The purpose of this project was to collect data about volunteers who do citizen science projects, particilarly the number and type of projects that these participants do, and what demographic communities these volunteers represent. There were four data sources: digital trace data from the website "SciStarter.org," a survey distributed to SciStarter volunteers, a survey distributed to volunteers with the project "The Christmas Bird Count" and volunteers with the project "Candid Critters." We used this data to create a list of citizen science projects, which we categorized according to disciplinary topic (ecology, astronomy, etc.) and participation mode (online or offline). We then categorized each volunteer in our data source according to how many projects they did, and whether the project(s) they did were from multiple disciplinary topics and modes. Finally, we used regression to assess what demographics and other factors predicted joining multiple projects, joining projects from multip...

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data.

Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set.

Exceptions to this are:

• Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence.
• Data from Gateway to Research is made available under an Open Government Licence (Version 3.0).

Contents

• Survey data & analysis: esrc_data-survey-analysis-data.zip
• Other data: esrc_data-other-data.zip
• Transcripts: esrc_data-transcripts.zip
• Data Management Plan: esrc_data-dmp.zip

Survey data & analysis

The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data.

The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled.

The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly.

A pdf copy of the survey questions is available on GitHub.

The survey data has been decoupled into:

• survey-results-key.csv - maps a question number and the responses to the actual question values.
• q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16).
• q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23).
• q17-institutions.csv - the institution/location of the respondent (Q17).
• q18-funding.csv - funding sources within the last 5 years (Q18).

Please note the code that has been used to do the analysis will not run with the decoupled survey data.

Other data files included

• CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered.
• DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021.
• projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence.
• locations.csv - latitude and longitude for the institutions in the cleaned locations.
• subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot.
• topics.csv - topic classification for the ESRC projects for the 24th February data snapshot.

Interview transcripts

The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts:

• 1269794877.md
• 1578450175.md
• 1792505583.md
• 2964377624.md
• 3270614512.md
• 40983347262.md
• 4288358080.md
• 4561769548.md
• 4938919540.md
• 5037840428.md
• 5766299900.md
• 5996360861.md
• 6422621713.md
• 6776362537.md
• 7183719943.md
• 7227322280.md
• 7336263536.md
• 75909371872.md
• 7869268779.md
• 8031500357.md
• 9253010492.md

Data Management Plan

The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

Early Postwar Canadian Census Data Creation Project Files. Contains digitized census tract boundary files and associated tabular data, with codebooks, for Census years 1951, 1956, 1961, and 1966.

OCUL’s Historical Census Working Group (part of the OCUL Data Community) is working on scoping a comprehensive bilingual inventory of Canadian census data. Our dream is to eventually build a bilingual and openly available discovery platform for census data & statistical tables (print & digital) going back to the earliest Canadian censuses. This presentation will provide a status update on the project and dedicate time for attendees to discuss the project and provide input.

There is a duality of trust in participatory science (citizen science) projects in which the data produced by volunteers must be trusted by the scientific community and participants must trust the scientists who lead projects. Facilitator organizations can diversify recruitment and broaden learning outcomes. We investigated the degree to which they can broker trust in participatory science projects. In Crowd the Tap, we recruited participants through partnerships with facilitators, including high schools, faith communities, universities, and a corporate volunteer program. We compared data quality (a proxy for scientists’ trust in the project) and participant privacy preferences (a proxy for participants’ trust in the project leaders) across the various facilitators as well as to those who came to the project independently (unfacilitated). In general, we found that data quality differed based on the project’s level of investment in the facilitation partner in terms of both time and money..., The data was collected through an IRB approved survey in which Crowd the Tap participants submitted data on the types of pipes they had, the age of their home, water aesthetics, and demographic information. As part of this process, participants also indicated if they came to the project through a partner organization (what we call facilitator organizations). Using the data available to us, we determined how completely, accurately, and informatively (understandability) they participated in the project to assess data quality. We also asked if they had interest in being publically associated with the project or if they referred to remain private. We used this and the number of times they selected "Prefer not to say" as indicators of privacy. We compared data quality and privacy preferences to the facilitator organization through which they came to the project. , , # Data from: The dual nature of trust in participatory science: An investigation into data quality and household privacy preferences

The dataset contains data on participation in Crowd the Tap, a large-scale participatory science (citizen science) project focused on identifying and addressing lead contamination in household drinking water. The project crowdsources information on plumbing materials, age of home, water aesthetics, and demographic data to learn more about the geographic spread of lead plumbing and social and environmental correlates to lead plumbing. We investigated how data quality (completeness, accuracy, and understandability) and participant privacy (whether or not they select to be public or private, the number times they select “prefer not to say†) preferences differed by facilitators. Data quality relates to scientists’ trust in the project, and privacy relates to the trust that participants have in the project leadership team. As participatory science projects inc...

