The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

State, territorial, and county executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance.

Data were collected to determine when individuals in states, territories, and counties were subject to executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require or recommend people stay in their homes.

These data are derived from the publicly available state, territorial, and county executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require or recommend individuals stay at home found by the CDC, COVID-19 Community Intervention and At-Risk Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 15 through May 5, 2020. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. These data do not include mandatory business closures, curfews, or limitations on public or private gatherings. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Context

The Caselaw Access Project makes 40 million pages of U.S. caselaw freely available online from the collections of Harvard Law School Library.

Learn more: https://case.law/api/

Access Limits: https://case.law/api/#limits

Content

This dataset includes all published U.S. caselaw from the state of Illinois (I.L.) in Text and XML format.

Acknowledgements

The Caselaw Access Project is by the Library Innovation Lab at Harvard Law School Library.

Inspiration

People are using CAP data to create research, applications, and more. We're sharing examples in our gallery.

Have something to share? We're excited to hear about it.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when members of the public in states and territories were subject to state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public. “Members of the public” are defined as individuals operating in a personal capacity. “In public” is defined to mean either (1) anywhere outside the home or (2) both in retail businesses and in restaurants/food establishments. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require individuals to wear masks in public found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program, and Max Gakh, Assistant Professor, School of Public Health, University of Nevada, Las Vegas from April 10, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the dates provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not include data on counties that have opted out of their state mask mandate pursuant to state law. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2016 dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance.

Data were collected to determine when members of the public in states and territories were subject to state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public. “Members of the public” are defined as individuals operating in a personal capacity. “In public” is defined to mean either (1) anywhere outside the home or (2) both in retail businesses and in restaurants/food establishments. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level.

These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require individuals to wear masks in public found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program, and Max Gakh, Assistant Professor, School of Public Health, University of Nevada, Las Vegas from April 10, 2020 through July 20, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the dates provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not include data on counties that have opted out of their state mask mandate pursuant to state law. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

This study was undertaken to provide current information on work and family issues from the police officer's perspective, and to explore the existence and prevalence of work and family training and intervention programs offered nationally by law enforcement agencies. Three different surveys were employed to collect data for this study. First, a pilot study was conducted in which a questionnaire, designed to elicit information on work and family issues in law enforcement, was distributed to 1,800 law enforcement officers representing 21 municipal, suburban, and rural police agencies in western New York State (Part 1). Demographic information in this Work and Family Issues in Law Enforcement (WFILE) questionnaire included the age, gender, ethnicity, marital status, highest level of education, and number of years in law enforcement of each respondent. Respondents also provided information on which agency they were from, their job title, and the number of children and step-children they had. The remaining items on the WFILE questionnaire fell into one of the following categories: (1) work and family orientation, (2) work and family issues, (3) job's influence on spouse/significant other, (4) support by spouse/significant other, (5) influence of parental role on the job, (6) job's influence on relationship with children, (7) job's influence on relationships and friendships, (8) knowledge of programs to assist with work and family issues, (9) willingness to use programs to assist with work and family issues, (10) department's ability to assist officers with work and family issues, and (11) relationship with officer's partner. Second, a Police Officer Questionnaire (POQ) was developed based on the results obtained from the pilot study. The POQ was sent to over 4,400 officers in police agencies in three geographical locations: the Northeast (New York City, New York, and surrounding areas), the Midwest (Minneapolis, Minnesota, and surrounding areas), and the Southwest (Dallas, Texas, and surrounding areas) (Part 2). Respondents were asked questions measuring their health, exercise, alcohol and tobacco use, overall job stress, and the number of health-related stress symptoms experienced within the last month. Other questions from the POQ addressed issues of concern to the Police Research and Education Project -- a sister organization of the National Association of Police Organizations -- and its membership. These questions dealt with collective bargaining, the Law Enforcement Officer's Bill of Rights, residency requirements, and high-speed pursuit policies and procedures. Demographic variables included gender, age, ethnicity, marital status, highest level of education, and number of years employed in law enforcement. Third, to identify the extent and nature of services that law enforcement agencies provided for officers and their family members, an Agency Questionnaire (AQ) was developed (Part 3). The AQ survey was developed based on information collected from previous research efforts, the Violent Crime Control and Law Enforcement Act of 1994 (Part W-Family Support, subsection 2303 [b]), and from information gained from the POQ. Data collected from the AQ consisted of whether the agency had a mission statement, provided any type of mental health service, and had a formalized psychological services unit. Respondents also provided information on the number of sworn officers in their agency and the gender of the officers. The remaining questions requested information on service providers, types of services provided, agencies' obstacles to use of services, agencies' enhancement of services, and the organizational impact of the services.

