The U.S. AI Training Dataset Market size was valued at USD 590.4 million in 2023 and is projected to reach USD 1880.70 million by 2032, exhibiting a CAGR of 18.0 % during the forecasts period. The U. S. AI training dataset market deals with the generation, selection, and organization of datasets used in training artificial intelligence. These datasets contain the requisite information that the machine learning algorithms need to infer and learn from. Conducts include the advancement and improvement of AI solutions in different fields of business like transport, medical analysis, computing language, and money related measurements. The applications include training the models for activities such as image classification, predictive modeling, and natural language interface. Other emerging trends are the change in direction of more and better-quality, various and annotated data for the improvement of model efficiency, synthetic data generation for data shortage, and data confidentiality and ethical issues in dataset management. Furthermore, due to arising technologies in artificial intelligence and machine learning, there is a noticeable development in building and using the datasets. Recent developments include: In February 2024, Google struck a deal worth USD 60 million per year with Reddit that will give the former real-time access to the latter’s data and use Google AI to enhance Reddit’s search capabilities. , In February 2024, Microsoft announced around USD 2.1 billion investment in Mistral AI to expedite the growth and deployment of large language models. The U.S. giant is expected to underpin Mistral AI with Azure AI supercomputing infrastructure to provide top-notch scale and performance for AI training and inference workloads. .

BackgroundGenerative artificial intelligence (Gen-AI) has emerged as a transformative tool in research and education. However, there is a mixed perception about its use. This study assessed the use, perception, prospect, and challenges of Gen-AI use in higher education.MethodsThis is a prospective, cross-sectional survey of university students in the United Kingdom (UK) distributed online between January and April 2024. Demography of participants and their perception of Gen-AI and other AI tools were collected and statistically analyzed to assess the difference in perception between various subgroups.ResultsA total of 136 students responded to the survey of which 59% (80) were male. The majority were aware of Gen-AI and other AI use in academia (61%) with 52% having personal experience of the tools. Grammar correction and idea generation were the two most common tasks of use, with 37% being regular users. Fifty-six percent of respondents agreed that AI gives an academic edge with 40% holding a positive overall perception about the use in academia. Comparatively, there was a statistically significant difference in overall perception between different age ranges (I2 = 27.39; p = 0.002) and levels of education (I2 = 20.07; p 

The U.S. Department of Education’s graduation rate, which is reported through the Integrated Postsecondary Education Data System (IPEDS), is a nationally recognized and commonly used metric in higher education. Graduation rate is calculated as the percentage of first‐time, full‐time, degree/certificate seeking students that complete a CCC program within 150% of the estimated time it takes to complete the program.

U.S. AI training dataset market size will be valued at USD 2,137.26 Million in 2032 and is projected to grow at a (CAGR) of 17.7%.

Analysis of ‘Learning Model by School District’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4b261ce4-e39e-45fe-b035-b9bffb392cce on 28 January 2022.

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This dataset includes the learning models (in-person, hybrid, and remote) by grade level by public school district during a given week of the school year.

When an asterisk is displayed, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Additional notes: Starting in the reporting period for 11/16-11/20, the columns "Percentage of students who are fully remote" and "Total number of students in the district" will no longer be updated due to changes in the data being collected from schools. More detailed data on student enrollment and attendance data is available at the school-level (https://data.ct.gov/Education/School-Attendance-by-School-2020-2021/jahr-cskc) and district level (https://data.ct.gov/Education/School-Attendance-by-District-2020-2021/a4ya-h6eq) on the Open Data Portal and on the EdSight page here: http://edsight.ct.gov/relatedreports/Supporting%20Student%20Participation%20in%202020-21.html.

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First, please create a public dataset. Resources that do not require authorization can be uploaded directly to the resources area. If a resource requires authorization to access, please store it in the cloud drive provided by the account membership system, which supports access control. Next, dataset administrators should add a custom field named "needapply" with a value of "true" to the dataset. This enables an application process. Once approved, authorized users will be able to download the restricted data.

Analysis of ‘EA034 - Percentage of 20 Year Olds Usually Resident and Present in the State Who Were Full Time Students 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6e3e936b-bf26-4fb8-81dd-74bf69f6d4b6 on 19 January 2022.

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Percentage of 20 Year Olds Usually Resident and Present in the State Who Were Full Time Students 2016

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Analysis of ‘Performance Metrics - City Colleges of Chicago - Course Success Rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b979587f-b421-4924-8b0e-c4d521c8d89e on 26 January 2022.

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Course Success rate is the percent of students obtaining grades A‐C and P out of the total number of students enrolled at the beginning of the term. Course success is the building block toward student program completion. Without successful completion of courses, City Colleges of Chicago students will not be able to earn credits toward a degree or certificate, nor will they progress from remedial to college-level coursework.

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Analysis of ‘2018 Graduation Rates - Charter Schools - All Students’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6ffb7203-173a-4a66-b4d3-68763452944b on 26 January 2022.

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2018 Graduation Rates - Charter Schools - All Students

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High School graduation rates for the 2015-2016 school year by census tract for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Graduation rates were originally obtained on a school level and aggregated to tract by Data Driven Detroit. The graduation rates were calculated by Data Driven Detroit, using the count of students per cohort per school divided by the count of students who graduated.Click here for metadata (descriptions of the fields).

