data file in SAS format

The Fiscal Intermediary maintains the Provider Specific File (PSF). The file contains information about the facts specific to the provider that affects computations for the Prospective Payment System. The Provider Specific files in SAS format are located in the Download section below for the following provider-types, Inpatient, Skilled Nursing Facility, Home Health Agency, Hospice, Inpatient Rehab, Long Term Care, Inpatient Psychiatric Facility

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

The raw data for each of the analyses are presented. Baseline severity difference (probands only) (Figure A in S1 Dataset), Repeated measures analysis of change in lesion severity (Figure B in S1 Dataset). Logistic regression of survivorship (Figure C in S1 Dataset). Time to cure (Figure D in S1 Dataset). Each data set is given as a SAS code for the data itself, and the equivalent analysis to that performed in JMP (and reported in the text). Data are presented in SAS format as this is a simple text format. The data and code were generated as direct exports from JMP, and additional SAS code added as needed (for instance, JMP does not export code for post-hoc tests). Note, however, that SAS rounds to less precision than JMP, and can give slightly different results, especially for REML methods. (DOCX)

This SAS code extracts data from EU-SILC User Database (UDB) longitudinal files and edits it such that a file is produced that can be further used for differential mortality analyses. Information from the original D, R, H and P files is merged per person and possibly pooled over several longitudinal data releases. Vital status information is extracted from target variables DB110 and RB110, and time at risk between the first interview and either death or censoring is estimated based on quarterly date information. Apart from path specifications, the SAS code consists of several SAS macros. Two of them require parameter specification from the user. The other ones are just executed. The code was written in Base SAS, Version 9.4. By default, the output file contains several variables which are necessary for differential mortality analyses, such as sex, age, country, year of first interview, and vital status information. In addition, the user may specify the analytical variables by which mortality risk should be compared later, for example educational level or occupational class. These analytical variables may be measured either at the first interview (the baseline) or at the last interview of a respondent. The output file is available in SAS format and by default also in csv format.

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

This data zip file consists of three different data sets using SAS format 1) Data of 105+ thousand adopters - 2 million and 795 thousand records 2) Data of 7+ thousand video game profiles 3) Data of 93+ thousand posts about video games Please unzip the file before using data. The data sets require at least 4 GB.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

The Emerging Pathogens Initiative (EPI) database contains emerging pathogens information from the local Veterans Affairs Medical Centers (VAMCs). The EPI software package allows the VA to track emerging pathogens on the national level without additional data entry at the local level. The results from aggregation of data can be shared with the appropriate public health authorities including non-VA and the private health care sector allowing national planning, formulation of intervention strategies, and resource allocations. EPI is designed to automatically collect data on emerging diseases for Veterans Affairs Central Office (VACO) to analyze. The data is sent to the Austin Information Technology Center (AITC) from all Veterans Health Information Systems and Technology Architecture (VistA) systems for initial processing and combination with related workload data. VACO data retrieval and analysis is then carried out. The AITC creates two file structures both in Statistical Analysis Software (SAS) file format, which are used as a source of data for the Veterans Affairs Headquarters (VAHQ) Infectious Diseases Program Office. These files are manipulated and used for analysis and reporting by the National Infectious Diseases Service. Emerging Pathogens (as characterized by VACO) act as triggers for data acquisition activities in the automated program. The system retrieves relevant, predetermined, patient-specific information in the form of a Health Level Seven (HL7) message that is transmitted to the central data repository at the AITC. Once at that location, the data is converted to a SAS dataset for analysis by the VACO National Infectious Diseases Service. Before data transmission an Emerging Pathogens Verification Report is produced for the local sites to review, verify, and make corrections as needed. After data transmission to the AITC it is added to the EPI database.

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

This online supplement contains data files and computer code, enabling the public to reproduce the results of the analysis described in the report titled “Thrifty Food Plan Cost Estimates for Alaska and Hawaii” published by USDA FNS in July 2023. The report is available at: https://www.fns.usda.gov/cnpp/tfp-akhi. The online supplement contains a user guide, which describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis; a data file in CSV format, which contains the most detailed information on food price differentials between the mainland U.S. and Alaska and Hawaii derived from Circana (formerly Information Resources Inc) retail scanner data as could be released without disclosing proprietary information; SAS and R code, which use the provided data file to reproduce the results of the report; and an excel spreadsheet containing the reproduced results from the SAS or R code. For technical inquiries, contact: FNS.FoodPlans@usda.gov. Resources in this dataset:

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement User Guide File name: TFPCostEstimatesForAlaskaAndHawaii-UserGuide.pdf Resource description: The online supplement user guide describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Data File File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementDataFile.csv Resource description: The online supplement data file contains food price differentials between the mainland United States and Anchorage and Honolulu derived from Circana (formerly Information Resources Inc) retail scanner data. The data was aggregated to prevent disclosing proprietary information.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement R Code File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementRCode.R Resource description: The online supplement R code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the R programming language.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement SAS Code (zipped) File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementSASCode.zip Resource description: The online supplement SAS code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the SAS programming language. This SAS file is provided in zip format for compatibility with Ag Data Commons; users will need to unzip the file prior to its use.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Reproduced Results File name: TFPCostEstimatesforAlaskaandHawaii-ReproducedResults.xlsx Resource description: The online supplement reproduced results are output from either the online supplement R or SAS code and contain the results of the analysis described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report.

