The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Interest Rates, Discount Rate for United States (INTDSRUSM193N) from Jan 1950 to Aug 2021 about discount, interest rate, interest, rate, and USA.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gasoline tax rates were last changed on July 1, 2022. The current rates are:

• unleaded gasoline - 9¢ per litre

Effective July 1, 2022 until June 30, 2025, the gasoline tax rate on unleaded gasoline will be reduced from 14.7 cents per litre to 9.0 cents per litre, representing a cut of 5.7 cents per litre.

• leaded gasoline - 17.7¢ per litre

The Aviation fuel tax rate was last changed on April 1, 2017. The current rate is 6.7¢ per litre.

Effective January 1, 2020, a new rate was established for Northern Ontario. The rate for Northern Ontario is 2.7¢ per litre.

The Propane tax rate was last changed on January 1, 1990. The current rate is 4.3¢ per litre.

You can download the dataset to view the historical price points for these taxes.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

This dataset tracks annual graduation rate from 2012 to 2022 for Cut Bank High School vs. Montana and Cut Bank High School District

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

As of 2023, approximately 2.4% of American Airlines' flights were canceled, according to data from the U.S. Department of Transportation. ☎️+1 (855) 217-1878 This rate reflects a variety of operational challenges, including weather, staffing, and air traffic control restrictions. ☎️+1 (855) 217-1878 Compared to its competitors, American ranks somewhere in the middle—not the best, but not the worst.

Over 200,000 scheduled flights take off annually under the American Airlines brand, and even a 2.4% cancellation rate means thousands of flights are affected. ☎️+1 (855) 217-1878 These disruptions can leave travelers scrambling for alternatives or stuck in airports. ☎️+1 (855) 217-1878 The cancellation rate becomes especially relevant during peak seasons and holidays.

On average, the cancellation rate increases to 4%–6% during major weather events or holidays like Thanksgiving and Christmas. ☎️+1 (855) 217-1878 That means during these times, the chance of being affected nearly doubles. ☎️+1 (855) 217-1878 Always plan accordingly by having backup options and checking forecasts ahead of time.

The causes of cancellations fall under two categories: controllable and uncontrollable. In American’s case, about 60% of cancellations are due to controllable factors like crew scheduling. ☎️+1 (855) 217-1878 That’s why the airline may be responsible for accommodations or rebooking in such cases. ☎️+1 (855) 217-1878 Uncontrollable causes, like bad weather, usually don’t require reimbursement.

American Airlines' main hubs—such as Dallas-Fort Worth (DFW), Charlotte (CLT), and Chicago O'Hare (ORD)—experience higher rates of cancellations due to operational complexity. ☎️+1 (855) 217-1878 These high-traffic hubs are also more sensitive to ripple effects caused by a single cancellation. ☎️+1 (855) 217-1878 Monitor your departure and connection airports before flying.

Statistics also show that early morning flights (before 9 AM) have a slightly lower cancellation rate. ☎️+1 (855) 217-1878 As the day progresses, delays and cancellations tend to increase, mostly due to cascading logistical challenges. ☎️+1 (855) 217-1878 Booking early can be a strategic move to avoid problems.

American Airlines publishes performance metrics monthly, which include on-time performance and cancellation rates. In August 2023, the airline saw a temporary spike with 3.1% cancellations. ☎️+1 (855) 217-1878 This was largely due to nationwide weather issues and increased summer travel demand. ☎️+1 (855) 217-1878 It's helpful to look at these trends before booking.

FlightAware, a real-time flight tracking service, often reports higher cancellation rates on busy travel days. On Memorial Day weekend, American had over 650 canceled flights nationwide. ☎️+1 (855) 217-1878 When the system is stressed, airline performance typically suffers. ☎️+1 (855) 217-1878 Consider flexibility in your travel schedule for such times.

Despite its cancellations, American has improved operational resilience in recent years. In 2021, the cancellation rate was over 5.5%, which has now been nearly cut in half. ☎️+1 (855) 217-1878 That suggests investment in technology, staffing, and better coordination is paying off. ☎️+1 (855) 217-1878 Still, no airline is immune to problems.

