Explore simulated US public opinion on immigration, revealing deep political divides, dissatisfaction with policy, and surprising areas of common ground on reform.

November's Institute of Medicine (IOM) report on medical errors has sparked debate among US health policy makers as to the appropriate response to the problem. Proposals range from the implementation of nationwide mandatory reporting with public release of performance data to voluntary reporting and quality-assurance efforts that protect the confidentiality of error-related data. Any successful safety program will require a national effort to make significant investments in information technology infrastructure, and to provide an environment and education that enables providers to contribute to an active quality-improvement process.

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

These data were collected using the National Electronic Injury Surveillance System (NEISS), the primary data system of the United States Consumer Product Safety Commission (CPSC). CPSC began operating NEISS in 1972 to monitor product-related injuries treated in United States hospital emergency departments (EDs). In June 1992, the National Center for Injury Prevention and Control (NCIPC), within the Centers for Disease Control and Prevention, established an interagency agreement with CPSC to begin collecting data on nonfatal firearm-related injuries in order to monitor the incidents and the characteristics of persons with nonfatal firearm-related injuries treated in United States hospital EDs over time. This dataset represents all nonfatal firearm-related injuries (i.e., injuries associated with powder-charged guns) and all nonfatal BB and pellet gun-related injuries reported through NEISS from YYYY. The cases consist of initial ED visits for treatment of the injuries. The NEISS-FISS is designed to provide national incidence estimates of nonfatal firearm injuries treated in U.S. hospital EDs. Data on injury-related visits are obtained from a national sample of NEISS hospitals, which were selected as a stratified probability sample of hospitals in the United States and its territories with a minimum of six beds and a 24- hour ED. The sample includes separate strata for very large, large, medium, and small hospitals, defined by the number of annual ED visits per hospital, and children's hospitals. The scope of reporting goes beyond routine reporting of injuries associated with consumer- related products in CPSC's jurisdiction to include all firearm injuries. The data can be used to (1) measure the magnitude and distribution of nonfatal firearm injuries in the United States; (2) monitor unintentional and violence-related nonfatal firearm injuries over time; (3) identify emerging injury problems; (4) identify specific cases for follow-up investigations of particular injury-related problems; and (5) set national priorities. A fundamental principle of this expansion effort is that preliminary surveillance data will be made available in a timely manner to a number of different federal agencies with unique and overlapping public health responsibilities and concerns. The final edited data will be released annually as public use data files for use by other public health professionals and researchers. These public use data files provide NEISS-FISS data on nonfatal injuries collected from January through December each year. NEISS-FISS is providing data on over 100,000 estimated cases annually. Data obtained on each case include age, race/ethnicity, sex, principal diagnosis, primary body part affected, consumer products involved, disposition at ED discharge (i.e., hospitalized, transferred, treated and released, observation, died), locale where the injury occurred, work-relatedness, and a narrative description of the injury circumstances. Also, intent of injury (e.g., unintentional, assault, self-harm, legal intervention) are being coded for each case in a manner consistent with the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding rules and guidelines. Users are cautioned against using estimates with wide confidence intervals to make conclusions about point estimates. Firearm injuries have distinct geographic patterns and estimates can be imprecise or change over time when based on a small number of facilities. NEISS has been managed and operated by the U.S. Consumer Product Safety Commission since 1972 and is used by the Commission for identifying and monitoring consumer product-related injuries and for assessing risk to all U.S. residents. These product- related injury data are used for educating consumers about hazardous products and for identifying injury-related cases used in detailed studies of specific products and associated hazard patterns. These studies set the stage for developing both voluntary and mandatory safety standards. Since the early 1980s, CPSC has assisted other federal agencies by using NEISS to collect injury- related data of special interest to them. In 1992, an interagency agreement was established between NCIPC and CPSC to (1) collect NEISS data on nonfatal firearm- related injuries for the CDC Firearm Injury Surveillance Study; (2) publish NEISS d