InfoGroup’s Historical Business Backfile consists of geo-coded records of millions of US businesses and other organizations that contain basic information on each entity, such as: contact information, industry description, annual revenues, number of employees, year established, and other data. Each annual file consists of a “snapshot” of InfoGroup’s data as of the last day of each year, creating a time series of data 1997-2019. Access is restricted to current Harvard University community members. Use of Infogroup US Historical Business Data is subject to the terms and conditions of a license agreement (effective March 16, 2016) between Harvard and Infogroup Inc. and subject to applicable laws. Most data files are available in either .csv or .sas format. All data files are compressed into an archive in .gz, or GZIP, format. Extraction software such as 7-Zip is required to unzip these archives.

These data were gathered to provide information on Kahn and Antonucci's life-span developmental model, "convoys of social support," which explores interpersonal relationships over time. Older adults (aged 50+) were interviewed on their health status, labor force status, and other demographic characteristics, and on the composition and degree of closeness of members of their current support network (e.g., spouses, children, friends). Three concentric circles of closeness were defined, varying in terms of transcendence of the relationship beyond role requirements, stability over the life span, and exchange of many different types of support (confiding, reassurance, respect, care when ill, discussion when upset, and talk about health). The principal respondents named a total of 6,341 network members, ranging in age from 18 to 96 years. Detailed structural and functional characteristics were collected from the principal respondents on the first ten named members of each support network. Similar interviews were then conducted with one to three network members of those 259 principal respondents who were 70+ years old. Two data files are provided: Part 1 contains merged data from the interviews of both the principal respondents aged 70+ and their network members, and Part 2 contains data from the principal respondents aged 50+. Datasets: DS0: Study-Level Files DS1: Principals, Aged 70+/Network Data DS2: Principals, Aged 50+ Data DS3: SAS Proc Format Statements for Principals, Aged 70+/Network Data DS4: SAS Input Statements for Principals, Aged 70+/Network Data DS5: SAS Format Statements for Principals, Aged 70+/Network Data DS6: SAS Label Statements for Principals, Aged 70+/Network Data DS7: SAS Missing Value Statements for Principals, Aged 70+/Network Data DS8: SPSS Data List Statements for Principals, Aged 70+/Network Data DS9: SPSS Variable Label Statements for Principals, Aged 70+/Network Data DS10: SPSS Value Label Statements for Principals, Aged 70+/Network Data DS11: SPSS Missing Value Statements for Principals, Aged 70+/Network Data DS12: SAS Proc Format Statements for Principals, Aged 50+ Data DS13: SAS Input Statements for Principals, Aged 50+ Data DS14: SAS Format Statements for Principals, Aged 50+ Data DS15: SAS Label Statements for Principals, Aged 50+ Data DS16: SAS Missing Value Statements for Principals, Aged 50+ Data DS17: SPSS Data List Statements for Principals, Aged 50+ Data DS18: SPSS Variable Label Statements for Principals, Aged 50+ Data DS19: SPSS Value Label Statements for Principals, Aged 50+ Data DS20: SPSS Missing Value Statements for Principals, Aged 50 Data Multistage national probability sample of households with at least one member aged 50 years or older and an oversampling of all household members aged 70 years or older. Additionally, up to three network members were interviewed for each of the respondents aged 70+ (as well as one child and one grandchild if not already named), for a total of 497 network members. There was some overlap between principal respondents and network members: 102 network members were also principal respondents, and 40 were named by more than one principal respondent. The age distribution of the 718 principal respondents was 50-64 years (N = 333), 65-74 years (N = 227), and 75-95 years (N = 158). Persons 50 years and older in households of the United States.

The Semantic Artist Similarity dataset consists of two datasets of artists entities with their corresponding biography texts, and the list of top-10 most similar artists within the datasets used as ground truth. The dataset is composed by a corpus of 268 artists and a slightly larger one of 2,336 artists, both gathered from Last.fm in March 2015. The former is mapped to the MIREX Audio and Music Similarity evaluation dataset, so that its similarity judgments can be used as ground truth. For the latter corpus we use the similarity between artists as provided by the Last.fm API. For every artist there is a list with the top-10 most related artists. In the MIREX dataset there are 188 artists with at least 10 similar artists, the other 80 artists have less than 10 similar artists. In the Last.fm API dataset all artists have a list of 10 similar artists. There are 4 files in the dataset.mirex_gold_top10.txt and lastfmapi_gold_top10.txt have the top-10 lists of artists for every artist of both datasets. Artists are identified by MusicBrainz ID. The format of the file is one line per artist, with the artist mbid separated by a tab with the list of top-10 related artists identified by their mbid separated by spaces.artist_mbid \t artist_mbid_top10_list_separated_by_spaces mb2uri_mirex and mb2uri_lastfmapi.txt have the list of artists. In each line there are three fields separated by tabs. First field is the MusicBrainz ID, second field is the last.fm name of the artist, and third field is the DBpedia uri.artist_mbid \t lastfm_name \t dbpedia_uri There are also 2 folders in the dataset with the biography texts of each dataset. Each .txt file in the biography folders is named with the MusicBrainz ID of the biographied artist. Biographies were gathered from the Last.fm wiki page of every artist.Using this datasetWe would highly appreciate if scientific publications of works partly based on the Semantic Artist Similarity dataset quote the following publication:Oramas, S., Sordo M., Espinosa-Anke L., & Serra X. (In Press). A Semantic-based Approach for Artist Similarity. 16th International Society for Music Information Retrieval Conference.We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research. https://www.upf.edu/web/mtg/semantic-similarity

