Survey conducted in April-May 2020 during the initial peak phase of the COVID-19 pandemic in the United States. Students in clinical training years were surveyed.

Publications:

Lee CM, Juarez M, Rae G, Jones L, Rodriguez RM, Davis JA, Boysen-Osborn M, Kashima KJ, Krane NK, Kman N, Langsfeld JM, Harries AJ. Anxiety, PTSD, and stressors in medical students during the peak of the COVID-19 pandemic. In production.

Harries AJ, Lee CM, Jones L, Rodriguez RM, Davis JA, Boysen-Osborn M, Kashima KJ, Krane NK, Rae G, Kman N, Langsfeld JM, Juarez M. Effects of the COVID-19 pandemic on medical students: a multicenter quantitative study. BMC Medical Education. January 2021.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

Note: CoP, WHO Global Code of Practice on the International Recruitment of Health Personnel; US IMGs, citizens of the US who graduated from non-US medical schools; non-US IMGs, foreign nationals who graduated from non-US medical schools.Data sources: Educational Commission for Foreign Medical Graduates [27–30].Numbers and percentages of US and non-US citizens who graduated from international medical schools in the National Residency Match Program after the 2010 launch of the CoP.

The objective of this study was to evaluate the feasibility of providing a voluntary assignment list to third-year medical students in their pediatric clerkship. This is a retrospective single-center cross-sectional analysis of voluntary assignment completion during the 2019–2020 academic year. In total, 132 subjects who were part of our school’s traditional curriculum and rotated at the pediatric clerkship’s primary site and at our off-campus affiliate sites were included in this study. Subjects who were part of our integrated longitudinal curriculum were excluded.

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!

Survey questions and unprocessed raw answers for two cross-sectional surveys on the impact of the COVID-19 pandemic on the education of medical and biomedical graduates based at US and Swedish universities. This dataset relates to two manuscripts by the authors published at "BMC Medical Education" and "Biochemistry and Molecular Biology Education". The survey was assessed by the Swedish Ethical Review Authority (Dnr 2021-00481) and the Institutional Review Board of the University of California San Diego (Project #201972XX), and found to be exempt by both. Participants provided informed consent to publication of the anonymous survey results and we followed the general principles and recommendations provided by the Helsinki Declaration and the Belmont Report. The dataset is from anonymous participants and does not contain any personally identifiable information.

BackgroundProfessional societies use metrics to evaluate medical schools' policies regarding interactions of students and faculty with the pharmaceutical and medical device industries. We compared these metrics and determined which US medical schools' industry interaction policies were associated with student behaviors.Methods and FindingsUsing survey responses from a national sample of 1,610 US medical students, we compared their reported industry interactions with their schools' American Medical Student Association (AMSA) PharmFree Scorecard and average Institute on Medicine as a Profession (IMAP) Conflicts of Interest Policy Database score. We used hierarchical logistic regression models to determine the association between policies and students' gift acceptance, interactions with marketing representatives, and perceived adequacy of faculty–industry separation. We adjusted for year in training, medical school size, and level of US National Institutes of Health (NIH) funding. We used LASSO regression models to identify specific policies associated with the outcomes. We found that IMAP and AMSA scores had similar median values (1.75 [interquartile range 1.50–2.00] versus 1.77 [1.50–2.18], adjusted to compare scores on the same scale). Scores on AMSA and IMAP shared policy dimensions were not closely correlated (gift policies, r = 0.28, 95% CI 0.11–0.44; marketing representative access policies, r = 0.51, 95% CI 0.36–0.63). Students from schools with the most stringent industry interaction policies were less likely to report receiving gifts (AMSA score, odds ratio [OR]: 0.37, 95% CI 0.19–0.72; IMAP score, OR 0.45, 95% CI 0.19–1.04) and less likely to interact with marketing representatives (AMSA score, OR 0.33, 95% CI 0.15–0.69; IMAP score, OR 0.37, 95% CI 0.14–0.95) than students from schools with the lowest ranked policy scores. The association became nonsignificant when fully adjusted for NIH funding level, whereas adjusting for year of education, size of school, and publicly versus privately funded school did not alter the association. Policies limiting gifts, meals, and speaking bureaus were associated with students reporting having not received gifts and having not interacted with marketing representatives. Policy dimensions reflecting the regulation of industry involvement in educational activities (e.g., continuing medical education, travel compensation, and scholarships) were associated with perceived separation between faculty and industry. The study is limited by potential for recall bias and the cross-sectional nature of the survey, as school curricula and industry interaction policies may have changed since the time of the survey administration and study analysis.ConclusionsAs medical schools review policies regulating medical students' industry interactions, limitations on receipt of gifts and meals and participation of faculty in speaking bureaus should be emphasized, and policy makers should pay greater attention to less research-intensive institutions.Please see later in the article for the Editors' Summary

Dataset Description

The 3rd annual MD+ datathon is a national month-long event hosted by MD+ and sponsors to foster innovative thinking about complex healthcare problems and their data-driven solutions. Medical students, graduate students, and trainees from all levels work together across disciplines to generate insights and engineer solutions from patient datasets. In contrast to prior years, the 2024 MD+ Datathon will be divided into 3 separate competition tracks, each using a… See the full description on the dataset page: https://huggingface.co/datasets/mdplus/Datathon2024.

