Dataset

This dataset was created by Wonjun Park

Contents

This collection consists of modified records from CENSUS OF POPULATION AND HOUSING, 1960 PUBLIC USE SAMPLE [UNITED STATES]: ONE-IN-ONE HUNDRED SAMPE (ICPSR 7756). The original records consisted of 120-character household records and 120-character person records, whereas the new modified records are rectangular (each person record is combined with the corresponding household record) with a length of 188, after the deletion of some items. Additional information was added to the data records including typical educational requirement for current occupation, occupational prestige score, and group identification code. This version differs from the original public-use sample in the following ways: ages of persons 15-74 are included, 10 percent of the Black population from each file is included, and Mexican Americans (identified by a Spanish surname) from outside Arizona, California, Colorado, New Mexico, and Texas are not included. This dataset uses the 1970 equivalent occupational codes. The Census Bureau originally used two separate codes for the 1970 and 1960 files, but these have been modified and are now identical.

The table TX-Demographic-2025-08-07 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 17485944 rows across 698 variables.

Dataset

This dataset was created by Wonjun Park

Contents

Abstract (en): This data collection contains extracts of the original DUALabs Special Fifth Count ED/BG Summary Tapes. They are comprised of limited demographic and socioeconomic variables for 27 states in the continental United States. Data are provided at the county, minor civil division, enumeration district, and block group levels for total population and Spanish heritage population for the following states: Minnesota, Nevada, Wyoming, Indiana, Kansas, Nebraska, Oklahoma, South Dakota, Colorado, Arizona, Utah, North Dakota, Montana, Idaho, Missouri, Washington, Iowa, Louisiana, Arkansas, Ohio, Michigan, Wisconsin, Illinois, Oregon, Texas, New Mexico, and California. Demographic variables provide information on race, age, sex, country and place of origin, income, and family status and size. The data were obtained by ICPSR from the National Chicano Research Network, Survey Research Center, Institute for Social Research, University of Michigan. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. A total of 27 states in the continental United States. 2011-08-18 SAS, SPSS, and Stata setups have been added to this data collection.2006-01-12 All files were removed from dataset 28 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 28 and flagged as study-level files, so that they will accompany all downloads.

The Hispanic EPESE provides data on risk factors for mortality and morbidity in Mexican Americans in order to contrast how these factors operate differently in non-Hispanic White Americans, African Americans, and other major ethnic groups. The Wave 7 dataset comprises the sixth follow-up of the baseline Hispanic EPESE (HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: [ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS] [ICPSR 2851]). The baseline Hispanic EPESE collected data on a representative sample of community-dwelling Mexican Americans, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The public-use data cover demographic characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of dental, hospital, and nursing home services, and depression. Subsequent follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization, and other changes in living arrangements, as well as changes in life situations and quality of life issues. During this 7th Wave (dataset 1), 2010-2011, re-interviews were conducted either in person or by proxy, with 659 of the original respondents. This Wave also includes 419 re-interviews from the additional sample of Mexican Americans aged 75 years and over with higher average-levels of education than those of the surviving cohort who were added in Wave 5, increasing the total number of respondents to 1,078. The Wave 7 Informant Interviews dataset (dataset 2) includes data which corresponds to the sixth follow-up of the baseline Hispanic EPESE Wave 7 and included re-interviews with 1,078 Mexican Americans aged 80 years and older. During these interviews, participants were asked to provide the name and contact information of the person they are "closer to" or they "depend on the most for help." These INFORMANTS were contacted and interviewed regarding the health, function, social situation, finances, and general well-being of the ongoing Hispanic EPESE respondents. Information was also collected on the informant's health, function, and caregiver responsibilities and burden. This dataset includes information from 925 informants, more than two-thirds of whom were children of the respective respondents.

The table TX-Voter-History-2025-06-11 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 17485952 rows across 936 variables.

