The Survey of Doctorate Recipients (SDR) provides demographic, education, and career history information from individuals with a U.S. research doctoral degree in a science, engineering, or health (SEH) field. The SDR is sponsored by the National Center for Science and Engineering Statistics and by the National Institutes of Health. Conducted since 1973, the SDR is a unique source of information about the educational and occupational achievements and career movement of U.S.-trained doctoral scientists and engineers in the United States and abroad. This dataset includes SDR assets for 2021.

The Survey of Doctorate Recipients (SDR) is a dataset created by the National Center for Science and Engineering Statistics (NCSES) under the U.S. National Science Foundation (NSF). It provides comprehensive data on individuals who earned research doctorates in science, engineering, or health (SEH) fields from U.S. academic institutions. The survey captures demographic information, educational background, career trajectories, employment status, and work experiences of doctorate holders, both within the U.S. and abroad. Its primary purpose is to inform policy and research on the SEH workforce, offering insights into career patterns, labor market dynamics, and the long-term impacts of doctoral education. Key features include its representative sampling of doctorate recipients (including those retired or seeking work), expanded coverage of specialized fields, and an online format to enhance data quality and participation. Unique aspects include integrated data on international and domestic recipients, enabling analysis of global career trends. The SDR is widely used by researchers, policymakers, and institutions to track workforce development, assess the return on investment in higher education, and shape STEM (science, technology, engineering, and mathematics) initiatives. Regular updates ensure relevance to evolving scientific and economic landscapes.

The National Science Foundation Survey of Earned Doctorates (SED) is an annual census conducted by the National Center for Science and Engineering Statistics (NCSES) within the NSF, in collaboration with the National Institutes of Health, U.S. Department of Education, and National Endowment for the Humanities. Established in 1957, it collects data on all individuals earning research doctorates from accredited U.S. institutions in a given year, covering demographics, field of study, institutional details, funding sources, and post-graduation employment. The dataset serves to track trends in doctoral education, inform science and workforce policy, and support research on academic and career pathways. Its long-term scope (spanning over six decades) and comprehensive coverage of U.S. doctorates make it a critical resource for analyzing educational attainment, diversity in STEM fields, and labor market outcomes. Unique features include the Doctorate Records File (DRF), a historical database dating to 1920, and tools like the Restricted Data Analysis System (RDAS), which enables customized data queries. The SED is widely used by researchers, policymakers, and institutions to assess workforce development, funding effectiveness, and demographic shifts in graduate education. Recent reports highlight growing doctoral awards in fields like computer science and health sciences, underscoring its relevance for evidence-based decision-making.

As part of the American Community Survey (ACS), the U.S. Census Bureau collects information regarding respondents' educational attainment. Educational attainment refers to the highest level of education that all individuals age 25 and older have completed. Response categories include no schooling completed; nursery school, grades 1 through 11; 12th grade but no diploma; regular high school diploma; GED or alternative credential; some college credit, but less than one year of college; one or more years of college credit, no degree; associate's degree; bachelor's degree; master's degree, professional degree beyond bachelor's degree; and doctorate degree. Data from the 2000 Decennial Census is also summarized.

The data in this library comes from the National Survey of Recent College Graduates. Included is information about employment numbers, major information, and the earnings of different majors. Many majors were not available before 2010, so their values have been recorded as 0 (note that this may affect the averages shown in the bar charts).

Data Dictionary

This dataset relates to the PhD study, "The voices of home educated adolescents: a participatory research study exploring their home education experiences". The study is a participatory research project with young people aged 13–17 who are home educated. They used vlogs, blogs, or visual boards to collect data on their experiences of being home educated, with a particular focus on their perceptions of their educational outcomes and social development.

The dataset includes resources created by participants, including a vlog, three blogs and three visual boards.

The vlog is an insight into how playing video games is an opportunity of learning for the participant: it demonstrates his interest in historical events and weaponry. Furthermore, the research project and creating the vlog itself was a new experience for him and was seen as a learning opportunity and became integrated into his home education experience. To align with the research methodology and remain socially and culturally appropriate, the participant used this method of data collection as an insight into his lived experience as home educated. Home educated young people have the autonomy and flexibility to learn through various mediums and learning tools that interest and relate to them. Therefore this vlog demonstrates that doing research with children can include various data collection methods that relate to the child's lived experience.

The visual boards are representations of participants' experiences being home educated and their perceptions of their educational outcomes. The blogs are a collection of thoughts or diary entries of their experience being home educated.