Survey demographics as compared to US medical school graduates who applied to EM.

PurposePoint-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance.MethodsThe authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups.ResultsThe results showed an advantage in quality indictor users’ median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views.ConclusionsThe AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

Hospital Care Quality Measures

Timely & Effective Care Across the U.S

By Health [source]

About this dataset

This dataset includes provider-level data revealing the quality of timely and effective care from hospitals across the United States. It allows us to analyze heart attack, heart failure, pneumonia, surgical, emergency department, preventive care for children's asthma and stroke prevention and treatment data for pregnancy and delivery care courtesy of the Centers for Medicare & Medicaid Services. With this dataset you can analyze hospital's performance on all these areas using Hospital Name, Addresss , City , State , ZIP Code , County Name , Phone Number as well as scores creditable to Measure Name , Sample size from which it was derived a Footnote explanation based on location. Dig deep into each provider's level of care with this dataset to understand their performance on providing timely effective care

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How to use the dataset

To get the most out of this dataset, it is important to understand each column in the dataset: Hospital Name identifies the health care facility; Address provides the address of the hospital; City identifies the city where it is located; State specifies which state it belongs to; ZIP Code denotes its specific zip code; County Name mentions what county it belongs to; Phone Number connects you with an immediate contact at the facility if needed; Condition categorizes types of tests/treatments being monitored in that case study; Measure Name outlines all related measures under said condition umbrella or metric(s) studied as part of that investigative research project/condition category (i.e., infection prevention); Score grades out how well that measure was doing compared against expectations or goals for quality & safe patient protections (higher scores are indicative of better performance on those surveyed & tracked items); Sample details how many patients were involved in this particular study topic component and involved participant sample size selection & unit evaluation criteria definition considerations during research recruitment and retention efforts associated with a particular area of specialty treatment/testing cluster system activity factors reviewed directionally by researchers via cohort based review activities over time [note: matching non-patients or control subject population reference points also sometimes may be used depending on written scope descriptions outlined by investigators]; Footnotes can amplify additional evaluations/CAVEATS sometimes noted regarding high-lighted findings(-such as improvement yet still not meeting standards), etc.; Measure Start Date defines when all test students were allowed entry into their respective study groups associated with one another for convergence analysis purposes within a defined subject patient group prospectively selected category designation feature component selection batch cases (new patients added mid-project have crossed design frontiers at random intervals sometimes necessary). Lastly, Measure End Date reflects terminal endpoint lead review periods cut off times when no new data entries can be accepted post-data collection stopped official time period specifications if designated by protocol order via institutional clinical trial board IRB approved advanced notification statements issued throughout any official project undertaking design process stages at its multiplex points).

Understanding each column's features will assist you in selecting relevant variables from this dataset according to your research needs. Additionally, using Location can help narrow down search results geographically. With this information researchers can gain valuable insight into overall trends regarding timely and effective care in different hospitals across different states

Research Ideas

• Create an interactive heatmap to visualize provider-level data across different states. This can allow researchers, consumers and policy makers to identify areas of excellence as well as opportunities for improvement in timely and effective care measures.
• Develop a web app that allows users to locate hospitals in their area based on any given health condition, measure name, score or timeframe data provided by this dataset. This could give patients access to quality care options and help them make informed decisions while seeking medical attention.
• Utilizing the geographic coordinates data included in the Location column, create a virtual tour function that lets people virtually explore the interior of hospital facilities associated with this dataset...

Dataset Title: Urban Air Quality and Health Impact Dataset: A Comprehensive Overview of U.S. Cities

Description:

This dataset provides an extensive collection of synthetic data related to urban air quality and its potential health impacts across major U.S. cities. The data has been augmented to include a wide range of features, making it a valuable resource for research and analysis in the fields of environmental science, public health, and urban studies.

