Student enrolment in elementary and secondary schools across the province, aggregated by gender and school board or school authority. Includes: * board number * board name * elementary male enrolment * elementary female enrolment * secondary male enrolment * secondary female enrolment * total male enrolment * total female enrolment Enrolment data is reported by schools to the Ontario School Information System (OnSIS), October Submissions. The following school types are included: * public * Catholic To protect privacy, numbers are suppressed in categories with less than 10 students. Note: * Starting 2018-2019, enrolment numbers have been rounded to the nearest five. * Where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals. ## Related * College enrolment * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

Dataset name: asppl_dataset_v2.csv

Version: 2.0

Dataset period: 06/07/2018 - 01/14/2022

Dataset Characteristics: Multivalued

Number of Instances: 8118

Number of Attributes: 9

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Occupational Classification (CBO) (Brasil, 2022b);

• National Registry of Health Establishments (CNES) (Brasil, 2022c);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).

Table 1: Description of AVASUS dataset features.

Attributes	Description	datatype	Value
gender	Gender of the course participant.	Categorical.	Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)
course_progress	Percentage of completion of the course.	Numerical.	Range from 0 to 100.
course_evaluation	A score given to the course by the participant.	Numerical.	0, 1, 2, 3, 4, 5 or NaN.
evaluation_commentary	Comment made by the participant about the course.	Categorical.	Free text or NaN.
region	Brazilian region in which the participant resides.	Categorical.	Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).
CNES	The CNES code refers to the health establishment where the participant works.	Numerical.	CNES Code or NaN.
health_care_level	Identification of the health care network level for which the course participant works.	Categorical.	“ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”, “ALTA COMPLEXIDADE”, and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")
year_enrollment	Year in which the course participant registered.	Numerical.	Year (YYYY).
CBO	Participant occupation.	Categorical.	Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)

Dataset name: prison_syphilis_and_population_brazil.csv

Dataset period: 2017 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 13

Missing Values: No

Source:

• National Penitentiary Department (DEPEN) (Brasil, 2022d);

Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.

Table 2: Description of DEPEN dataset Features.

Attributes	Description	datatype	Value
Region	Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.	Categorical.	Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.
syphilis_2017	Number of syphilis cases in the prison system in 2017.	Numerical.	Number of syphilis cases.
syphilis_rate_2017	Normalized rate of syphilis cases in 2017.	Numerical.	Syphilis case rate.
syphilis_2018	Number of syphilis cases in the prison system in 2018.	Numerical.	Number of syphilis cases.
syphilis_rate_2018	Normalized rate of syphilis cases in 2018.	Numerical.	Syphilis case rate.
syphilis_2019	Number of syphilis cases in the prison system in 2019.	Numerical.	Number of syphilis cases.
syphilis_rate_2019	Normalized rate of syphilis cases in 2019.	Numerical.	Syphilis case rate.
syphilis_2020	Number of syphilis cases in the prison system in 2020.	Numerical.	Number of syphilis cases.
syphilis_rate_2020	Normalized rate of syphilis cases in 2020.	Numerical.	Syphilis case rate.
pop_2017	Prison population in 2017.	Numerical.	Population number.
pop_2018	Prison population in 2018.	Numerical.	Population number.
pop_2019	Prison population in 2019.	Numerical.	Population number.
pop_2020	Prison population in 2020.	Numerical.	Population number.

Dataset name: students_cumulative_sum.csv

Dataset period: 2018 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 7

Missing Values: No

Source:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.

Table 3: Description of Students dataset Features.

Private school (elementary, secondary, and combined*) enrolment numbers are organized by student gender and school level for each private school. The number captures the enrolment as of October 31st for the given school year. To be included, a student must be actively enrolled to attend the private school as their main school as of October 31. Data includes: * academic year * school number * school name * school level * elementary male enrolment * elementary female enrolment * secondary male enrolment * secondary female enrolment * total male enrolment * total female enrolment Source: As reported by private schools in the Ontario School Information System (OnSIS), October submission. Data includes private, First Nations, overseas, secondary and combined schools. *Combined schools offer both elementary and secondary education. Data does not include publicly funded elementary and secondary schools, hospital and provincial schools and care, treatment and correctional facilities. Small cells have been suppressed: * where fewer than 10 students are in a given category, the data is depicted with (<10) * suppressed totals are depicted with (SP) * the report may not be used in any way that could lead to the identification of an individual Note: * starting 2018-2019, enrolment numbers have been rounded to the nearest five. * where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals.

