In the academic year of 2021/22, about 880,250 students were awarded a Master's degree in the United States. This figure is projected to increase by the academic year of 2031/32, when it is forecasted that 1,000,460 students will be awarded a Master's degree.

In 2023, 24 percent of prospective graduate business students in the United States were interested in hybrid programs, an increase from 16 percent in 2019. However, the overall preference in 2023 was for in-person business school programs, at 60 percent.

The COVID-19 data sets and associated Jupyter Hub notebooks are support for a manuscript describing how data science was shown to be effective in developing a transdisciplinary team and the production of novel outputs in part due to the common learning process of all team members being part of an online professional data science and analytics master’s degree program. This online curriculum helped the team members to find a common process that allowed them learn in common (Kläy, Zimmermann, & Schneider, 2015), transdisciplinary learning a key component of transdisciplinary teamwork (Yeung, 2015). Our team's Jupyter Hub files with complete coding and data set explanations are uploaded to document this teamwork and the outputs of the team.

This data collection contains information on degrees earned at a sample of postsecondary institutions in the United States. The survey collected data on the number of completions of academic, vocational, and continuing professional educational programs by award category. There are three files in the collection. Part 1, Response Status Information, contains response status information to the completions survey for active institutions in the sample. Part 2, Postsecondary Completions: Awards/Degrees Conferred, contains the number of degrees and other awards granted by the institution in each field of study (CIP code), by level of award/degree, and sex of recipient. Part 3, Postsecondary Completions by Major Discipline (Two-Digit CIP Codes), contains the number of degrees and other awards conferred by major discipline (two-digit CIP code), award level, race/ethnicity, and sex of recipient.

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

According to a 2023 survey, 70 percent of undergraduate students who were studying online in the United States were White while 23 percent were Black or African-American. In comparison, 69 percent of graduate students studying online in the United States in that year were White while 24 percent were Black or African American.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

The global massive open online course (MOOC) market size is calculated to advance at a CAGR of 32% through 2034, which is set to increase its market value from US$ 13.2 billion in 2024 to US$ 212.7 billion by the end of 2034.

Report Attribute	Detail
MOOC Market Size (2024E)	US$ 13.2 Billion
Projected Market Value (2034F)	US$ 212.7 Billion
Global Market Growth Rate (2024 to 2034)	32% CAGR
China Market Value (2034F)	US$ 23.3 Billion
Japan Market Growth Rate (2024 to 2034)	32.6% CAGR
North America Market Share (2024E)	23.9%
East Asia Market Value (2034F)	US$ 49.1 Billion
Key Companies Profiled	Alison; Coursera Inc; edX Inc; Federica.EU; FutureLearn; Instructure; Intellipaat; iverity; Jigsaw Academy; Kadenze.

Country Wise Insights

Attribute	United States
Market Value (2024E)	US$ 1.4 Billion
Growth Rate (2024 to 2034)	32.5% CAGR
Projected Value (2034F)	US$ 23.6 Billion

Attribute	China
Market Value (2024E)	US$ 1.5 Billion
Growth Rate (2024 to 2034)	32% CAGR
Projected Value (2034F)	US$ 23.3 Billion

Category-wise Insights

Attribute	xMOOC
Segment Value (2024E)	US$ 9.3 Billion
Growth Rate (2024 to 2034)	30.8% CAGR
Projected Value (2034F)	US$ 136.1 Billion

Attribute	Degree & Master Programs
Segment Value (2024E)	US$ 6.4 Billion
Growth Rate (2024 to 2034)	30.2% CAGR
Projected Value (2034F)	US$ 89.3 Billion

This resource contains the survey questions, compiled results, and code for Fisher's exact test, as associated with the following manuscript:

"Faculty Perspectives on a Collaborative, Multi-Institutional Online Hydrology Graduate Student Training Program" by Anne J. Jefferson, Steven P. Loheide, and Deanna H. McCay. Submitted to Frontiers in Water, in the research topic: “Innovations in Remote and Online Education by Hydrologic Scientists", May 2022

