This dataset provides comprehensive information about various Data Science and Analytics master's programs offered in the United States. It includes details such as the program name, university name, annual tuition fees, program duration, location of the university, and additional information about the programs.

Column Descriptions:

• Subject Name: The name or field of study of the master's program, such as Data Science, Data Analytics, or Applied Biostatistics.

• University Name: The name of the university offering the master's program.

• Per Year Fees: The tuition fees for the program, usually given in euros per year. For some programs, the fees may be listed as "full" or "full-time," indicating a lump sum for the entire program or for full-time enrollment, respectively.

• About Program: A brief description or overview of the master's program, providing insights into its curriculum, focus areas, and any unique features.

• Program Duration: The duration of the master's program, typically expressed in years or months.

• University Location: The location of the university where the program is offered, including the city and state.

• Program Name: The official name of the master's program, often indicating its degree type (e.g., M.Sc. for Master of Science) and format (e.g., full-time, part-time, online).

A file that holds the master records for all online training courses nominated for reimbursement.

The quantitative data shows student enrolment and academic performance for a fully online postgraduate diploma in public health. Based on the enrolment data, trends and patterns in student retention were plotted. The qualitative data was gathered through a questionnaire based on a Distance Education Student Progress Inventory (Kember, 1995).

The aim of this survey was to collect feedback about existing training programmes in statistical analysis for postgraduate researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate researchers across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2017. 90 responses were received. The Scoping Statistical Analysis Support project, funded by Information Services Innovation Fund, aims to increase visibility and raise the profile of the Research Data Service by: understanding how statistical analysis support is conducted across University of Edinburgh Schools; scoping existing support mechanisms and models for students, researchers and teachers; identifying services and support that would satisfy existing or future demand.

This dataset contains the non-aggregated data accompanying the SEFI Paper: From Curriculum To Career: Analysing The Contribution Of Delft University’s Robotics MSc Programme To The Career Path Of Its Alumni in .xlsx format of the qualitative questionnaire results. The research objective for the study was: to assess whether the programme’s goal of producing versatile robotics engineers has been met, by gathering feedback from the initial alumni by using a questionnaire which was fielded in the spring of 2023. The primary research questions were: Did the alumni consciously choose to pursue careers in the robotics field? If so, why ? If not, why not? What insights can be gleaned from alumni feedback and perceptions concerning the MSc Robotics Programme? The data was collected using a Qualtrics online survey and both qualitative and quantitative data such as alumni satisfaction and employment details were collected. The work was reported in a conference paper: Saunders-Smits, G., Bossen, L., & De Winter, J. (2023). From Curriculum To Career: Analysing The Contribution Of Delft University’s Robotics Msc Programme To The Career Path Of Its Alumni. European Society for Engineering Education (SEFI). DOI: 10.21427/3VA6-M479

This data collection contains energy commodity production statistics for approximately 200 United Nations reporting countries for the years 1970-1979. In this file, each record refers to an individual reporting country and the quantity of its various transactions (e.g., production, imports, exports, bunkers, additions to stocks, and capacity) for a given energy commodity in a given year. Only annual data are included. The 70 types of commodities reported include solid fuels (e.g., coal, peat, and charcoal), liquid fuels (e.g., crude petroleum, gasoline, and kerosene), gases, uranium, and both industrial and public types of geothermal, hydro, and nuclear generated electricity. Information is also included on the population (in thousands) of the reporting country, the quantity of the commodity per transaction, and the date of the transaction. Supplementary data not contained in this data collection are in the introduction and footnotes of the individual tables published in the YEARBOOK OF WORLD ENERGY STATISTICS, 1979.

