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

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

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.

ABSTRACT The health care model based on the Family Health Strategy, created in the early 1990s, encouraged changes in health education, highlighting the need to create lato and stricto sensu postgraduate courses aimed at empowering professionals that foster comprehensive health care. Periodic evaluations are carried out and encouraged by Capes/MEC in order to maintain the quality of postgraduate courses, but evaluations of recently-introduced professional master’s degree courses in family health remain scarce. Objectives To describe the academic profile, contribution, motivations and expectations of graduates of a Professional Master’s in Family Health. Method Cross-sectional and quantitative study to analyze the results of 102 questionnaires answered by graduates of the Professional Master’s Degree in Family Health of the Estácio de Sá University (RJ), who had concluded the course between 2007 and 2012. The instrument consisted of open-ended and closed-ended questions, sent by e-mail and made available online through the electronic platform Survey Monkey. The study evaluated age, gender, regional origin, academic background, as well as the contributions, expectations and motivations related to the course. Results The survey sample was formed predominantly by female graduates, aged over 30, from 13 Brazilian states and, mainly from Medicine and Nursing courses. The contribution of the master’s degree to the graduate’s professional life was evaluated as excellent by 77% of the interviewees. The expectations regarding the course were positively evaluated and the main reasons for seeking the qualification were scientific-technical improvement and personal satisfaction, rather than better salaries or job stability. Conclusion The course was evaluated positively by the graduates, having exceeded their expectations and satisfied the interests that led them to it, thus producing changes to their personal and professional life. A longitudinal analysis of the impact of the professional master’s degree in the career of graduates will require a sequence of similar studies, as has been stimulated by Capes/MEC in recent years.

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.

The Region 2 schools GIS layer contains unique records with basic identification information and best available locational information, for Region 2 schools found in the EPA Facility Registry System, which has imported records for schools in the National Center for Education Statistics database. Fields included in the layer include FRS_Name, FRS_Address, Facility Registry System ID), and locational information (latitude, longitude, and locational metadata). The locational data source for this and all R2 Regulated FACILITY and R2 Regulated PERMIT GIS Layers is the Locational Reference Tables (LRT) database, of Envirofacts augmented by R2 Locational Data Improvement records that may not yet have been cycled into the LRT. The Facility Registry System (FRS) is a centrally managed database developed by EPA's Office of Environmental Information (OEI). It provides Internet access to a single source of comprehensive information about facilities subject to environmental regulations or of environmental interest. The FRS contains accurate and authoritative facility identification records which are subjected to rigorous verification and data management quality assurance procedures. FRS records are continuously reviewed and enhanced by a Regional Data Steward network and active State partners. The facility records are based on information from EPA's national program systems and State master facility records and enhanced by other Web information sources.

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.

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.

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.

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.

