(SAS7BDAT)

This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It enables researchers to both reproduce the initial results and to undertake further analyses. The publication includes introductory chapters explaining the statistical theories and concepts required to analyse the PISA data, including full chapters on how to apply replicate weights and undertake analyses using plausible values; worked examples providing full syntax in SAS®; and a comprehensive description of the OECD PISA 2003 international database. The PISA 2003 database includes micro-level data on student educational performance for 41 countries collected in 2003, together with students’ responses to the PISA 2003 questionnaires and the test questions. A similar manual is available for SPSS users.

Imbjddd/sas dataset hosted on Hugging Face and contributed by the HF Datasets community

SAS Project

## Overview

SAS Project is a dataset for object detection tasks - it contains Person Gun Knife Fire Bat annotations for 7,868 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset contains data from 3,786 patients. It is not available for download here, but registered in the FAIR4Health Platform portal.

One of four dataset to replicate numbers for tables and figures in the article "Mammography screening: eliciting the voices of informed citizens" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

This dataset comprises the data collected for the Sub-state Autonomy Scale (SAS). The SAS is an indicator measuring the autonomy demands and statutes of sub-state communities in kind (whether competences are administrative or legislative), in degree (how much each dimension is present) and by competences (as a function of the extent of comprised policy domains). Definitions: -By 'sub-state community', I refer to sub-state entities within countries for which autonomous institutions have been demanded by a significant regionalist or traditional (centrist, liberal or socialist main-stream) political party (>5%) or to which autonomous institutions have been conferred. -By 'autonomy statutes', I refer to the legal autonomy prerogatives obtained by sub-state communities. -For 'autonomy demands', I distinguish between the legal autonomy prerogatives demanded by the regionalist party with the highest vote share and those demanded by the traditional party with the largest autonomy demand. Detailed conceptual presentation: see the Regional Studies article cited below (the open access author version can be found in the files section). Specifications: -Unit of analysis: sub-state communities by yearly intervals. -Country coverage: Belgium, Spain, United Kingdom (31 sub-state communities). -Time coverage: 1707-2020 (starting dates vary across sub-state communities). *For the full list of sub-state communities and their respective time coverage, see the codebook. Citation and acknowledgement: when using the data, please cite the Regional Studies article listed below. Latest version: 1.0 [01.02.2022].

One of three dataset to replicate numbers for tables and figures in the article "Using a Deliberative Poll on breast cancer screening to assess and improve the decision quality of laypeople" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

SAS PROC used to evaluate SSMT data

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle) In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2016. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2016. Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016. Study spatial scale (size of replicates and spatial scale of study area) In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2016 is a participant dataset consisting of 8,815,472 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 8.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The nationally representative sample is composed of 60,003 records. The distribution by category is 5,449 pregnant women, 4,661 breastfeeding women, 3,904 postpartum women, 13,999 infants, and 31,990 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016.   Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: WIC Participant and Program Characteristics 2016. File Name: wicpc_2016_public.csvResource Description: The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.Resource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 Codebook. File Name: WICPC2016_PUBLIC_CODEBOOK.xlsxResource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data. File Name: WIC_PC_2016_SAS_SPSS_STATA_Files.zipResource Description: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data

After the number of flights decreased by ** percent in 2020 due to the impact of the coronavirus pandemic and fell further in 2021, flight numbers began to recover in 2022. In 2022, SAS operated ******* scheduled flights. The positive trend persisted in the subsequent year, 2023, with a total of ******* flights.

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)

The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

These SAS files are sample code used for the Monte Carlo studies in a manuscript on statistical properties of four effect size measures for the mediated effect.Citation:Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, H. C. (2016). The bias and efficiency of five effect size measures for mediation models. Under review at Behavior Research Methods.

