The Survey of Earned Doctorates (SED) is an annual census conducted since 1957 of all individuals receiving a research doctorate from an accredited U.S. institution in a given academic year. The SED is sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF) and by three other federal agencies: the National Institutes of Health, Department of Education, and National Endowment for the Humanities. The SED collects information on the doctoral recipient's educational history, demographic characteristics, and postgraduation plans. Results are used to assess characteristics of the doctoral population and trends in doctoral education and degrees. This dataset includes SED assets for 2022.

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.

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 Survey of Doctorate Recipients is a longitudinal biennial survey conducted since 1973 that provides demographic and career history information about individuals with a research doctoral degree in a science, engineering, or health (SEH) field from a U.S. academic institution. The survey follows a sample of individuals with SEH doctorates throughout their careers from the year of their degree award until age 76.

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 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.

US State Education Analysis for Tech Product Launch

About This Dataset

This comprehensive dataset provides detailed educational attainment and demographic analysis across all 50 US states from 2021-2023, specifically designed for tech companies planning strategic market entry and product launch decisions.

Dataset Overview

• 150 rows of data (50 states × 3 years)
• 17 columns of educational, demographic, and economic indicators
• Complete coverage of all US states from 2021-2023
• Ready-to-analyze format with calculated percentages and rankings

Key Features

🎯 Strategic Market Intelligence

• Educational attainment levels by degree type (Bachelor's, Master's, Professional, Doctoral)
• Calculated education scores and state rankings for quick market prioritization
• Median household income data for purchasing power assessment

📊 Comprehensive Demographics

• Population data for adults 25+ (primary tech consumer demographic)
• Household count data for market sizing
• College graduate percentages for targeted marketing

🔢 Advanced Analytics Ready

• Pre-calculated composite education scores
• State rankings based on education levels
• Percentage breakdowns for immediate insights

Column Definitions

Use Cases

🚀 Tech Product Launches

• Identify states with highest concentrations of educated early adopters
• Prioritize markets based on education levels and income
• Size potential customer segments by state

📈 Market Research & Analysis

• Compare educational demographics across regions
• Analyze trends in educational attainment over time
• Correlate education levels with income potential

🎯 Customer Segmentation

• Target high-value customer segments (graduate degree holders)
• Develop region-specific marketing strategies
• Plan B2B tech sales territories

📊 Business Intelligence

• Regional expansion planning
• Competitive market analysis
• Investment and resource allocation decisions

Data Quality & Sources

• Primary Sources: US Census Bureau American Community Survey (ACS), Bureau of Labor Statistics
• Data Validation: Cross-referenced against multiple official sources
• Calculation Methodology: All percentages and scores calculated using consistent formulas
• Update Frequency: Annual updates as new official data becomes available

Sample Insights

The dataset reveals that Massachusetts consistently ranks #1 in education metrics with: - 47.78% college graduation rate (2023) - 22.42% graduate degree holders - $99,858 median household income - Education score of 28.76

Perfect for identifying premium tech markets and highly-educated consumer bases for sophisticated technology products.

This dataset is ideal for data scientists, market researchers, business analysts, and tech companies looking to make data-driven decisions about market entry, customer targeting, and regional strategy.

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.

This dataset consists of raw fsa files obtained from fragment analysis carried out on DNA amplified using 15 pairs of microsatellite primers. Samples were analysed using a 3730 genetic analyser and samples were pooled into groups of products from 5 loci. Groupings and dyes corresponding to specific loci are outlined in an excel spreadsheet included with the dataset. DNA was amplified from tissue samples taken from 122 and 127 Duma florulenta and Acacia stenophylla individuals, respectively. Samples were collected from sites that were located on 7 rivers in the northern Murray-Darling Basin (Balonne River, Bokhara River, Birrie River, Culgoa River, Warrego River, Paroo River and Darling River).

Results of qualitative data analysis of a field study of a smart wearable system (Grippy) aiming to help people deal with daily stress. This dataset has been anonymized.