This dataset contains water-use estimates for 2015 that are aggregated to the county level in the United States. The U.S. Geological Survey's (USGS's) National Water Use Science Project is responsible for compiling and disseminating the Nation's water-use data. Working in cooperation with local, State, and Federal agencies, the USGS has published an estimate of water use in the United States every 5 years, beginning in 1950. Water-use estimates aggregated to the State level are presented in USGS Circular 1441, "Estimated Use of Water in the United States in 2015" (Dieter and others, 2018). This dataset contains the county-level water-use data that support the state-level estimates in Dieter and others 2018. This dataset contains data for public supply, domestic, irrigation, thermoelectric power, industrial, mining, livestock, and aquaculture water-use categories. First posted September 28, 2017, ver. 1.0 Revised June 19, 2018, ver. 2.0 Version 2.0: This version of the dataset contains total population data and water-use estimates for 2015 for the following categories: Public supply, domestic, irrigation, thermoelectric power, industrial, mining, livestock, and aquaculture. Data are aggregated to the county level. A value of "--" denotes that values were not estimated for an optional attribute. Some values in the public supply and domestic categories have been updated from those found in version 1.0 of this dataset. Version 1.0: This version of the dataset contains total population data and water-use estimates for the public supply and domestic categories for 2015 that are aggregated to the county level in the United States. A "--" in the attributes "PS-GWPop" or "PS-SWPop" denotes that values were not estimated for an optional attribute. All other occurrences of "--" denote data for an attribute in a water-use category that has not yet been released. Version 1.0 data are available upon request.

This is a hybrid gridded dataset of demographic data for China from 1979 to 2100, given as 21 five-year age groups of population divided by gender every year at a 0.5-degree grid resolution.

The historical period (1979-2020) part of this dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4, UN WPP-Adjusted Population Count) with gridded population from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, Histsoc gridded population data).

The projection (2010-2100) part of this dataset is resampled directly from Chen et al.’s data published in Scientific Data.

This dataset includes 31 provincial administrative districts of China, including 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government (Taiwan, Hong Kong, and Macao were excluded due to missing data).

Method - demographic fractions by age and gender in 1979-2020

Age- and gender-specific demographic data by grid cell for each province in China are derived by combining historical demographic data in 1979-2020 with the national population census data provided by the National Statistics Bureau of China.

To combine the national population census data with the historical demographics, we constructed the provincial fractions of demographic in each age groups and each gender according to the fourth, fifth and sixth national population census, which cover the year of 1979-1990, 1991-2000 and 2001-2020, respectively. The provincial fractions can be computed as:

\(\begin{align*} \begin{split} f_{year,province,age,gender}= \left \{ \begin{array}{lr} POP_{1990,province,age,gender}^{4^{th}census}/POP_{1990,province}^{4^{th}census} & 1979\le\mathrm{year}\le1990\\ POP_{2000,province,age,gender}^{5^{th}census}/POP_{2000,province}^{5^{th}census} & 1991\le\mathrm{year}\le2000\\ POP_{2010,province,age,gender}^{6^{th}census}/POP_{2010,province}^{6^{th}census}, & 2001\le\mathrm{year}\le2020 \end{array} \right. \end{split} \end{align*}\)

Where:

- \( f_{\mathrm{year,province,age,gender}}\)is the fraction of population for a given age, a given gender in each province from the national census from 1979-2020.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province,age,gender}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for a given age, a given gender in each province from the X^th national census.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for all ages and both genders in each province from the X^th national census.

Method - demographic totals by age and gender in 1979-2020

The yearly grid population for 1979-1999 are from ISIMIP Histsoc gridded population data, and for 2000-2020 are from the GPWv4 demographic data adjusted by the UN WPP (UN WPP-Adjusted Population Count, v4.11, https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11), which combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP to improve accuracy. These two gridded time series are simply joined at the cut-over date to give a single dataset - historical demographic data covering 1979-2020.