The table TX-Voter-History-2025-08-07 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 17485944 rows across 936 variables.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Dataset Containing 173 College Common Data Sets

Contains Common Data Sets for the Following Schools:

• Alabama State University
• Angelo State University
• Arapahoe Community College
• Arkansas Tech University
• Aurora University
• Baldwin Wallace University
• Beloit College
• Bemidji State University
• Berea College
• Binghamton University
• Boston University
• Bucknell University
• Cabrini University
• California Baptist University
• California State University, Bakersfield
• California State University, Long Beach
• California State University, Los Angeles
• California State University, Sacramento
• Carnegie Mellon University
• Case Western Reserve University
• Christopher Newport University
• Clark University
• Colby College
• College of Charleston
• Collin College
• Colorado College
• Colorado School of Mines
• Colorado State University-Pueblo
• Columbia College
• Concordia University Texas
• Cornell University
• Davidson College
• Delaware Technical Community College
• DeSales University
• Dickinson College
• Drake University
• Drew University
• Duquesne University
• East Central University
• Eastern Washington University
• Embry Riddle Aeronautical University-Daytona Beach
• Fairfield University
• Florida Gulf Coast
• Florida International University
• Fort Hays State University
• Georgia Institute Of Technology
• Gettysburg College
• Hamilton College
• Hollins University
• Humboldt State University
• Iowa State University
• Jackson State University
• John Jay College of Criminal Justice
• Kennesaw State University
• Lafayette College
• Lane College
• Lee University
• Le Moyne College
• Lenoir Rhyne University
• Life University
• Loyola University Maryland
• Lubbock Christian University
• Lycoming College
• Lynn University Common Data Set
• Malone University
• Marlboro College
• Maryville University
• Massachusetts Maritime Academy
• Metropolitan State University of Denver
• Michigan Technological University
• Middlebury College
• Millersville University
• Mississippi State University
• Mott Community College
• Neumann University
• Northeastern State University
• Northern Arizona University
• Northern Kentucky University
• Nyack College
• Oklahoma Christian University
• Oklahoma State University
• Old Dominion University
• Oral Roberts University
• Pepperdine University
• Pomona College
• Prescott College
• Providence College
• Reed College
• Regis University
• Rensselaer Polytechnic Institute
• Rice University
• Rochester College
• Rutgers University
• Saint Vincent College
• San Francisco State University
• Santa Clara University
• Scripps College
• Seton Hill University
• Sewanee
• Shippensburg University
• Simpson University
• Slippery Rock University
• Smith College
• Sonoma State University
• Southeastern Community College
• Southeastern Oklahoma State University
• Southwestern Oklahoma State University
• Springfield College
• St. Ambrose University
• Stanford University
• Stephen F. Austin State University
• SUNY Oneonta
• SUNY Potsdam
• Sweet Briar College
• Taylor University
• Temple University
• Tennessee Wesleyan University
• Texas A&M University - Kingsville
• Texas A&M University
• Texas Wesleyan University
• The College at Brockport
• The College of New Jersey
• The University of Scranton
• The University of Southern Mississippi
• The University of Tennessee
• Trinity University
• Tufts University
• Tulane University
• Tulsa Community College
• University at Buffalo
• University Enrollment
• University of California - Davis
• University of California, Riverside
• University of Colorado Boulder, 2015
• University of Delaware
• University of Kentucky
• University of Louisville
• University of Maine
• University of Missouri
• University of Montana
• University of Mount Olive: 2016
• University of Nebraska Kearney
• University of Nebraska-Lincoln
• University of Nevada, Reno
• University of New Hampshire
• University of New Mexico
• University of North Alabama
• University of North Carolina at Charlotte
• University of Pennsylvania
• University of Pikeville
• University of Puget Sound
• University of Science and Arts
• University of Texas Rio
• University of the Sciences in Philadelphia
• University of Wisconsin
• University Wide Common Data Set 2015
• Villanova University
• Virginia Commonwealth University
• Washburn University
• Washington and Lee University
• Washington College
• Weber State University
• Wellesley College
• Wesleyan University
• Westfield State University
• Westminster College
• Wheaton College
• Whitman College
• Widener University
• Worcester Polytechnic Institute
• Xavier University of Louisiana
• Xavier University
• Yale University

Congressional districts of the 99th Congress are matched to census geographic areas in this file. The areas used are those from the 1980 census. Each record contains geographic data, a congressional district code, and the total 1980 population count. Ten states were redistricted for the 99th Congress: California, Hawaii, Louisiana, Maine, Mississippi, Montana, New Jersey, New York, Texas, and Washington. The data for the other 40 states and the District of Columbia are identical to that for the 98th Congress. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08404.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

DEC. 22, 2022 – After a historically low rate of change between 2020 and 2021, the U.S. resident population increased by 0.4%, or 1,256,003, to 333,287,557 in 2022, according to the U.S. Census Bureau’s Vintage 2022 national and state population estimates and components of change released today.

Net international migration — the number of people moving in and out of the country — added 1,010,923 people between 2021 and 2022 and was the primary driver of growth. This represents 168.8% growth over 2021 totals of 376,029 – an indication that migration patterns are returning to pre-pandemic levels. Positive natural change (births minus deaths) increased the population by 245,080.