Abstract (en): The numbers contained in this study are released pursuant to the order of the United States Court of Appeals for the Ninth Circuit in Carter v. Department of Commerce, 307 F.3d 1084. These numbers are not official Census 2000 counts. These numbers are estimates of the population based on a statistical adjustment method, utilizing sampling and modeling, applied to the official Census 2000 figures. The estimates utilized the results of the Accuracy and Coverage Evaluation (A.C.E.), a sample survey intended to measure net over- and undercounts in the census results. The Census Bureau has determined that the A.C.E. estimates dramatically overstate the level of undercoverage in Census 2000, and that the adjusted Census 2000 data are, therefore, not more accurate than the unadjusted data. On March 6, 2001, the Secretary of Commerce decided that unadjusted data from Census 2000 should be used to tabulate population counts reported to states and localities pursuant to 13 U.S.C. 141(c) (see 66 FR 14520, March 13, 2001). The Secretary's decision endorsed the unanimous recommendation of the Executive Steering Committee for A.C.E. Policy (ESCAP), a group of 12 senior career professionals within the Census Bureau. The ESCAP, in its recommendation against the use of the statistically adjusted estimates, had noted serious reservations regarding their accuracy. In order to inform the Census Bureau's planned October 2001 decision regarding the potential use of the adjusted estimates for non-redistricting purposes, the agency conducted extensive analyses throughout the summer of 2001. These extensive analyses confirmed the serious concerns the agency had noted earlier regarding the accuracy of the A.C.E. estimates. Specifically, the adjusted estimates were determined to be so severely flawed that all potential uses of these data would be inappropriate. Accordingly, the Department of Commerce deems that these estimates should not be used for any purpose that legally requires use of data from the decennial census and assumes no responsibility for the accuracy of the data for any purpose whatsoever. The Department, including the U.S. Census Bureau, will provide no assistance in the interpretation or use of these numbers. The collection contains four tables: (1) a count of all persons by race (Table PL1), (2) a count of Hispanic or Latino and a count of not Hispanic or Latino by race of all persons (Table PL2), (3) a count of the population 18 years and older by race (Table PL3), and (4) a count of Hispanic or Latino and a count of not Hispanic or Latino by race for the population 18 years and older (Table PL4). 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.. All persons and housing units in the United States in 2000. 2013-05-24 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 90 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 86 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 84 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 83 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2004-08-26 All the data definition statements (Parts 83, 84, and 90) were replaced because of errors. The codebook was replaced with an updated one from the Bureau of the Census. The data are provided in three segments (files) per state: the Geographic Header, Tables PL1 and PL2, and Tables PL3 and PL4. The Geographic Header segments are fixed-format ASCII text files, while the Table segments are comma-delimited ASCII files. The Geographic Header has 80 variables and the Table segments have 149 variables each, for a total of 378 variables when the segments a...

Context

This data set contains measles vaccination rate data for 46,412 schools in 32 states across the US.

Content

Vaccination rates are for the 2017-201818 school year for the following states: - Colorado - Connecticut - Minnesota - Montana - New Jersey - New York - North Dakota - Pennsylvania - South Dakota - Utah - Washington

Rates for other states are for the time period 2018-2019.

The data was compiled by The Wall Street Journal.

Acknowledgements

The data was originally compiled by The Wall Street Journal, and then downloaded and wrangled by the TidyTuesday community. The R code used for wrangling can be accessed here.

Inspiration

Please remember that you are welcome to explore beyond the provided data set, but the data is provided as a "toy" data set to practice techniques on. The data may require additional cleaning and wrangling!

The study was conducted to determine law enforcement's role in protecting American agriculture from terrorism. In particular, the study looked at what effect a widespread introduction of Foot and Mouth disease to America's livestock supply would have on the nation's economy, and law enforcement's ability to contain such an outbreak. The study had two primary components. One component of the study was designed to take an initial look at the preparedness of law enforcement in Kansas to respond to such acts. This was done through a survey completed by 85 sheriffs in Kansas (Part 1). The other component of the study was an assessment of the attitudes of persons who work in the livestock industry with regard to their attitudes about vulnerabilities, prevention strategies, and working relationships with public officials and other livestock industry affiliates. This was done through a survey completed by 133 livestock industry members in Kansas (Parts 2-3, 6-9, 12-13), Oklahoma (Parts 4, 10, 14), and Texas (Parts 5, 11, 15).