Next, historical demographic data are mapped onto the grid scale to obtain provincial data by using gridded provincial code lookup data and name lookup table. The age- and gender-specific fraction were multiplied by the historical demographic data at the provincial level to obtain the total population by age and gender for per grid cell for china in 1979-2020.

Method - demographic totals and fractions by age and gender in 2010-2100

The grid population count data in 2010-2100 under different shared socioeconomic pathway (SSP) scenarios are drawn from Chen et al. published in Scientific Data with a resolution of 1km (~ 0.008333 degree). We resampled the data to 0.5 degree by aggregating the population count together to obtain the future population data per cell.

This previously published dataset also provided age- and gender-specific population of each provinces, so we calculated the fraction of each age and gender group at provincial level. Then, we multiply the fractions with grid population count to get the total population per age group per cell for each gender.

Note that the projected population data from Chen’s dataset covers 2010-2020, while the historical population in our dataset also covers 2010-2020. The two datasets of that same period may vary because the original population data come from different sources and are calculated based on different methods.

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. Spatial or temporal consistency across dataset boundaries cannot be guaranteed.

Abstract (en): This study contains teaching materials developed over a period of years for a four-week workshop, Longitudinal Analysis of Historical Demographic Data (LAHDD), offered through the ICPSR Summer Program in 2006, 2007, 2009, 2011 and 2013, with one-day alumni workshops in 2010, 2012, and 2014. Instructors in the workshops are listed below. Funding was provided by The Eunice Kennedy Shriver National Institute of Child Health and Human Development, grants R25-HD040525 and R25-HD-049479, the ICPSR Summer Program and the ICPSR Director. The course was designed to teach students the theories, methods, and practices of historical demography and to give them first-hand experience working with historical data. This training is valuable not only to those interested in the analysis historical data. The techniques of historical demography rest on methodological insights that can be applied to many problems in population studies and other social sciences. While historical demography remains a flourishing research area with publications in key journals like Demography, Population Studies, and Population, practitioners were dispersed, and training was not available at any of the population research centers in the U.S. or elsewhere. One hundred and ten participants from around the globe took part in the workshops, and have gone on to establish courses of their own or teach in other workshops. We offer these materials here in the hopes that others will find them useful in developing courses on historical demography and/or longitudinal data analysis. The workshop was organized in three tracks: A brief tour of historical demography, event-history analysis, and data management for longitudinal data using Stata and Microsoft Access. The data management track includes 13 exercises designed for hands-on learning and reinforcement. Included in this project are the syllabii and reading lists for the three tracks, datasets used in the exercises, documents setting out each exercise, a file with the expected results, and for many of the exercises, an explanation. Video tutorials helpful with the Access exercises are accessible from ICPSR's YouTube channel https://www.youtube.com/playlist?list=PLqC9lrhW1Vvb9M1QpQH23z9UlPYxHbUMF. Users are encouraged to use these materials to develop their own courses and workshops in any of the topics covered. Please acknowledge NICHD R25-HD040525 and R25-HD-049479 whenever appropriate. Historical demography instructors: Myron P. Gutmann, University of Colorado Boulder Cameron Campbell, Hong Kong University of Science and Technology J. David Hacker, University of Minnesota Satomi Kurosu, Reitaku University Katherine A. Lynch, Carnegie Mellon University Event history instructors: Cameron Campbell, Hong Kong University of Science and Technology Glenn Deane, State University of New York at Albany Ken R. Smith, Huntsman Cancer Institute and University of Utah Database management instructors: George Alter, University of Michigan Susan Hautaniemi Leonard, University of Michigan Teaching Assistants: Mathew Creighton, University of Massachusetts Boston Emily Merchant, University of Michigan Luciana Quaranta, Lund University Kristine Witkowski, University of Michigan Project Manager: Susan Hautaniemi Leonard, University of Michigan Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (R25 HD040525).

Overview

This dataset provides comprehensive information on customer visits to restaurants, including demographic details, visit-specific metrics, and customer satisfaction ratings. It is designed to facilitate predictive modeling and analytics in the hospitality industry, focusing on factors that drive customer satisfaction.

