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 Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

File List ECO101_sample_data.xls ECO101_sample_data.txt SAS_Code.rtf

Please note that ESA cannot guarantee the availability of Excel files in perpetuity as it is proprietary software. Thus, the data file here is also supplied as a tab-delimited ASCII file, and the other Excel workbook sheets are provided below in the description section. Description -- TABLE: Please see in attached file. --

This is the SAS 9.4 data set and the same data set as an excel file that is the basis and starting point for all analyses

The SAS code (Supplementary File 1) and R program code (Supplementary File 2). For the analysis to proceed, this code requires an input data file (Supplementary File 3-5) prepared in excel format (CSV). Data can be stored in any format such as xlsx, txt, xls and others. Economic values in the SAS code are entered manually in the code, but in the R code are stored in an Excel file (Supplementary File 6).

Cordier Excel Uccoar Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Comparative primer efficiency as calculated by geNorm Excel and geNorm SAS.

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.

Data from Evans et al. these are the data sets that produced the parameter estimates in Table 3.Survey excel file has the data producing the frequency diagrams in Figs. 2, 3, 4, 5. Survival data excel file contains the data yielding the regression fits in those same figures. The SAS code for each experiment produced the nonlinear fits.

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

Excel Pest Control Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This is a front end to a database containing all service personnel recorded on JPA. The system contains snapshots of data from April 2006 to the present. It is used for producing statistics on the service manpower state. The main repository for the data is on the NEMESIS system. However throughout the agency there is a huge volume of files, SAS, Excel, Access, MySQL containing extracts from the production data. These are held on the various Asante fileservers as evidence for particular Parliamentary Questions (PQ) that have been answered from the data. These extracts probably run into the 1000s. They are retained indefinitely as DASA policy is that they need to be able to re-create any information used to answer a PQ.

This is a front end to a database containing all service personnel recorded on JPA. The system contains snapshots of data from April 2006 to the present. It is used for producing statistics on the service manpower state. The main repository for the data is on the NEMESIS system. However throughout the agency there is a huge volume of files, SAS, Excel, Access, MySQL containing extracts from the production data. These are held on the various Asante fileservers as evidence for particular Parliamentary Questions (PQ) that have been answered from the data. These extracts probably run into the 1000s. They are retained indefinitely as DASA policy is that they need to be able to re-create any information used to answer a PQ.

Transects in backwaters of Navigation Pools 4 and 8 of the Upper Mississippi River (UMR) were established in 1997 to measure sedimentation rates. Annual surveys were conducted from 1997-2002 and then some transects surveyed again in 2017-18. Changes and patterns observed were reported on in 2003 for the 1997-2002 data, and a report summarizing changes and patterns from 1997-2017 will be reported on at this time. Several variables are recorded each survey year and placed into an Excel spreadsheet. The spreadsheets are read with a SAS program to generate a SAS dataset used in SAS programs to determine rates, depth loss, and associations between depth and change through regression.

Dataset and SAS code to replicate the associated results:

PHYTODIV_DATASET --> Excel file with all the databases to conduct the analysis

Sequestration.csv --> Caterpillar cardenolide sequestration data

Sequestration.sas --> SAS code to run the analysis of caterpillar growth and sequestration

MonteCarlo_seqgrwt.csv --> Caterpillar growth and cardenolide sequestration data for regression analysis

MonteCarlo_seqgrwt.sas --> SAS code to perform growth-damage regression, and growth-sequestration regression of observed data and compare to 10.000 simulations of regression analysis

CAFEassay.csv --> Data of fly survival and feeding rate when feeding on toxic diets

CAFE_assay.sas --> SAS code to analyze fly survival and feeding rate when feeding on toxic diets

Enzyme_assay.csv --> Data of sodium pump inhibition due to cardenolide toxins

Enzyme_assay.sas --> SAS code to analyze and compare the sodium pump inhibition of single vs toxin mixtures

The Washington SAC provides access to crime statistics through several methods; CrimeStats Online, the Uniform Crime Report (UCR), and the National Incident Based Reporting System (NIBRS). Queries are web-based interfaces that allow users to query Washington crime data online. For more detailed analyses, the UCR and NIBRS data are available in Excel spreadsheets and SAS datasets. County-level summaries from the Criminal Justice Data Book are available in Excel as well.

Sabotaging milkweed by monarch caterpillars (Danaus plexippus) is a famous textbook example of disarming plant defence. By severing leaf veins, monarchs are thought to prevent the flow of toxic latex to their feeding site. Here, we show that sabotaging by monarch caterpillars is not only an avoidance strategy. While young caterpillars appear to avoid latex, late-instar caterpillars actively ingest exuding latex, presumably to increase sequestration of cardenolides used for defence against predators. Comparisons with caterpillars of the related but non-sequestering common crow butterfly (Euploea core) revealed three lines of evidence supporting our hypothesis. First, monarch caterpillars sabotage inconsistently and therefore the behaviour is not obligatory to feed on milkweed, whereas sabotaging precedes each feeding event in Euploea caterpillars. Second, monarch caterpillars shift their behaviour from latex avoidance in younger to eager drinking in later stages, whereas Euploea caterpil..., , , Readme for the statistical documentation for the publication: Monarchs sabotage milkweed to acquire toxins, not to disarm plant defense Authors: Anja Betz, Robert Bischoff, Georg Petschenka

For the statistical documentation, we provide the following files: This readme gives a brief outline of the different files and data provided in the statistical documentation Subfolders for each experiment containing

• Excel files with just the data, SAS code files for analysis of each dataset with comments SAS dataset files (sas7bdat) a data dictionary.txt that defines all variables of all datasets

Disclaimer: Excel automatically formats numbers. We do not take any responsibility for automatic formatting of the numbers by Excel. This might lead to different results, if the Excel files are used for analysis. The sas7bdat files, or data at the start of the individual sas-analysis files should be resistant to automatic formatting, so we suggest using them for analysis.

The datasets co...

Use these maps and tables to understand the geographic boundaries used in the2016-2018 National Surveys on Drug Use and Health (NSDUH) substate estimates. The resource is available as a PDF or HTM file. There is also additional documentation in SAS and Excel.

Documentation (Word file), SAS 9.4 program files, Excel spreadsheets, HTML, GIF, and PDFs used in generating a staff paper and a web-based database of Minnesota farmland sales prices and acreages by township for 2021. If you don't have SAS and would like to view the .sas program files, one approach is to make a copy of the file, rename it with a .txt extension, and open it in Notepad. The SAS database files can also be exported using R if you don't have SAS.

This model was developed and applied by the EFSA Working Group on Working Group on the evaluation of substances used to remove microbial contamination from product of animal origin during the preparatory work on the Scientific Opinion ‘Evaluation of the safety and efficacy of the organic acids lactic and acetic acids to reduce microbiological surface contamination on pork carcasses and pork cuts' (see http://doi.org/10.2903/j.efsa.2018.5482).

The code (SAS and R) has been used to evaluate the efficacy of two organic acids, lactic and acetic acid, intended to be used individually by food business operators during processing to reduce microbiological surface contamination on carcasses and cuts from pork. The reduction is expressed as log₁₀ reduction, i.e. the difference between the means of the log₁₀ concentrations of control group and treated group and corresponding 95% confidence interval (95% CI) when this information was available.

The code may be run using the input data from the excel table 'Data extraction.xlsx'.