Combined (standard score normalization over original data sets), fully anonymised data for article manuscript. Includes 3 original data sets:

1. Data set 1: 2486 respondents, aged 15-19, from both the basic and secondary education and students from the preparatory training groups, collected in 2014 - 2016.

2. Data set 2: 9,316 basic education level students, ages 15-17, collected in 2017-2019.

3. Data set 3: 3,090 secondary education students, ages 15-19, collected in 2017.

Includes a total of 14,892 young Finns, aged 15-19. Contains information on adolescents' activity in six usage categories (maintaining social relationships, using digital services, following current affairs, online communicating, digital gaming, and searching for information on a scale of 0 = never, 1 = occasionally, 2 = weekly, 3 = daily, 4 = several hours daily) and an indicator of their digital skills. For usage categories both initial and z-score values, for digital skills only z-score values.

Background variables: Age, Gender (0 = female, 1 = male, missing value = don't want to share the information), LevelOfEducation (1 = basic education, 2 = secondary education), FormOfEducation (0 = comprehensive school, 1 = preparatory training for vocational education, 2 = vocational upper secondary school, 3 = general upper secondary school), and RerularityOfEducation (0 = regular transition, 1 = delayed transition).

Abstract copyright UK Data Service and data collection copyright owner.Gender and Adolescence: Global Evidence (GAGE) is a ten-year (2015-2025) research programme, funded by UK Aid from the UK Foreign, Commonwealth and Development Office (FCDO), that seeks to combine longitudinal data collection and a mixed-methods approach to understand the lives of adolescents in particularly marginalized regions of the Global South, and to uncover 'what works' to support the development of their capabilities over the course of the second decade of life, when many of these individuals will go through key transitions such as finishing their education, starting to work, getting married and starting to have children.GAGE undertakes longitudinal research in seven countries in Africa (Ethiopia, Rwanda), Asia (Bangladesh, Nepal) and the Middle East (Jordan, Lebanon, Palestine). Sampling adolescent girls and boys aged between 10‐19‐year olds, the quantitative survey follows a global total of 18,000 adolescent girls and boys, and their caregivers and explores the effects that programme have on their lives. This is substantiated by in‐depth qualitative and participatory research with adolescents and their peers. Its policy and legal analysis work stream studies the processes of policy change that influence the investment in and effectiveness of adolescent programming.Further information, including publications, can be found on the Overseas Development Institute GAGE website. In Jordan, GAGE recruited a sample of 4,101 adolescent girls and boys in two separate cohorts (younger adolescents aged 10-12 years and older adolescents age 15-18 years at baseline). GAGE surveyed the adolescents, as well as their adult female caregivers and, for those enrolled in formal schooling, conducted surveys at their schools. This sample includes Syrian refugees living in refugee camps, informal tented settlements (ITS) and host communities, as well as Palestinian refugees living in refugee camps and host communities, vulnerable Jordanian adolescents living in communities hosting refugees, and a small group of adolescents of other nationalities (Egyptian, Iraqi, and others) living in Jordan. The research sample was recruited during 2018 and 2019. Additional information about the sample and the baseline Jordan data are available in the GAGE Jordan Baseline Sample Overview and Data Use Manual (2021) available from UK Data Archive SN 8866. Gender and Adolescence: Global Evidence: Jordan Baseline School Survey, 2019-2020 contains data collected at baseline from an additional survey conducted in adolescents' communities, which focused on formal primary and secondary schools. Specific schools where Core Respondents attended were identified and linked based on the details collected from the Core Respondent baseline survey. Where schools consented to participate, questionnaires were administered to a key school informant, such as the principal or head teacher, in September 2019 through January 2020. Main Topics: Youth; adolescence; gender; longitudinal impact evaluation of youth programming Purposive selection/case studies Convenience sample Face-to-face interview: Computer-assisted (CAPI/CAMI)

Statistics Canada Census Data from 2021. This dataset includes the individual income in 2019 data provided by Statistics Canada joined with the census tracts. Each topic covered by the census was exported as a separate table. Each table contains the total, male, and female characteristics as fields for each census tract. Topics range from population, age and sex, immigration, language, family and households, income, education, and labour. For more information on definitions of terms used in the tables and other notes, refer to Statistics Canada's 2021 Census.

