Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Between 2020 and 2024, the data protection supervisory authorities in Cyprus had the highest change in budget among the European Union countries, as their authority's budget grew by 130 percent during the measured period. The second-highest increase in budget was recorded at the Austria's data protection authority.

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 .

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

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)

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 .

Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.

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.

In a shared spectrum environment, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS), the secondary users with lower priority are managed by independent spectrum access systems (SASs) in order to protect the incumbents with higher priority from interference. The interference protection is guaranteed in terms of a percentile of the aggregate interference power. The current practice requires each SAS to obtain a global snapshot of interference and use a common algorithm to manage it. We present a simplified method to permit each SAS to independently manage its users while still meeting overall aggregate interference protection requirements. The data include statistical upper bound on aggregate interference for some known distributions, which is the core idea of the proposed method. The data also include numerical results of using the proposed interference protection criterion in terms of two metrics, the total number of users moved from the channel in order to protect the incumbent (i.e., the size of the move list) and the realized aggregate interference of all co-channel users at the incumbent. The data is associated with the letter, "Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment," M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

In the interest of efficiency, clarity and standardization of stock assessment materials, the stock assessment reports for the 2015 Groundfish update have been streamlined. Additional information is now available through the SASINF website, a public web based repository of information supplemental to assessment update summary documents. Managers, stakeholders, and other interested parties can...

Mailclub SAS Whois Database, discover comprehensive ownership details, registration dates, and more for Mailclub SAS with Whois Data Center.

7365 Global import shipment records of Sas Hard Disk Drives with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Pedestrian Crash Data Study (PCDS) collected detailed data on motor vehicle vs pedestrian crashes.

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.

PurposeChronic Respiratory Disease Questionnaire Self-Administered Standardized (CRQ-SAS) is a valid and reliable tool that evaluates the health-related quality of life among the adult population affected with chronic respiratory disorders (CRDs) and has been translated into many languages as per need. The main objective of this study was to translate the CRQ-SAS into the Urdu language and evaluate its psychometric properties.MethodologyIt was a two-staged study that consisted of translating the original version into Urdu language and then psychometric testing of the translated version. The reliability of the translated questionnaire was assessed by measuring its internal consistency, test-retest reliability, standard error of mean (SEM) & minimal detectable change (MDC). Validity was determined by evaluating its content for content validity, construct (convergent and discriminative) validity, and exploratory factor analysis. Data was analyzed using SPSS v 28 with an alpha level < 0.05 considered to be significant.ResultsCRQ-SAS U had an excellent internal consistency (Cronbach’s Alpha α = 0.89), test-retest reliability (ICC2,1) = 0.91 of all items, and low SEM = 0.11 and MDC = 0.65. S-CVI was 0.9, with statistically significant difference across the response of COPD patients and healthy subjects, and a high degree of correlation with St Georges Respiratory Questionnaire (r = 0.7–0.9) proving CRQ-SAS U content, discriminant and convergent valid respectively. Exploratory factor analysis identified two factors responsible for 80% of the variance.ConclusionCRQ-SAS U demonstrated optimal psychometric properties which renders it to be used in Urdu speaking populations with COPD.

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.

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

Urine osmolarity UOSM, the exposure of main interest, is included in all models. The initial set of adjustment variables for these models was selected by the disjunctive cause criterion. Hazard ratios (HR), confidence limits (CI) and p-values are given. Model stability was evaluated by bootstrap inclusion frequencies (based on bootstrap resamples). UOSM, creatinine clearance, and proteinuria were log2-transformed and therefore, corresponding hazard ratios are per doubling of each variable.Abbreviations and symbols: , significance threshold; ABE, augmented backward elimination; ACEI/ARBs, use of angiotensin-converting enzyme inhibitors and Angiotensin II type 1 receptor blockers; BE, backward elimination; CI, confidence interval; HR, hazard ratio; , change-in-estimate threshold; Uosm, urine osmolarity (mosm/L).Urine osmolarity example: final models selected by backward elimination (BE) with a significance threshold , augmented backward elimination (ABE) with and a change-in-estimate threshold , and unselected model (No selection).