In the period of November 2023 to October 2024, the SAS Group employed 6,421 men and 3,936 women. The largest group of employees, 3,900 people, was recorded in Denmark, although these numbers also include international employees outside Denmark, Sweden or Norway.

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.

SAS script and input files for calculations of sensitivity and specificity based on different model settings and weather data in the weather data file supplied here.

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

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.

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

Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

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

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

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.

SAS PROC used to evaluate SSMT data

The most common type of aircraft in the SAS Scandinavian Airlines fleet in 2024 was the Airbus A320 family, with 87 planes. Meanwhile, the airline had another 46 aircrafts of various models.

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

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 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).

SAS-Bench represents the first specialized benchmark for evaluating Large Language Models (LLMs) on Short Answer Scoring (SAS) tasks. Utilizing authentic questions from China's National College Entrance Examination (Gaokao), our benchmark offers:

1,030 questions spanning 9 academic disciplines 4,109 expert-annotated student responses Step-wise scoring with Step-wise error analysis Multi-dimensional evaluation (holistic scoring, step-wise scoring, and error diagnosis consistency)

Sas Msi Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

In 2023, the number of pilots employed by Scandinavian Airlines was 781, representing a decrease of 24 full-time equivalent pilots compared to the previous years. Since 2020, Scandinavian airlines have had one of the lowest numbers of pilots in their 14 years history.

Serial Attached Storage (SAS) Solid State Drive (SSD) Market Outlook 2032

The global serial attached storage (SAS) solid state drive (SSD) market size was USD 3.03 Billion in 2023 and is likely to reach USD 4.32 Billion by 2032, expanding at a CAGR of 4.01 % during 2024–2032. The market growth is attributed to the rising demand for high-performance computing and the increasing adoption of cloud computing.



Increasing adoption of cloud computing is expected to boost the market. SAS SSDs offer high performance and low latency, which are crucial for cloud computing applications that require fast data access and processing for handling large amounts of data and high user loads. Thus, the rising adoption of cloud computing is propelling the market.



SAS SSDs are known for their reliability and endurance as they have no moving parts, which reduces the risk of mechanical failures. This encourages IT & telecom, BFSI, and other industries to adopt SAS SSDs. Additionally, SSDs consume less power compared to traditional hard disk drives (HDDs), which leads to significant energy savings, especially in large-scale data centers. This increases the adoption of SSDs in data centers.

Impact of Artificial Intelligence (AI) on Serial Attached Storage (SAS) Solid State Drive (SSD) Market

Artificial Intelligence (AI) is significantly influencing the SAS SSD market by driving the need for faster and efficient data storage solutions. AI applications and workloads demand high-speed data access and processing capabilities, which SAS SSDs deliver. These drives handle the large volumes of data generated by AI algorithms and provide the necessary speed for real-time data analysis. Furthermore, AI-driven predictive analytics enhance the performance and lifespan of SAS SSDs by identifying potential issues before they become critical, thereby reducing downtime and maintenance costs. Thus, the rise of AI is creating substantial demand in the SAS SSD market and is expected to continue to do so as AI adoption increases across various industries.

Serial Attached Storage

7005 Global import shipment records of Sas Hard Disk Drives with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.