Analysis of ‘SFI Gender Dashboard 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/25adf0c5-e1bc-48e9-9677-ad1112af9737 on 10 January 2022.

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

The data here include SFI research programmes from 2011 that were managed end-to-end in SFI’s Grants and Awards Management System. Programmes were gradually managed through the Grants and Awards Management System from 2011, and therefore awards made under programmes prior to 2011 were excluded as these data were not available. Furthermore, non-research funded programmes (e.g. education and public engagement grants) and programmes where SFI simply provided the funding to another organisation who solicit and process the applications, for example the Wellcome Trust, Royal Society, Joint Funding initiatives etc., were also excluded.

The data include awards offered by SFI, irrespective of whether the award was accepted or declined by the applicant, as this best represents completion of the SFI peer review process. Where awards were transferred or underwent different ownership after their inception, data were based on the lead applicant’s self-declared gender at the time the award decision was made and currently reflects a binary categorisation of gender, e.g. male or female (with exclusions as described previously) between 2011 and 2018.

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

Abstract The bibliographic study that supports this paper mapped Education articles on the relations between gender and Mathematics, published in Brazilian academic journals between 2009 and 2019.The analyzed corpus was made up of 25 papers and their analysis sought to show potentials and weaknesses, questions and commitments, ruptures and entanglements that involve two different analytical gender approaches identified in the texts. We distinguished them in two groups: 1. those who use gender as a variable to analyze different aspects that permeate educational processes in relation to mathematics; 2. those who use gender as an analytical category to understand gender and Mathematics relations at school and in other social spaces. The results suggest a growing concern about gender relations in Mathematics Education studies, but they also warn about the risk of such studies being inserted in a citational chain that reiterates principles that presuppose and produce the difference between men and women, boys and girls, concerning mathematical practices. However, they announce possibilities of cracks in this discursive chain, which can lead to more detailed questions and investigations about how the process of difference production takes place on mathematics and gender, and subsidize the reflection on what is taken as a mathematical parameter in these practices. They also point out the need to activate other categories (such as race, ethnicity, sexuality, generation, territory) in their intersection with gender and mathematics, to expand understanding and strengthen the confrontation of inequalities.

The dataset presents information on the number of primary and secondary school students divided by year of course, school time and gender, belonging to the public schools of the Municipality of Milan. The data collected have as reference period the school year 2019-2020 The data within the dataset are: * School Year: Numerical School year of reference school registry; * CodeSchool: Text Code of the school (plexus); * NameSchool: Name (name) of the school (plexus) * AddressSchool:School address * OrderSchool: Indicates the school order (degree of education) of the school. No data are collected on kindergarten * YearCourse: Indicates the year of course in reference to the school order. For Primary school from 1 to 5. For lower secondary school grades 1 to 3. For upper secondary school from 1 to 6 * TimeSchool: Description of the address for I and II grade secondary school or primary school time * PupilsMale: Number of male pupils * PupilsFemale: Number of female pupils * ZIP code: Postal code *MUNICIPALITY: City Hall * ID_NIL: Local Identity Core Identifier * NIL:Local Identity Unit * LONG_X_4326: Longitude * LAT_Y_4326: Latitude * Location: Latitude and Longitude

