which two annotators independently tagged and then collectively resolved any disagreements. This subset of recordings with verified annotations intersects with all three recording splits. All of the recordings in the test set have these verified annotations. In v2 version of this dataset, we have also included coarse spatiotemporal context information to aid in tag prediction when time and location is known. For more details on the motivation and creation of this dataset see the DCASE 2020 Urban Sound Tagging with Spatiotemporal Context Task website.

Audio data

The provided audio has been acquired using the SONYC acoustic sensor network for urban noise pollution monitoring. Over 60 different sensors have been deployed in New York City, and these sensors have collectively gathered the equivalent of over 50 years of audio data, of which we provide a small subset. The data was sampled by selecting the nearest neighbors on VGGish features of recordings known to have classes of interest. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings. To maintain privacy, we quantized the spatial information to the level of a city block, and we quantized the temporal information to the level of an hour. We also limited the occurrence of recordings with positive human voice annotations to one per hour per sensor.

Label taxonomy

The label taxonomy is as follows:

engine 1: small-sounding-engine 2: medium-sounding-engine 3: large-sounding-engine X: engine-of-uncertain-size

machinery-impact 1: rock-drill 2: jackhammer 3: hoe-ram 4: pile-driver X: other-unknown-impact-machinery

non-machinery-impact 1: non-machinery-impact

powered-saw 1: chainsaw 2: small-medium-rotating-saw 3: large-rotating-saw X: other-unknown-powered-saw

alert-signal 1: car-horn 2: car-alarm 3: siren 4: reverse-beeper X: other-unknown-alert-signal

music 1: stationary-music 2: mobile-music 3: ice-cream-truck X: music-from-uncertain-source

human-voice 1: person-or-small-group-talking 2: person-or-small-group-shouting 3: large-crowd 4: amplified-speech X: other-unknown-human-voice

dog 1: dog-barking-whining

The classes preceded by an X code indicate when an annotator was able to identify the coarse class, but couldn’t identify the fine class because either they were uncertain which fine class it was or the fine class was not included in the taxonomy. dcase-ust-taxonomy.yaml contains this taxonomy in an easily machine-readable form.

Data splits

This release contains a training subset (13538 recordings from 35 sensors), and validation subset (4308 recordings from 9 sensors), and a test subset (669 recordings from 48 sensors). The training and validation subsets are disjoint with respect to the sensor from which each recording came. The sensors in the test set will not disjoint from the training and validation subsets, but the test recordings are displaced in time, occurring after any of the recordings in the training and validation subset. The subset of recordings with verified annotations (1380 recordings) intersects with all three recording splits. All of the recordings in the test set have these verified annotations.

Annotation data

The annotation data are contained in annotations.csv, and encompass the training, validation, and test subsets. Each row in the file represents one multi-label annotation of a recording—it could be the annotation of a single citizen science volunteer, a single SONYC team member, or the agreed-upon ground truth by the SONYC team (see the annotator_id column description for more information). Note that since the SONYC team members annotated each class group separately, there may be multiple annotation rows by a single SONYC team annotator for a particular audio recording.

Columns

split

The data split. (train, validate, test)

sensor_id

The ID of the sensor the recording is from.

audio_filename

The filename of the audio recording

annotator_id

The anonymous ID of the annotator. If this value is positive, it is a citizen science volunteer from the Zooniverse platform. If it is negative, it is a SONYC team member. If it is 0, then it is the ground truth agreed-upon by the SONYC team.

year

The year the recording is from.

week

The week of the year the recording is from.

day

The day of the week the recording is from, with Monday as the start (i.e. 0=Monday).

hour

The hour of the day the recording is from

borough The NYC borough in which the sensor is located (1=Manhattan, 3=Brooklyn, 4=Queens). This corresponds to the first digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL).

block

The NYC block in which the sensor is located. This corresponds to digits 2—6 digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL).

latitude

The latitude coordinate of the block in which the sensor is located.

longitude

The longitude coordinate of the block in which the sensor is located.

-_presence

Columns of this form indicate the presence of fine-level class. 1 if present, 0 if not present. If -1, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset.

_presence

Columns of this form indicate the presence of a coarse-level class. 1 if present, 0 if not present. If -1, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. These columns are computed from the fine-level class presence columns and are presented here for convenience when training on only coarse-level classes.

-_proximity

Columns of this form indicate the proximity of a fine-level class. After indicating the presence of a fine-level class, citizen science annotators were asked to indicate the proximity of the sound event to the sensor. Only the citizen science volunteers performed this task, and therefore this data is not included in the verified annotations. This column may take on one of the following four values: (near, far, notsure, -1). If -1, then the proximity was not annotated because either the annotation was not performed by a citizen science volunteer, or the citizen science volunteer did not indicate the presence of the class.

Conditions of use

Dataset created by Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, and Juan Pablo Bello

The SONYC-UST dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/

The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-UST dataset or any part of it.

Feedback

Please help us improve SONYC-UST by sending your feedback to:

Mark Cartwright: mcartwright@gmail.com

In case of a problem, please include as many details as possible.

Acknowledgments

We would like to thank all the Zooniverse volunteers who continue to contribute to our project. This work is

Track real-time 1 Year Treasury Rate yields and explore historical trends from year start to today. View interactive yield curve data with YCharts.

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