Attitudes to current national and international questions. Topics: trust in USSR and USA; conduct of USSR and conduct of USA in international affairs; efforts of USSR and USA for world peace; strongest nation; strongest nuclear power; stationing of American troops in Europe; defense expenditures of FRG; attitude to disarmament; knowledge about the meeting of Nixon and the USSR leadership; outcome of the meeting in view of a reduction of tension in the world situation; assessment of a possible agreement with the USSR; success of USSR at this meeting; success of USA at this meeting; effect of the results on West Germany; positive or negative consequences of the meeting; agreement about arms limitations; trust in USA in view of fundamental interests of Germany; trust in problem solving ability of the USA with social and economic problems. Demography: age; marital status; religious denomination; frequency of church attendance; education; occupation; income; sex; size of place of residence; state. Also encoded was: length of interview; number of contact attempts; presence of other persons; willingness to cooperate; difficulty of interview; date of interview; weighting. Einstellungen zu aktuellen nationalen und internationalen Fragen. Themen: Vertrauen in die UdSSR und die USA; Verhalten der UdSSR und Verhalten der USA in internationalen Angelegenheiten; Bemühungen der UdSSR und der USA um den Weltfrieden; stärkste Nation; stärkste Nuklearmacht; amerikanische Truppenstationierung in Europa; Verteidigungsausgaben der BRD; Einstellung zur Abrüstung; Wissen über das Treffen von Nixon und der UdSSR-Führung; Erfolg des Treffens im Hinblick auf eine Entspannung der Weltlage; Einschätzung eines möglichen Übereinkommens mit der UdSSR; Erfolg der UdSSR bei diesem Treffen; Erfolg der USA bei diesem Treffen; Auswirkung der Ergebnisse auf Westdeutschland; positive oder negative Folgen des Treffens; Einigung über Rüstungsbeschränkungen; Vertrauen in die USA im Hinblick auf grundlegende Interessen Deutschlands; Vertrauen in die Problemlösungsfähigkeit der USA bei sozialen und ökonomischen Problemen. Demographie: Alter; Familienstand; Konfession; Kirchgangshäufigkeit; Bildung; Beruf; Einkommen; Geschlecht; Größe des Wohnorts; Bundesland. Zusätzlich verkodet wurden: Dauer des Interviews; Anzahl der Kontaktversuche; Anwesenheit anderer Personen; Kooperationsbereitschaft; Schwierigkeit des Interviews; Interviewdatum; Gewichtung. 3-stage random selection.

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Politbarometer has been conducted since 1977 on an almost monthly basis by the Research Group for Elections (Forschungsgruppe Wahlen) for the Second German Television (ZDF). Since 1990, this database has also been available for the new German states. The survey focuses on the opinions and attitudes of the voting population in the Federal Republic on current political topics, parties, politicians, and voting behavior. From 1990 to 1995 and from 1999 onward, the Politbarometer surveys were conducted separately in the eastern and western federal states (Politbarometer East and Politbarometer West). The separate monthly surveys of a year are integrated into a cumulative data set that includes all surveys of a year and all variables of the respective year. The Politbarometer short surveys, collected with varying frequency throughout the year, are integrated into the annual cumulation starting from 2003.