If your flight is canceled, American typically offers rebooking on the next available flight or a full refund if you choose not to travel. ☎️+1 (855) 217-1878 Call ☎️+1 (855) 217-1878 to request compensation or assistance from an agent. This line provides direct help with disrupted travel plans.

Weekly updated dataset of Lloyds mortgage products including interest rates, LTVs, APRC and product fees.

this graph was created in OurDataWorld:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fc099e1eee01f448c6c8f40c2d83de5eb%2Fgraph1.png?generation=1715453565007185&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Facca62b608d0c4817206ea2d9f671a77%2Fgraph3.png?generation=1715453571722599&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fdb52a1fa0b9db9f7d84bacedb217e160%2Fgraph2.png?generation=1715453577861078&alt=media" alt="">

What you should know about this indicator This GDP per capita indicator provides information on economic growth and income levels in the very long run. Some country estimates are available as far back as 1 CE and regional estimates as far back as 1820 CE. This data is adjusted for inflation and for differences in the cost of living between countries. This data is expressed in international-$ at 2011 prices, using a combination of 2011 and 1990 PPPs for historical data. Time series for former countries and territories are calculated forward in time by estimating values based on their last official borders. For more regularly updated estimates of GDP per capita, see the World Bank's indicator.

Real GDP per capita in 2011$

In two ways, this analysis leads to departures from the original Maddison approach and closer to the multiple benchmark approach as developed by the PWT. There is, to begin with, no doubt that the 2011 PPPs and the related estimates of GDP per capita reflect the relative levels of GDP per capita in the world economy today better than the combination of the 1990 benchmark and growth rates of GDP per capita according to national accounts. This information should be taken into account. At the same time, the underlying rule within the current Maddison Database is that economic growth rates of countries in the dataset should be identical or as close as possible to growth rates according to the national accounts (which is also the case for the pre 1990 period). For the post-1990 period we therefore decided to integrate the 2011 benchmarks by adapting the growth rates of GDP per capita in the period 1990–2011 to align the two (1990 and 2011) benchmarks. We estimated the difference between the combination of the 1990 benchmark and the growth rates of GDP (per capita) between 1990 and 2011 according to the national accounts, and annual growth rate from the 1990 benchmark to the 2011 benchmark. This difference is then evenly distributed to the growth rate of GDP per capita between 1990 and 2011; in other words, we added a country specific correction (constant for all years between 1990 and 2011) to the annual national account rate of growth to connect the 1990 benchmark to the 2011 benchmark. Growth after 2011 is, in the current update, exclusively based on the growth rates of GDP per capita according to national accounts.

We also use the collected set of historical benchmark estimates to fine tune the dataset for the pre-1940 period, but only in those cases where the quality of the benchmark was high and there were multiple benchmarks to support a revision. The most important correction concerns the US/UK comparison. The conventional picture, based on the original 1990 Maddison estimates, indicated that the US overtook the UK as the world leader in the early years of the 20th century. This finding was first criticized by Ward and Devereux (2003), who argued, based on alternative measures of PPP-adjusted benchmarks between 1870 and 1930, that the United States was already leading the United Kingdom in terms of GDP per capita in the 1870s. This conclusion was criticized by Broadberry (2003).

New evidence, however, suggests a more complex picture: in the 18th century, real incomes in the US (settler colonies only, not including indigenous populations) were probably higher than those in the UK (Lindert & Williamson, 2016a). Until about 1870, growth was both exten- sive (incorporating newly settled territory) and intensive (considering the growth of cities and industry at the east coast), but on balance, the US may—in terms of real income—have lagged behind the UK. After 1870, intensive growth becomes more important, and the US slowly gets the upper hand. This pattern is consistent with direct benchmark comparison of the income of both countries for the period 1907–1909 (Woltjer, 2015). This shows that GDP per capita for the United States in those years was 26% higher than in the United Kingdom. We have used Woltjer’s (2015) benchmark to correct the GDP series of the two countries. Projecting this benchmark into the 19th century with the series of GDP per capita of both countries results in the two countries achieving parity in 1880. This is close to Prados de la Escosura’s conjecture based on his short- cut method (Prados de la Escosura, 2000), and even closer to the Lindert and Williamson (2016a) results.