Abstract: The CUAHSI Virtual University is an interinstitutional graduate training framework that was developed to increase access to specialized hydrology courses for graduate students from participating institutions. The program was designed to capitalize on the benefits of collaborative teaching, allowing students to differentiate their learning and access subject matter experts at multiple institutions, while enrolled in a single course at their home institution, through a framework of reciprocity. Although the CUAHSI Virtual University was developed prior to the covid-19 pandemic, the resilience of its online education model to such disruptions to classroom teaching increases the urgency of understanding how effective such an approach is at achieving its goals and what challenges multi-institutional graduate training faces for sustainability and expansion within the water sciences or in other disciplines. To gain faculty perspectives on the program, we surveyed water science faculty who had served as instructors in the program, as well as water science faculty who had not participated and departmental chairs of participating instructors. Our data show widespread agreement across respondent types that the program is positive for students, diversifying their educational opportunities and increasing access to subject matter experts. Concerns and factors limiting faculty participation revolved around faculty workload and administrative barriers, including low enrollment at individual institutions. If these barriers can be surmounted, the CUAHSI Virtual University has the potential for wider participation within hydrology and adoption in other STEM disciplines.

Audiovisual feedback in Higher Education. In this study, carried out in a face-to-face and an online university, several subjects were selected to provide bachelor’s and master’s degree students with audio-visual feedback on certain activities. After feedback had been given on each activity, an anonymous, voluntary questionnaire was sent to each student to ascertain their level of satisfaction with the feedback received. Each teacher also gave her opinion on the feedback provided. This dataset contains two files: one file in Word format containing the two questionnaires sent to the students and the teachers; and another file in Excel format containing the students' and teachers' answers.

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

According to an online survey conducted in February 2025 in the United States, ********* of LinkedIn users held a bachelor degree or equivalent. Additionally, ** percent of LinkedIn users in the U.S. held a masters degree or equivalent.

Introduction: In an age of increasingly face-to-face, blended, and online Health Professions Education, students have more selections of where they will receive a degree. For an applicant, oftentimes, the first step is to learn more about a program through its website. Websites allow programs to convey their unique voice and to share their mission and values with others, such as applicants, researchers, and academics. Additionally, as the number of Health Professions Education programs rapidly grows, websites can share the priorities of these programs. Methods: In this study, we conducted a website review of 158 Health Professions Education websites to explore their geographical distributions, missions, educational concentrations, and various programmatic components. Results: We compiled this information and synthesized pertinent aspects, such as program similarities and differences, or highlighted the omission of critical data. Conclusion: Given that websites are often the first point of contact for prospective applicants, curious collaborators, and potential faculty, the digital image of HPE programs matters. We believe our findings demonstrate opportunities for growth within institutions and assist the field in identifying the priorities of HPE programs. As programs begin to shape their websites with more intentionality, they can reflect their relative divergence/convergence compared to other programs as they see fit and, therefore, attract individuals to best match this identity. Periodic reviews of the breadth of programs, such as those undergone here, are necessary to capture diversifying goals, and serve to help advance the field of Health Professions Education as a whole. Methods Our team deduced that most HPE programs would have a website, and that this would serve as a representation of how individuals within the program choose to view themselves and hope to be viewed by others. Further, our team determined that these websites would be an efficient means of collecting programmatic information for the purposes of learning more about program growth, diversity, and values. We conducted the website review from August 2021 to April 2022 using a list of worldwide Health Professions Education programs, which was acquired from the Foundation of Advancement of International Medical Education and Research’s (FAIMER’s) website. FAIMER was chosen as the origin source of programs studied due to its use in another published study evaluating HPE programs. Each master's degree in HPE offered by a university was counted separately, allowing us to note the differences in course and time requirements across all programs. Only HPE master's programs were selected for this study. Certificate and Ph.D. programs were excluded. Next, we developed a data extraction tool. Categories were jointly identified for data collection by three of our authors (JS, SW, and HM). JS, SW, and HW worked independently through a set of three HPE programs, obtaining the data for our selected categories. Afterward, we cross-checked each other's work for verification purposes. For example, if JS obtained the information, SW or HM, who were blinded to JS’s findings, would independently find the answers to the same questions/ topics. This was performed until an agreement between pre and post-review information was above 95%. There was no discovered information that was not agreed upon after discussion. Once 100% agreement was reached with this method, the total number of HPE programs analyzed was split between JS and SW, and the raw data was obtained for the same categories. This data then underwent a review by the other two researchers to ensure high accuracy. This review consisted of information verification on individual program websites where it was originally obtained. For example, if JS found the information about a program, SW and HM (now not blinded) would both have to independently find the same information. Any identified discrepancies were rectified through discussion, and three-way agreement was mandatory for the team to move on to the next program.