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

This dataset is based on the model developed with the Ph.D. students of the Communication and Information Sciences Ph.D. program at the University of Hawaii at Manoa, intended to help new students get relevant information. The model was first presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students " by Stanislava Gardasevic and Rich Gazan (available at: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/9eebcea7-06fd-4db3-b420-347883e6379e/content)The database is created in Neo4J, and the .dump file can be imported to the cloud instance of this software. The dataset (.dump) contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph demonstrating progress through a student's program in this degree, and activities they have done while in the program. This dataset was made possible with the huge help of my collaborator, Petar Popovic, who ingested the data in the database.The model and dataset were developed while involving the end users in the design and are based on the actual information needs of a population. It is intended to allow researchers to investigate multigraph visualization of the data modeled by the said model.The knowledge graph was evaluated with CIS student population, and the study results show that it is very helpful for decision-making, information discovery, and identification of people in one's surroundings who might be good collaborators or information points. We provide the .json file containing the Neo4J Bloom perspective with styling and queries used in these evaluation sessions.

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.

Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Context

Pakistan has a large number of public and private universities offering degrees in multiple disciplines. There are 162 universities out of which 64 are in private sector and 98 are public sector/government universities recognized by the Higher Education Commission of Pakistan (HEC).

According to HEC, Pakistani universities are producing over half a million graduates per year, which include over more than 10,000 Computer Science/IT graduates.

From year 2001 to 2015 there is a mass increase in number of enrollment in universities. The recent statistics shows that in 2015, 1,298,600 students enrolled in different levels of degree, 869,378 in Bachelors (16 years), 63,412 in Bachelors (17 years), 219,280 in Masters (16 years), 124,107 in M.Phil/MS, 14,373 in Ph.D, and 8,319 in P.G.D. However, in 2014 the number of doctoral degree awarded were 1,351 only.

Moreover, according to HEC report, in 2014-2015 there are over 10,125 fulltime Ph.D. faculty teaching in Pakistan in all disciplines. Computer Science and related disciplines are widely taught in Pakistan with over 90 universities offering this discipline with qualified faculty. According to our dataset, there are 504 PhD faculty members in Computer Science in Pakistan for 10,000 students. So we have a PhD faculty member for every 20 students on average in computer science program.

Current Student to PhD Professor Ratio in Pakistan is 130:1 (while India is going towards 10:1 in Post-Graduate and 25:1 in Undergrad education).

Here is world's Top 100 universities with Student to Staff Ratio.

Content

Dataset: The dataset contains list of computer science/IT professors from 89 different universities of Pakistan.

Variables: The dataset contains Serial No, Teacher’s Name, University Currently Teaching, Department, Province University Located, Designation, Terminal Degree, Graduated from (university for professor), Country of graduation, Year, Area of Specialization/Research Interests, and some Other Information

Acknowledgements

Data has been collected from respective university websites. Some of the universities did not mention about their faculty profiles or were unavailable (hence the limitation of this dataset). The statistics mentioned above are gathered by Higher Education Commission of Pakistan (HEC) website and other web resources.

Inspiration

Here is what I like you to do:

1. Which area of interest/expertise is in abundance in Pakistan and where we need more people?
2. How many professors we have in Data Sciences, Artificial Intelligence, or Machine Learning?
3. Which country and university hosted majority of our teachers?
4. Which research areas were most common in Pakistan?
5. How does Pakistan Student to PhD Professor Ratio compare against rest of the world, especially with USA, India and China?
6. Any visualization and patterns you can generate from this data

Let me know how I can improve this dataset and best of luck with your work

The dataset contains academic year-, gender- and state-wise compiled data on number of students who have passed out in certificate, diploma, integrated, pg diploma, undergraduate, post graduate, m.phil and ph.d educational courses from the year 2010-11 to 2020-21. In addition, the dataset also contains separate data on number of students who have passed out with 60% or more marks

Overview of educational characteristics of Indigenous populations in Canada, provinces, territories and cities, with percent distribution of highest certificate, diploma or degree.

AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data was reported at 1.396 % in 2023. This records a decrease from the previous number of 1.670 % for 2022. AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data is updated yearly, averaging 1.150 % from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 1.670 % in 2022 and a record low of 0.850 % in 2014. AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

The dataset was created to investigate the speech and cognitive performance of people with varying degrees of cognitive impairment, primarily dementia. The dataset contains a comprehensive set of data including the results of standardized neuropsychological tests (RBANS, ALBA, POBAV, MASTCZ), speech tasks focused on comprehension, memory, naming, and repetition, and demographic data (age, gender, education).