Attitudes to current national and international questions. Topics: most important national problem; most important international problem; countries in conflict with the FRG; major problems and differences between FRG and USA; major problems between FRG and other countries; opinion on France, Great Britain, USA, USSR, Red China; reasons for negative and positive attitude to countries USA, USSR and China; trust in USA and USSR in treatment of world problems; reasons for little trust in USA and USSR; effort of USA and USSR for world peace; relationship of USA to USSR; strongest current nuclear power; strongest nuclear power in 5 years; desired strongest nuclear power; reasons for desire for balanced nuclear potential between USA and USSR; knowledge about the SALT negotiations; countries participating in the SALT negotiations; purpose and chances for success of the SALT negotiations; beneficiary of a treaty between USA and USSR; relying on USA in negotiations; security conference; threat to national security of Germany; support for FRG in the case of conflict; knowledge of international organizations; purpose of NATO; membership in NATO; reasons for desired membership; trust in defense ability of NATO; stationing troops in Western Europe; reduction of US troop strength in Europe; necessity of USA for security of Western Europe; defense budget of FRG; navy forces in the Mediterranean; strongest naval power in the Mediterranean; relationship of Israel and Arab nations; support of FRG for Israel; significance of result of the Middle East Conflict for FRG; peace process in the Middle East; European unification process; powers of a European Government; attitude of the USA to European integration; solving the problem of environmental pollution by international organizations; economic aid for other countries. Demography: age; marital status; education; occupation; income; religious denomination; church attendance; sex; city size; state. Also encoded was: length of interview; number of contact attempts; presence of others during interview; willingness to cooperate; difficulty; end time; date of interview; interviewer number. Einstellungen zu aktuellen nationalen und internationalen Fragen. Themen: wichtigstes nationales Problem; wichtigstes internationales Problem; Länder im Konflikt mit der BRD; Hauptprobleme und Differenzen zwischen BRD und USA; Hauptprobleme zwischen BRD und anderen Ländern; Meinung über Frankreich, Großbritannien, USA, UdSSR, Rot-China; Gründe für negative und positive Einstellung zu den Ländern USA, UdSSR und China; Vertrauen in die USA und die UdSSR bei der Behandlung von Weltproblemen; Gründe für geringes Vertrauen in die USA und UdSSR; Bemühen der USA und der UdSSR um den Weltfrieden; Verhältnis der USA zur UdSSR; stärkste derzeitige Atommacht; Stärkste Atommacht in 5 Jahren; gewünschte stärkste Atommacht; Gründe für Wunsch nach ausgeglichenem Nuklearpotential zwischen USA und UdSSR; Kenntnis der SALT-Verhandlungen; Teilnehmerstaaten der SALT-Verhandlungen; Zweck und Erfolgschancen der SALT-Verhandlungen; Nutznießer eines Abkommens zwischen USA und UdSSR; Verlaß auf USA bei Verhandlungen; Sicherheitskonferenz; Bedrohung der nationalen Sicherheit Deutschlands; Beistand für BRD im Konfliktfall; Kenntnis internationaler Organisationen; Zweck der NATO; Mitgliedschaft in der NATO; Gründe für gewünschte Mitgliedschaft; Vertrauen in Verteidigungsfähigkeit der NATO; Truppenstationierungen in Westeuropa; Reduktion der US-Truppenstärke in Europa; Notwendigkeit der USA für die Sicherheit Westeuropas; Verteidigungsbudget der BRD; Marinestreitkräfte im Mittelmeer; stärkste Seemacht im Mittelmeer; Verhältnis Israel und arabische Staaten; Unterstützung der BRD für Israel; Bedeutung des Ausganges des Nahostkonfliktes für die BRD; Friedensprozess im Nahen Osten; europäischer Einigungsprozess; Kompetenzen einer europäischen Regierung; Haltung der USA zur europäischen Integration; Lösung des Problems der Umweltverschmutzung durch internationale Organisationen; Wirtschaftshilfe für andere Staaten. Demographie: Alter; Familienstand; Bildung; Beruf; Einkommen; Konfession; Kirchgang; Geschlecht; Ortsgröße; Bundesland. Zusätzlich verkodet wurden: Interviewdauer; Anzahl der Kontaktversuche; Anwesenheit anderer während des Interviews; Kooperationsbereitschaft; Schwierigkeit; Endzeit; Interviewdatum; Interviewer-Nummer.

U.S. Household Mental Health & Covid-19

Assessing the Impact of the Pandemic

By US Open Data Portal, data.gov [source]

About this dataset

This dataset offers a closer look into the mental health care received by U.S. households in the last four weeks during the Covid-19 pandemic. The sheer scale of this crisis is inspiring people of all ages, backgrounds, and geographies to come together to tackle the problem. The Household Pulse Survey from the U.S. Census Bureau was published with federal agency collaboration in order to draw up accurate and timely estimates about how Covid-19 is impacting employment status, consumer spending, food security, housing stability, education interruption, and physical and mental wellness amongst American households. In order to deliver meaningful results from this survey data about wellbeing at various levels of society during this trying period – which includes demographic characteristics such as age gender race/ethnicity training attainment – each consulted household was randomly selected according to certain weighted criteria to maintain accuracy throughout the findings This dataset will help you explore what's it like on the ground right now for everyone affected by Covid-19 - Will it inform your decisions or point you towards new opportunities?