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

The Serial Attached SCSI (SAS) Solid State Drive (SSD) market is experiencing robust growth, driven by increasing demand for high-performance storage solutions in enterprise data centers and high-transaction environments. While precise market sizing data is unavailable, leveraging industry reports and observed trends in related markets (e.g., NVMe SSDs, enterprise storage), a reasonable estimate for the 2025 market size places it around $5 billion. This sector is projected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033, indicating substantial future expansion. This growth is fueled by several factors, including the growing adoption of cloud computing, the expanding need for faster data processing in demanding applications like financial transactions and high-performance computing, and the inherent advantages of SAS SSDs over traditional hard disk drives (HDDs) in terms of speed, reliability, and durability. The market’s segmentation reflects the diverse applications of SAS SSDs across various industry sectors. The key players in the SAS SSD market, including ADATA, Kingston, KIOXIA, Micron, Seagate, SK Hynix, Western Digital, and others, are continuously innovating to improve performance, density, and cost-effectiveness. However, the market also faces certain restraints, such as the relatively higher cost of SAS SSDs compared to other SSD technologies like SATA and the emergence of competing technologies like NVMe, which offer higher bandwidth. Nevertheless, the unique strengths of SAS SSDs in terms of reliability, predictability, and backward compatibility ensure its continued relevance and growth within specific enterprise niches, particularly those prioritizing data integrity and predictable performance. The continued growth in cloud infrastructure and the expansion of big data analytics will remain significant drivers of SAS SSD market growth over the forecast period.

In the fiscal year of 2023, Scandinavian Airlines accounted for ** percent of all flights to, from, and within Scandinavia. According to the company, the main operating focus of Scandinavian departures is maintained within the hubs of Stockholm-Arlanda, Copenhagen-Kastrup, and Oslo-Gardermoen.

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology). Topics: most important political problems of the country; political interest; party inclination; behavior at the polls in the Federal Parliament election 1972; political participation and willingness to participate in political protests. Demography: age; sex; marital status; religious denomination; school education; interest in politics; party preference. Übungsdatensatz zum SAS-Buch von Uehlinger. Auswahl einzelner Variablen und Fälle aus dem Datensatz der ZA-Studie 0757 (Politische Ideologie). Themen: Wichtigste politische Probleme des Landes; politisches Interesse; Parteineigung; Wahlverhalten bei der Bundestagswahl 1972; politische Partizipation und Teilnahmebereitschaft an politischen Protesten. Demographie: Alter; Geschlecht; Familienstand; Konfession; Schulbildung; Politikinteresse; Parteipräferenz. Random selection Zufallsauswahl Oral survey with standardized questionnaire

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

This database is the Third Small Astronomy Satellite (SAS-3) Y-Axis Pointed Observation Log. It identifies possible pointed observations of celestial X-ray sources which were performed with the y-axis detectors of the SAS-3 X-Ray Observatory. This log was compiled (by R. Kelley, P. Goetz and L. Petro) from notes made at the time of the observations and it is expected that it is neither complete nor fully accurate. Possible errors in the log are (i) the misclassification of an observation as a pointed observation when it was either a spinning or dither observation and (ii) inaccuracy of the dates and times of the start and end of an observation. In addition, as described in the HEASARC_Updates section, the HEASARC added some additional information when creating this database. Further information about the SAS-3 detectors and their fields of view can be found at: http://heasarc.gsfc.nasa.gov/docs/sas3/sas3_about.html Disclaimer: The HEASARC is aware of certain inconsistencies between the Start_date, End_date, and Duration fields for a number of rows in this database table. They appear to be errors present in the original table. Except for one entry where the HEASARC corrected an error where there was a near-certainty which parameter was incorrect (as noted in the 'HEASARC_Updates' section of this documentation), these inconsistencies have been left as they were in the original table. This database table was released by the HEASARC in June 2000, based on the SAS-3 Y-Axis pointed Observation Log (available from the NSSDC as dataset ID 75-037A-02B), together with some additional information provided by the HEASARC itself. This is a service provided by NASA HEASARC .