Charlie839242/SAS dataset hosted on Hugging Face and contributed by the HF Datasets community

Sas

## Overview

Sas is a dataset for object detection tasks - it contains Sasas annotations for 2,737 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

(SAS7BDAT)

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

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.

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 SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

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.

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

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

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 .

View details of Fresh Yucca Import Data of Exportandina Sas Supplier to US with product description, price, date, quantity, major us ports, countries and more.

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.

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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

In the period of ************* to ************, the SAS Group employed ***** men and ***** women. The largest group of employees, ***** people, was recorded in Denmark, although these numbers also include international employees outside Denmark, Sweden or Norway.

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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.

SAS Expander Market Outlook

According to our latest research, the global SAS Expander market size reached USD 1.35 billion in 2024, reflecting robust demand from data-intensive sectors. The market is projected to expand at a CAGR of 7.2% during the forecast period, reaching approximately USD 2.52 billion by 2033. This growth is primarily driven by the exponential rise in data generation, increasing adoption of high-performance storage solutions, and the expanding footprint of cloud computing and enterprise storage infrastructure worldwide.

One of the key growth factors propelling the SAS Expander market is the surging demand for scalable storage architectures in data centers and cloud service environments. As organizations continue to digitize operations and accumulate vast amounts of structured and unstructured data, there is a critical need for storage systems that offer high throughput, reliability, and flexibility. SAS Expanders, which enable multiple devices to connect to a single host, have become essential in building large-scale, cost-effective storage networks. The ongoing transformation of data centers from traditional on-premises models to hybrid and cloud-based architectures further amplifies the need for advanced SAS Expander solutions that can support dynamic workloads and rapid scaling.

Another significant factor fueling market expansion is the proliferation of enterprise applications and emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies generate unprecedented data volumes, necessitating robust backend storage frameworks. SAS Expanders play a pivotal role in facilitating seamless data transfer and storage management, ensuring optimal performance and minimal latency. Additionally, the increasing adoption of virtualization and containerization in enterprise IT environments is placing additional emphasis on storage scalability and flexibility, further accelerating the uptake of SAS Expander products across various industries.

Technological advancements and continuous innovation in SAS Expander design are also contributing to market growth. Vendors are focusing on enhancing product features such as higher port densities, improved data transfer rates, and advanced error correction mechanisms to meet the evolving needs of enterprise customers. The integration of SAS Expanders with NVMe and SSD technologies is creating new opportunities for performance optimization, particularly in mission-critical applications. Furthermore, the trend towards software-defined storage and hyper-converged infrastructure is driving the demand for SAS Expanders that can seamlessly integrate with next-generation storage platforms.

The role of SAS HBA (Host Bus Adapter) in the SAS Expander ecosystem is crucial as it serves as the interface between the server and the SAS Expander. SAS HBAs are designed to manage data flow between the connected storage devices and the host system, ensuring efficient data transfer and communication. With the increasing complexity and scale of data storage environments, the demand for high-performance SAS HBAs is growing. These adapters are essential for optimizing the performance of SAS Expanders by providing the necessary bandwidth and connectivity options to support multiple storage devices. As organizations continue to expand their storage infrastructure, the integration of advanced SAS HBAs becomes vital in maintaining system reliability and performance.

From a regional perspective, North America remains the dominant market for SAS Expander solutions, driven by the presence of leading technology firms, expansive data center infrastructure, and early adoption of advanced storage technologies. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, significant investments in data center construction, and the proliferation of cloud services in countries such as China, India, and Japan. Europe also presents substantial growth opportunities, supported by stringent data regulations and increasing adoption of enterprise storage solutions across various sectors. Collectively, these regional dynamics underscore the global relevance and growth trajectory of the SAS Expander market.

The table presents the percentage of problems where SAS-Pro performed better than, or at par with CE, SSM, and STSA. In addition, the table presents the average improvement in the RMSD, SI, SAS scores for these problems when SAS-Pro is used instead of other solvers.