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

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.

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

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.

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.

High rates of mental ill-health in postgraduate researchers (PGRs) represent a significant barrier to life satisfaction and academic success. Nevertheless, there is little knowledge about the extent and origins of mental health problems of PGRs in the UK. The current study addresses this gap by assessing investigating the prevalence and provenance of anxiety, depression, sleep problems, subjective mental wellbeing, and suicide behaviours of PGRs in the UK. An online survey (N=479) was used to measure the mental health outcomes and assess their relationship with influence of demographic, trait and academic variables, and social support. We found a high prevalence of mental ill-health and low levels of wellbeing in the current sample. Factors associated with poorer outcomes were female and non-binary gender, non-heterosexual identity, maladaptive perfectionism, workaholism and being in the 5th year of study or above. Resilience, adaptive perfectionism, higher levels of social support and positive evaluations of progress and preparation, departmental climate, and supervisory relationship were associated with more positive outcomes. The current findings contribute new knowledge about the prevalence of mental health symptoms in PGRs in the UK, implying that institutional efforts to improve PGR wellbeing should include strategies to promote equality, diversity, resilience, integration and work-life balance of PGRs.

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/

Research on women in STEM fields has primarily focused on North America and Europe, overlooking the experiences of women in Sub-Saharan Africa. This mixed-methods study helps address this gap by examining the challenges faced by women postgraduate students in STEM fields at African institutions and how they navigate these challenges to succeed. Quantitative data were collected through an online survey in 2020 with 163 women who had completed STEM PhDs at 40 African universities. Qualitative data were gathered through seven focus group discussions in 2019/2020 with female STEM postgraduate students (MSc and PhD) at four African universities. The findings reveal that African women in STEM face common challenges such as financial stress, lack of role models, gender stereotypes, societal pressure, work-life balance issues, and sexual harassment. Passion for their field, determination to persist, a realistic outlook, and family support are key factors contributing to success in a challenging learning environment. The study highlights best practices in Africa that support women in STEM and offers insights for advancing women in higher education globally.

Sample frame distribution by year of data collection.

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.

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.