Between 2022 and 2023, Facebook and Instagram saw an increase in conversations around body positivity, autonomy, and self-expression. Overall, conversations about epilators saw a year-on-year increase of 512 percent, and body modification discussions saw a year-on-year increase of 258 percent. On Instagram, conversations over chemical depilatory rose by 123 percent. Additionally, discussions about body positivity increased by 47 percent, year-on-year.

Data Set

The data set contains the qualitative analysis of fourteen design concepts aiming to support positive activities as Active Design in consumer technology.

Codebook

The codebook specifies two classification schemes for a) design mechanisms and b) drivers of behavior that were used to analyze the data set.

Financial overview and grant giving statistics of Positive Presence Unlimited

About this dataset

Context

The dataset tabulates the population of Good Thunder by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Thunder. The dataset can be utilized to understand the population distribution of Good Thunder by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Thunder. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Good Thunder.

Key observations

Largest age group (population): Male # 55-59 years (69) | Female # 50-54 years (29). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Good Thunder population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Thunder is shown in the following column.
• Population (Female): The female population in the Good Thunder is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Good Thunder for each age group.

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 Good Thunder Population by Gender. You can refer the same here

Financial overview and grant giving statistics of Negative To Positive

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

According to a survey conducted in 2024, half of French respondents believed refugees did not make a positive contribution to France. On the other hand, ** percent of respondents thought the opposite.

Shorelines are continuously moving in response to winds, waves, tides, sediment supply, changes in relative sea level, and human activities. Shoreline changes are generally not constant through time and frequently switch from negative (erosion) to positive (accretion) and vice versa. Cyclic and non-cyclic processes change the position of the shoreline over a variety of timescales, from the daily and seasonal effects of winds and waves, to changes in sea level over a century to thousands of years. The shoreline "rate of change" statistic thus reflects a cumulative summary of the processes that altered the shoreline for the time period analyzed.

In a survey conducted in 2023, ** percent of young people in Australia stated that they felt positive or very positive about the future. This is almost exactly the same result of the previous year, where **** percent of young people were positive or very positive.

About this dataset

Context

The dataset tabulates the population of Good Hope by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Hope. The dataset can be utilized to understand the population distribution of Good Hope by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Hope. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Good Hope.

Key observations

Largest age group (population): Male # 65-69 years (25) | Female # 30-34 years (38). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Good Hope population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Hope is shown in the following column.
• Population (Female): The female population in the Good Hope is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Good Hope for each age group.

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 Good Hope Population by Gender. You can refer the same here

Financial overview and grant giving statistics of Positive Impact Inc

Campaign advertisements by party, office, and DMA for presidential, senatorial, and gubernatorial elections from 1996-2008. Not all years are available for all elections. General elections only, excludes primaries. Ads are coded by Wisconsin Advertising Project as Promoting, Contrasting, or Attacking. Includes vote totals and demographic information for CBSA matched to DMA advertising data.

Historical Dataset of Positive Pathways Transition Center is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2011-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2013-2023),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2011-2023),White Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2011-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2011-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2023),Overall School Rank Trends Over Years (2010-2022),Graduation Rate Comparison Over Years (2013-2023)

A survey conducted in August 2022, found that young people (those aged between 18 and 29 years old) in the United States were more likely to have a positive impression of socialism, with ** percent viewing socialism positively. About ** percent of those aged 65 and over had a positive impression of capitalism.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ## Data includes: * date * age group * average testing percent positive **Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool ** This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

United States SBP: TW: COVID-19 Impact: Large Positive Effect data was reported at 1.500 % in 04 Oct 2020. This records a decrease from the previous number of 2.100 % for 27 Sep 2020. United States SBP: TW: COVID-19 Impact: Large Positive Effect data is updated weekly, averaging 1.400 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 2.200 % in 09 Aug 2020 and a record low of 0.400 % in 30 Aug 2020. United States SBP: TW: COVID-19 Impact: Large Positive Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).

This dataset is published by the Colorado Department of Public Health and Environment and contains the number of COVID-19 positive cases by county, county rate of infection per 100,000 persons, death data by county, statewide COVID-19 prevalence data and associated statewide COVID-19 related statistics. Data is assembled and published Monday-Friday beginning July 26, 2021. Further information concerning case data can be found at https://covid19.colorado.gov/data/.

United States SBP: CT: COVID-19 Impact: Large Positive Effect data was reported at 1.200 % in 04 Oct 2020. This records an increase from the previous number of 0.500 % for 27 Sep 2020. United States SBP: CT: COVID-19 Impact: Large Positive Effect data is updated weekly, averaging 0.800 % from Apr 2020 (Median) to 04 Oct 2020, with 15 observations. The data reached an all-time high of 1.200 % in 04 Oct 2020 and a record low of 0.300 % in 07 Jun 2020. United States SBP: CT: COVID-19 Impact: Large Positive Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S046: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).

These tables compile data provided to DDS by California's 21 regional centers. Updates received from each regional center every business day include information for individuals known to them to have tested positive for COVID-19. Data is provisional and may change as regional centers provide updates. Details regarding gender, age group, and self-reported ethnicity are retrieved from separate databases of information for all regional center consumers.