Features

Demographic Information

• CustomerID: Unique identifier for each customer.
• Age: Age of the customer.
• Gender: Gender of the customer (Male/Female).
• Income: Annual income of the customer in USD.

Visit-specific Variables

• VisitFrequency: How often the customer visits the restaurant (Daily, Weekly, Monthly, Rarely).
• AverageSpend: Average amount spent by the customer per visit in USD.
• PreferredCuisine: The type of cuisine preferred by the customer (Italian, Chinese, Indian, Mexican, American).
• TimeOfVisit: The time of day the customer usually visits (Breakfast, Lunch, Dinner).
• GroupSize: Number of people in the customer's group during the visit.
• DiningOccasion: The occasion for dining (Casual, Business, Celebration).
• MealType: Type of meal (Dine-in, Takeaway).
• OnlineReservation: Whether the customer made an online reservation (0: No, 1: Yes).
• DeliveryOrder: Whether the customer ordered delivery (0: No, 1: Yes).
• LoyaltyProgramMember: Whether the customer is a member of the restaurant's loyalty program (0: No, 1: Yes).
• WaitTime: Average wait time for the customer in minutes.

Satisfaction Ratings

• ServiceRating: Customer's rating of the service (1 to 5).
• FoodRating: Customer's rating of the food (1 to 5).
• AmbianceRating: Customer's rating of the restaurant ambiance (1 to 5).

Target Variable

• HighSatisfaction: Binary variable indicating whether the customer is highly satisfied (1) or not (0).

Potential Applications

• Predictive modeling of customer satisfaction.
• Analyzing factors that drive customer loyalty and satisfaction.
• Identifying key areas for improvement in service, food, and ambiance.
• Optimizing marketing strategies to attract and retain satisfied customers.

Usage

This dataset is ideal for data scientists and hospitality analysts looking to explore and model customer satisfaction in the restaurant industry. It can be used for machine learning projects, customer segmentation, and strategic planning.

Dataset Usage and Attribution Notice

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

Exclusive Synthetic Dataset

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

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling and person

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Every separate and independent structure that has been constructed or converted for use as temporary or permanent housing. This includes any class of fixed or mobile shelter used as a place of lodging at the time of enumeration. A dwelling can be a) a private house, apartment, floor in a house, room or group of rooms, ranch, etc. designed to give lodging to one person or a group of people or b) a boat, vehicle, railroad car, barn, shed, or any other type of shelter occupied as a place of lodging at the time of enumeration. - Households: All the occupying members of a family or private dwelling that live together as family. In most cases, a household is made up of a head of the family, relatives of this person (wife or partner, children, grand-children, nieces and nephews, etc.), close friends, guests, lodgers, domestic employees and all other occupants. Households with five or fewer lodgers are considered private,but households with six or more lodgers are considered a non-family group. - Group quarters: Accommodation for a group of people who are not usually connected by kinship ties who live together for reasons of discipline, healthcare, education, mlitary activity, religion, work or other dwellings such as reform schools, boarding schools, barracks, hopsitals, guest houses, nursing homes, workers camps, etc.

Universe

Population in private and communal housing

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 268,248

Mode of data collection

Face-to-face [f2f]

Research instrument

Single record that includes housing and population questionnaires

This dataset contains comprehensive information on 2,392 high school students, detailing their demographics, study habits, parental involvement, extracurricular activities, and academic performance. The target variable, GradeClass, classifies students' grades into distinct categories, providing a robust dataset for educational research, predictive modeling, and statistical analysis.

Table of Contents

1. Student Information
   • Student ID
   • Demographic Details
   • Study Habits
2. Parental Involvement
3. Extracurricular Activities
4. Academic Performance
5. Target Variable: Grade Class

Student Information

Student ID

• StudentID: A unique identifier assigned to each student (1001 to 3392).

Demographic Details

• Age: The age of the students ranges from 15 to 18 years.
• Gender: Gender of the students, where 0 represents Male and 1 represents Female.
• Ethnicity: The ethnicity of the students, coded as follows:
  • 0: Caucasian
  • 1: African American
  • 2: Asian
  • 3: Other
• ParentalEducation: The education level of the parents, coded as follows:
  • 0: None
  • 1: High School
  • 2: Some College
  • 3: Bachelor's
  • 4: Higher

Study Habits

• StudyTimeWeekly: Weekly study time in hours, ranging from 0 to 20.
• Absences: Number of absences during the school year, ranging from 0 to 30.
• Tutoring: Tutoring status, where 0 indicates No and 1 indicates Yes.

