Output from programming code written to summarize 2018 monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MM_SOP2_for_SAS.csv' and is stored in ServCat reference 136485. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

Dr. Kevin Bronson provides a second experiment year of Field 13 nitrogen and water management in cotton agricultural research data for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs. The reflectance data is good. There are some errors in the CS data. See included README file for operational details and further description of the measured data signals. Summary: Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2015 cotton season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to _location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional data illustration is offered as a report file with annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Public Access is a general level of public access permitted in the area - Open, Restricted (permit, seasonal), Closed, Unknown.PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.This layer displays protected areas symbolized by the public access field. Symbology classes include closed, open, and restricted access.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by public accessCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: USGS Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

SAS based field microsimulation model used principally for forecasting North Sea oil and gas revenues. Includes survey data of oil and gas companies' production and expenditure, as well as parameters and determinants. Updated: Every six months

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Output from programming code written to summarize immature monarch butterfly, milkweed and nectar plant abundance from monitoring data acquired using a grid of 1 square-meter quadrats at custom 2017 GRTS draw sites within select monitoring areas (see SOP 3 in ServCat reference 103368 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MonMonSOP3DS1_forSAS.csv' and is stored in ServCat reference 137698. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

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.

Dr. Kevin Bronson provides a small area nitrogen and water management in Guayule agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis table of correlated and calculated parameters with laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw sensor recorded logger outputs. This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result. Summary: Active optical proximal cotton canopy sensing spatial data and including additional related metrics such as thermal are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first dual sliding arm configuration was deployed and platform clearance raised successfully as design improvements. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined 20 or more weeks in the season with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

Dr. Kevin Bronson provides this dataset representing the first of three consecutive years of cotton and nitrogen management experimentation in Field 113. Included, is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs. See included README file for operational details and further description of the measured data signals. Summary - Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2016 cotton season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of high-throughput plant phenotyping. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

To perform a statistically rigorous meta-analysis of research results on the response by herbaceous vegetation to increased atmospheric CO2 levels, a multiparameter database of responses was compiled from the published literature. Seventy-eight independent CO2-enrichment studies, covering 53 species and 26 response parameters, reported mean response, sample size, and variance of the response (either as standard deviation or standard error). An additional 43 studies, covering 25 species and 6 response parameters, did not report variances. This numeric data package accompanies the Carbon Dioxide Information Analysis Center's (CDIAC's) NDP- 072, which provides similar information for woody vegetation. This numeric data package contains a 30-field data set of CO2- exposure experiment responses by herbaceous plants (as both a flat ASCII file and a spreadsheet file), files listing the references to the CO2-exposure experiments and specific comments relevant to the data in the data sets, and this documentation file (which includes SAS and Fortran codes to read the ASCII data file; SAS is a registered trademark of the SAS Institute, Inc., Cary, North Carolina 27511). For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp073/ndp073.html

Abstract (en): This study contains data on 779 dyadic fatal quarrels for the period 1809-1949. The study represents one of the earlier attempts at quantification of historical conflict behavior. A dyadic deadly quarrel is a situation involving a pair of opponents or belligerents which causes death to humans. Each quarrel is identified by its beginning date and magnitude. The magnitude of a quarrel is measured by the logarithm to the base 10 of the number of deaths. The range of magnitude of quarrels in this study is from 2.50 to 7.50, the latter representing the figure for nations involved in World War II. For each quarrel, the nominal variables include the type of quarrel, as well as political, cultural, and economic similarities and dissimilarities between the pair of combatants. A total of 779 dyadic, fatal quarrels in the period 1809-1949. 2016-02-10 The column locations provided in the codebook do not represent the current structure of the data. Users should refer to the SPSS or SAS setup files provided with the ASCII data for the correct column locations for the data. The column locations provided in the codebook do not represent the current structure of the data. Users should refer to the SPSS or SAS setup files provided with the ASCII data for the correct column locations for the data.

The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) routinely releases radiosondes two times per day (00 and 12 UTC) and does occasional special releases at other times of day (usually 18 UTC) from sites across the United States. This data set includes the quality controlled NWS soundings released from 16 stations located within the Southeast Atmosphere Study (SAS) domain for the SAS field phase (1 June to 15 July 2013). A total of 1438 quality controlled, high resolution (1-second) soundings are contained in the final SAS data set. These data were converted into the EOL Sounding Composite (ESC) format (columnar ASCII).