Hate crime incidents reached the highest levels ever in the United States. The FBI's Crime Data Explorer (CDE) aims to provide transparency, create easier access, and expand awareness of criminal, and noncriminal, law enforcement data sharing; improve accountability for law enforcement; and provide a foundation to help shape public policy with the result of a safer nation. Use the CDE to discover available data through visualizations, download data in .csv format, and other large data files. https://cde.ucr.cjis.gov/LATEST/webapp/#/pages/home

Despite public attention to the problem of human trafficking, it has proven difficult to measure the problem. Improving the quality of information about human trafficking is critical to developing sound anti-trafficking policy. In support of this effort, in 2013 the Federal Bureau of Investigation incorporated human trafficking offenses in the Uniform Crime Reporting (UCR) program. Despite this achievement, there are many reasons to expect the UCR program to underreport human trafficking. Law enforcement agencies struggle to identify human trafficking and distinguishing it from other crimes. Additionally, human trafficking investigations may not be accurately classified in official data sources. Finally, human trafficking presents unique challenges to summary and incident-based crime reporting methods. For these reasons, it is important to understand how agencies identify and report human trafficking cases within the UCR program and what part of the population of human trafficking victims in a community are represented by UCR data. This study provides critical information to improve law enforcement identification and reporting of human trafficking. Coding criminal incidents investigated as human trafficking offenses in three US cities, supplemented by interviews with law and social service stakeholders in these locations, this study answers the following research questions: How are human trafficking cases identified and reported by the police? What sources of information about human trafficking exist outside of law enforcement data? What is the estimated disparity between actual instances of human trafficking and the number of human trafficking offenses reported to the UCR?

This program evaluation study is intended to capture fully the universe of drug enforcement tactics available in the United States and to assess trends in drug enforcement. The primary objective of the study was to learn more about the application of anti-drug tactics by police: What tactics are used by police to address drug use problems? How widely are these tactics used? What new and innovative tactics are being developed and applied by police? What anti-drug tactics are most effective or show some promise of effectiveness? To answer these questions, state and local law enforcement agencies serving populations of 50,000 or more were mailed surveys. The survey was administered to both patrol and investigation units in the law enforcement agencies. This dual pattern of administration was intended to capture the extent to which the techniques of one unit had been applied by another. The questionnaire consisted primarily of dichotomous survey questions on anti-drug tactics that could be answered "yes" or "no". In each of the 14 categories of tactics, respondents were encouraged to add other previously unidentified or unspecified tactics in use in their agencies. These open-ended questions were designed to insure that a final list of anti-drug tactics would be truly comprehensive and capture the universe of drug tactics in use. In addition to questions regarding structural dimensions of anti-drug tactics, the survey also collected standardized information about the law enforcement agency, including agency size, demographic characteristics and size of the agency's service population, and a description of the relative size and nature of the jurisdiction's drug problems.

Census Blocks from 2020. Redistricting Data (P.L. 94-171).Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: Census BlocksCurrent Vintage: 2020P.L. 94-171 Table(s): P1. Race; P2. Hispanic or Latino, and Not Hispanic or Latino by Race; P3. Race for the Population 18 Years and Over; P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over; P5. Group Quarters Population by Major Group Quarters Type; H1. Housing Occupancy StatusData downloaded from: https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.htmlNational Figures: data.census.govPublic Law 94-171, enacted in 1975, directs the U.S. Census Bureau to make special preparations to provide redistricting data needed by the 50 states.1 It specifies that within 1 year following Census Day, the Census Bureau must send the governor and legislative leadership in each state the data they need to redraw districts for the U.S. Congress and state legislatures. To meet this legal requirement, the Census Bureau set up a program that affords state officials an opportunity before each decennial census to define the small areas for which they wish to receive census population totals for redistricting purposes. Officials may receive data for voting districts (e.g., election precincts, wards) and state house and senate districts, in addition to standard census geographic areas such as counties, cities, census tracts, and blocks. State participation in defining areas is voluntary and nonpartisan. For further information on Public Law 94-171 and the 2020 Census Redistricting Data Program, see:www.census.gov/programs-surveys/decennial-census/about/rdo/program -management.htmlData processed using R statistical package and ArcGIS Desktop.