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

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.

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.

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

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

SAS PROC used to evaluate SSMT data

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 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).

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

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.

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.

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.

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

This research work studied the effect of timing constraint and overloading of Spectrum Access System (SAS) on the SAS-CBSD protocol. Specifically, it studies how Heartbeat and Grant Request fail as number of CBSDs served by a SAS becomes large for a given service rate. It also looks at the time taken by CBSDs to vacate a channel (on which an incumbent has appeared) at different Heartbeat Interval. These study results are captured in the following files. (1) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 40 requests/sec (2) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 requests/sec (3) Number of CBSD vs number of failed grants when SAS service rate is 40 requests/sec (4) Number of CBSD vs number of failed grants when SAS service rate is 60 requests/sec (5) CDF of duration of CBSDs vacating a channel when number of CBSD=700, mean heartbeat interval = 90 s (6) CDF of duration of CBSDs vacating a channel when number of CBSD=1200, mean heartbeat interval = 150 s (7) CDF of duration of CBSDs vacating a channel when number of CBSD=1500, mean heartbeat interval = 220 s

This dataset contains the scrubbed chat logs from the Southeast Atmosphere Study (SAS) project, including NOMADSS (Nitrogen, Oxidants, Mercury and Aerosol Distributions, Sources and Sinks), from May 30 - July 17, 2013. The chat logs contain conversations between scientists and other field project participants regarding data collection within the SAS-NOMADSS project.

Explore the historical Whois records related to sas.download (Domain). Get insights into ownership history and changes over time.

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 .

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

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].

The Spectrum Access System (SAS) service market is projected to reach a value of XXX million by 2033, expanding at a CAGR of XX% during the forecast period (2025-2033). The demand for SAS services is primarily driven by the proliferation of 4G and 5G networks, coupled with the increasing adoption of private networks in enterprise settings. These technologies require efficient and flexible spectrum management, which SAS effectively provides. Additionally, the growing need for seamless connectivity and enhanced network performance is contributing to the market's growth. Key market trends include the increasing deployment of 5G networks, the adoption of cloud-native SAS solutions, and the integration of artificial intelligence (AI) and machine learning (ML) for optimizing spectrum utilization. Moreover, the emergence of private networks and the need for spectrum sharing are further driving market expansion. The major players in the market include Google, Federated Wireless, Amdocs, Sony, Fairspectrum, Ericsson, Tecore, among others. North America and Europe are expected to dominate the market, followed by Asia Pacific and the Middle East & Africa.