Raw data for the manuscript entitled: European Agrifood and Forestry Education for a Sustainable Future - Gap Analysis from an Informatics Approach Abstract Purpose: To evaluate how well European agrifood and forestry Masters program websites use vocabulary associated with the NextFood Project ‘categories of skills’. Methodology: Web-scraping Python scripts were used to collect texts from European Masters programs websites, which were then analysed using statistical tools including Partial Least Squares Regression and contextual relation analysis. A total of fourteen countries, twenty-seven universities, 1303 European Masters programs, 3305 web-pages and almost two million words were studied using this approach. Findings: While agrifood and forestry Masters programs used vocabulary from the NextFood Project ‘categories of skills’ in most cases equal to or more often than non-agrifood and forestry Masters programs, we found evidence for the relative underuse of words associated with networking skills, with least use among agriculture-related Masters programs. Practical Implications: The informatic approach provides evidence that European agrifood and forestry Masters programs are for the most part following the educational paths for meeting future challenges as outlined by the NextFood Project, with the possible exception of networking skills. Theoretical Implications: This text-based, informatic approach complements the more targeted approaches taken by the NextFood Project in studying the skilling-pathways, which involved focus-group interviews, surveys of stakeholders, interviews of individuals with expert-knowledge and literature reviews. Originality: A text-based, web-scraping informatic approach has thus far been limited in the study of agrifood and forestry higher education, especially relative to recent advances made in the social sciences.

More information about the context and the methodology can be found in the README.md file and online at this link: https://github.com/sdgis-edu-tud/fair-data-publication-groupf.

Along with the Elbe river, Dresden comprises a dense network of streams, which are spread out across its fabric. Presently, the streams are secluded from being a valuable part of the city. The problems are characterised by ecological issues, inappropriate land use by residents, and artificial channeling. They, along with the Elbe river hold potential to become elements of integrating the ecological and social functions of the city by reclaiming the historical identity of waterfronts and restoring natural habitats. Therefore, there arises a need to understand how to integrate these streams into the network of protected green areas and public spaces, while maximising their contribution to biodiversity while adapting to the risk of flooding within and around the city.

These concerns and identified potentials beg the question that, how can urban streams be restored and integrated in Dresden's fabric, such that there is a synergy between human activities and the natural environment?

This is investigated by adopting an integrated approach for biodiversity, climate adaptation and quality of life.

Based on the three criteria that we decided to tackle, we came up with numerical indicators that we could use to evaluate them. These numerical indicators are called attributes and have to be normalised—in our case between 0 and 1—so that they can be compared, weighted and thereafter clustered properly depending on their relevance and similarities.

The spatial units used in this study are hexagons with a dimension of 250 meters. The study area of Dresden is divided using a complete surface of a hexagonal pattern. Then it is overlaid with the water stream network and river body from OpenStreetMap to keep only the hexagons that intersect with at least one stream. Finally, the isolated hexagons were removed.

Two data-driven methods were used to conduct the analysis:

• S-MCDA (Spatial Multi-Criteria Decision Analysis) — S-MCDA was used to weigh the different attributes against each other. The method supports decision-making by evaluating and ranking alternatives (the attributes) within the three objectives of biodiversity, climate adaptation and quality of life.
• Typology Construction — Typology construction is used to group attributes into homogenous types based on similarities. This was used to identify patterns in data and make clusters of attributes that show similarity, which can thereafter be used to understand the type of interventions which would be impactful.

This dataset contains both the values computed for the attributes in each spatial unit and the final results of the two methods.

Abstract

High quality postgraduate training in science, technology, engineering and mathematics (STEM) related disciplines in sub-Saharan Africa (SSA) is important to strengthen research evidence to advance development and ensure countries achieve the Sustainable Development Goals (SDGs). Equally, participation of women in STEM careers is vital, to ensure that countries develop economies that work for all their citizens. However, women and girls remain underrepresented in STEM due to gender stereotyping, lack of visible role models, and unsupportive policies and work environments. Therefore, there is a need to consolidate information on participation and experiences of women in STEM related postgraduate training and careers in SSA to enhance their contribution to realizing the SDGs. The primary objective of this study is to examine the participation and experiences of women in postgraduate training, and their subsequent recruitment, retention and progression in STEM careers in East Africa. A secondary objective is to establish the gender gaps in training and career engagement in selected STEM related academic disciplines in East Africa. The descriptive study will employ a mixed methods approach, including a scoping review, qualitative interviews, and quantitative analysis of secondary data. We will synthesize results to inform the development of an effective gendered approach and framework to improve participation and experiences of women in STEM training and career engagements in SSA. We will conduct the study over a period of five years.