This dataset has been carried out for the subject Typology and data life cycle, and is part of the master's degree in Data Science at the Open University of Catalonia. To obtain this dataset, web scraping techniques are applied using the Python programming language to extract data from the web datosmacro and generate a data set with data on COVID-19 and vaccines.

The One Health concept is increasingly employed to combat zoonotic diseases. This study assessed the knowledge, attitudes, and practices regarding One Health and zoonotic diseases among key sector professionals to identify gaps and opportunities for enhancing One Health strategies in Bhutan. A cross-sectional, facility-based study was used to conduct a nationwide online questionnaire survey using a validated and pre-tested questionnaire among professionals from the Ministry of Health, the Ministry of Agriculture and Livestock, and universities. Descriptive and summary statistics were calculated. Respondents were categorised into binary groups based on their knowledge, attitude, and practice scores relative to the mean. Multivariable logistic regression analyses were conducted to identify the demographic factors associated with knowledge, attitudes, and practices regarding One Health and zoonotic diseases. The survey achieved a 35% response rate, with 964 responses. The majority of respondents demonstrated above-average knowledge (63%), attitudes (50%), and practices (66%) regarding One Health. Professionals with master’s degree or higher (Adjusted Odds Ratio [AOR] = 2.39; 95% Confidence Interval [CI] = 1.16–5.12) were more likely to have above-average knowledge. Regarding zoonotic diseases, approximately half of the respondents had above-average knowledge (51%), attitudes (52%), and two-thirds (66%) had above-average practices. Professionals with mid-level job experience (6–10 years; AOR = 2.13; 95% CI = 1.37–3.30 and 11 to 15 years; AOR = 2.11; 95% CI = 1.31–3.40) were more likely to possess above-average knowledge. Gaps in training, communication, funding, research, and collaborations were identified. Targeted educational interventions, enhanced communication, strengthened collaboration, expanded research, and improved funding are essential for improving One Health approaches and zoonotic disease prevention and control in Bhutan. Our study findings provide valuable insights that can inform global efforts to enhance One Health systems, particularly in regions where resources are limited but disease risks are significant.

IntroductionThe assessment of student outcomes is essential for monitoring the quality of graduate programs in healthcare sciences. As such, this study focused on developing a self-employed questionnaire that allowed for the evaluation of elements focused on career impact and levels of satisfaction regarding graduate program education. Following, this instrument was utilized in a cross-sectional study design with alumni that had obtained their degree (MSc or PhD) over a 25-year span (1995–2020) from a graduate program in dentistry located in Brazil.MethodsThe employed instrument comprised a total of 43 questions presenting a mix of both close and open-ended questions coupled with 5-point Likert scales. The questionnaire was hosted online and a total of 528 alumni were invited to participate through e-mail and social media outreach.Results376 alumni answered the questionnaire (71.2% response rate). The majority were female (69.9%), and with a MSc (58.5%). Levels of satisfaction towards the program as well the impact in career and life were higher in alumni that had obtained a PhD degree compared to MSc. After obtaining the degree, an increase in involvement in teaching/research positions (3.4% vs 21.5%, p 