Parental Involvement

• ParentalSupport: The level of parental support, coded as follows:
  • 0: None
  • 1: Low
  • 2: Moderate
  • 3: High
  • 4: Very High

Extracurricular Activities

• Extracurricular: Participation in extracurricular activities, where 0 indicates No and 1 indicates Yes.
• Sports: Participation in sports, where 0 indicates No and 1 indicates Yes.
• Music: Participation in music activities, where 0 indicates No and 1 indicates Yes.
• Volunteering: Participation in volunteering, where 0 indicates No and 1 indicates Yes.

Academic Performance

• GPA: Grade Point Average on a scale from 2.0 to 4.0, influenced by study habits, parental involvement, and extracurricular activities.

Target Variable: Grade Class

• GradeClass: Classification of students' grades based on GPA:
  • 0: 'A' (GPA >= 3.5)
  • 1: 'B' (3.0 <= GPA < 3.5)
  • 2: 'C' (2.5 <= GPA < 3.0)
  • 3: 'D' (2.0 <= GPA < 2.5)
  • 4: 'F' (GPA < 2.0)

Conclusion

This dataset offers a comprehensive view of the factors influencing students' academic performance, making it ideal for educational research, development of predictive models, and statistical analysis.

Dataset Usage and Attribution Notice

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

Exclusive Synthetic Dataset

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

Companion DATA

Title: Using social media and personality traits to assess software developers' emotional polarity

Authors: Leo Moreira Silva Marília Gurgel Castro Miriam Bernardino Silva Milena Santos Uirá Kulesza Margarida Lima Henrique Madeira

Journal: PeerJ Computer Science

Github: https://github.com/leosilva/peerj_computer_science_2022

The folders contain:

Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.

/analysis analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset alldata.json: contains the dataset used in the paper

/ethics_committee committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version. committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version. committee_submission_form_english_version.pdf: the project submitted to the committee. English version. committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version. consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version. consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version. data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version. data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.

/notebooks General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version. Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version. Demographic_Survey_answers.xlsx: participants' demographic survey answers ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_answers.xlsx: participantes' and psychologists' answers for BFI

We have removed from dataset any sensible data to protect participants' privacy and anonymity. We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

blockgroupdemographics A selection of variables from the US Census Bureau's American Community Survey 5YR and TIGER/Line publications. Overview The U.S. Census Bureau published it's American Community Survey 5 Year with more than 37,000 variables. Most ACS advanced users will have their personal list of favorites, but this conventional wisdom is not available to occasional analysts. This publication re-shares 174 select demographic data from the U.S. Census Bureau to provide an supplement to Open Environments Block Group publications. These results do not reflect any proprietary or predictive model. Rather, they extract from Census Bureau results. For additional support or more detail, please see the Census Bureau citations below. The first 170 demographic variables are taken from popular variables in the American Community Survey (ACS) including age, race, income, education and family structure. A full list of ACS variable names and definitions can be found in the ACS 'Table Shells' here https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html. The dataset includes 4 additional columns from the Census' TIGER/Line publication. See Open Environment's 2023blockgroupcartographics publication for the shapes of each block group. For each block group, the dataset includes land area (ALAND), water area (AWATER), interpolated latitude (INTPTLAT) and longitude (INTPTLON). These are valuable for calculating population density variables which combine ACS populations and TIGER land area. Files The resulting dataset is available with other block group based datasets on Harvard's Dataverse https://dataverse.harvard.edu/ in Open Environment's Block Group Dataverse https://dataverse.harvard.edu/dataverse/blockgroupdatasets/. This data simply requires csv reader software or pythons pandas package. Supporting the data file, is acsvars.csv, a list of the Census variable names and their corresponding description. Citations “American Community Survey 5-Year Data (2019-2023).” Census.gov, US Census Bureau, https://www.census.gov/data/developers/data-sets/acs-5year.html. 2023 "American Community Survey, Table Shells and Table List” Census.gov, US Census Bureau, https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html Python Package Index - PyPI. Python Software Foundation. "A simple wrapper for the United States Census Bureau’s API.". Retrieved from https://pypi.org/project/census/

Student Depression Dataset: Analyzing Mental Health Trends and Predictors Among Students

Overview
This dataset compiles a wide range of information aimed at understanding, analyzing, and predicting depression levels among students. It is designed for research in psychology, data science, and education, providing insights into factors that contribute to student mental health challenges and aiding in the design of early intervention strategies.