Geographic coverage

Regional coverage (East Africa Region)

Analysis unit

Individual Women in STEM

Universe

Qualitative data: Women in Science Technology Engineering and Mathematics (STEM) in postgraduate training and career Quantitative data: Postgraduate students, faculty, reseachers and supervisors (both men and women) in STEM in Inter-University Council for East Africa (IUCEA) member Universitiies

Sampling procedure

The study utilized a purposive sampling technique and targeted all universities that offered doctoral programs in applied sciences, technology, engineering, and mathematics. At the time, only 23 of the 74 universities in Kenya—equivalent to 30%—offered doctoral degrees in STEM. It was assumed that a similar or lower percentage would be found in the other five countries, namely Uganda, Tanzania, Rwanda, Burundi, and South Sudan.

Purposive sampling was used to recruit participants from purposively selected universities and national higher education commissions and agencies for the study. In universities, all students enrolled in doctoral programs in STEM were considered. Additionally, female and male students' lecturers, supervisors, mentors, and other faculty members and researchers in the identified institutions were also considered for participation in the study.

Purposive sampling of doctoral students, faculty, and early career researchers (post-doctoral fellows within the first six years since receiving their PhD) was conducted using the following inclusion criteria:

Inclusion criteria i. Worked in a STEM field/discipline ii. Enrolled in a doctoral program within a STEM field iii. Early career researchers in a STEM field in research organizations iv. Faculty in a STEM field at a university

Additionally, registrars, postgraduate training coordinators, heads of departments, and officials from national agencies and ministries related to postgraduate training and research were purposively selected from all the identified universities to provide input on existing policies, guidelines, and enrollment data. For each of the mentioned groups, 7-12 interviews were conducted, totaling 60 interviews.

Sampling deviation

Qualitative For the Key informant interviews one participant was interviewed from the engineers board despite the scope being Inter-University Council for East Africa (IUCEA) member Universities.

Quantitative The online survey was completed by some researchers not working/teaching in IUCEA member universities

Mode of data collection

Other [oth]

Research instrument

Quantitative data collection A. Online Survey This was carried out through an online survey questionnaire that was circulated via email and other digital platforms such as WhatsApp. The questionnaire had various parts: Part A - Participants characteristics This section mainly collected demographic details such as age, gender, nationality, residence, marital status, income, highest level of education completed, year of study, supervision and mentoship relationship, field of study in STEM (Science, Technology, Enginnering and Mathematics), mode of funding of postgraduate degree,

Part B - Status of Gender equality This section collected information on students enrollment and graduation in masters and PhD in STEM looking at gender distribution,

Part C - Factors that contribute to participation of women in STEM This section collected information on the factors or situations encountered while pursuing career in STEM in your specific discipline

Part D - Strategies for Optimizing Women's Engagement in STEM This section collected information on the strategies can maximize engagement of women in STEM training PhD level and subsequent careers

Part E - Effect of the COVID-19 pandemic on women's progression In this section collected information on COVID-19 pandemic affect on research progress or deadline for submission of thesis, COVID-19 pandemic affect on current research funding, COVID-19 pandemic caused researchers to work from home, working from affected progress in studies, any direct responsibilities caring for children, number of children being taken care of, change of domestic work responsibilities since the COVID-19 outbreak, change of domestic work responsibilities since the COVID-19 outbreak on studies, COVID-19 pandemic affect on access to these research tools which inlude: Computer or laptop, Reliable Internet, Assistive Technology, Laboratory equipment, University Library, Archives/special collections and Access to patients/research participants. It als collected information on: any benefits to COVID-19 pandemic for your work, some ways one thinks their supervisor or line manager could support or help one manage the impacts of COVID-19 on studies