Abstract copyright UK Data Service and data collection copyright owner.Gender and Adolescence: Global Evidence (GAGE) is a ten-year (2015-2025) research programme, funded by UK Aid from the UK Foreign, Commonwealth and Development Office (FCDO), that seeks to combine longitudinal data collection and a mixed-methods approach to understand the lives of adolescents in particularly marginalized regions of the Global South, and to uncover 'what works' to support the development of their capabilities over the course of the second decade of life, when many of these individuals will go through key transitions such as finishing their education, starting to work, getting married and starting to have children.GAGE undertakes longitudinal research in seven countries in Africa (Ethiopia, Rwanda), Asia (Bangladesh, Nepal) and the Middle East (Jordan, Lebanon, Palestine). Sampling adolescent girls and boys aged between 10‐19‐year olds, the quantitative survey follows a global total of 18,000 adolescent girls and boys, and their caregivers and explores the effects that programme have on their lives. This is substantiated by in‐depth qualitative and participatory research with adolescents and their peers. Its policy and legal analysis work stream studies the processes of policy change that influence the investment in and effectiveness of adolescent programming.Further information, including publications, can be found on the Overseas Development Institute GAGE website. Gender and Adolescence: Global Evidence: Ethiopia Round 2, 2019-2020 extends the GAGE quantitative research in Ethiopia for a second round. A sample of nearly 8,600 adolescent boys and girls was sought, including nearly 7,000 adolescents surveyed in an earlier Baseline round (available from the UK Data Archive under SN 8597), as well as approximately 1,600 new adolescents. The main purpose of this survey was to gather information on the lives of Ethiopian adolescents living in urban and rural locations in the Amhara, Oromiya, and Afar regions, and to understand their changing lives and challenges. At the time of data collection, adolescents were primarily aged 12-14 and 17-19. The sample includes both randomly and purposefully sampled adolescents, and their female caregivers were also surveyed where possible. The current data release includes information for the subset of individuals who are not part of an ongoing randomized evaluation of adolescent-centric programming. A total of nearly 5,000 adolescents and their caregivers are included in the current release. Main Topics: The Core Respondent (CR) dataset contains data from the survey administered to the CR and covers education, time allocation, paid work, health and nutrition, psychosocial and mental health, mobility and voice, social inclusion, marriage and relationships, financial inclusion and economic empowerment, and information and communication technologies. The Adult Female (AF) dataset contains information on the household, including the household roster, family background, durable goods, dwelling characteristics, access to productive capital, recent positive and negative shocks, and household access to programs and support. In addition, the AF survey contains detailed information about the AF herself, such as parenting, health and nutrition, attitudes to gender equality, marriage, fertility and social norms. Purposive selection/case studies Multi-stage stratified random sample Face-to-face interview: Computer-assisted (CAPI/CAMI) 2019 2020 ACCESS TO EDUCATION ACCESS TO HEALTH SE... ACCESS TO INFORMATI... ACTIVITIES OF DAILY... ADOLESCENCE ADOLESCENTS AGE ALCOHOL USE ANIMAL HUSBANDRY ANXIETY ARRANGED MARRIAGES ATTITUDES BANK ACCOUNTS BIRTH CONTROL CHILDREN CREDIT DEVELOPING COUNTRIES DEVELOPMENT PROGRAMMES DISABILITIES EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL FACILITIES EDUCATIONAL STATUS EMOTIONAL STATES ENERGY CONSUMPTION Education Ethiopia FAMILY INFLUENCE FAMILY PLANNING FATHER S EDUCATIONA... FATHERS FINANCIAL DIFFICULTIES FOOD FOOD AND NUTRITION GENDER EQUALITY GENDER ROLE Gender and gender r... HEADS OF HOUSEHOLD HEALTH STATUS HEARING IMPAIRMENTS HOUSEHOLD BUDGETS HOUSEHOLDERS HOUSEHOLDS HOUSING CONDITIONS ILL HEALTH INFORMAL CARE INFORMATION SOURCES INTERNAL MIGRATION INTERNET ACCESS INTERNET USE LAND OWNERSHIP LAVATORIES LEISURE TIME ACTIVI... LIFE SATISFACTION LITERACY LIVESTOCK LOANS MARITAL HISTORY MARITAL STATUS MENSTRUATION MOBILE PHONES MORAL VALUES MOTHERS PARENTAL ENCOURAGEMENT PARENTAL ROLE PERSONAL FINANCE MA... PERSONAL SAFETY PHYSICAL MOBILITY PLACE OF BIRTH PREGNANCY QUALITY OF LIFE RELIGIOUS AFFILIATION RELIGIOUS BEHAVIOUR RESIDENTIAL MOBILITY ROOMS SAVINGS SCHOOL PUNISHMENTS SCHOOLS SEX SEX DISCRIMINATION SOCIAL ATTITUDES SOCIAL INEQUALITY SOCIAL VALUES STRUCTURAL ELEMENTS... STUDENT EMPLOYMENT STUDENT TRANSPORTATION Society and culture TELEVISION VIEWING TIME BUDGETS TRUANCY UNEARNED INCOME VISION IMPAIRMENTS WATER RESOURCES Youth