“There was a sizeable uptick in population growth last year compared to the prior year’s historically low increase,” said Kristie Wilder, a demographer in the Population Division at the Census Bureau. “A rebound in net international migration, coupled with the largest year-over-year increase in total births since 2007, is behind this increase.”

Regional Patterns The South, the most populous region with a resident population of 128,716,192, was the fastest-growing and the largest-gaining region last year, increasing by 1.1%, or 1,370,163. Positive net domestic migration (867,935) and net international migration (414,740) were the components with the largest contributions to this growth, adding a combined 1,282,675 residents.

The West was the only other region to experience growth in 2022, having gained 153,601 residents — an annual increase of 0.2% for a total resident population of 78,743,364 — despite losing 233,150 residents via net domestic migration (the difference between residents moving in and out of an area). Natural increase (154,405) largely accounted for the growth in the West.

The Northeast, with a population of 57,040,406, and the Midwest, with a population of 68,787,595, lost 218,851 (-0.4%) and 48,910 (-0.1%) residents, respectively. The declines in these regions were due to negative net domestic migration.

Changes in State Population Increasing by 470,708 people since July 2021, Texas was the largest-gaining state in the nation, reaching a total population of 30,029,572. By crossing the 30-million-population threshold this past year, Texas joins California as the only states with a resident population above 30 million. Growth in Texas last year was fueled by gains from all three components: net domestic migration (230,961), net international migration (118,614), and natural increase (118,159).

Florida was the fastest-growing state in 2022, with an annual population increase of 1.9%, resulting in a total resident population of 22,244,823.

“While Florida has often been among the largest-gaining states,” Wilder noted, “this was the first time since 1957 that Florida has been the state with the largest percent increase in population.”

It was also the second largest-gaining state behind Texas, with an increase of 416,754 residents. Net migration was the largest contributing component of change to Florida’s growth, adding 444,484 residents. New York had the largest annual numeric and percent population decline, decreasing by 180,341 (-0.9%). Net domestic migration (-299,557) was the largest contributing component to the state’s population decline.

Eighteen states experienced a population decline in 2022, compared to 15 and DC the prior year. California, with a population of 39,029,342, and Illinois, with a population of 12,582,032, also had six-figure decreases in resident population. Both states’ declining populations were largely due to net domestic outmigration, totaling 343,230 and 141,656, respectively.

Puerto Rico Population Changes In 2022, Puerto Rico’s population was 3,221,789. This reflects a decrease of 1.3%, or 40,904 people, between 2021 and 2022.

Puerto Rico’s population decline resulted from negative net international migration (-26,447) and negative natural change (-14,457), where deaths outnumber births.

                            **###Components of Change for States**

In 2022, 24 states experienced negative natural change, or natural decrease. Florida had the highest natural decrease at -40,216, followed by Pennsylvania (-23,021) and Ohio (-19,543). In 2021, 25 states had natural decrease.

Of the 26 states and the District of Columbia where births outnumbered deaths, Texas (118,159), California (106,155) and New York (35,611) had the highest natural increase.

All 50 states and the District of Columbia saw positive net international migration with California (125,715), Florida (125,629) and Texas (118,614) having the largest gains.

The biggest gains from net domestic migration last year were in Florida (318,855), Texas (230,961) and North Carolina (99,796), while the biggest losses were in California (-343,230), New York (-299,557) and Illinois...