Participants were divided into four groups based on clinical assessment: healthy individuals, healthy individuals with possible mild cognitive impairment, patients with mild cognitive impairment, and patients with dementia. All recordings and examinations were managed as part of routine clinical practice in the neurological outpatient clinic – Memory Disorders Advisory Unit, at the Neurological Clinic of the Faculty Hospital Královské Vinohrady. The dataset containing 268 examinations was divided into a training and test part using stratification by clinical group, age, gender, and level of education to ensure an even distribution of these key characteristics in both parts of the data.

The aim of the dataset is to support the development of methods for automated detection of cognitive disorders based on speech analysis and cognitive performance. The data are suitable for research in the areas of clinical neuropsychology, computational linguistics, and machine learning. The dataset is intended for non-commercial research purposes.

Offline reinforcement learning (RL) is a promising direction that allows RL agents to be pre-trained from large datasets avoiding recurrence of expensive data collection. To advance the field, it is crucial to generate large-scale datasets. Compositional RL is particularly appealing for generating such large datasets, since 1) it permits creating many tasks from few components, and 2) the task structure may enable trained agents to solve new tasks by combining relevant learned components. This submission provides four offline RL datasets for simulated robotic manipulation created using the 256 tasks from CompoSuite Mendez et al., 2022. In every task in CompoSuite, a robot arm is used to manipulate an object to achieve an objective all while trying to avoid an obstacle. There are for components for each of these four axes that can be combined arbitrarily leading to a total of 256 tasks. The component choices are * Robot: IIWA, Jaco, Kinova3, Panda* Object: Hollow box, box, dumbbell, plate* Objective: Push, pick and place, put in shelf, put in trashcan* Obstacle: None, wall between robot and object, wall between goal and object, door between goal and object The four included datasets are collected using separate agents each trained to a different degree of performance, and each dataset consists of 256 million transitions. The degrees of performance are expert data, medium data, warmstart data and replay data: * Expert dataset: Transitions from an expert agent that was trained to achieve 90% success on every task.* Medium dataset: Transitions from a medium agent that was trained to achieve 30% success on every task.* Warmstart dataset: Transitions from a Soft-actor critic agent trained for a fixed duration of one million steps.* Medium-replay-subsampled dataset: Transitions that were stored during the training of a medium agent up to 30% success. These datasets are intended for the combined study of compositional generalization and offline reinforcement learning. Methods The datasets were collected by using several deep reinforcement learning agents trained to the various degrees of performance described above on the CompoSuite benchmark (https://github.com/Lifelong-ML/CompoSuite) which builds on top of robosuite (https://github.com/ARISE-Initiative/robosuite) and uses the MuJoCo simulator (https://github.com/deepmind/mujoco). During reinforcement learning training, we stored the data that was collected by each agent in a separate buffer for post-processing. Then, after training, to collect the expert and medium dataset, we run the trained agents for 2000 trajectories of length 500 online in the CompoSuite benchmark and store the trajectories. These add up to a total of 1 million state-transitions tuples per dataset, totalling a full 256 million datapoints per dataset. The warmstart and medium-replay-subsampled dataset contain trajectories from the stored training buffer of the SAC agent trained for a fixed duration and the medium agent respectively. For medium-replay-subsampled data, we uniformly sample trajectories from the training buffer until we reach more than 1 million transitions. Since some of the tasks have termination conditions, some of these trajectories are trunctated and not of length 500. This sometimes results in a number of sampled transitions larger than 1 million. Therefore, after sub-sampling, we artificially truncate the last trajectory and place a timeout at the final position. This can in some rare cases lead to one incorrect trajectory if the datasets are used for finite horizon experimentation. However, this truncation is required to ensure consistent dataset sizes, easy data readability and compatibility with other standard code implementations. The four datasets are split into four tar.gz folders each yielding a total of 12 compressed folders. Every sub-folder contains all the tasks for one of the four robot arms for that dataset. In other words, every tar.gz folder contains a total of 64 tasks using the same robot arm and four tar.gz files form a full dataset. This is done to enable people to only download a part of the dataset in case they do not need all 256 tasks. For every task, the data is separately stored in an hdf5 file allowing for the usage of arbitrary task combinations and mixing of data qualities across the four datasets. Every task is contained in a folder that is named after the CompoSuite elements it uses. In other words, every task is represented as a folder named

UGU stands for "Evaluation Through Follow-up" and is one of the country's largest research study in the field of education, the country's largest survey of schools. As part of the national evaluation of the school, our study contributes with nationally representative data that can be linked to other databases. Researchers in psychology, economics, political science and pedagogy use our longitudinal data to know more about the Swedish school system and the labour market.