The questionnaire was developed in English and was latertranslated into French to accommodate the French speaking countries i.e Burundi and Rwanda. The French questionnaire was backtlanslated to English to ensure the questions still maintained their original meaning. This work was done by an external consultant and the French questionnaires were reviewed by the research assistant from Burundi and tested among postgraduate students in Light University.

All questionnares and modules are provided as external resources.

Cleaning operations

Qualitative The data was collected through qualitative interviews (In-depth interviews) and focus group discussions. They were audio recorded and the recordings were transcribed on Ms Ofiice.The transcript were subjected to data quality checks and the clean transcripts were anonyzed for data protection.

QUANTITATIVE Secondary data The data was collected from the five countries in an Ms Excel designed data abstraction sheet. The data abstraction sheet helped the universities administrators and rergistrars to directly enter the data only in the required field and for the defined or specific variables. For the dataset that was in hardcopy format the data entry was also done using the data abstraction sheets. The data sets were subjected to data quality checks for data quality. We used a standard template to ensure data editing took place during data entry.

Online survey Data entry was in form of responding to the survey. Data editing was done while cleaning the data.

Response rate

Quantitaive The online survey link was circulated using contacts within universities and research institutions in East Africa via email and social media platforms such as WhatApp hence it is impossible to track those who received the survey and hence it is not possible t calculate the survey response rate.

Sampling error estimates

NA

This dataset contains responses from an online survey designed to evaluate how consistently and neutrally ChatGPT’s English and Arabic answers align across ten prompts (seven politically sensitive, three non-sensitive). Each row captures one participant’s ratings of sentiment and factual consistency between the two language outputs, neutrality scores for each response and the prompt itself, and optional comments. The data were collected via Qualtrics from English- and Arabic-fluent respondents who compared side-by-side model answers, providing quantitative Likert-scale ratings to assess multilingual consistency and neutrality of Generative AI output in a human evaluation study.

Descriptive statistics-aggregate data.

ABSTRACT The objective of this study is to evaluate the impact of training in online searching in Medical Literature Analysis and Retrieval System Online (MedLine) and Biblioteca Virtual em Saúde (BVS) Research Portal databases, on the information seeking behavior of graduate students and residents of the Health Science Campus of the Federal University of Minas Gerais. The research used the Kirkpatrick model for evaluating training specifically focusing on first and second levels of this model. The basic descriptive research used a quantitative approach. The research used a non-random sample consisting of the graduate students and residents who agreed to participate in the course. This training had a course load of 15 hours and was offered to postgraduate students of the Graduate Campus Health of the Federal University of Minas Gerais and residents of Obstetric Nursing Residency Program of the Federal University of Minas Gerais School of Nursing courses. Data collection was conducted using two questionnaires at the beginning and at the end of the training. We compared the answers given by the respondents in these questionnaires. The results showed that the training had a positive impact on the informational seeking behavior because students have acquired new knowledge and research skills.

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

The Master Facility Inventory (MFI) data collection provides a comprehensive list of hospital facilities in the United States in 1976. The criteria for inclusion were that a facility provided medical, nursing, personal, or custodial care to groups of unrelated persons on an inpatient basis and was licensed or operated by federal or state agencies. The American Hospital Association conducted the survey, supplying the resulting data to the National Center for Health Statistics in order to update its Master Facility Inventory on the number and kinds of hosptals in the United States and the changes in the list since the last MFI survey. Information gathered is for the previous calendar year and includes facility identification information, ownership, number of full- and part-time staff, number of beds per unit, number of adult and pediatric inpatients, numbers in newborn nursery, outpatient utlilization (e.g., emergency care and clinics), major and minor surgical operations, hospital classification (e.g., government, non-government, investor-owned), and finances (e.g., total revenue, expenses, and assets) for 7,271 institutions.

