We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

Net-Income Time Series for Believe SAS. Believe S.A. provides digital music services for independent labels and local artists in France, Germany, rest of Europe, the Americas, Asia, Oceania, and Pacific. It operates through two segments, Premium Solutions and Automated Solutions. The company engages in the sale, promotion, and delivery of digital content provided by artists and labels by developing their catalog on digital platforms and social media; administration of copyrights; provision of synchronization services comprising the use of recorded music in advertising, films and series, video games and television; and organization of musical events. It also offers TuneCore digital platform for artists to distribute their audio content in an automated manner to streaming and social media platforms. Believe S.A. was incorporated in 2005 and is headquartered in Paris, France.

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

PLOSsyphThis is an ASCII file that is space delimited that was created in SAS. It has the variables that were used in the published paper. The readme.sas file is a .sas file that reads the data. You will need to change the infile statement to reflect the path to where you put the data.

File List Code_and_Data_Supplement.zip (md5: dea8636b921f39c9d3fd269e44b6228c) Description The supplementary material provided includes all code and data files necessary to replicate the simulation models other demographic analyses presented in the paper. MATLAB code is provided for the simulations, and SAS code is provided to show how model parameters (vital rates) were estimated.

  The principal programs are Figure_3_4_5_Elasticity_Contours.m and Figure_6_Contours_Stochastic_Lambda.m which perform the elasticity analyses and run the stochastic simulation, respectively.


  The files are presented in a zipped folder called Code_and_Data_Supplement. When uncompressed, users may run the MATLAB programs by opening them from within this directory. Subdirectories contain the data files and supporting MATLAB functions necessary to complete execution. The programs are written to find the necessary supporting functions in the Code_and_Data_Supplement directory. If users copy these MATLAB files to a different directory, they must add the Code_and_Data_Supplement directory and its subdirectories to their search path to make the supporting files available.


  More details are provided in the README.txt file included in the supplement.


  The file and directory structure of entire zipped supplement is shown below.

  Folder PATH listing
Code_and_Data_Supplement
|  Figure_3_4_5_Elasticity_Contours.m
|  Figure_6_Contours_Stochastic_Lambda.m
|  Figure_A1_RefitG2.m
|  Figure_A2_PlotFecundityRegression.m
|  README.txt
|  
+---FinalDataFiles
+---Make Tables
|    README.txt
|    Table_lamANNUAL.csv
|    Table_mgtProbPredicted.csv
|    
+---ParameterEstimation
|  |  Categorical Model output.xls
|  |  
|  +---Fecundity
|  |    Appendix_A3_Fecundity_Breakpoint.sas
|  |    fec_Cat_Indiv.sas
|  |    Mean_Fec_Previous_Study.m
|  |    
|  +---G1
|  |    G1_Cat.sas
|  |    
|  +---G2
|  |    G2_Cat.sas
|  |    
|  +---Model Ranking
|  |    Categorical Model Ranking.xls
|  |    
|  +---Seedlings
|  |    sdl_Cat.sas
|  |    
|  +---SS
|  |    SS_Cat.sas
|  |    
|  +---SumSrv
|  |    sum_Cat.sas
|  |    
|  \---WinSrv
|      modavg.m
|      winCatModAvgfitted.m
|      winCatModAvgLinP.m
|      winCatModAvgMu.m
|      win_Cat.sas
|      
+---ProcessedDatafiles
|    fecdat_gm_param_est_paper.mat
|    hierarchical_parameters.mat
|    refitG2_param_estimation.mat
|    
\---Required_Functions
  |  hline.m
  |  hmstoc.m
  |  Jeffs_Figure_Settings.m
  |  Jeffs_startup.m
  |  newbootci.m
  |  sem.m
  |  senstuff.m
  |  vline.m
  |  
  +---export_fig
  |    change_value.m
  |    eps2pdf.m
  |    export_fig.m
  |    fix_lines.m
  |    ghostscript.m
  |    license.txt
  |    pdf2eps.m
  |    pdftops.m
  |    print2array.m
  |    print2eps.m
  |    
  +---lowess
  |    license.txt
  |    lowess.m
  |    
  +---Multiprod_2009
  |  |  Appendix A - Algorithm.pdf
  |  |  Appendix B - Testing speed and memory usage.pdf
  |  |  Appendix C - Syntaxes.pdf
  |  |  license.txt
  |  |  loc2loc.m
  |  |  MULTIPROD Toolbox Manual.pdf
  |  |  multiprod.m
  |  |  multitransp.m
  |  |  
  |  \---Testing
  |    |  arraylab13.m
  |    |  arraylab131.m
  |    |  arraylab132.m
  |    |  arraylab133.m
  |    |  genop.m
  |    |  multiprod13.m
  |    |  readme.txt
  |    |  sysrequirements_for_testing.m
  |    |  testing_memory_usage.m
  |    |  testMULTIPROD.m
  |    |  timing_arraylab_engines.m
  |    |  timing_matlab_commands.m
  |    |  timing_MX.m
  |    |  
  |    \---Data
  |        Memory used by MATLAB statements.xls
  |        Timing results.xlsx
  |        timing_MX.txt
  |        
  +---province
  |    PROVINCE.DBF
  |    province.prj
  |    PROVINCE.SHP
  |    PROVINCE.SHX
  |    README.txt
  |    
  +---SubAxis
  |    parseArgs.m
  |    subaxis.m
  |    
  +---suplabel
  |    license.txt
  |    suplabel.m
  |    suplabel_test.m
  |    
  \---tight_subplot
      license.txt
      tight_subplot.m

Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data. Our objective was to establish an algorithm to identify pregnant women and their pregnancies in claims data. We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes, in health encounter reporting on 10/1/2015. We selected women in Merative MarketScan commercial databases aged 15–49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages were assigned using the constellation of service dates, code types, pregnancy outcomes, and linkage to infant records. We describe pregnancy outcomes and gestational ages, as well as maternal age, census region, and health plan type. In a sensitivity analysis, we compared our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures. Among 5,812,699 identified pregnancies, most (77.9%) were livebirths, followed by spontaneous abortions (16.2%); 3,274,353 (72.2%) livebirths could be linked to infants. Most pregnancies were among women 25–34 years (59.1%), living in the South (39.1%) and Midwest (22.4%), with large employer-sponsored insurance (52.0%). Outcome distributions were similar across ICD-9 and ICD-10 eras, with some variation in gestational age distribution observed. Sensitivity analyses supported our algorithm’s framework; algorithm- and fertility procedure-derived LMP estimates were within a week of each other (mean difference: -4 days [IQR: -13 to 6 days]; n = 107,870). We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.

Abstract (en): This data collection provides comparable measures of state appellate and trial court caseloads by type of case for the 50 states, the District of Columbia, and Puerto Rico. Court caseloads are tabulated according to generic reporting categories developed by the Court Statistics Project Committee of the Conference of State Court Administrators. These categories describe differences in the unit of count and the point of count when compiling each court's caseload. Major areas of investigation include (1) case filings in state appellate and trial courts, (2) case processing and dispositions in state appellate and trial courts, and (3) appellate opinions. Within each of these areas of state government investigation, cases are separated by main case type, including civil cases, capital punishment cases, other criminal cases, juvenile cases, and administrative agency appeals. 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.; Checked for undocumented or out-of-range codes.. State appellate and trial court cases in the United States. 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.2003-08-27 Part 45, Appellate Court Data, 2001, and Part 46, Trial Court Data, 2001, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2002-08-13 Part 43, Appellate Court Data, 2000, and Part 44, Trial Court Data, 2000, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2001-10-31 Part 41, Appellate Court Data, 1999, and Part 42, Trial Court Data, 1999, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2000-03-23 Part 39, Appellate Court Data, 1998, and Part 40, Trial Court Data, 1998, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.1999-07-16 Part 37, Appellate Court Data, 1997, and Part 38, Trial Court Data, 1997, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks. Funding insitution(s): State Justice Institute (SJI-91-N-007-001-1). United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. The Court Statistics Project Web page is: http://www.ncsconline.org/D_Research/csp/CSP_Main_Page.html.A user guide containing court codes and variable descriptions for the 1987 data and the codebooks for the 1995-2001 data are provided as Portable Document Format (PDF) files, and the codebooks for the 1988-1992 data are available in both ASCII text and PDF versions.

This is a special file prepared by the Economic Research Service of the U.S. Department of Agriculture. This file was donated to CISER by Mark Lancelle, Department of Rural Sociology, Cornell, in 1984. The file was received as an SPSS file. It was converted to an SAS system file. The only documentation for this file is the SAS Contents Listing. According to that listing, this file contains county level data for various time periods between 1960 and 1980. The Source Statements indicate that the file contains data from the Bureau of Economic Analysis (BEA) Personal Income and Employment series. No such variables can be found in the SAS dataset.

Diluted-EPS Time Series for Believe SAS. Believe S.A. provides digital music services for independent labels and local artists in France, Germany, rest of Europe, the Americas, Asia, Oceania, and Pacific. It operates through two segments, Premium Solutions and Automated Solutions. The company engages in the sale, promotion, and delivery of digital content provided by artists and labels by developing their catalog on digital platforms and social media; administration of copyrights; provision of synchronization services comprising the use of recorded music in advertising, films and series, video games and television; and organization of musical events. It also offers TuneCore digital platform for artists to distribute their audio content in an automated manner to streaming and social media platforms. Believe S.A. was incorporated in 2005 and is headquartered in Paris, France.