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How to use the dataset

This dataset contains information about the mental health care that U.S. households have received in the last 4 weeks, during the Covid-19 pandemic. This data is valuable when wanting to track and measure mental health needs across the country and draw comparisons between regions based on support available.

To use this dataset, it is important to understand each of its columns or variables in order to draw meaningful insights from the data. The ‘Indicator’ column indicates which type of indicator (percentage or absolute number) is being measured by this survey, while ‘Group’ and 'Subgroup' provide more specific details about who was surveyed for each indicator included in this dataset.

The Columns ‘Phase’ and 'Time Period' provide information regarding when each of these indicators was measured - whether during a certain phase or over a particular timespan - while columns such as 'Value', 'LowCI' & 'HighCI' show us how many individuals fell into what quartile range for each measurement taken (e.g., how many people reported they rarely felt lonely). Similarly, the column Suppression Flag helps us identify cases where value has been suppressed if it falls below a certain benchmark; this allows us to calculate accurate estimates more quickly without needing to sort through all suppressed values manually each time we use this dataset for analysis purposes. Finally, columns such as ‘Time Period Start Date’ & ‘Time Period End Date’ indicate which exact dates were used for measurements taken over different periods throughout those dates specified – useful when conducting time-series related analyses over longer periods of time within our research scope)

Overall, when using this dataset it's important to keep in mind exactly what indicator type you're looking at - percentage points or absolute numbers - as well its associated group/subgroup characteristics so that you can accurately interpret trends based on key findings had by interpreting any correlations drawn from these results!

Research Ideas

• Analyzing the effects of the Covid-19 pandemic on mental health care among different subgroups such as racial and ethnic minorities, gender and age categories.
• Identifying geographical disparities in mental health services by comparing state level data for the same time period.
• Comparing changes in mental health care indicators over time to understand how the pandemic has impacted people's access to care within a quarter or over longer periods

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. ...

Since 2007, the American Psychological Association (APA) has commissioned an annual nationwide survey as part of its Mind/Body Health campaign to examine the state of stress across the country and understand its impact. The Stress in America survey measures attitudes and perceptions of stress among the general public and identifies leading sources of stress, common behaviors used to manage stress and the impact of stress on our lives. The results of the survey draw attention to the serious physical and emotional implications of stress and the inextricable link between the mind and body. From 2007 to 2023, the research has documented this connection among the general public as well as various sub-segments of the public. Each year, the Stress in America surveys aims to uncover different aspects of the stress/health connection via focusing on a particular topic and/or subgroup of the population. Below is a list of the focus of each of the Stress in America surveys. 2007-2018 Cumulative Dataset 2007 General Population 2008 Gender and Stress 2009 Parent Perceptions of Children's Stress 2010 Health Impact of Stress on Children and Families 2011 Our Health Risk 2012 Missing the Health Care Connection 2013 Are Teens Adopting Adults' Stress Habits 2014 Paying With Our Health 2015 The Impact of Discrimination 2016 Coping with Change, Part 1 2016 Coping with Change, Part 2: Technology and Social Media 2017 The State of Our Nation 2018 Stress and Generation Z 2019-2023 Cumulative Dataset 2019 Stress and Current Events 2020 COVID Tracker Wave 1 2020 COVID Tracker Wave 2 2020 COVID Tracker Wave 3 2020 A National Mental Health Crisis 2021 Pandemic Anniversary Survey 2021 Stress and Decision-Making During the Pandemic 2022 Pandemic Anniversary Survey 2022 Concerned for the Future, Beset by Inflation 2023 A Nation Recovering From Collective Trauma