Data in this collection examine the processing of federal offenders. The Cases Terminated files (Parts 1-3 and 25-28) contain information about defendants in criminal cases filed in the United States Federal District Court and terminated in the calendar years indicated. Defendants in criminal cases may either be individuals or corporations, and there is one record for each defendant in each case terminated. Information on court proceedings, date the case was filed, date the case was terminated, most serious charge, and reason for termination is included. The Docket and Reporting System files (Parts 4-7, 31-34, and 42) include information on suspects in investigative matters that took an hour or more of a United States Attorney's time with one of the following outcomes: (1) the United States Attorney declined to prosecute, (2) the case was filed in Federal District Court, or (3) the matter was disposed by a United States magistrate. Codes for each disposition and change of status are also provided. The Pretrial Services data (Parts 8, 22, 43, and 47) present variables on the circuit, district, and office where the defendant was charged, type of action, year of birth and sex of the defendant, major offense charge, and results of initial and detention hearings. The Parole Decisions data (Part 9) contain information from various parole hearings such as court date, appeal action, reopening decision, sentence, severity of sentence, offense, and race and ethnicity of the defendant. The Offenders Under Supervision files (Parts 15-16 and 37-40) focus on convicted offenders sentenced to probation supervision and federal prisoners released to parole supervision. The Federal Prisoner files (Parts 18 and 20) supply data on when an offender entered and was released from confinement, as well as the amount of time served for any given offense. The Administrative Office of the United States Courts data files (Parts 44, 52, and 53) contain records of defendants in criminal cases filed in Federal District Court and terminated in the calendar years indicated. There is one record for each defendant in each case. Variables include the date the case was filed, offense level, AO (Administrative Office) codes, and disposition date. The Bureau of Prisons data (both the Master and Detail files, Parts 45, 46, and 54-57 -- formerly known as the Federal Prisoner files) contain records of sentenced prisoners admitted to or released from federal prison during 1992-1994. These files consist of separate records for each prisoner's commitment to federal prison, and for each sentence imposed on a prisoner for a given commitment to federal prison. The Central System (CS) and Central Charge (CC) files of the Executive Office for United States Attorneys (EOUSA) include information about suspects in criminal matters and defendants in criminal cases in 1993-1994. Each defendant in a criminal matter has a master Central System record (Parts 50 and 51) and may have one or more Central Charge records (Parts 48 and 49). The Federal Probation/Supervision Data files (Parts 58 and 59) provide information on supervision procedures and the sequence of events and proceedings in 1992-1994 from the time a case was opened for supervision until the case was terminated. These include reports of parole violations, transfers of supervision to other districts, and case removals due to, for example, rearrest or hospitalization. The Sentencing Commission data (Parts 60 and 61) contain information on federal criminal cases sentenced in 1992-1994 under the Sentencing Guidelines and Policy Statements of the Sentencing Reform Act of 1984.

This repository contains a dataset of higher education institutions in Germany. This includes 400 higher education institutions in Germany, including universities, universities of applied sciences and Higher Institutes as Higher Institute of Engineering, Higher Institute of biotechnologies and few others. This dataset was compiled in response to a cybersecurity investigation of Germany higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

The data includes the following fields for each institution:

• ETER_Id: A unique identifier assigned to each institution.
• Name: The full name of the institution.
• Category: Indicates whether the institution is public or private.
• Institution_Category_Standardized: Indicates whether the institution is University, University of applied sciences or other.
• Member_of_European_University_alliance: Indicates if the institution is member of European University Alliance (A kind of collaborative higher education institutions network in Europe).
• Url: The website of the institution.
• NUTS2: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS2 being the second level.
• NUTS2_Label_2016: Refers to the classification of regions at the NUTS2 level according to the 2016 criteria set by the European Union.
• NUTS2_Label_2021: Refers to the classification of regions at the NUTS2 level according to the 2021 criteria set by the European Union.
• NUTS3: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS3 being the third level.
• NUTS3_Label_2016: Refers to the classification of regions at the NUTS3 level according to the 2016 criteria set by the European Union.
• NUTS3_Label_2021: Refers to the classification of regions at the NUTS3 level according to the 2021 criteria set by the European Union.

The methodology for creating the dataset involved obtaining data from two sources: The European Higher Education Sector Observatory (ETER)[3]. The data was collected on December 26, 2024, the Eurostat for NUTS - Nomenclature of territorial units for statistics 2013-16[4] and 2021[5].

This section outlines the methodology used to create the dataset for Higher Education Institutions (HEIs) in France. The dataset consolidates information from various sources, processes the data, and enriches it to provide accurate and reliable insights.

Data Sources

1. ETER Database: The primary dataset was sourced from the ETER database, containing detailed information about HEIs in Europe.
   • File: eter-export-2021-DE.xlsx
2. Eurostat NUTS Data: Two datasets from Eurostat were used for regional information:
   • NUTS 2013-2016 regions: NUTS2013-NUTS2016.xlsx
   • NUTS 2021 regions: NUTS2021.xlsx

Data Cleaning and Preprocessing Column Renaming Columns in the raw dataset were renamed for consistency and readability. Examples include:

• ETER ID → ETER_ID
• Institution Name → Name
• Legal status → Category

Value Replacement

1. HEI Categories: The Category column was cleaned, with government-dependent institutions classified as "public."
2. Standardized Institution Categories: Mapped numerical values to descriptive labels such as "University" and "University of applied sciences."
3. European University Alliance Membership: Replaced binary values with "Yes" or "No."