Features:

• DateTime: Timestamp of the recorded data.
• City: The U.S. city where the data was recorded (e.g., Phoenix, San Diego, New York City).
• Temp_Max: Maximum temperature for the day (°F).
• Temp_Min: Minimum temperature for the day (°F).
• Temp_Avg: Average temperature for the day (°F).
• Feels_Like_Max: Maximum "feels like" temperature for the day (°F).
• Feels_Like_Min: Minimum "feels like" temperature for the day (°F).
• Feels_Like_Avg: Average "feels like" temperature for the day (°F).
• Dew_Point: Dew point temperature (°F).
• Humidity: Relative humidity percentage.
• Precipitation: Total precipitation for the day (inches).
• Precip_Prob: Probability of precipitation (percentage).
• Precip_Cover: Coverage of precipitation (percentage).
• Precip_Type: Type of precipitation (e.g., rain, snow).
• Snow: Amount of snowfall (inches).
• Snow_Depth: Snow depth (inches).
• Wind_Gust: Maximum wind gust speed (mph).
• Wind_Speed: Average wind speed (mph).
• Wind_Direction: Wind direction (degrees).
• Pressure: Atmospheric pressure (hPa).
• Cloud_Cover: Cloud cover percentage.
• Visibility: Visibility distance (miles).
• Solar_Radiation: Solar radiation (W/m²).
• Solar_Energy: Solar energy received (kWh).
• UV_Index: UV index level.
• Severe_Risk: Risk level of severe weather (e.g., low, moderate, high).
• Sunrise: Sunrise time (HH:MM:SS).
• Sunset: Sunset time (HH:MM:SS).
• Moon_Phase: Phase of the moon (e.g., new moon, full moon).
• Conditions: General weather conditions (e.g., clear, cloudy).
• Description: Detailed description of the weather conditions.
• Icon: Weather icon representation.
• Stations: Weather stations reporting data.
• Source: Data source information.
• Temp_Range: Temperature range for the day (difference between max and min temperatures).
• Heat_Index: Heat index value for the day.
• Severity_Score: Score representing the severity of weather conditions.
• Condition_Code: Code representing specific weather conditions.
• Month: Month of the year.
• Season: Season of the year (e.g., winter, spring).
• Day_of_Week: Day of the week.
• Is_Weekend: Indicator if the day is a weekend.
• Health_Risk_Score: Score representing the potential health risk based on weather and air quality conditions.

Usage:

This dataset is intended for researchers, data scientists, and analysts interested in studying the relationships between air quality, weather conditions, and public health. It can be used for developing predictive models, conducting statistical analyses, and creating visualizations to better understand urban environmental impacts.

Source:

The data is synthesized and augmented based on real-world weather data from major U.S. cities and is intended to serve as a comprehensive resource for urban air quality and health impact studies.

Notes:

• The dataset is synthetic and has been generated to provide a broad range of scenarios for analysis.
• Ensure to validate any findings with real-world data when applying the insights to practical applications. .

Purpose: To determine if medical students of different races/ethnicities or genders have different perceptions of bias in the United States (US). Methods: An IRB-approved, anonymous survey was sent to US medical students from November 2022 through February 2024. Students responded to statements regarding perceptions of bias toward them from attendings, patients, and classmates. Chi-square tests, or Fisher’s exact tests, when appropriate, were used to calculate if significant differences exist among genders or races/ethnicities in response to these statements. Results: 370 students responded to this survey. Most respondents were women (n=259, 70%), and nearly half were White (n=164, 44.3%). 8.5% of women agreed that they felt excluded by attendings due to their gender, compared to 2.9% of men (p=0.018). 87.5% and 73.3% of Hispanic and Black students agreed that bias due to race negatively impacted research opportunities compared to 37.2% of White students (p<0.001). 87% and 85.7% of W..., This data was collected through Google Forms, and respondents were asked to log in with their email addresses to make sure that they could only submit their responses once. Data was processed in R studio., , # Experiences of US medical students - a national survey

https://doi.org/10.5061/dryad.cz8w9gjbq

This dataset contains responses to an anonymous, IRB-approved survey sent to medical students across the country. The survey included demographic information and students' responses to various questions regarding their medical school experience.Â

Description of the data and file structure

The data is structured so that each row is an individual response. A researcher could analyze the data to see what demographic factors are related to various survey responses.Â

There are certain questions on the survey that respondents could respond "NA" to if the question did not apply to them. For example, the last question on the survey asks,

...

When the COVID-19 pandemic began, U.S. college students reported increased anxiety and depression. This study examines mental health among U.S college students during the subsequent 2020–2021 academic year by surveying students at the end of the fall 2020 and the spring 2021 semesters. Our data provide cross-sectional snapshots and longitudinal changes. Both surveys included the PSS, GAD-7, PHQ-8, questions about students’ academic experiences and sense of belonging in online, in-person, and hybrid classes, and additional questions regarding behaviors, living circumstances, and demographics. The spring 2021 study included a larger, stratified sample of eight demographic groups, and we added scales to examine relationships between mental health and students’ perceptions of their universities’ COVID-19 policies. Our results show higher-than-normal frequencies of mental health struggles throughout the 2020–2021 academic year, and these were substantially higher for female college students, but by spring 2021, the levels did not vary substantially by race/ethnicity, living circumstances, vaccination status, or perceptions of university COVID-19 policies. Mental health struggles inversely correlated with scales of academic and non-academic experiences, but the struggles positively correlated with time on social media. In both semesters, students reported more positive experiences with in-person classes, though all class types were rated higher in the spring semester, indicating improvements in college students’ course experiences as the pandemic continued. Furthermore, our longitudinal data indicate the persistence of mental health struggles across semesters. Overall, these studies show factors that contributed to mental health challenges among college students as the pandemic continued.