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!Version 8 release notes:Adds 2019 dataVersion 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2022).

Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw sensors records and logger outputs. This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result. See included README file for operational details and further description of the measured data signals. Summary: Active optical proximal wheat canopy sensing spatial data and including additional related metrics such as thermal are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2013 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled. Acquired data using USDA Maricopa first mobile platforms, such as the Proximal Sensing Cart Mark 1, where the first cluster sensor bracket design and rickshaw inspired operator's handle were successfully employed. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where intermediate data tabulation and analysis is available. The weekly proximal sensing data collected include canopy reflectance at six wavelengths, ultrasonic distance sensing of canopy height, and infrared thermometry. Ten levels gradient irrigation application from linear move sprinkler system were applied. Soil physical texture and fertility chemistry results are available. Yield and seed information is presented.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In addition to core items, new content includes questions on values, political knowledge, and attitudes on racial policy, as well as more general attitudes conceptualized as antecedent to these opinions on racial issues. The Main Data File also contains vote validation data that were expanded to include information from the appropriate election office and were attached to the records of each of the respondents in the post-election survey. The expanded data consist of the respondent's post case ID, vote validation ID, and two variables to clarify the distinction between the office of registration and the office associated with the respondent's sample address. The second data file, Bias Nonresponse Data File, contains respondent-level field administration variables. Of 3,833 lines of sample that were originally issued for the 1990 Study, 2,176 resulted in completed interviews, others were nonsample, and others were noninterviews for a variety of reasons. For each line of sample, the Bias Nonresponse Data File includes sampling data, result codes, control variables, and interviewer variables. Detailed geocode data are blanked but available under conditions of confidential access (contact the American National Election Studies at the Center for Political Studies, University of Michigan, for further details). This is a specialized file, of particular interest to those who are interested in survey nonresponse. Demographic variables include age, party affiliation, marital status, education, employment status, occupation, religious preference, and ethnicity. 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: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this study is 67.7 percent. The study was in the field until January 31, although 67 percent of the interviews were taken by November 25, 80 percent by December 7, and 93 percent by December 31. All United States households in the 50 states. National multistage area probability sample. 2015-11-10 The study metadata was updated.2009-01-09 YYYY-MM-DD Part 1, the Main Data File, incorporates errata that were posted separately under the Fourth ICPSR Edition. Part 2, the Bias Nonresponse Data File, has been added to the data collection, along with corresponding SAS, SPSS, and Stata setup files and documentation. The codebook has been updated by adding a technical memorandum on the sampling design of the study previously missing from the codebook. The nonresponse file contains respondent-level field administration variables for those interested in survey nonresponse. The collection now includes files in ASCII, SPSS portable, SAS transport (CPORT), and Stata system formats.2000-02-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. Additionally, the Voter Validation Office Administration Interview File (Expanded Version) has been merged with the main data file, and the codebook and SPSS setup files have been replaced. Also, SAS setup files have been added to the collection, and the data collection instrument is now provided as a PDF file. Two files are no longer being released with this collection: the Voter Validation Office Administration Interview File (Unexpanded Version) and the Results of First Contact With Respondent file. Funding insitution(s): National Science Foundation (SOC77-08885 and SES-8341310). face-to-face interviewThere was significantly more content in this post-election survey than ...

The Lockman Hole represents the sky area of lowest Galactic line-of-sight columns density. It was observed by the XMM-Newton X-ray observatory in 18 pointings performed between April 2000 and December 2002. The total exposure time spent on the field was 1.16Ms (EPIC pn detector; EPIC MOS detector: 1.30 Ms). The effective exposure after removal of times of high particle background is 637ks (EPIC pn detector; EPIC MOS detector: 765ks). The catalogue lists positions, count rates, fluxes, hardness ratios, and partial optical classifications of 409 X-ray point sources detected in the central 0.196{deg}2 of the field down to a detection likelihood threshold of 10 (3.9sigma). The analysis was performed using the XMM-Newton SAS data analysis package version 6.0. Cone search capability for table J/A+A/479/283/table3 (Point source catalogue)