The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who do and do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete the Youth Interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Units (PSUs) and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0001 (DS0001) contains the data from the Public-Use File Master Linkage File (PUF-MLF). This file contains 93 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Public-Use Files and Special Collection Public-Use Files. Dataset 0002 (DS0002) contains the data from the Restricted-Use File Master Linkage File (RUF-MLF). This file contains 198 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Restricted-Use Files, Special Collection Restricted-Use Files, and Biomarker Restricted-Use Files.

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled primary sampling units (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Wave 4.5 was a special data collection for youth only who were aged 12 to 17 at the time of the Wave 4.5 interview. Wave 4.5 was the fourth annual follow-up wave for those who were members of the Wave 1 Cohort. For those who were sampled at Wave 4, Wave 4.5 was the first annual follow-up wave. Wave 5.5, conducted in 2020, was a special data collection for Wave 4 Cohort youth and young adults ages 13 to 19 at the time of the Wave 5.5 interview. Also in 2020, a subsample of Wave 4 Cohort adults ages 20 and older were interviewed via the PATH Study Adult Telephone Survey (PATH-ATS). Wave 7.5 was a special collection for Wave 4 and Wave 7 Cohort youth and young adults ages 12 to 22 at the time of the Wave 7.5 interview. For those who were sampled at Wave 7, Wave 7.5 was the first annual follow-up wave. Dataset 1002 (DS1002) contains the data from the Wave 4.5 Youth and Parent Questionnaire. This file contains 1,617 variables and 13,131 cases. Of these cases, 11,378 are continuing youth having completed a prior Youth Interview. The other 1,753 cases are "aged-up youth" having previously been sampled as "shadow youth" Datasets 1112, 1212, and 1222, (DS1112, DS1212, and DS1222) are data files comprising the weight variables for Wave 4.5. The "all-waves" weight file contains weights for participants in the Wave 1 Cohort who completed a Wave 4.5 Youth Interview and completed interviews (if old enough to do so) or verified their information with the study (if not old enough to be interviewed) in Waves 1, 2, 3, and 4. There are two separate files with "single wave" weights: one for the Wave 1 Cohort and one for the Wave 4 Cohort. The "single-wave" weight file for the Wave 1 Cohort contains weights for youth who c

Dataset Card for PathVQA

  Dataset Description

PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions. The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a publicly-available digital library: "Pathology… See the full description on the dataset page: https://huggingface.co/datasets/flaviagiammarino/path-vqa.

The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative longitudinal cohort study of tobacco use and how it affects the health of people in the United States. It was launched in 2011 as a collaboration between the National Institutes of Health (NIH) and the Food and Drug Administration (FDA); data collection began in 2013 and is planned through 2024. Participants were recruited by a stratified address-based, area-probability sampling design, oversampling adult tobacco users, young adults (18–24 years), and African American adults. For the baseline wave (Wave 1), the study sampled over 150,000 mailing addresses across the United States to create the national sample of tobacco users and non-users. At Waves 4 and 7, probability samples were recruited from residential addresses not selected during previous waves in the same sampled Primary Sampling Units (PSUs) and segments using similar within-household procedures; these "replenishment samples" were combined for estimation and purposes with adult and youth respondents from their respective waves.

Each case in an Adult data file represents a single, completed interview. Each case in a Youth data file represents one youth and his or her parent's responses about that youth. When multiple youth from the same household were selected to be in the study, the parent(s) completed separate interviews about each youth. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Respondents are asked to complete an interview at each follow-up wave. Additionally, "shadow youth" (youth ages 9 to 11 sampled at Wave 1) are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. Adult interviews covered use of tobacco and nicotine products, peer and family opinions of tobacco use, health and quality of life outcomes, and tobacco product advertising. Youth interviews also included questions about media use and use of other substances; parents were asked about their youth's home and school life as well as their own use of tobacco products.

Questions about the collection, content, weighting, documentation, or structure of PATH Study data may be submitted to PATHDataUserQuestions@Westat.com. NOTE: This email address is not for questions about statistical analysis or analytic guidance. For analytic questions, researchers may wish to consult with statisticians and analysts at their institutions.