This data was derived from postgraduate students of International Islamic University Malaysia (IIUM).The researcher employed a quantitative research design as the research is statistical in nature. In order to determine the extent of IIUM postgraduate students’ awareness knowledge of MOOCs (Massive Open Online Courses), how-to knowledge of MOOCs, perceived usefulness of MOOCs, attitude towards MOOCs, actual usage of MOOCs and their intention to use MOOCs for academic purposes, survey developed by Davis (1989), Chen et al. (2008), Venkatesh (2000), Hu and Chau (2001), Lin and Overbaugh (2009), Taylor and Todd (1995) were adapted and used. The researcher following a systematic procedure, self-developed some items in order to appropriately address the research question. The instrument in all contains 32 items measuring all the constructs under study. The respondents gave their degree of agreement and disagreement on 5-Likert scale with response category (strongly disagree 1, disagree 2, neutral 3, agree 4, and strongly agree 5). The sample comprised 190 postgraduate students selected from six kulliyyahs using stratified random sampling. The data collection procedure was done through self-administered questionnaires during lecture hours after a permission was granted by the lecturers and IIUM Center for Postgraduate Studies. Students were also invited to respond to the questionnaires after their classes at the various lecture halls in their respective Kulliyyahs, at the hostels, library, and cafeterias. The Statistical Package for the Social Sciences (SPSS) version 17 software was utilised in analysing the data. The analysis procedure made use of descriptive statistics to analyse the demographic characteristics of the respondents as well as determine the percentages, frequencies, means, and standard deviations of the extent of awareness knowledge, how-to knowledge of MOOCs, perceived usefulness, attitude, actual usage and intention to use MOOCs for academic purposes.

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.

Workshop date: 22nd November 2018Time: 2pmVenue: Science Library Seminar Room1. PowerPoint presentation slides2. Paper version of Data Management Planning (DMP) tool. Online DMP tool here: https://dmp.otago.ac.nz/

According to latest figures, around ***** million undergraduate students were enrolled in degree programs at public colleges and universities in China in 2024. In 2023, around ***** million students were studying in bachelor's degree programs, while ***** million were enrolled in more practically oriented short-cycle degree programs. The number of graduates from these programs reached around ***** million in 2023. On a postgraduate level, there were almost *** million master's and doctor's degree students studying at public institutions in China in 2023. Development of enrollment figures Since the beginning of the reform era in 1979, the number of students enrolled at institutions of tertiary education in China has increased tremendously. While the gross enrollment rate in tertiary education ranged at only *** percent in 1990, it reached ** percent of related age groups in 2023. This is the result of a thorough governmental plan aimed at increasing the number of specialists China needed for its economic development. The quality of university education in China also increased a lot throughout these years. Nowadays, two Chinese institutions, Tsinghua University and Peking University, regularly reach the highest positions in international university rankings, while a broad group of institutions are continuously improving into the midfield of international universities. However, competition for admission to the elite universities is fierce and the quality of many lower level colleges is not comparable to higher international standards. Types of tertiary education in China China generally differentiates between universities, providing four-year bachelor, master and doctorate programs, and higher vocational colleges, providing more practically oriented three-year, short-cycle degree programs. In addition, it is possible to obtain degrees at public institutes for adult education and from online and self-learning courses provided by public institutions. The number of students enrolled in degree programs at all different levels of public tertiary education in China reached more than **** million in 2023. In addition to public institutions, there is also a growing number of students enrolled at private colleges and universities. However, these private institutions are generally not as esteemed and work on a lower level than their public counterparts.

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.

Table View of Master_OP_EXP - Budgets and Actuals from FY 2016, 2017, 2018, 2019, and FYTD 2020. This View is the data source for Expense Dashboards. Update Schedule: Once per Month.