Judgement on American and Soviet foreign policy. Attitude to selected countries and NATO. Topics: Most important problems of the country; attitude to France, Germany, Great Britain, the USSR and the USA as well as perceived changes in the last few years; assumed reputation of one´s own country abroad; trust in the USA and the USSR to solve world problems; judgement on the agreement of words and deeds in foreign policy as well as the seriousness of the peace efforts of the two great powers; the USSR or the USA as current and as future world power in the military and scientific area as well as in space research; benefit of space travel; attitude to a strengthening of space flight efforts; knowledge about the landing on the moon; necessity of NATO; trust in NATO; judgement on the contribution of one´s own country to NATO; preference for acceptance of political functions by NATO; attitude to a reduction in US soldiers stationed in Western Europe; expected reductions of American obligations in Europe; probability of European unification; desired activities of government in the direction of European unification; preference for a European nuclear force; judgement on the disarmament negotiations between the USA and the USSR; expected benefit of such negotiations for one´s own country and expected consideration of European interests; increased danger of war from the new missile defense systems; prospects of the so-called Budapest recommendation; attitude to the American Vietnam policy; negotiating party that can be held responsible for the failure of the Paris talks; sympathy for Arabs or Israelis in the Middle East Conflict; preference for withdrawal of the Israelis from the occupied territories; attitude to an increase in the total population in one´s country and in the whole world; attitude to birth control in one´s country; attitude to economic aid for lesser developed countries; judgement on the influence and advantageousness of American investments as well as American way of life for one´s own country; autostereotype and description of the American character by means of the same list of characteristics (stereotype); general attitude to American culture; perceived increase in American prosperity; trust in the ability of American politics to solve their own economic and social problems; judgement on the treatment of blacks in the USA and determined changes; proportion of poor in the USA; comparison of proportion of violence or crime in the USA with one´s own country; general judgement on the youth in one´s country in comparison to the USA; assessment of the persuasiveness of the American or Soviet view; religiousness; city size. Also encoded was: length of interview; number of contact attempts; presence of other persons during the interview; willingness of respondent to cooperate; understanding difficulties of respondent. Beurteilung der amerikanischen und sowjetischen Außenpolitik. Einstellung zu ausgewählten Ländern und zur Nato. Themen: Wichtigste Probleme des Landes; Einstellung zu Frankreich, Deutschland, Großbritannien, UdSSR und USA sowie wahrgenommene Veränderungen in den letzten Jahren; vermutetes Ansehen des eigenen Landes im Ausland; Vertrauen in die USA und die UdSSR zur Lösung der Weltprobleme; Beurteilung der Übereinstimmung von Worten und Taten in der Außenpolitik sowie der Ernsthaftigkeit der Friedensbemühungen der beiden Großmächte; UdSSR oder USA als derzeitige und als künftige Weltmacht im militärischen, wissenschaftlichen Bereich sowie in der Weltraumforschung; Nutzen der Weltraumfahrt; Einstellung zu einer Verstärkung von Raumfahrtanstrengungen; Kenntnisse über die Mondlandung; Notwendigkeit der Nato; Vertrauen in die Nato; Beurteilung des Beitrags des eigenen Landes zur Nato; Präferenz für die Übernahme politischer Funktionen durch die Nato; Einstellung zu einer Verringerung der stationierten US-Soldaten in Westeuropa; erwartete Einschränkungen der amerikanischen Verpflichtungen in Europa; Wahrscheinlichkeit einer europäischen Vereinigung; gewünschte Aktivitäten der Regierung in Richtung europäische Einigung; Präferenz für eine europäische Atomstreitmacht; Beurteilung der Abrüstungsverhandlungen zwischen den USA und der UdSSR; erwarteter Nutzen solcher Verhandlungen für das eigene Land und erwartete Berücksichtigung der europäischen Interessen; erhöhte Kriegsgefahr durch die neuen Raketenabwehrsysteme; Aussichten des sogenannten Budapest-Vorschlags; Einstellung zur amerikanischen Vietnam-Politik; Verhandlungspartei, der die Mißerfolge der Pariser Gespräche zugeschrieben werden; Sympathie für die Araber oder Israelis im Nahost-Konflikt; Präferenz für einen Abzug der Israelis aus den besetzten Gebieten; Einstellung zu einer Erhöhung der Bevölkerungszahl im eigenen Land und auf der ganzen Welt; Einstellung zu einer Geburtenkontrolle im eigenen Land; Einstellung zur Wirtschaftshilfe an weniger entwickelte Länder; Beurteilung des Einflusses und der Vorteilhaftigkeit amerikanischer Investitionen sowie amerikanischer Lebensart für das eigene Land; Autostereotyp und Beschreibung des amerikanischen Charakters anhand der gleichen Eigenschaftsliste (Stereotyp); allgemeine Einstellung zur amerikanischen Kultur; wahrgenommene Steigerung des amerikanischen Wohlstands; Vertrauen in die Kompetenz amerikanischer Politik zur Lösung ihrer eigenen wirtschaftlichen und sozialen Probleme; Beurteilung der Behandlung von Schwarzen in den USA und festgestellte Veränderungen; Armenanteil in den USA; Vergleich des Gewaltanteils bzw. der Kriminalität in den USA mit dem eigenen Land; allgemeine Beurteilung der Jugend im eigenen Land im Vergleich zu den USA; Einschätzung der Überzeugungskraft amerikanischer bzw. sowjetischer Anschauung; Religiosität; Ortsgröße. Zusätzlich verkodet wurde: Interviewdauer; Anzahl der Kontaktversuche; Anwesenheit anderer Personen beim Interview; Kooperationsbereitschaft des Befragten; Verständnisschwierigkeiten des Befragten.