The dataset displays information regarding the number of primary and lower secondary school students divided by course year, school time and gender, belonging to private schools in the Municipality of Milan. The data collected refer to the 2018-2019 school year. The data in the dataset are: * School Year: Numeric Reference school year in the school registry; * CodiceScuola: Code text of the school (plexus); * DenominazioneScuola: Denomination (name) of the school (plexus) * AddressSchool: Delivery address of the school * OrdineScuola: Indicates the school order (level of education) of the school. No data relating to kindergarten were collected. * AnnoCorso: Indicates the year of the course with reference to the school order. For primary school from 1 to 5. For lower secondary school from 1 to 3. For upper secondary school from 1 to 6 Primary * PupilsMale: Number of pupils male * PupilsFemale: Number of pupils female * CAP: Postal code * MUNICIPALITY: Municipality * ID_NIL: Local identity nucleus identifier * NIL: Local identity nucleus * LONG_X_4326: Longitude * LAT_Y_4326: Latitude * Location: Latitude and Longitude

This dataset contains self-efficacy survey responses from undergraduate biology students enrolled in three different course formats (lecture, introductory field, and intensive field) at the University of California, Santa Cruz from 2016-2019. The data structure includes pre/post survey responses measuring students' self-efficacy across four skill areas (species identification, experimental design, oral presentation, and field research), demographic information (URM status, first-generation status, gender, and Educational Opportunity Program status), and calculated change scores for 564 students. The dataset demonstrates how Differential Item Functioning (DIF) analysis can quantify both the magnitude and demographic patterns of educational interventions with greater precision than traditional assessment methods. Analysis revealed field course students were significantly more likely to report higher self-efficacy ratings compared to lecture course students (odds ratios ranging from 2-167 ..., Study Context and Participants We conducted several sets of new analyses on student response data from a pre/post survey previously administered by Beltran et al. (2020) at the University of California, Santa Cruz. The original survey response data ranges from Fall 2016 through Spring 2019 and assessed three undergraduate biology courses with different levels of the intervention (field-based learning): (1) BIOE 20C Ecology and Evolution (lecture course; no field-based learning; n = 81); (2) BIOE 82 Introduction to Field Research and Conservation (introductory field-based learning; n = 190); and (3) California Ecology and Conservation (intensive field-based learning; n = 293). Complete course descriptions are provided in Supplement 1. For demographic analyses, we compared five dichotomous groups based on self-reported student identities: (1) URM versus non-URM status, (2) FIF versus non-FIF status, (3) Female versus male gender identity, (4) Educational Opportunity Program (EOP) versus n..., # Refining impact assessment in undergraduate STEM education: Differential item functioning analysis of field-based learning interventions

Dataset DOI: 10.5061/dryad.zcrjdfnqs

Description of the data and file structure

This dataset contains self-efficacy survey responses from undergraduate biology students enrolled in three different course formats at the University of California, Santa Cruz from Fall 2016 through Spring 2019. The data was collected to analyze how field-based learning experiences impact students' self-efficacy development across demographic groups. The dataset includes pre/post survey responses measuring students' self-efficacy in several areas, demographic information, and course enrollment data.

This data is an extension of research originally published by Beltran et al. (2020) and archived at Dryad (https://doi.org/10.7291/D1DM3P). The current dataset includes additional analyses examining differenti..., We received explicit consent from participants to publish the de-identified data in the public domain and de-identified data through our approved UCSC IRB protocol #HS3230.

StudentIDs were anonymized and do not represent a means to identify participants.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - 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.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

Summary

The State of Early Education and Care in Boston: Supply, Demand, Affordability, and Quality, is the first in what is planned as a recurrent landscape survey of early childhood, preschool and childcare programs in every neighborhood of Boston. It focuses on potential supply, demand and gaps in child-care seats (availability, quality and affordability). This report’s estimates set a baseline understanding to help focus and track investments and policy changes for early childhood in the city.