Severe burns are one of the most complex forms of traumatic injury. People with burn injuries often require long-term rehabilitation. Survivors of a burn injury often have a wide range of physical and psychosocial problems that can affect their quality of life. The Burn Model System (BMS) program began in 1994, with funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), in the Administration of Community Living and the U.S. Department of Education. The BMS program seeks to improve, through research, care and outcomes for people with burn injuries. Its research programs are housed in clinical burn centers that provide a coordinated and multidisciplinary system of rehabilitation care, including emergency medical, acute medical, post-acute, and long-term follow-up services. In addition, and with funding from NIDILRR, each BMS center conducts research and contributes follow-up data to the BMS National Data and Statistical Center (BMS NDSC). The four BMS centers are: Boston-Harvard Burn Injury Model System (BH-BIMS) in Boston, Massachusetts North Texas Burn Rehabilitation Model System (NTBRMS) in Dallas, Texas Northwest Regional Burn Model System (NWRBMS) in Seattle, Washington; andSouthern California Burn Model System (SCBMS) in Los Angeles, CaliforniaPast centers include the University of Texas Medical Branch Burn Injury Rehabilitation Model System in Galveston, Texas, the Johns Hopkins University Burn Model System in Baltimore, Maryland, the University of Colorado Denver National Data and Statistical Center, and the University of Colorado Denver Burn Model System Center.The BMS NDSC supports the research teams in the clinical burn centers. It also manages data collected by the BMS centers on more than 7,000 people who have received medical care for burn injuries. The data include a wide range of information—including pre-injury; injury; acute care; rehabilitation; recovery; and outcomes at 6, 12, 24 months, and every five years after the burn injury. To be included in the database, the burn injuries of participants must meet several criteria (as of 2015): ·More than 10% total body surface area (TBSA) burned, 65 years of age and older with burn surgery for wound closure;More than 20% TBSA burned, 0–64 years of age with burn surgery for wound closure; Electrical high voltage/lightning injury with burn surgery for wound closure; or Hand burn and/or face burn and/or feet burn with burn surgery for wound closure.In 2015, the BMS began a major initiative to collect data every five years after the injury and to collect new psychometrically sound, patient-reported outcome measures. On December 31, 2023, the database contained information for 4,913 adults (18 years of age and older at the time of burn) and 2,402 children (17 years of age and younger at the time of burn). The BMS program disseminates evidence-based information to patients, family members, health care providers, educators, policymakers, and the general public. The BMS centers provide information in many ways: peer-reviewed publications, presentations at national professional meetings, fact sheets about different aspects of living with a burn injury, newsletters for patients on BMS research and center events, outreach satellite clinics for patients living in rural areas, and peer-support groups. The BMS program also collaborates with the NIDILRR-funded Model Systems Knowledge Translation Center to promote the adoption of research findings by rehabilitation professionals, policymakers, and persons with burn injuries and their family members. The BMS program establishes partnerships to increase the overall impact of research; information dissemination; and training of clinicians, researchers, and policymakers. Current partners include the American Burn Association (ABA) and the Phoenix Society. Together, these partners help the BMS to ensure that NIDILRR-funded research addresses issues that are relevant to people with burn injuries.

This dataset contains historical and recent unemployment data from various sources, including the Current Population Survey (Household Survey). It features unemployment rates and levels for the United States and specific states like California and Texas, covering different demographic groups and seasonal adjustments. Data is provided in monthly and quarterly frequencies. Key metrics include the overall unemployment rate, insured unemployment, and the noncyclical rate of unemployment. This dataset is useful for economic analysis and forecasting unemployment trends.

Column Descriptions:

id: Unique identifier for each data series. realtime_start: The start date when the data was recorded in real-time. realtime_end: The end date when the data was recorded in real-time. title: Title of the data series. observation_start: The starting date of the observation period for the data series. observation_end: The ending date of the observation period for the data series. frequency: The frequency at which data is recorded (e.g., Monthly, Quarterly). frequency_short: Shortened notation for the frequency (e.g., M for Monthly, Q for Quarterly). units: Units of measurement for the data (e.g., Percent, Thousands of Persons). units_short: Shortened notation for units (e.g., %, Thous. of Persons). seasonal_adjustment: Indicates whether the data is seasonally adjusted. seasonal_adjustment_short: Shortened notation for seasonal adjustment (e.g., SA for Seasonally Adjusted, NSA for Not Seasonally Adjusted). last_updated: The date and time when the data series was last updated. popularity: Popularity score of the data series, indicating its usage or reference frequency. notes: Additional notes providing context or details about the data series.

Physical Strength & Fear-Related Personality

Exploring Gender Differences across Five University Samples

By [source]

About this dataset

This dataset offers a fascinating insight into gender differences in fear-related personality traits and their correlation with physical strength across five university samples. It includes demographic information such as age, gender, and ethnicity, as well as physical strength measures – grip strength and chest strength – taken from undergraduate students from the University of California Santa Barbara, Oklahoma State University, University of Texas Austin, and Arizona State University. Additionally, the dataset includes self-report measures of HEXACO Emotionality to explore the effects of physical strength on fear-related personality traits - which is key information to consider when designing interventions for mental health issues. With this data we could discover how temperament affects physiological parameters such as grip or chest strength: Does having a fearful personality predispose someone to have decreased levels of physical power? How does this route differ dependeing on sex? Answering these questions could allow us to gain valuable insights into how greater bodily prowess affects unique psychological conditions that differ depending on gender. Do not miss out on this amazing opportunity to learn more about fear-induced personality features associated with physical force!