This dataset provides the 4-Year graduation rates in Iowa by cohort (represented by graduating class) and public school district starting with the Class of 2009. A cohort in the graduation rate calculation starts with a group of students entering ninth grade for the first time. The cohort is adjusted to add students that transfer in and subtract students that transfer out during a four year time period for calculating a graduation rate.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

This page provides data for the 3rd Grade Reading Level Proficiency performance measure.The dataset includes the student performance results on the English/Language Arts section of the AzMERIT from the Fall 2017 and Spring 2018. Data is representive of students in third grade in public elementary schools in Tempe. This includes schools from both Tempe Elementary and Kyrene districts. Results are by school and provide the total number of students tested, total percentage passing and percentage of students scoring at each of the four levels of proficiency. The performance measure dashboard is available at 3.07 3rd Grade Reading Level Proficiency.Additional InformationSource: Arizona Department of EducationContact: Ann Lynn DiDomenicoContact E-Mail: Ann_DiDomenico@tempe.govData Source Type: Excel/ CSVPreparation Method: Filters on original dataset: within "Schools" Tab School District [select Tempe School District and Kyrene School District]; School Name [deselect Kyrene SD not in Tempe city limits]; Content Area [select English Language Arts]; Test Level [select Grade 3]; Subgroup/Ethnicity [select All Students] Remove irrelevant fields; Add Fiscal YearPublish Frequency: Annually as data becomes availablePublish Method: ManualData Dictionary

Table 1. Number and percentage distribution of private schools, students, and full-time equivalent (FTE) teachers, by selected school characteristics: United States, 2017–18

The AI Training Dataset In Healthcare Market size was valued at USD 341.8 million in 2023 and is projected to reach USD 1464.13 million by 2032, exhibiting a CAGR of 23.1 % during the forecasts period. The growth is attributed to the rising adoption of AI in healthcare, increasing demand for accurate and reliable training datasets, government initiatives to promote AI in healthcare, and technological advancements in data collection and annotation. These factors are contributing to the expansion of the AI Training Dataset In Healthcare Market. Healthcare AI training data sets are vital for building effective algorithms, and enhancing patient care and diagnosis in the industry. These datasets include large volumes of Electronic Health Records, images such as X-ray and MRI scans, and genomics data which are thoroughly labeled. They help the AI systems to identify trends, forecast and even help in developing unique approaches to treating the disease. However, patient privacy and ethical use of a patient’s information is of the utmost importance, thus requiring high levels of anonymization and compliance with laws such as HIPAA. Ongoing expansion and variety of datasets are crucial to address existing bias and improve the efficiency of AI for different populations and diseases to provide safer solutions for global people’s health.

This measure reports the percentage of offenders who are currently living in residential facilities supervised by Iowa Community Based Corrections who have or are working towards a post-secondary education degree. It includes offenders where the highest level of education completed is one of the following: In College, Freshman level college, Sophomore level college, Junior level college, Vocational/Technical Student, Technical Training Completion, Vocational Program/Technical Certificate, Associate's Degree, Bachelor's Degree, Master's Degree, or Doctorate.

We tested whether the proportion of AI versus auditors in fraud selection matters for fairness, and whether there is an impact of transparency (explanations). We found that a higher proportion of AI was more procedurally fair, mostly through bias suppression and consistency, and that the attitude toward AI and trust in the administration explained most variance. Transparency (explanations) had no impact. We also found two small negative interaction effects concerning trust and procedural fairness: with high trust in the tax administration, fairness increased less (as AI increased). Conversely, with low trust, fairness increased more (as AI increased). Dataset 1 was used for the pilot (with students and professionals) Dataset 2 was a representative dataset for the Flemish population. Sample 1: Convenience sample Sample 2: Convenience sample drawn from their database with 100,000 participants

This dataset includes three distinct subsets of text:Open Access Academic Articles: A collection of 100 open-access articles from various academic journals focused on mental health and psychiatry published between 2016-2018. The articles are selected from reputable journals including JAMA, The Lancet Psychiatry, WPJ, and AM J Psy.ChatGPT-Generated Texts: Discussion section samples generated by ChatGPT (GPT-4 model, version as of August 3, 2023, OpenAI) that are designed to imitate the style and content of academic articles in the field of mental health and psychiatry.Claude-Generated Texts: Discussion section samples generated by Claude (Version 2, Anthropic) with the aim of imitating academic articles in the same field.Additionally, the dataset contains the results of tests performed using ZeroGPT and Originality.AI to evaluate the AI texts vs the academic articles for the percentage of texts identified as being AI-generated.Please cite this dataset if you make use of it in your research.

The data here is from the report entitled Trends in Enrollment, Credit Attainment, and Remediation at Connecticut Public Universities and Community Colleges: Results from P20WIN for the High School Graduating Classes of 2010 through 2016. The report answers three questions: 1. Enrollment: What percentage of the graduating class enrolled in a Connecticut public university or community college (UCONN, the four Connecticut State Universities, and 12 Connecticut community colleges) within 16 months of graduation? 2. Credit Attainment: What percentage of those who enrolled in a Connecticut public university or community college within 16 months of graduation earned at least one year’s worth of credits (24 or more) within two years of enrollment? 3. Remediation: What percentage of those who enrolled in one of the four Connecticut State Universities or one of the 12 community colleges within 16 months of graduation took a remedial course within two years of enrollment? Notes on the data: CT Remed: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

Decrease the percentage of students enrolled in remedial coursework in college from 39.38% in 2014 to 35% by 2018.