This dataset comprises the contents of a CD-ROM which was enclosed with the thesis when it was first submitted in 2005. For further information on the files, please refer to the thesis "Salvage rites: making memory on a Montana homestead" on ORO. The preservation of selected sites and artefacts privileges certain forms of cultural memory. Other material cultures, no longer useful and deemed unworthy of preservation, accumulate in overlooked places. Abandoned in a state of unfinished disposal, these objects and structures can generate unpredictable and unruly effects. Such degraded materialities may trigger apprehensions of cultural memory in a mode unfamiliar to the museum or the heritage park. This study takes up the residual material culture of a homestead in Western Montana to explore how history and memory are made, and remade, through interactions between people and things. Theories of performativity and intersubjectivity inform a move away from a broadly representational or semiotic understanding of material culture. In this study, experimental methodologies access the different ways in which material engagements animate the potential effects of a given artefact. One approach explores the potential for inclusive, artful inventory practice. Another engages in a process of associative storytelling which assembles disparate objects in constellations of meaning. A third approach observes the way in which sensory or haptic memory arises out of embodied action and practical reclamation. Finally, the thesis considers the nature of cultural memory and the processes of decay that obscure certain residues of knowledge even as they expose others. In conclusion, the thesis considers the social and political implications of such non-essentialist encounters with memory and materiality. The thesis argues that these active, creative encounters with objects open up the possibility for an ethical relation to the past-a salvage both of cultural artefacts and of overlooked histories.

Dataset

This dataset was created by Umer Majeed

Contents

Scientist studying diet of piscivorous animales found difficulties determining fish species from skeletal remains. The purpose of this project was to find an identification method for cyprinids of the Iberian Peninsula using their bony structures (scales, cleithra and operculum) and use them to determine the size and body mass of these fish (Miranda 1997).

More than 1000 individuals belonging to 26 species of cyprinidae family present in Spain were analysed. Description of anatomical features related with cleithra, opercula and scales morphology allowed the specific discrimination of individuals and the elaboration of an identification key for the family.

Context

PhD candidates typically get paid to study.

Content

Self-reported PhD salaries from phdstipends.com.

['University', 'Department', 'Overall Pay', 'Living Wage Ratio', 'Academic Year', 'Program Year', etc.]

Acknowledgements

Data from http://www.phdstipends.com/csv

License: Unknown + https://twitter.com/pfforphds/status/1222921605493313537?s=12

Banner Photo by Photo by Good Free Photos on Unsplash

This dataset contains entries scraped from GradCafe.Along with this,two existing datasets were merged which were https://github.com/aditya-sureshkumar/University-Recommendation-System and https://github.com/tramatejaswini/University_Recommendation_System.This dataset contains fields like: University Name Term GRE AWA GRE Quant GRE Verbal GPA Degree Course Name Publication Work Experience Research Experience Toefl

The dataset contains 58049 entries.

Here is one beginner-friendly dataset of the latest UK PhD studentship opportunity scrapped from https://www.jobs.ac.uk/phd

Data Content:

1. Title: The title of each studentship, providing insight into the research focus.
2. Employer: The university or institution offering the studentship.
3. Department: The specific department or school within the institution associated with the opportunity.
4. Salary: The annual stipend or payment associated with the studentship.
5. Location: The city where the studentship is based.
6. Posting Date: The date when the job listing was posted.
7. Closing Date: The deadline for application submissions.
8. URL: A direct link to the job listing for further details and application.

Context

This dataset was created during my PhD (http://www.tdg-seville.info/fogallego/Personal%20Info) at the University of Seville. We didn't found any datasets with labelled conditions so we decided to build one since our main goal for the PhD was to be able to identify conditions without relying on user-defined patterns or requiring any specific-purpose dictionaries, taxonomies, or heuristics.

We presented this dataset in a poster session during Machine Learning Summer School Madrid 2018 (http://mlss.ii.uam.es/mlss2018/posters.html).

Content

The reviews in English and Spanish were randomly gathered from ciao.com between April 2017 and May 2017. The sentences were classified into 15 domains according to their sources, namely: adults, baby care, beauty, books, cameras, computers, films, headsets, hotels, music, ovens, pets, phones, TV sets, and video games.

Our dataset consist of two files: sentences.csv and conditions.csv. The first one contains the whole set of sentences and the second one the manually labelled conditions.

In order to better understand the meaning of each column, I'll explain them in detail:

sentence.csv:

• sentence_uuid: the unique identifier of the sentence
• sentence_text: the text of the sentence
• language: the language of the sentence
• domain: the domain of the sentence
• labelled: whether the sentence was labelled or not

conditions.csv:

• sentence_uuid: the unique identifier of the corresponding labelled sentence
• condition_uuid: the unique identifier of the condition
• begin_connective: the character position where the connective of the condition starts
• end_connective: the character position where the connective of the condition ends
• begin_condition: the character position where the rest of the condition starts
• end_condition: the character position where the rest of the condition ends
• language: the language of the corresponding labelled sentence
• domain: the domain of the corresponding labelled sentence

Acknowledgements

My PhD and this dataset were supported by Opileak.com and the Spanish R&D programme (grants TIN2013- 40848-R and TIN2013-40848-R).