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Research Ideas

• Using the physical strength and fear-related personality trait measures, universities can identify and target students who might need extra support in an intervention to improve mental health and wellbeing.
• Exploring correlations between physical strength measures, overall HEXACO Emotionality scores, as well as the Anxiousness, Fearfulness, Sentimentalism, and Emotional Dependence facets of HEXACO Emotionality to understand how gender differences in fear-related personality traits may be affected by physical strength.
• Comparing the distributions of simple demographic measures such as age and ethnicity across five different university samples to explore commonalities or potential differences among student populations at different universities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Sample_1.csv | Column name | Description | |:--------------|:----------------------------------------------------------------| | age | Age of the participant. (Numeric) | | female | Gender of the participant (1 = Female, 0 = Male). (Categorical) | | ethnicity | Ethnicity of the participant. (Categorical) | | grip | Grip strength of the participant. (Numeric) | | chest | Chest strength of the participant. (Numeric) | | e_anx_1 | Fearfulness score of the participant. (Numeric) | | e_anx_2 | Anxiety score of the participant. (Numeric) | | e_anx_3 | Sentimentalism score of the participant. (Numeric) | | e_anx_4 | Emotional Dependence score of the participant. (Numeric) | | e_anx_5 | Fearfulness score of the participant. (Numeric) | | e_anx_6 | Anxiety score of the participant. (Numeric) | | e_anx_7 | Sentimentalism score of the participant. (Numeric) | | e_anx_8 | Emotional Dependence score of the participant. (Numeric) | | e_anx_9 | Fearfulness score of the participant. (Numeric) | | e_anx_10 | Anxiety score of the participant. (Numeric) | | e_dep_1 | Sentimentalism score of the participant. (Numeric) | | e_dep_2 | Emotional Dependence score of the participant. (Numeric) | | e_dep_3 | Fearfulness score of the participant. (Numeric) | | e_dep_4 | Anxiety score of the participant. (Numeric) | | e_dep_5 | Sentimentalism score of the participant. ...

The dataset contains the details of Patent Litigation Cases in the United States from 2000 to 2021. The team collected the litigation data in two phases. The first phase looked at data from 2010, specifically within Texas's Western and Eastern Districts. Unified Patent's Portal includes litigation data that each plaintiff has been marked as NPE (Patent Assertion Entity), NPE (Small Company), or NPE (Individual).

Using the definitions, Unified first focused on identifying what NPEs were aggregators and then if they involved third-party financing. NPE aggregators were defined as NPEs with more than one affiliated subsidiary bringing patent litigation. An example of this would be IP Edge and the various limited liability companies underneath IP Edge's control that have brought numerous litigations against operating companies. Third-party financing was defined as evidence of any third party with a financial interest other than the assertors.

With a narrow focus on the Western and Eastern District of Texas, Unified then used several public databases, such as Edgar, USPTO Assignment Records, the NPE Stanford Database, press releases, and its database of NPEs to identify any aggregator and any third-party financial interest, as well as various secretary of state corporate filings or court-ordered disclosures. After these two districts were identified, Unified expanded the data to cover the top five most litigious venues for patents, including the Western and Eastern Districts of Texas, Delaware, and the North and Central Districts of California. (On average, over the past five years, these districts have seen about 70% of all patent litigation.) Once that was completed, that dataset was then expanded to include all jurisdictions from 2010 and on.

The final step was to complete the data set from 2000 to 2009. The team followed a similar data collection process using Lex Machina, the NPE Stanford Database, and Unified's Portal. Unified identified all of the litigation known to be NPE-related. Using the top five jurisdictions' aggregation and financing data, aggregator entities—such as Intellectual Ventures—were identified using the same methodology. The current dataset covers 2000-2021, determines who is an NPE, notes which NPEs are aggregators, and identifies which aggregators are known to have third-party financing.

Note: there are currently no reporting requirements Federally, at the state level, or in the courts to publicly disclose the financing details of nonpublic entities. Thus, any data analysis of which litigations are funded or financed is incomplete, as many of these arrangements are closely held, private, and unknown even to the courts and the parties to the actions. This data set describes the minimum known amount of third-party-funded patent litigation. It is necessarily underinclusive of all nonpublic deals for which there is no available evidence or insight. For further generalized industry information on the size and scope of litigation funding for patent litigations, private sources often report on the size and scope of the burgeoning industry in the aggregate. For example, see Westfleet Advisor's 2021 Litigation Finance Report, available at https://www.westfleetadvisors.com/publications/2021-litigation-finance-report/.