This publication is a culmination of efforts by a diverse data committee representing providers, parents, funding agencies, policymakers, advocates, and researchers. The report includes data from several sources, such as American Community Survey, Massachusetts Department of Early Education and Care, Massachusetts Department of Elementary & Secondary Education, Boston Public Health Commission, City of Boston, among others. For detailed information on methodology, findings and recommendations, please access the full report here

The first dataset contains all Census data used in the publication. Data is presented by neighborhoods:

• Population 0 – 5 years;
• Population 0 – 2 years;
• Population 3 – 5 years;
• Race/ethnicity for children 0 – 4 years (White, non-Hispanic; Black; Asian; Hispanic/Latinx);
• Family type (married couples, female householder, male householder);
• Poverty status;
• Family median income in the past 12 months;
• Average cost of care as a percentage of median family income (infant, preschool);
• Share of families that cannot afford care (infant, preschool)

The Boston Planning & Development Agency Research Division analyzed 2013-2017 American Community Survey data to estimate numbers by ZIP-Code. The Boston Opportunity Agenda combined that data by the approximate neighborhoods and estimated cost of care and affordability.

Additional notes:

• Record Type: Each record represents a ZIP-Code defined neighborhood. See list below for detailed information on Boston ZIP-Codes used to create each one of the 15 neighborhoods.
• Data Quality: Numbers presented here came from 2013-2017 American Community Survey data. Therefore, these are ESTIMATES and have margin of errors. The smaller the geographical unit, the greater the margin of error. The Boston Planning & Development Agency analyzed the data to estimate numbers by ZIP-Code.
• Race/Ethnicity: Non-White Hispanics may be double counted due to data limitations.
• Cost of Care: The average cost of care as a percentage of median family income was computed assuming the annual average cost of infant care was $19,877 and the average cost of preschool care was $ 13,771 (Childcare Aware of America, 2019). For each neighborhood we estimated the impact of child care (infant and preschool) on its median annual family income.
• Affordability: The Department of Health and Human Services (DHHS) sets a standard regarding the affordability of child care, where the annual cost of child care should not exceed 10 percent of household annual income. Using this 10% threshold, we estimated that to afford market rate infant care, a family’s annual income would have to be at least $198,770. The census income bracket closest to this income was a family income of $150,000– 199,999. To afford preschool care, a family's annual income should be at least $137,710. Thus, the census income bracket that encompass this income is $125,000 - 149,999. For both infant and preschool care, we underestimated the number of families that can afford care.

The dataset takes data from 145 university students from Cyprus in 2019. The collected data are expressed by 31 variables with the following structure:

• Variables 1-10 are personal questions.
• Variables 11-16 are questions about the student's family.
• The remaining variables are about study habits

CSV files

Additionally, this dataset includes three CSV files:

• student_performance_only_index : This is the original dataset.
• student_performance_index: This is the cleaned dataset with headers renamed using the variable names.
• student_performance_value: This is the dataset cleaned with the values specified below.

Variables and Values

1. Student age (1: 18-21, 2: 22-25, 3: above 26)
2. Sex (1: female, 2: male)
3. Graduated high school type (1: private, 2: state, 3: other)
4. Scholarship type (1: none, 2: 25%, 3: 50%, 4: 75%, 5: 100%)
5. Additional work (1: yes, 2: no)
6. Regular artistic or sports activity (1: yes, 2: no)
7. Do you have a partner (1: yes, 2: no)
8. Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above USD 410)
9. Transportation to the university (1: bus, 2: private car/taxi, 3: bicycle, 4: other)
10. Accommodation type in Cyprus (1: rental, 2: dormitory, 3: with family, 4: other)
11. Mother's education (1: primary school, 2: secondary school, 3: high school, 4: university, 5: master of science, 6: doctor of philosophy)
12. Father's education (1: primary school, 2: secondary school, 3: high school, 4: university, 5: master of science, 6: doctor of philosophy)
13. Number of sisters/brothers if available (1: 1, 2: 2, 3: 3, 4: 4, 5: 5 or above)
14. Parental status (1: married, 2: divorced, 3: died - one of them or both)
15. Mother's occupation (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other)
16. Father's occupation (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other)
17. Weekly study hours (1: none, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: +20 hours)
18. Reading frequency non-scientific books/journals (1: none, 2: sometimes, 3: often)
19. Reading frequency scientific books/journals (1: None, 2: Sometimes, 3: Often)
20. Attendance to the seminars/conferences related to the department (1: yes, 2: no)
21. Impact of your projects/activities on your success (1: positive, 2: negative, 3: neutral)
22. Attendance to classes (1: always, 2: sometimes, 3: never)
23. Preparation to midterm exams 1 (1: alone, 2: with friends, 3: not applicable)
24. Preparation to midterm exams 2 (1: closest date to the exam, 2: regularly during the semester, 3: never)
25. Taking notes in classes (1: never, 2: sometimes, 3: always)
26. Listening in classes (1: never, 2: sometimes, 3: always)
27. Discussion improves my interest and success in the course (1: never, 2: sometimes, 3: always)
28. Flip classroom (1: not useful, 2: useful, 3: not applicable)
29. Cumulative grade point average in the last semester (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)
30. Expected cumulative grade point average in the graduation (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)
31. ID course
32. Output grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)