Batteries experience two aging modes: calendar aging at rest and cyclic aging during the passage of current. Existing empirical aging models treat these as independent, but degradation may be sensitive to their order and periodicity – a phenomenon that has been called ‘path dependence’. This long-term dataset was collected to study the influence of path dependence in commercially available lithium-ion 18650 cells with nickel cobalt aluminium oxide (NCA) positive electrodes and graphite negative electrodes. Four groups of 3 cells each were subjected to combined load profiles comprising fixed periods of calendar and cyclic aging applied in various orders. Cells in groups 1 and 2 were exposed to one day of cycling followed by five days of calendar aging at C/2 and C/4 respectively. Cells in groups 3 and 4 were exposed to two days of cycling followed by ten days of calendar aging at C/2 and C/4 respectively. All cycling in the combined load profiles was conducted under constant current (CC) conditions and calendar aging was conducted at 90% state of charge (SoC). Cells in group 5 were exposed to continuous CC cycling at C/2 while group 6 consists of a single cell calendar aged at 90% SoC.

This dataset is a continuation of the 'Path Dependent Battery Degradation Dataset Part 1, DOI: 10.5287/bodleian:v0ervBv6p ' dataset. Path Dependent Battery Degradation Dataset Part 1 includes data collected over 1.5 years from the beginning of life to the middle of life. Path Dependent Battery Degradation Dataset Part 2 covers the remaining 1.5 years of data from the middle of life to end of life. The data collected while the cells were exposed to the combined profiles as well as the reference performance tests and electrochemical impedance spectroscopy data is included in this dataset.

Further information is available in the readme.txt file.

Path Detection V2

## Overview

Path Detection V2 is a dataset for instance segmentation tasks - it contains Paths annotations for 325 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data has been collected as part of a larger project by the City of Austin's Watershed Protection and Development Review Department to inventory its drainage infrastructure and create a GIS to store this information. The project includes an internal team developing a GIS based on record documents and an external team locating ground level appurtenances using GPS field collection units. The data in this data set represents the former.

Choose Your Path

## Overview

Choose Your Path is a dataset for object detection tasks - it contains Red Green Yellow annotations for 4,920 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Path Plan

## Overview

Path Plan is a dataset for object detection tasks - it contains Words annotations for 855 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is a source code file and the code language is MATLAB, Propose an improved algorithm based on the traditional A* algorithm, which expands the search step and search angle - Improv-A*. This algorithm not only improves the search speed but also enhances search efficiency, reducing the total planning distance. In order to achieve a combination of static global path planning and dynamic local path planning, we attempt to integrate Improv-A* algorithm with artificial potential field method to achieve dynamic path planning for unmanned aerial vehicles.

The Population Assessment of Tobacco and Health (PATH) Study began originally surveying 45,971 adult and youth respondents. The PATH Study was launched in 2011 to inform Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort.Dataset 0001 (DS0001) contains the data from the Master Linkage file. This file contains 14 variables and 67,276 cases. The file provides a master list of every person's unique identification number and what type of respondent they were for each wave. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Public-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts.Dataset 1001 (DS1001) contains the data from the Wave 1 Adult Questionnaire. This data file contains 1,732 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1002 (DS1002) contains the data from the Youth and Parent Questionnaire. This file contains 1,228 variables and 13,651 cases.Dataset 2001 (DS2001) contains the data from the Wave 2 Adult Questionnaire. This data file contains 2,197 variables and 28,362 cases. Of these cases, 26,447 also completed a Wave 1 Adult Questionnaire. The other 1,915 cases are "aged-up adults" having previously completed a Wave 1 Youth Questionnaire. Dataset 2002 (DS2002) contains the data from the Wave 2 Youth and Parent Questionnaire. This data file contains 1,389 variables and 12,172 cases. Of these cases, 10,081 also completed a Wave 1 Youth Questionnaire. The other 2,091 cases are "aged-up youth" having previously been sampled as "shadow youth." Dataset 3001 (DS3001) contains the data from the Wave 3 Adult Questionnaire. This data file contains 2,139 variables and 28,148 cases. Of these cases, 26,241 are continuing adults having completed a prior Adult Questionnaire. The other 1,907 cases are "aged-up adults" having previously completed a Youth Questionnaire. Dataset 3002 (DS3002) contains the data from t

This dataset provides information about the number of properties, residents, and average property values for Albavar Path cross streets in Inver Grove Heights, MN.