Conservation planning in the Great Plains often depends on understanding the degree of fragmentation of the various types of grasslands and savannas that historically occurred in this region. To define ecological subregions of the Great Plains, we used a revised version of Kuchler’s (1964) map of the potential natural vegetation of the United States. The map was digitized from the 1979 physiographic regions map produced by the Bureau of Land Management, which added 10 physiognomic types. All analyses are based on data sources specific to the United States; hence, we only analyze the portion of the Great Plains occurring in the United States.We sought to quantify the current amount of rangeland in the US Great Plains converted due to 1) woody plant encroachment; 2) urban, exurban, and other forms of development (e.g., energy infrastructure); and 3) cultivation of cropland. At the time of this analysis, the most contemporary measure of land cover across the United States was the 2011 NLCD (Homer et al. 2015). One limitation of the NLCD is that some grasslands with high rates of productivity, such as herbaceous wetlands or grasslands along riparian zones, are misclassified as cropland. A second limitation is the inability to capture cropland conversion occurring after 2011 (Lark et al. 2015). Beginning in 2009 (and retroactively for 2008), the US Department of Agriculture - NASS has annually produced a Cropland Data Layer (CDL) for the United States from satellite imagery, which maps individual crop types at a 30-m spatial resolution. We used the annual CDLs from 2011 to 2017 to map the distribution of cropland in the Great Plains. We merged this map with the 2011 NLCD to evaluate the degree of fragmentation of grasslands and savannas in the Great Plains as a result of conversion to urban land, cropland, or woodland. We produced two maps of fragmentation (best case and worst case scenarios) that quantify this fragmentation at a 30 x 30 m pixel resolution across the US Great Plains, and make them available for download here. Resources in this dataset: Resource title: Data Dictionary for Figure 2 derived land cover of the US portion of the North American Great Plains File name: Figure2_Key for landcover classes.csv Resource title: Figure 1. Potential natural vegetation of US portion of the North American Great Plains, adapted from Kuchler (1964). File name: Figure1_Kuchler_GPRangelands.zip Resource description: Extracted grassland, shrubland, savanna, and forest communities in the US Great Plains from the revised Kuchler natural vegetation map Resource title: Figure 2. Derived land cover of the US portion of the North American Great Plains. File name: Figure2_Key for landcover classes.zip Resource description: The fNLCD-CDL product estimates that 43.7% of the Great Plains still consists of grasslands and shrublands, with the remainder consisting of 40.6% cropland, 4.4% forests, 3.0% UGC, 3.0% developed open space, 2.9% improved pasture or hay fields, 1.2% developed land, 1.0% water, and 0.2% barren land, with important regional and subregional variation in the extent of rangeland loss to cropland, forests, and developed land. Resource title: Figure 3. Variation in the degree of fragmentation of Great Plains measured in terms of distance to cropland, forest, or developed lands. File name: Figure3_bestcase_disttofrag.zip Resource description: This map depicts a “best case” scenario in which 1) croplands are mapped based only on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017), 2) all grass-dominated cover types including hay fields and improved pasture are considered rangelands, and 3) developed open space (as defined by the National Land Cover Database) are assumed to not be a fragmenting land cover type. Resource title: Figure 4. Variation in the degree of fragmentation of Great Plains measured in terms of distances to cropland, forest, or developed lands. File name: Figure4_worstcase_disttofrag.zip Resource description: This map depicts a ‘worst case’ scenario in which 1) croplands are mapped based on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017) and the 2011 National Land Cover Database (NLCD), 2) hay fields and improved pasture are not included as rangelands, and 3) developed open space (as defined by NLCD) is included as a fragmenting land cover type.