Net-Income-Applicable-To-Common-Shares Time Series for Believe SAS. Believe S.A. provides digital music services for independent labels and local artists in France, Germany, rest of Europe, the Americas, Asia, Oceania, and Pacific. It operates through two segments, Premium Solutions and Automated Solutions. The company engages in the sale, promotion, and delivery of digital content provided by artists and labels by developing their catalog on digital platforms and social media; administration of copyrights; provision of synchronization services comprising the use of recorded music in advertising, films and series, video games and television; and organization of musical events. It also offers TuneCore digital platform for artists to distribute their audio content in an automated manner to streaming and social media platforms. Believe S.A. was incorporated in 2005 and is headquartered in Paris, France.

Composition of the control diet and its ingredients1.

File List Variable Importance Simulation.txt Variable Importance Simulation.sas hierpart.txt hierpart.sas Description "Variable Importance Simulation.sas" is a simulation to evaluate the relative importance of random variables using Akaike weights, standardized regression coefficients, partial and semi-partial correlation coefficients, and hierarchical partitioning. Remember to change the subdirectory where hierpart.sas is called from the include statement. The file "hierpart.sas" is a macro that executes hierarchical partitioning analysis as described by Chevan and Sutherland in American Statistician, 1991, Vol. 45, no. 2, pp. 90–96. This macro was written by Kim Murray Berger and Mary M. Conner based on a Dominance Analysis macro written by Razia Azen and Robert Ceurvorst (http://www.uwm.edu/~azen/damacro.html). Hierarchical Partitioning analysis quantifies the importance of each predictor as its average contribution to the model r-square, across all possible models. Note: This program is limited to at most 10 predictors!

Provides data about the most suitable and clinically appropriate stoma related products supplied under the Stoma Appliance Scheme (SAS) to assist people affected with stomas to better manage their condition. These products are provided free of charge through regional stoma associations across Australia. The Stoma Appliance Scheme (SAS), established in 1975 and subsidised by the Australian Government.

Excel spreadsheets of stoma related products and information on each product such as a SAS code, company code, brand name, description, pack size, maximum issue and lists the set price that the Australian Government pays for products ; product utilisation and expenditure.

Biochemistry exams of the control group and obese group before and after weight loss.

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. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. 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.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 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. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

Guaranteed analysis and ingredients of the weight loss diet of the present study.

Weighted logistic regression results, stratified by experimental condition.

The GLM SAS results for the effect of hemoglobin on morphometric traits.

Abstract (en): This dataset presents information on the relationship between drug and alcohol use and contacts with police for persons in Racine, Wisconsin, born in 1955. The collection is part of an ongoing longitudinal study of three Racine, Wisconsin, birth cohorts: those born in 1942, 1949, and 1955. Only those born in 1955 were considered to have potential for substantial contact with drugs, and thus only the younger cohort was targeted for this collection. Data were gathered for ages 6 to 33 for the cohort members. The file contains information on the most serious offense during the juvenile and adult periods, the number of police contacts grouped by age of the cohort member, seriousness of the reason for police contact, drugs involved in the incident, the reason police gave for the person having the drugs, the reason police gave for the contact, and the neighborhood in which the juvenile was socialized. Other variables include length of residence in Racine of the cohort member, and demographic information including age, sex, and race. 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: Checked for undocumented or out-of-range codes.. All individuals born in 1955 in Racine, Wisconsin, and those who had migrated there by the age of 6. The sample includes all individuals born in 1955 and attending school (i.e., appearing in the Racine school census records) in 1966. 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.2001-12-21 SAS and SPSS data definition statements were added to the collection and the documentation was converted into PDF format. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (87-IJ-CX-0045). (1) Other datasets that are part of this ongoing study include: JUVENILE DELINQUENCY AND ADULT CRIME, 1948-1977 [RACINE, WISCONSIN]: THREE BIRTH COHORTS (ICPSR 8163), JUVENILE DELINQUENCY AND ADULT CRIME, 1948-1977 [RACINE, WISCONSIN]: CITY ECOLOGICAL DATA (ICPSR 8164), and SANCTIONS IN THE JUSTICE SYSTEM, 1942-1977: THE EFFECTS ON OFFENDERS IN RACINE, WISCONSIN (ICPSR 8530). (2) Users should note that police contact, rather than the individual, is the unit of analysis in this collection, and that each contact is a record. Therefore, there can be multiple records (contacts) per individual. Each individual is identified by the variable UID (Unique Identification Number). (3) 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.