Path Finder

## Overview

Path Finder is a dataset for instance segmentation tasks - it contains Trail Path annotations for 270 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset provides information about the number of properties, residents, and average property values for Deer Path cross streets in Knoxville, MD.

About this dataset

Context

The dataset tabulates the Honea Path population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Honea Path across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Honea Path was 3,848, a 1.61% increase year-by-year from 2022. Previously, in 2022, Honea Path population was 3,787, an increase of 1.34% compared to a population of 3,737 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Honea Path increased by 188. In this period, the peak population was 3,848 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Honea Path is shown in this column.
• Year on Year Change: This column displays the change in Honea Path population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Honea Path Population by Year. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Avalon Path cross streets in Inver Grove Heights, MN.

N/A

Financial overview and grant giving statistics of Path

Path Guidance Market Outlook

The global Path Guidance market size is projected to grow from USD 7.5 billion in 2023 to USD 18.7 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 10.5% during the forecast period. This significant growth can be attributed to the increasing advancement in autonomous technologies and the rising demand for efficient navigation systems across various sectors.

One of the primary growth factors driving the Path Guidance market is the rapid development and adoption of autonomous vehicles, particularly in the automotive and logistics sectors. Autonomous vehicles, whether for personal use or commercial transportation, rely heavily on sophisticated path guidance systems to navigate safely and efficiently. As these technologies become more advanced, the demand for reliable and accurate path guidance solutions is expected to surge, contributing significantly to market growth.

Additionally, the healthcare sector is increasingly adopting path guidance systems for various applications, such as robotic surgery, patient management, and equipment logistics within hospitals. The precision and reliability of these systems ensure enhanced operational efficiency and patient safety, which is a crucial factor for their increasing deployment. This trend is expected to continue, further driving the growth of the Path Guidance market.

Technological advancements in sensor technologies, such as LiDAR, radar, and vision-based systems, are also pivotal in propelling the Path Guidance market forward. These technologies offer high accuracy and reliability, which are essential for the effective functioning of path guidance systems. Continuous innovation and development in these sensor technologies are likely to create new opportunities and expand their application range, thereby boosting market growth.

Regionally, North America holds a substantial share of the Path Guidance market, owing to the presence of leading technology companies and high adoption rates of advanced technologies. The Asia Pacific region is also expected to witness significant growth, driven by industrial expansion, government initiatives supporting technological adoption, and increasing investments in automation and robotics. Europe, with its stringent regulations and strong automotive industry, also presents promising growth opportunities.

Component Analysis

The Path Guidance market is segmented by components into hardware, software, and services. The hardware segment includes various sensors, controllers, and other physical devices essential for path guidance systems. This segment is expected to hold a considerable market share due to the continuous advancements in sensor technologies such as LiDAR, radar, and cameras. These advancements are crucial for the development of more sophisticated and reliable path guidance systems, thus driving demand for hardware components.

Software is another critical segment, encompassing the algorithms, artificial intelligence (AI), and machine learning (ML) technologies that process data from hardware components to generate precise navigation paths. The software segment is poised to grow at a significant rate due to the increasing complexity and capabilities of path guidance systems. Enhanced software solutions enable better decision-making, real-time processing, and integration with other systems, thereby enhancing the overall efficiency of path guidance solutions.

Services form the third component segment, including installation, maintenance, and consulting services. As the deployment of path guidance systems becomes more widespread, the demand for professional services to support these systems is also expected to grow. This segment is crucial for ensuring the long-term reliability and performance of path guidance systems, and it includes training programs, technical support, and system upgrades.

The integration of hardware, software, and services is essential for delivering comprehensive path guidance solutions. Companies in the market are focusing on developing integrated solutions that offer seamless performance and reliability. This holistic approach is expected to drive growth across all component segments, as customers increasingly seek end-to-end solutions that can be easily implemented and maintained.

Report Scope

This dataset provides information about the number of properties, residents, and average property values for Deer Path cross streets in New Lisbon, WI.