RealMedQA

RealMedQA is a biomedical question answering dataset consisting of realistic question and answer pairs. The questions were created by medical students and a large language model (LLM), while the answers are guideline recommendations provided by the UK's National Institute for Health and Care Excellence (NICE). The full paper describing the dataset and the experiments has been accepted to the American Medical Informatics Association (AMIA) Annual Symposium and is… See the full description on the dataset page: https://huggingface.co/datasets/k2141255/RealMedQA.

Data sources: World Health Organization [5]; 2002 AMA Physician Masterfile as per Hagopian et al. [19]; American Medical Association [115].a2002 data were reported by Hagopian et al. [19] except for the numbers of IMGs trained in Cameroon, Tanzania, and Sudan. Their numbers are included in brackets because they are not part of the total counts reported in the last row of the table. These migrants were identified among SSA-IMGs in the 2011 AMA Physician Masterfile who completed residency by 2002. But the number of physicians available in Cameroon, Sudan, and Tanzania in 2002 came from the Hagopian et al. paper. In their dataset, “other” includes 12 countries with “at least one graduate in the US.” In our 2011 dataset, except otherwise specified, “other” refers to the 16 sub-Saharan African countries with fewer than 15 SSA-IMGs each in the 2011 AMA Physician Masterfile. The numbers of physicians in source countries for the year 2011 are from the Global Health Workforce Statistics of the World Health Organization [5]. “Active” emigration rate is the emigration rate among potentially active physicians. We defined all migrant physicians age ≤70 as potentially active.

MedX Q&A - Medical Reasoning Dataset 🩺 🧠

MedX Q&A is an open-source synthetic text dataset for research and development of medical LLMs, focusing on medical question-answering with AI reasoning. The dataset comprises curated health-related questions sourced from simulated students and patients, paired with corresponding AI-generated reasoning paths and answers. The core aim of MedX Q&A is to provide a valuable resource for training and evaluating models capable of understanding… See the full description on the dataset page: https://huggingface.co/datasets/mavenintel/MedXQA.

The Graduate Students and Postdoctorates in Science and Engineering survey is an annual census of all U.S. academic institutions granting research-based master's degrees or doctorates in science, engineering, and selected health fields as of fall of the survey year. The survey, sponsored by the National Center for Science and Engineering Statistics within the National Science Foundation and by the National Institutes of Health, collects the total number of master's and doctoral students, postdoctoral appointees, and doctorate-level nonfaculty researchers by demographic and other characteristics such as source of financial support. Results are used to assess shifts in graduate enrollment and postdoc appointments and trends in financial support.

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.

Solution Publishing by Allforce Ivy League Business Pros (ILBP) Elite Ivy League Graduate Database for Precision Networking Solution Publishing by Allforce offers a premium database connecting you to over 150,000 Ivy League alumni. This exclusive dataset enables targeted outreach to graduates from Harvard, Yale, Princeton, Columbia, Brown, Dartmouth, UPenn, and Cornell. Core Dataset Features

Comprehensive Alumni Coverage: Direct access to 150,000+ verified Ivy League graduates Detailed Educational Profiles: Information on degrees, graduation years, and specialized programs Advanced Segmentation Options: Filter by industry, job function, and seniority level Regular Data Verification: Continuous updates ensure data accuracy and compliance

Strategic Applications

Brand Elevation: Connect your offerings with the prestige of Ivy League institutions Targeted Alumni Engagement: Perfect for fundraising, events, and institutional outreach Executive Recruitment: Access to top-tier talent across various professional fields

Institutional Coverage Our database spans prestigious institutions and their graduate schools, including:

Harvard (Law, Business, Medical, Education) Yale (Law, Management, Medicine, Divinity) Princeton (Public Affairs, Theological Seminary) Columbia (Law, Business, Medicine, Journalism) Brown (Medicine, Public Health, Graduate School) Dartmouth (Medicine, Tuck Business School) UPenn (Law, Wharton, Perelman School of Medicine) Cornell University

Solution Publishing by Allforce Ivy League Business Pros provides unmatched access to this elite professional network, enabling sophisticated marketing and recruitment strategies targeting this influential demographic.RetryClaude can make mistakes. Please double-check responses.