Handling Missing or Incorrect Data

1. Specific entries with missing or incorrect data were updated manually based on their ETER_ID. For instance:
   • Adjusted URLs for entries like DE0012 (updated to www.zeppelin-university.com)
   • Adjusted URLs for entries like FR0906 (updated to hmtm.de)
   • Adjusted URLs for entries like FR0104 (updated to www.dhfpg.de)
   • Adjusted URLs for entries like FR0466 (updated to fhf.brandenburg.de)
   • Adjusted URLs for entries like FR0907 (updated to hr-nord.niedersachsen.de)
   • Adjusted URLs for entries like FR0333 (updated to www.srh-university.de)

Regional Data Integration

1. Merged NUTS 2016 and NUTS 2021 data to enrich the dataset with regional labels.

Final Dataset The final dataset was saved as a CSV file: germany-heis.csv, encoded in UTF-8 for compatibility. It includes detailed information about HEIs in France, their categories, regional affiliations, and membership in European alliances.

Summary This methodology ensures that the dataset is accurate, consistent, and enriched with valuable regional and institutional details. The final dataset is intended to serve as a reliable resource for analyzing French HEIs.

This data is available under the Creative Commons Zero (CC0) license and can be used for any purpose, including academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.7614862

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Instituto Politécnico de Viana do Castelo, Portugal.

1. Pending
2. S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]
3. The European Higher Education Sector Observatory, Dec 2024. Available: ETER
4. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2013-2016
5. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2021.

This repository contains a dataset of higher education institutions in Italy. This includes 206 higher education institutions in Italy, including universities, universities of applied sciences and Higher Institutes as Higher Institute of Engineering, Higher Institute of biotechnologies and few others. This dataset was compiled in response to a cybersecurity investigation of Italy higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

The data includes the following fields for each institution:

• ETER_Id: A unique identifier assigned to each institution.
• Name: The full name of the institution.
• Category: Indicates whether the institution is public or private.
• Institution_Category_Standardized: Indicates whether the institution is University, University of applied sciences or other.
• Member_of_European_University_alliance: Indicates if the institution is member of European University Alliance (A kind of collaborative higher education institutions network in Europe).
• Url: The website of the institution.
• NUTS2: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS2 being the second level.
• NUTS2_Label_2016: Refers to the classification of regions at the NUTS2 level according to the 2016 criteria set by the European Union.
• NUTS2_Label_2021: Refers to the classification of regions at the NUTS2 level according to the 2021 criteria set by the European Union.
• NUTS3: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS3 being the third level.
• NUTS3_Label_2016: Refers to the classification of regions at the NUTS3 level according to the 2016 criteria set by the European Union.
• NUTS3_Label_2021: Refers to the classification of regions at the NUTS3 level according to the 2021 criteria set by the European Union.

The methodology for creating the dataset involved obtaining data from two sources: The European Higher Education Sector Observatory (ETER)[3]. The data was collected on December 26, 2024, the Eurostat for NUTS - Nomenclature of territorial units for statistics 2013-16[4] and 2021[5].

This section outlines the methodology used to create the dataset for Higher Education Institutions (HEIs) in France. The dataset consolidates information from various sources, processes the data, and enriches it to provide accurate and reliable insights.

Data Sources

1. ETER Database: The primary dataset was sourced from the ETER database, containing detailed information about HEIs in Europe.
   • File: eter-export-2021-IT.xlsx
2. Eurostat NUTS Data: Two datasets from Eurostat were used for regional information:
   • NUTS 2013-2016 regions: NUTS2013-NUTS2016.xlsx
   • NUTS 2021 regions: NUTS2021.xlsx

Data Cleaning and Preprocessing Column Renaming Columns in the raw dataset were renamed for consistency and readability. Examples include:

• ETER ID → ETER_ID
• Institution Name → Name
• Legal status → Category

Value Replacement

1. HEI Categories: The Category column was cleaned, with government-dependent institutions classified as "public."
2. Standardized Institution Categories: Mapped numerical values to descriptive labels such as "University" and "University of applied sciences."
3. European University Alliance Membership: Replaced binary values with "Yes" or "No."