Data Description
- Format: CSV (each row represents an individual student)
- Features:
- ID: Unique identifier for each student
- Demographics: Age, Gender, City
- Academic Indicators: CGPA, Academic Pressure, Study Satisfaction
- Lifestyle & Wellbeing: Sleep Duration, Dietary Habits, Work Pressure, Job Satisfaction, Work/Study Hours
- Additional Factors: Profession, Degree, Financial Stress, Family History of Mental Illness, and whether the student has ever had suicidal thoughts
- Target Variable:
- Depression_Status: A binary indicator (0/1 or Yes/No) that denotes whether a student is experiencing depression

Key Highlights
- Multifaceted Data: Integrates demographic, academic, and lifestyle factors to offer a comprehensive view of student wellbeing.
- Ethical Considerations: Data collection adhered to strict ethical standards with an emphasis on privacy, informed consent, and anonymization.
- Research & Practical Applications: Ideal for developing predictive models, conducting statistical analyses, and informing mental health intervention strategies in educational environments.

Usage & Potential Applications
- Academic Research: Explore correlations between academic pressures and mental health trends.
- Data Science Projects: Build predictive models to identify at-risk students based on various indicators.
- Policy Making: Inform the development of targeted mental health support programs within academic institutions.

Ethical Note
Due to the sensitive nature of the data, please ensure that any analysis or published results respect privacy and ethical guidelines. Users of this dataset should be mindful of the ethical implications when interpreting and sharing insights.

The Census@Leicester datasets include socio-demographic data from the 2001, 2011, and 2021 Leicester censuses to enable the exploration of recent historical trends. It also includes data from the 2021 census for both Nottingham and Coventry to enable comparisons with other cities.

This online resource that can be used for teaching and research purposes by staff and students and to create a legacy for the Census@Leicester Project.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Place in which people are living/ being sheltered, or are present on census day like a detached house; an apartment flat; a prefabricated house; a tent, a shack, etc.; a hotel, motel, hostel; a train, a boat, a bus, a terminal, etc.; a hospital, a health clinic; a military post, garrison, an officer's club, etc.; a boarding school, a dormitory; a child daycare facility, an orphanage, a nursing home; a prison, a reform school, or other places (a factory, an official office, an embassy, etc.). - Households: Social entities made up of one or more persons, whether bound by kinship or not, living in the same dwelling or in a portion of the same dwelling, participating in the provision of service or management to the household, who make no distinctions among themselves in terms of their income or expenses. - Group quarters: Places such as a hotel, a motel, a hostel, a train, a boat, a bus, a train station, a terminal, a port, a hospital, a health clinic, a military post, a garrison, an officer's club, a boarding school, a dormitory, a nursing home, a child daycare facility, an orphanage, a jail, a reform school, and others (a factory, an official office, an embassy, etc.).

Universe

All the persons present at places that constitute a household, that do not constitute a household like dormitories, military quarters, prisons, hospitals, hotels, etc., and the nomadic population (thus all the population within the boundaries of the country on the census day).

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: State Institute of Statistics of Turkey

SAMPLE DESIGN: Systematic random sampling by province

SAMPLE UNIT: Households, otherwise individuals if enumerated in non-household places on census day.

SAMPLE FRACTION: 5%

SAMPLE SIZE (person records): 3,388,218

Mode of data collection

Face-to-face [f2f]

Research instrument

Single form with 4 sections: address information, dwelling type information, household questions, and personal characteristics.

HLA-B27 status unknown for 5 probands, including one without sacroiliitis by modified New York criteria

Including 3 probands for whom a pelvic radiograph was not available

AxSpA probands who meet modified New York criteria are categorized as ankylosing spondylitis (AS), probands who do not fulfill the New York criteria for sacroiliitis are considered having non-radiographic axial SpA (axSpA).

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.