Today, UGU's working group consists of 10 people who work to varying degrees with the management, planning and development of the study. Metadata is available as codebooks on the website and increases the accessibility of data for interested researchers. Metadata increases the searchability and reuse of data in accordance with the FAIR principles.

The data set within the UGU project constitutes a valuable resource for the research community and for the education of students at different levels. Data are available on request for researchers and doctoral students at Swedish and foreign universities and colleges. There are restrictions on delivery to countries outside the EU. Inquiries about the data set are made by contacting the datamanager via email or telephone. Disclosure of data takes place after a formal request by filling in a user agreement. The agreement must be accompanied by a description of the project and a list of desired variables.

The number of postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED), age group and gender.

The Scientists and Engineers Statistical Data System (SESTAT) is a longitudinal dataset created by the National Center for Science and Engineering Statistics (NCSES) within the U.S. National Science Foundation (NSF). Established in 1993, it integrates data from three biennial surveys: the National Survey of College Graduates (NSCG), the Survey of Doctorate Recipients (SDR), and the discontinued National Survey of Recent College Graduates (NSRCG). SESTAT contains detailed demographic, educational, employment, and earnings data on individuals with college degrees in science, engineering, or related fields. Its primary purpose is to provide insights into the education-to-employment pathways of STEM professionals, supporting research on workforce trends, education outcomes, and labor market dynamics. Key features include longitudinal tracking (enabling analysis of career trajectories over time), integration of multiple surveys (enhancing data comprehensiveness), and microdata accessibility for granular analysis. Unique aspects also include its role in informing federal and state policies on STEM education and workforce development, as well as its use by researchers and institutions to study trends in scientific and technical professions. SESTAT’s data spans surveys conducted from 1993 to 2013, offering a historical perspective on STEM workforce evolution.

This dataset comprises five sets of data collected throughout the PhD Thesis project of Pelin Esnaf-Uslu.

Esnaf-Uslu, P. (2024). Design for Interpersonal Mood Regulation: Introducing a Framework and Three Tools to Support Mood-Sensitive Service Encounters. (Doctoral dissertation in review). Delft University of Technology, Delft, the Netherlands.

The research in this thesis is based on the premise that service providers can enhance their effectiveness in client interactions by acquiring a detailed understanding of IMR strategies and effectively applying this knowledge. To achieve this overall aim, the current research aimed to explore (1) the current role of mood in service encounters, (2) the IMR strategies used by service providers during service encounters in response to client’s moods, (3) how IMR strategies can be facilitated by means of tools for service providers and the (4) strengths and limitations of the developed materials.

This research was supported by VICI grant number 453-16-009 from The Netherlands Organization for Scientific Research (NWO), Division for the Social and Behavioral Sciences, awarded to Pieter M. A. Desmet.

The data is organized into folders corresponding to the chapters of the thesis. Each folder contains a README file with specific information about the dataset.

Chapter_2: This study investigates the role of mood in service encounters. Samples are collected from service providers experiences during service encounters and in-depth interviews are conducted. The dataset includes the blank diary and the interview protocol.

Chapter_3: This study investigates the clarity of the images developed representing Interpersonal Mood Regulation (IMR) strategies. The dataset includes anonymized scores from 27 and 29 participants, showing the associations between images representing nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants. Additionally, the dataset contains a screenshot of the workshop material used in the implementation study.

Chapter_4: This study examines the clarity of developed videos depicting IMR strategies. The dataset includes anonymized scores from 32 participants, showing the associations between videos depicting nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants. In addition, the dataset contains the workshop guideline developed for the implementation study.

Chapter_5: This study evaluates the clarity of character animations depicting Interpersonal Mood Regulation (IMR) strategies. The dataset includes anonymized scores from 39 participants, demonstrating the associations between videos illustrating nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants.

Chapter_6: This dataset comprises correspondence analysis files for each material, created for the purpose of comparison.

All the data is anonymized by removing the names of individuals and institutions.