About Cyprus

Cyprus is an island belonging to the European Union that belongs to two countries: Republic of Cyprus and the Turkish Republic of Northern Cyprus, the latter is important because the output grade of the students uses the Turkish grades metric.

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Chapter 3 looks at the institutional factors that contribute to explaining the relationship between parent’s education and children’s education. Data for 48 countries in total, from multiple harmonized surveys, are utilised. A total of 149 surveys are included. Using multivariate regressions, we first present the correlation coefficients of the relationship between parent’s education and children’s education. These coefficients then serve as the dependent variable in the regression analysis with the institutional factors at the second stage. To this end, secondary data are obtained from the household Demographic and Health Surveys (DHS), and from the U.S. Agency for International Development (USAID) and the World Bank data catalogue. The DHS are nationally representative cross-sectional surveys where data on impact evaluation indicators on the population, health, and nutrition in over 90 countries are represented. The primary respondents of the surveys are women of reproductive age, between 15-49 years, who respond to a household questionnaire and a woman’s questionnaire (DHS Program, 2020). The man’s questionnaire is responded to by men of reproductive age (typically 15 to 49, 54, or 59). In the household questionnaire, the respondent provides information on household membership, individual characteristics, household head, health, housing, consumer goods, and living conditions (DHS Program, 2020). The factors from the USAID and the World Bank data catalogue are part of the world development indicators (WDI) and the worldwide governance indicators (WGI). Corruption estimates, political stability estimates, and voice and accountability estimates are taken from the WGI while the others (GDP, prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, and mobile cellular subscriptions) are from the WDI. The WDI is a compilation of high-quality, relevant, and internationally comparable statistics about global development and the fight against poverty (World Bank, 2020b). 1600 time series indicators are contained in the database for 217 countries. These indicators are organized according to six main thematic areas that are poverty and inequality, people, environment, economy, states and markets, global links (World Bank, 2020b). The WGI are nationally comparable indicators of government selection, monitoring, replacement, effectiveness, and the respect of citizens and the state. The worldwide governance indicators generally report on six broad governance dimensions for over 215 countries and territories. These dimensions are government effectiveness, control of corruption, rule of law, voice and accountability, regulatory quality, and political stability and absence of violence (World Bank, 2019). Specifically, we focus on GDP, the prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, mobile cellular subscriptions, the extent of corruption, the extent of political stability, and the extent of voice and accountability. The factors used in this chapter are selected based on data availability. The process looks at the correlation between these factors and the intergenerational correlation of education. The results show that these institutional factors account for 39% of the explained cross-country variation in the intergenerational correlation of education. The pupil-teacher ratio, primary school duration, and compulsory years of education reduce intergenerational correlation of education by 0.03 years, 0.03 years, and 0.02 years respectively, following a one standard deviation change in the variables. Besides these variables, GDP, female-male labour force participation, and extent of voice and accountability reduce intergenerational correlation of education by 0.01 years, 0.03 years, and 0.03 years respectively, following a one standard deviation change in the variables. This confirms our second hypothesis on favourable institutional characteristics being able to reduce intergenerational correlation of education.