Site selection for cervical stabilization surgery in horses with spinal ataxia frequently relies on measurements derived from radiographic myelography. A variety of measurement criteria exist and can provide conflicting results. The main objectives of this study were to assess the correlation between two commonly used myelographic measures, dorsal contrast column reduction (DCCR) and dural diameter reduction (DDR), and their association with previously selected operative sites in a population of horses operated at a tertiary clinic. Secondary objectives were to determine if articular process joint (APJ) atrophy occurred in a subset of operated horses with radiographic follow-up, and to describe complications of cervical stabilization surgery and long term outcomes. The study was primarily cross-sectional using previously recorded medical information and images from horses operated between 2008 and 2022: three masked raters assessed previously acquired pre-operative myelograms obtained in neutral, flexed and extended neck positions from horses that had subsequently undergone stabilization surgery consisting of cervical interbody fusion via a Kerf-cut cylinder technique at one or two sites. A veterinary radiologist evaluated changes in APJ in radiographs obtained in a subset of horses re-evaluated >18 months after surgery. DCCR was unremarkable at nearly all articulations in all horses, while DDR met reduction criteria at over 50% of articulations in flexed position. Neither DCCR nor DDR distinguished operated from non-operated sites at most intervertebral junctions, except at the C6-7 articulation in neutral and extended position. The two measures were also poorly correlated at most sites and in most positions. Surgical complications included a high incidence of laryngeal hemiplegia. Comparison of operated to non-operated sites within individuals radiographed years later showed consistent, mildly reduced APJ opacity at most operated sites without a consistent decrease in APJ height or area ratios. Our results suggest that DCCR and DDR measures did not reliably predict surgical site selection in this surgical cohort except at C6-7, and that the two measures yielded conflicting diagnostic classification at many sites and positions. Complication rates from stabilization surgery were high; and predictable reduction in APJ height or area after surgery was not demonstrated by radiography in this study.

Introduction

Welcome to the Norwegian General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Norwegian speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Norwegian communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Norwegian speech models that understand and respond to authentic Norwegian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Norwegian. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Norwegian speech and language AI applications:

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset has been superseded by https://data.cnra.ca.gov/dataset/tre-altamira-insar-subsidence This dataset represents measurements of vertical ground surface displacement in Bulletin 118 groundwater basins between spring of 2015 and summer of 2017. Image resolution is 0.0008333 degrees, or approximately 92 meters in north-south direction, and 70-77 meters in east-west direction (low end of range applies to northern latitudes and higher end of range applies to lower latitudes). Vertical ground surface displacement rates are derived from Interferometric Synthetic Aperture Radar (InSAR) data that are collected by the European Space Agency (ESA) Sentinel-1A satellite and processed by the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL), under contract with to the California Department of Water Resources (DWR). JPL presented preliminary processing results in the Progress Report: Subsidence in California, March 2015 – September 2016, and submitted a later version of the processing results that are still preliminary to the California Department of Water Resources (DWR). These files provided by JPL to DWR are multiband floating point GeoTIFFs with each band representing a date. GeoTIFF pixel values are in inches equal to the cumulative vertical displacement from the first date. JPL processed Sentinel-1A InSAR data separately for three different geographic regions; The Sacramento Valley, the San Joaquin Valley, and the South Central Coast. DWR temporarily interpolated the JPL data to end-of-month values, merged the resulting rasters from all three regions into a single raster for each month, and clipped all rasters to Bulletin 118 groundwater basins. DWR derived rasters for total vertical displacement relative to May 31, 2015, as well as rasters for annual vertical displacement rates, both in monthly time steps. Data are considered public domain. DWR makes no warranties or guarantees — either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. This is an official DWR Image Service, published on 2/9/2018 by Ben Brezing of the DWR Division of Statewide Integrated Water Management, who may be contacted at Benjamin.brezing@water.ca.gov or (916) 651-9291. Date of acquisition: Between Spring of 2015 and Spring of 2017. Date of production: 2017. Date of delivery of product: Delivered from NASA JPL to DWR in September of 2017. Processing steps: See Progress Report: Subsidence in California, March 2015 – September 2016, Tom G. Farr, Cathleen E. Jones, Zhen Liu, Jet Propulsion Laboratory, 2016. Pixel value definitions: Vertical ground surface displacement in inches for time period specified above. Positional accuracy: See Progress Report: Subsidence in California, March 2015 – September 2016, Tom G. Farr, Cathleen E. Jones, Zhen Liu, Jet Propulsion Laboratory, 2016.

These data come from the Caisse d’Assurance Maladie, which makes these data available at the EPCI level (such as Saint-Louis Agglomération) throughout France. Below, the description of the data present on the portal is also valid for all data in France that are not displayed.

###

General information:

The data present information on vaccination against Covid-19 by public institution of intercommunal cooperation (EPCI) of residence of patients and age group.

For each EPCI and age group, are presented:

- patient numbers:

• having received at least one first injection; *whose vaccination has been completed;

- patient rates:

• having received at least one first injection; *whose vaccination has been completed.

These numbers and rates are proposed per week and cumulatively since the start of the vaccination campaign.

Source data used to build the dataset:

The available data are calculated from the information collected in the Vaccin Covid teleservice. Launched on 4 January 2021 by the Health Insurance, this information system centralises individual data on COVID-19 vaccination to ensure traceability for the purpose of pharmacovigilance and vaccination campaign management.

These individual data are pseudonymised and matched to the National Health Data System (NSDS) in order to provide vaccination rates based on the EPCI of the patient’s place of residence.

An anonymous aggregated dataset is then created.

The database of EPCI 2021 (source Insee) is used to create data cutting.

Points of attention:

• It is possible that a patient receives only one injection and his vaccination is thus completed: for example, injection vaccines or patients who have already had Covid-19;
• The reference population for the calculation of the number of vaccinated patients and for the calculation of rates is the population of the mapping of pathologies and expenditure of the Health Insurance, and not the population identified by INSEE whose breakdown by age group and commune dates from 2017;
• Only vaccinated patients who are part of the disease mapping and expenditure population of Sickness Insurance are included in the workforce (> 95 % of those vaccinated);
• Distribution by EPCI is not possible for some patients whose residence department or commune is unknown. These patients are attached to EPCI “999999999” in the dataset.

What is Health Insurance’s Pathology and Expenditure Mapping Population?

The mapping population lists all beneficiaries of compulsory health insurance, regardless of their membership scheme:

• having received at least one benefit in the year;
• and/or having stayed at least once in a public or private health facility during the year (stays in medicine, surgery, obstetrics, psychiatry, follow-up and rehabilitation care, outpatient procedures and consultations or in-home hospitalisation).

It makes it possible to obtain a population close to 67.7 million beneficiaries of all health insurance schemes, who have used reimbursed care. This population is used by the Caisse nationale de l’assurance Maladie (Cnam) for numerous studies and to produce data on certain pathologies and expenditure of sickness insurance. Thus, its use here makes it possible to calculate vaccination rates with the most recent population possible (December 2020).

For more information on mapping, see the following articles available in Studies and Data:

• presentation of the Mapping of Disease Insurance’s Pathologies and Expenditures
• method of mapping pathologies and expenditure of Sickness Insurance

Territorial cutting:

The attached EPCI is that of the patient’s place of residence, not the place of vaccination.

The data are returned on metropolitan France and DROMs. COM data are excluded due to too small numbers.

Population age:

The age is calculated on 1 July 2021.

Under 18 years of age are taken into account.

Statistical confidentiality:

Out of respect for statistical confidentiality (Law of 7 June 1951) and in order to make direct or indirect identification of individuals impossible, no information on numbers and rates of vaccination is provided when the number of vaccinated patients is less than 11. The value of the indicator is then empty in the dataset and set to “Not significant” in the graphical representations. The number of staff in excess of 10 is rounded to ten. Rates are calculated on the basis of actual staff.

Data update:

The data proposed for download in the “Export” tab is updated weekly.

The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.