This longitudinal data collection supplies information on the educational, vocational, and personal development of young people who were high school seniors in 1972 and examines the kinds of factors -- personal, familial, social, institutional, and cultural -- that may affect that development. The collection provides a broad spectrum of information on each student and covers areas such as ability, socioeconomic status, home background, community environment, ethnicity, significant others, current activity at time of survey, educational attainment, school experiences, school performance, work status, work performance and satisfaction, goal orientations, marriage and the family, and military experience. Data collected in the base-year (1972) focus on factors relating to the student's personal/family background, education and work experiences, plans, aspirations, attitudes, and opinions. The first follow-up, which was conducted in 1973, offers information on the respondent's activity state (education, work, etc.), socioeconomic status, work and educational experience since leaving high school, future plans, and expectations. The second follow-up (1974) probes respondents on similar measures but is augmented by additional variables pertaining to work and education. The third follow-up (1976) contains additional items on graduate school application and entry, job supervision, sex roles, sex and race biases, and a subjective rating of high school experiences. The fourth follow-up (1979) offers data similar to the other follow-ups but includes some variables that were modified to elicit unique information. For the fifth follow-up, the sample members averaged 32 years of age and had been out of high school for 14 years. In addition to covering the same subject areas as the previous surveys, this follow-up includes additional questions on marital history, divorce, child support, and economic relationships in modern families. Part 1 of this collection contains base-year data as well as data collected during four subsequent follow-ups undertaken in 1973, 1974, 1976, and 1979, while Part 12 contains fifth follow-up data for 1986. Part 2, the School File, contains information obtained from the respondent's high school and also from high school counselors. Data are available on school organization and enrollment, course offerings, special services and programs, library and other resources, time scheduling, and grading systems. Counselor information is supplied on work loads, counseling practices and facilities, experience with student financial aid programs, age, ethnicity, training, and experience. A supplementary School District Census File, Part 3, contains 1970 Census data tabulated by school district boundaries. In addition, the collection includes an FICE Code File and a CEEB Institutional Data Base File that can be used in conjunction with the student file to supply contextual information about respondents' colleges. The Institutional Data Base File offers data for colleges and universities on items such as enrollment, income and revenues, expenses, tuition and fees, and median student scores on standardized tests. Parts 6, 7, 9, and 10 contain transcript data from each postsecondary institution reported by sample members in the first through fourth follow-up surveys. Data are available for several types of postsecondary institutions, ranging from short-term vocational or occupational programs through major universities with graduate programs and professional schools. Data in these four rectangular files -- Student, Transcript, Term, and Course Files -- are organized to be used in combination hierarchically. Information is available on terms of attendance, fields of study, specific courses taken, and grades and credits earned. The Fifth Follow-Up Teaching Supplement (Parts 15-17) surveyed those members of the original 1972 sample who had obtained teaching certificates and/or who had teaching experience. Respondents were asked questions about their qualifications, experience, and attitudes toward teaching.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by educational degree, gender and age group, annual.

Dataset analyzed in the "Postgraduate education among family and community physicians in Brazil: the Trajetórias MFC project" manuscript.

File "family_physicians" has personally identifiable data on family and community physicians in Brazil, more specifically on their specialization (medical residency, specialist certification) and their master's and PhD degrees. File "postgraduate_programs" has data on the master's and PhD programs the family and community physicians graduated from.

All spreadsheets are in the CSV (comma-separated values) format, delmited with semicolons and encoded in UTF-8 (there are special characters due to the Portuguese language) with the byte-order mark (BOM). The spreadsheets can be opened with desktop or Web application software (LibreOffice Calc, Microsoft Excel, Google Sheets) or with statistical software such as R. Each dataset is accompanied with a data dictionary for interpreting the columns. See also the manuscript for background.