Context

ViC dataset is a collection for implementing a Dynamic Spectrum Access(DSA) system testbed in the CBRS band in the USA. This data is a DSA system which consists of a 2-tier user : Incident user: generating a chirp signal with a Radar system, Primary user: LTE-TDD signal with a CBSD base station system, and corresponds to signal waveforms in the band 3.55-3.56 GHz (Ch1), 3.56-3.57 GHz (Ch2) respectively. There are a total of 12 classes, excluding the assumption that two of the 16 cases are used by CBSD base stations, depending on the presence or absence of two users in two channels. The labels of each data have the following meanings :

0000 (0) : All off 0001 (1) : Ch2 - Radar on 0010 (2) : Ch2 - LTE on 0011 (3) : Ch2 – LTE, Radar on 0100 (4) : Ch1 – Radar on 0101 (5) : Ch1 – Radar on / Ch2 – Radar on 0110 (6) : Ch1 – Radar on /Ch2 – LTE on 0111 (7) : Ch1 – Radar on / Ch2 – LTE, Radar on 1000 (8) : Ch1 – LTE on 1001 (9) : Ch1 – LTE on / Ch2 – Radar on (X) 1010 (10) : Ch1 – LTE on / Ch2 – LTE on (X) 1011 (11) : Ch1 – LTE on / Ch2 – LTE, Radar on 1100 (12) : Ch1 – LTE, Radar on 1101 (13) : Ch1 – LTE, Radar on / Ch2 – Radar on (X) 1110 (14) : Ch1 – LTE, Radar on / Ch2 – LTE on (X) 1111 (15) : Ch1 – LTE, Radar on / Ch2 – LTE, Radar on

Content

This dataset has a total of 7 types consisting of one raw dataset expressed in two extensions, 4 processed datasets processed in different ways, and a label. Except for one of the datasets, all are Python version of numpy files, and the other is a csv file.

(Raw) The raw data is a IQ data generated from testbeds created by imitating the SAS system of CBRS in the United States. In the testbeds, the primary user was made using the LabView communication tool and the USRP antenna (Radar), and the secondary user was made by manufacturing the CBSD base station. This has both csv format and numpy format exist.

(Processed) All of these data except one are normalized to values between 0 and 255 and consist of spectrogram, scalogram, and IQ data. The other one is a spectrogram dataset which is not normalized. They are measured between 250us. In the case of spectrograms and scalograms, the figure formed at 3.56 GHz to 3.57 GHz corresponds to channel 1, and at 3.55 GHz to 3.56 GHz corresponds to channel 2. Among them, signals transmitted from the CBSD base station are output in the form of LTE-TDD signals, and signals transmitted from the Radar system are output in the form of Chirp signals.

(Label) All of the above five data share one label. This label has a numpy format.

Abstract (en): This collection examines the characteristics of users and sellers of crack cocaine and the impact of users and sellers on the criminal justice system and on drug treatment and community programs. Information was also collected concerning users of drugs other than crack cocaine and the attributes of those users. Topics covered include initiation into substance use and sales, expenses for drug use, involvement with crime, sources of income, and primary substance of abuse. Demographic information includes subject's race, educational level, living area, social setting, employment status, occupation, marital status, number of children, place of birth, and date of birth. Information was also collected about the subject's parents: education level, occupation, and place of birth. 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: Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Residents of two New York City neighborhoods, some of whom had been arrested for drug offenses, some of whom used drugs but had eluded arrest, and some of whom were participating in drug treatment programs. Respondents were selected through police records and snowball sampling methods. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2002-04-25 The data file was converted from card image to logical record length data format. SAS and SPSS data definition statements were created, and the codebook was converted to PDF format. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (87-IJ-CX-0064). The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

This layer contains census tract level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, BLOCK, BLKGRP, and TBLKGRP.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual tract level, since this data has been protected using differential privacy.**To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual tracts will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the tract itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

Output from programming code written to summarize fates of immature monarch butterflies collected and raised in captivity following SOP 4 (ServCat reference 103368). Collection and raising was conducted by crews from Neal Smith (IA), Necedah (WI) NWRs and near the town of Lamoni, Iowa. Results are given in tabular format in the excel file labeled as 2017 Metrics. Additional output from the SAS analysis code is given in the mht file.