Handling Missing or Incorrect Data

1. Specific entries with missing or incorrect data were updated manually based on their ETER_ID. For instance:
   • Adjusted URLs for entries like IT0162 (updated to www.conspaganini.it)
   • Adjusted URLs for entries like IT0203 (updated to www.conscremona.it)
   • Remove URLs for entries like IT0032
   • Remove URLs for entries like IT0178

Regional Data Integration

1. Merged NUTS 2016 and NUTS 2021 data to enrich the dataset with regional labels.

Final Dataset The final dataset was saved as a CSV file: italy-heis.csv, encoded in UTF-8 for compatibility. It includes detailed information about HEIs in France, their categories, regional affiliations, and membership in European alliances.

Summary This methodology ensures that the dataset is accurate, consistent, and enriched with valuable regional and institutional details. The final dataset is intended to serve as a reliable resource for analyzing French HEIs.

This data is available under the Creative Commons Zero (CC0) license and can be used for any purpose, including academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.7614862

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Instituto Politécnico de Viana do Castelo, Portugal.

1. Pending
2. S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]
3. The European Higher Education Sector Observatory, Dec 2024. Available: ETER
4. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2013-2016
5. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2021.

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 

Twenty European Universities currently offering specific mining engineering curricula were found: 16 bachelor’s programmes, 18 master’s programmes: in total 34 programmes. 968 courses both from bachelor’s and master’s mining degrees from european universities are listed and categorised using following keywords:

Basic engineering, Economics and Law courses (BEEL) Mathematics, Physics, Chemistry, Basic Computer training, Thermodynamics, Technical drawing, General Science, Orientational Courses, Robotics, Hydraulics, Economy, Accounting, Taxation, Legislation, Licensing and Intellectual Property Mining Engineering specific courses (MESC) Geomatics: Surveying, Geodesy, Deposit Modelling, Data Management; Geomechanics: Rock and Soil Mechanics, Geophysics, Numerical analysis; Geosciences: Geology, Mineralogy, Petrology, Earth science, Deposits, Hydrogeology; Materials: Metals, Ceramics, Building materials, High temperature processes; Operations: Open pit mining, Underground mining, Drilling & Blasting, Ventilation and Water Management, Equipment and Machines, Transport Systems, Historic and Worldwide mining; Processing: Waste treatment, Recycling, Plant operations, Sampling and analysis; Elective Courses specific to mining; Health, Safety, Environment and Sustainability courses (HSES) Occupational Health and Safety, Security and Risk Analysis, Sustainability, Environment, Rehabilitation, Reclamation, Recovery, Post Mining and Remediation; Social Skills, Problem based Learning, Scientific work (SPBLS) Project work, Studies, Seminars, Languages, Sociology, Politics, Organization and Strategy, Management, Internships, Field trips, Excursions, Scientific writing, Bachelors and Master Thesis and preparatory courses therefore; Digitalisation Information technology, Automation, Computer Science, Programming, CAD, Robotics, Algorithms, Data Handling, Data Structure, Simulations, Geo- and engineering statistics, Internet, computer, control engineering and numerical methods; Modelling: this keyword is not included here but courses containing the keyword could be using CAD and or geologic modelling software

List of Abbreviations:

MUL , Montanuniversität Leoben ; ULiège , Université de Liège ; UniZg , University of Zagreb ; VŠB-TUO , Technická univerzita Ostrava ; RWTH Aachen , Rheinisch-Westfälische Technische Hochschule Aachen ; TU Clausthal , Technische Universität Clausthal ; TUBAF , Technische Universität Bergakademie ; NTUA , Freiberg ; Miskolc , National Technical University Of Athens ; Torino , University of Miskolc ; AGH , Politecnico di Torino ; PolSl , Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie ; , Politechnika Śląska ; ULisboa , Universidade de Lisboa ; UPet , University of Petrosani ; TUKE , Technická univerzita v Košiciach ; UniLJ , Univerza v Ljubljani ; ULE , Universidad de León ; UPC , Universitat Politècnica de Catalunya · BarcelonaTech ; UPM , Universidad Politécnica de Madrid ; LTU , Lulea university of technology ; , ; BEEL , Basic engineering, Economics and Law courses ; MESC , Mining Engineering specific courses ; HSES , Health, Safety, Environment, Sustainability courses ; SPBLS , Social Skills, Problem based Learning, Scientific work courses ; ELEC , Elective Courses not specific to mining ;

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.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Data Bases And Internet Application Programming. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Data Bases And Internet Application Programming. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

AbstractBackgroundNurses comprise the dominant cadre of healthcare workers yet there remains an acute shortage of nurses globally with Africa most affected. However, access to higher nursing education in sub-Saharan Africa remains limited. We aimed to i) Assess the need for a Master of Nursing (MScN) program among graduate nurses in Kenya ii) Identify preferred MScN program options among graduate nurses intending to enrol for MScN in Kenya iii) Identify skills mismatch among graduate nurses in Kenya iv) Assess the relationship between intention to enrol in MScN program and job satisfaction among graduate nurses in Kenya.MethodsA cross-sectional design employing an online survey was used. Consenting nurses with a first degree in general nursing or any of the nursing speciality areas were included. Socio-demographic data were collected using a questionnaire. The ward organizational features scale was used to assess job satisfaction. The Program for the International Assessment of Adult Competencies background questionnaire was used to assess skills mismatch. The need for an MScN program was assessed by determining the proportion of participants who expressed the intention to join the MScN program. Preferred MScN program options were determined as frequencies and proportions. Skills mismatch was computed as frequencies and proportions. The relationship between the need for MScN and job satisfaction was assessed using the point biserial-correlation.ResultsA total of 420 volunteers were screened out of which 355 were enrolled. Overall, 337 (94.9% [95% CI: 92.1 – 96.9]) of volunteers expressed the desire to pursue MScN training. Among volunteers who expressed desire to pursue an MScN, majority preferred to study the critical care/renal specialty (24.9% [95% CI: 20.3 – 29.9]). A majority of the volunteers (319 [89.9%]) felt their skills were inferior to their responsibilities (under skilled). We found no significant correlation between the need for MScN and job satisfaction (r = 0.058; p = 0.269).ConclusionOur findings suggest a strong desire by graduate nurses in Kenya to pursue MScN with a preference for critical care specialization while part-time is the preferred learning mode. Additionally, most nurses felt their skills were inferior to their professional responsibilities. There was no correlation between the intention to pursue MScN and job satisfaction. There is a need to establish more MScN programs in Kenya coupled with the deployment of nurses as per the scope of practice.

ABSTRACT The teaching of public health in Brazil contributes to the technical-scientific development of the field, transcending the mere approach of health programs in a fragmented way. Thus, the objective of this study was to understand the operationalization of teaching in public health, in the stricto sensu undergraduate and graduate programs, in Brazil. This is an integrative literature review based on a search in PubMed, SCOPUS, PsycINFO, Latin American and Caribbean Health Sciences (LILACS), Scientific Electronic Library Online (SciELO) databases, and the Latin American and Caribbean Center on Health Sciences Information (BIREME). Twenty-one articles were analyzed by title, author, year, characterization and/or objective, methodology, and main results. It is concluded that the institutionalization of graduate courses in the field of public health followed the educational movement of the university and the Brazilian Health Reform, while undergraduate courses only took place in the last decade. The 1988 constitutional framework defines the ordering of human resources for professional training in and for the country’s health system.

List of all UGC-funded programmes.