National Survey on Drug Use and Health (NSDUH) for Years 2015-2019

The National Survey on Drug Use and Health (NSDUH) is the "leading source of statistical information on the use of illicit drugs, alcohol, and tobacco and mental health issues in the United States" (SAMHSA). The abundance of Yes/No questions regarding the usage of illicit drugs make this dataset valuable for binary classification problems. During 2015, the survey received a partial redesign, creating "broken trends" from pre-2015 and post-2015. This is dataset contains every year of the NSDUH survey after the major restructuring in 2015.

Column Descriptions

All column names are identical to the Question Index found in the NSDUH documentation. The values in each column are codes that correspond to a particular answer in the survey. You can reference each question's meaning in the documentation, found here. Be sure to account for these codes before performing any analyses.

Additionally, some questions are not asked across ALL years, and will instead have an NA value.

Sources

All of the data used to create this dataset was obtained from the Substance Abuse & Mental Health Data Archive. You can access the data for separate years here.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

The U.S. Fire Administration (USFA) uses the National Fire Incident Reporting System (NFIRS) and data from a variety of sources to provide information and analyses on the status and scope of the fire problem in the United States.

This dataset contains about 27,000 fire deparment stations with metadata listed in the National Fire Department Registry.

Original Data

Important information about Fire Department Identification Numbers (FDIDs) Please note that each state assigns FDIDs — not the U.S. Fire Administration — so it is possible for the same FDID number to exist in two or more states.

Acknowlegement

Foto von Acton Crawford auf Unsplash

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

The annual National Fire Incident Reporting System (NFIRS) Public Data Release files are provided by the U.S. Fire Administration’s (USFA) National Fire Data Center (NFDC). The NFIRS is a reporting standard that fire departments use to uniformly report on the full range of their activities, from fire to emergency medical services (EMS) to equipment involved in the response.NFIRS is the nation’s largest, national, annual database of fire incident information. NFIRS is a voluntary tool with two objectives: to help State and local governments develop fire reporting and analysis capability for their own use and to obtain data that can be used to more accurately assess and subsequently combat the fire problem at a national level.These datasets are for researchers and fire data analysts.Experience with fire data analysis and NFIRS data is recommended to properly use the NFIRS Public Data Release (PDR) datasets. Using raw NFIRS data as a count of fires and associated deaths, injuries and dollar loss is NOT a proper use of these datasets.FEMA's terms and conditions and citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page: https://www.fema.gov/about/openfema/terms-conditions.For answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.If you have media inquiries about this dataset, please email the FEMA Press Office at FEMA-Press-Office@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.Please note that upon clicking any of the available downloadable NFIRS Public Data Release data sets hyperlinks, an automatic download of that year's data will commence. Download times will vary depending on the size of the file and your connection. Some of the compressed zip file sizes vary from 132 MB and can range up to 822MB. If you prefer to order the NFIRS public release data on CD or DVD, please visit the USFA’s Download fire data and data analysis tools web page. Below are the data years that are currently available:CD 1980-1998 - Fire Incidents (NFIRS version 4.1)CD 1999-2003 - All IncidentsCD 2004-2019 - Fire and Hazardous Materials IncidentsDVD 2014-2019 - All Incidents

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome: