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 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.

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.

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.

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

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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.

In the Annual Budget Document, the Budget Office presents information about the annual cost of various city services/fees for the typical ratepayer. These services and fees include Austin Energy, Austin Water, Austin Resource Recovery, the Clean Community Fee, the Transportation User Fee, the Drainage Utility Fee, and the Property Tax Bill. This dataset supports the SD23 measure, "Dollar amount and percentage increase of major rates and fees for a range of customer types" (EOA.C.5.c). It contains the approved and amended rates for the typical ratepayer, the annual dollar change, and the annual percent change for each service/fee. This dataset can be used to help understand the cost of city services over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Dollar-Amount-and-Percentage-Increase-of-Major-Rat/56uv-46qi/

Heart Disease Deaths in Oklahoma

Current Trends and Target Rates

By Oklahoma [source]

About this dataset

This dataset contains an overview of historical heart disease death rates in Oklahoma from 2000 to 2018. The dataset consists of yearly figures and target figures for the numbers of deaths due to heart diseases, allowing a comparison between the expected rate and the actual rate over time. This data is important as it can be used to analyze trends in heart disease death rates, helping inform public health initiatives and policy decisions

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How to use the dataset

This dataset includes the number of death due to heart disease in Oklahoma. It provides a single, comprehensive data set that captures detailed information on the historical prevalence of heart disease death rates in the state. This dataset can be used for various research or analytical purposes such as epidemiological studies or health services planning.

To use this dataset, one must first understand that it contains three main pieces: the year of reported deaths, the actual number of deaths related to heart disease during each year and a target total for expected deaths from heart disease per year, which are used as reference points when analyzing other years. The years column includes all relevant dates while historical data column provides more specifics such as exact numbers and percentages related to those who perished due to heart-related conditions.

By utilizing this data set users can easily find out how many persons died due to cardiac-related diseases along with what risks were most prevalent at certain times over that period by comparing provided figures with reference targets at any given time slice in question (time point). Additionally, one can observe trends carefully within different groups such as males versus females or rural versus urban locations thus allowing them more robust insight into factors associated with mortality from cardiac conditions across different demographics

Research Ideas

• Identifying which geographic areas in Oklahoma are at highest risk for heart disease and creating targeted public health initiatives to reduce its incidence.
• Determining correlations between changes in vital health indicators (e.g., increase of physical activity) with changes in heart disease death rates to better inform policy and research direction.
• Analyzing overall mortality rates compared to other counties or states with comparable demographics to assess the effectiveness of existing public health interventions over time

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: res_heart_disease_deaths_kdjx-hayj.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------------------------------| | Years | The year associated with the data. (Integer) | | Historical Data | The number of deaths due to heart disease in Oklahoma in that particular year from 2000-2018. (Integer) | | Target | A value generated based on Historical Data indicating what should be targeted as a baseline performance measure going forward. (Integer) |

File: res_heart_disease_deaths_-_column_chart_3a28-gndr.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------------------------------| | Years | The year associated with the data. (Integer) | | Historical Data | The number of deaths due to heart disease in Oklahoma in that particular year from 2000-2018. (Integer) | | Target | A value generated based on Historical Data indicating what should be targeted as a baseline performance measure going forward. (Integer) |

Acknowledgements

...

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

The benchmark interest rate in Russia was last recorded at 20 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

Dataset Description:

The Pet Adoption Dataset provides a comprehensive look into various factors that can influence the likelihood of a pet being adopted from a shelter. This dataset includes detailed information about pets available for adoption, covering various characteristics and attributes.

Features:

1. PetID: Unique identifier for each pet.
2. PetType: Type of pet (e.g., Dog, Cat, Bird, Rabbit).
3. Breed: Specific breed of the pet.
4. AgeMonths: Age of the pet in months.
5. Color: Color of the pet.
6. Size: Size category of the pet (Small, Medium, Large).
7. WeightKg: Weight of the pet in kilograms.
8. Vaccinated: Vaccination status of the pet (0 - Not vaccinated, 1 - Vaccinated).
9. HealthCondition: Health condition of the pet (0 - Healthy, 1 - Medical condition).
10. TimeInShelterDays: Duration the pet has been in the shelter (days).
11. AdoptionFee: Adoption fee charged for the pet (in dollars).
12. PreviousOwner: Whether the pet had a previous owner (0 - No, 1 - Yes).
13. AdoptionLikelihood: Likelihood of the pet being adopted (0 - Unlikely, 1 - Likely).

Usage:

This dataset is ideal for data scientists and analysts interested in understanding and predicting pet adoption trends. It can be used for: - Predictive modeling to determine the likelihood of pet adoption. - Analyzing the impact of various factors on adoption rates. - Developing strategies to increase adoption rates in shelters.

Warning:

This dataset was collected during a specific period of time. Therefore, it cannot be used to generalize pet adoption behavior.

Conclusion:

This dataset aims to support research and initiatives focused on increasing pet adoption rates and ensuring that more pets find their forever homes.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Note: Find data at source. ・ Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates.This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The archived data consist of count rates from the sum of two hemispherical detectors covering 4 pi steradians and operating continuously. The detectors are 3 mm thick CsI scintillators coupled to photomultiplier tubes. The nominal energy range is 25-150 keV, but the below table should be consulted to find the accurate thresholds for any day of the mission. A more complete description of the instrument may be found in [HURLEYETAL1992] and [COTINETAL1983]. Although the prime objectives of this investigation are the study of solar and cosmic x- and gamma-ray bursts, it should be noted that the experiment is also sensitive to solar protons and electrons. The former deposit energy directly in the scintillator if they are energetic enough, while the latter may produce x- radiation locally by bremsstrahlung. Thus although the counting rates are generally stable at about 500 c/s over the long term, there are periods of weeks to a month or so when the rates increase considerably due to particles. Examples may be found in March 1991 (solar protons) and February 1992 (particles in the Jovian magnetosphere). The time resolution of the data takes on one of four values depending on the telemetry rate and instrument operating mode: 0.25, 0.5, 1, or 2 seconds.

The Texas Department of Insurance (TDI) collects and reports information about billing rates for emergency service providers by procedure code as set by the political subdivisions. The procedure codes include Healthcare Common Procedure Coding System (HCPCS) Codes and any other codes reported by the political subdivisions. This dataset lists the codes and the rates for residents of that political subdivision and for non-residents if that rate differs. There is a row for each procedure code and the rates set by a political subdivision. Political subdivisions with more than one code with a rate set will be listed in multiple rows. The data includes the year and quarter the information applies to as well as the date the political subdivision submitted their report. The Texas Legislature amended Texas Insurance Code Chapter 38 via Senate Bill 2476 during the 88th session to add reporting “relating to consumer protections against certain medical and health care billing by emergency medical services providers. A political subdivision may submit to the department a rate set, controlled, or regulated by the political subdivision for emergency services.” ► For contact information, refer to dataset:  Emergency Services Billing Rates - Contact List. ► For National Provider Identifier Standard (NPI) information reported in each political subdivision, refer to dataset: Emergency Services Billing Rates - NPI. ► For ZIP codes within political subdivisions, refer to dataset:  Emergency Services Billing Rates - ZIPs. Users are responsible for reviewing and updating data before the submission deadlines. Information entered or found in this dataset is subject to change. Visit TDI’s web site disclaimer for more information. For more information related to this data, visit TDI’s FAQ page.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Apr 2025 about savings, personal, rate, and USA.

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

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

Analysis of ‘🧑 Childhood Obesity in the US’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/childhood-obesity-in-the-use on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Childhood Obesity in the United States (1971-2014)

data source: http://www.cdc.gov/nchs/data/hestat/obesity_child_13_14/obesity_child_13_14.htm

Data Files

1. child_ob_gender.csv
2. obesity_child_age

Visualizations

Historical Childhood Obesity Rate by Gender

Boys tended to suffer from obesity at a higher rate than girls during 2000 through 2010. More recently however, between 2011 and 2014, boys' and girls' obesity rates converged as a result of an increase for girls and decrease for boys.

For both genders, obesity rates grew rapidly during the last two decades of the 20th century, but thankfully growth rates have lessened in recent years.

http://i.imgur.com/oyWAjys.png" alt="Imgur" style="">

Historical Childhood Obesity Rate by Age

The data show that older children have been afflicted by the obesity epidemic at a higher rate than very young children.

http://i.imgur.com/7W2Bsz3.png" alt="Imgur" style="">

This dataset was created by Health and contains around 100 samples along with Se, Percent Obese, technical information and other features such as: - Gender - Time - and more.

How to use this dataset

• Analyze Age in relation to Se
• Study the influence of Percent Obese on Gender
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Overview

This multi-subject and multi-session EEG dataset for modelling human visual object recognition (MSS) contains:

1. 122-channel EEG data collected on 32 participants during natural visual stimulation;
2. totally 100 sessions for 1.5 hours each;
3. each session consists of 4 RSVP runs and 4 low-speed presentation runs;
4. each participant completed between 1 to 5 sessions on different days, around one week apart.

More details about the dataset are described as follows.

Participants

32 participants were recruited from college students in Beijing, of which 4 were female, and 28 were male, with an age range of 21-33 years. 100 sessions were conducted. They were paid and gave written informed consent. The study was conducted under the approval of the ethical committee of the Institute of Automation at the Chinese Academy of Sciences, with the approval number: IA21-2410-020201.

Experimental Procedures

1. RSVP experiment: During the RSVP experiment, the participants were shown images at a rate of 5 Hz, and each run consisted of 2,000 trials. There were 20 image categories, with 100 images in each category, making up the 2,000 stimuli. The 100 images in each category were further divided into five image sequences, resulting in 100 image sequences per run. Each sequence was composed of 20 images from the same class, and the 100 sequences were presented in a pseudo-random order.

After every 50 sequences, there was a break for the participants to rest. Each rapid serial sequence lasted approximately 7.5 seconds, starting with a 750ms blank screen with a white fixation cross, followed by 20 or 21 images presented at 5 Hz with a 50% duty cycle. The sequence ended with another 750ms blank screen.

After the rapid serial sequence, there was a 2-second interval during which participants were instructed to blink and then report whether a special image appeared in the sequence using a keyboard. During each run, 20 sequences were randomly inserted with additional special images at random positions. The special images are logos for brain-computer interfaces.

1. Low-speed experiment: During the low-speed experiment, each run consisted of 100 trials, with 1 second per image for a slower paradigm. The 100 stimuli were presented in a pseudo-random order and included 20 image categories, each containing 5 images. A break was given to the participants after every 20 images for them to rest.

Each image was displayed for 1 second and was followed by 11 choice boxes (1 correct class box, 9 random class boxes, and 1 reject box). Participants were required to select the correct class of the displayed image using a mouse to increase their engagement. After the selection, a white fixation cross was displayed for 1 second in the centre of the screen to remind participants to pay attention to the upcoming task.

Stimuli

The stimuli are from two image databases, ImageNet and PASCAL. The final set consists of 10,000 images, with 500 images for each class.

Annotations

In the derivatives/annotations folder, there are additional information of MSS:

1. Videos of two paradigms.
2. Dataset_info: Main features of MSS.
3. Experiment_schedule: Schedule of each session.
4. Stimuli_source: Source categories of ImageNet and PASCAL.
5. Subject_info: Age and sex of participants.
6. Task_event: The meaning of eventID.

Preprocessing

The EEG signals were pre-processed using the MNE package, version 1.3.1, with Python 3.9.16. The data was sampled at a rate of 1,000 Hz with a bandpass filter applied between 0.1 and 100 Hz. A notch filter was used to remove 50 Hz power frequency. Epochs were created for each trial ranging from 0 to 500 ms relative to stimulus onset. No further preprocessing or artefact correction methods were applied in technical validation. However, researchers may want to consider widely used preprocessing steps such as baseline correction or eye movement correction. After the preprocessing, each session resulted in two matrices: RSVP EEG data matrix of shape (8,000 image conditions × 122 EEG channels × 125 EEG time points) and low-speed EEG data matrix of shape (400 image conditions × 122 EEG channels × 125 EEG time points).