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.

Live release rate for companion animals

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)

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

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

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.

This dataset includes profile discrete measurements of dissolved inorganic carbon, total alkalinity, pH on total scale, water temperature, salinity, dissolved oxygen and other parameters measured during the R/V Oden SAS-Oden 2021 (SO21) cruise (EXPOCODE 77DN20210725) in the Arctic Ocean from 2021-07-25 to 2021-09-20. The SAS-Oden 2021 expedition (SO21) with icebreaker Oden1 (IB Oden) is the Swedish contribution to the international scientist-driven initiative †Synoptic Arctic Survey†(SAS)2. SAS will collect primary ecosystem data in the Arctic Ocean in 2020-2022 from both ice-breaking and non-ice-breaking research vessels. The goal of SAS is to generate a comprehensive dataset that allows for an improved characterization of the Arctic Ocean with respect to its (1) physical oceanography, (2) marine ecosystems and (3) carbon cycle. The complete SAS dataset will provide a unique baseline that will allow for tracking climate change and its impacts as they unfold in the Arctic region over the coming years, decades and centuries.

To analyze these data as presented, you must have the SAS system software (e.g.SAS 2016) installed. Once you have unpacked the ZIP file, change the path within the SAS files to point to the directory where you have unpacked the data, and run the programs, which have .SAS extensions. Some data are in .csv files, but most are in SAS data sets. If you do not have SAS, you can still use conversion utilities in other software, such as R, to read that data.

SAS Institute, Inc. 2016.The SAS System for Windows, Release 9.4.SAS Institute, Cary, NC.

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

This data publication contains the data and SAS code corresponding to the examples provided in the publication "A tutorial on the piecewise regression approach applied to bedload transport data" by Sandra Ryan and Laurie Porth in 2007 (see cross-reference section). The data include rates of bedload transport and discharge recorded from 1985-1993 and 1997 at Little Granite Creek near Jackson, Wyoming as well as the bedload transport and discharge recorded during snowmelt runoff in 1998 and 1999 at Hayden Creek near Salida, Colorado. The SAS code demonstrates how to apply a piecewise linear regression model to these data, as well as bootstrapping techniques to obtain confidence limits for piecewise linear regression parameter estimates.These data were collected to measure rates of bedload transport in coarse grained channels.Original metadata date was 05/31/2007. Metadata modified on 03/19/2013 to adjust citation to include the addition of a DOI (digital object identifier) and other minor edits. Minor metadata updates on 12/20/2016.

Track the SAS 4 in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 403702010, IMO: 8921016

This dataset contains soil and vegetation data from experimental deer exclosures in hunted and unhunted properties in southeastern New York. This dataset is a contribution to the Cary Institute of Ecosystem Studies, and is part of the Long term monitoring of forest ecosystems: Nutrient cycling archive.

File list:

Deer Ex Graphs (sas).xlsx. Graphs from deer exclosure SAS output.

Deer Exclosure N-Min 1997.xlsx. Master spreadsheet for deer exclosure nitrogen mineralization.

Deer Exclosure N-min calculations 1997+GML.xlsx. Extraction calculations for deer exclosure nitrogen mineralization. Sheet 2 contains definitions of column headers.

Deer Exclosure Vegetation Data.xlsx. Deer exclosure vegetation database of the Cary Institute property. Sheet 2 contains definitions of column headers.

Key for spreadsheets_SHARE.pdf. Original metadata for deer exclosure vegetation database, including miscellaneous notes for other data sheets (public version).

DEEREXCL.xlsx. Rough spreadsheet of deer exclosure lab data.

DEREXWHC.xlsx. Water holding capacity spreadsheet for deer exclosure N-min.

rawdeer.xlsx. Master deer exclosure spreadsheet for SAS analysis.

SAS Deer Ex Graphs Part 2.xlsx. More graphs from deer exclosure SAS output.

Deer Exclosures.pptx. Power Point presentation of deer exclosure data.

deer.sas SAS job for deer exclosure data analysis.

DEER.sd2. SAS spreadsheet for deer exclosure data analysis.

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Research publications relating to this project are linked below.

The Cary Institute of Ecosystem Studies furnishes data under the following conditions: The data have received quality assurance scrutiny, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used.

Data citation: Please click on the Cite button on this page.

Those wishing to publish data from Cary Institute of Ecosystem Studies are encouraged to contact the data manager at datamanagement@caryinstitute.org or the Manager of Field Research & Outdoor Programs, Michael Fargione at fargionem@caryinstitute.org.

Transects in backwaters of Navigation Pools 4 and 8 of the Upper Mississippi River (UMR) were established in 1997 to measure sedimentation rates. Annual surveys were conducted from 1997-2002 and then some transects surveyed again in 2017-18. Changes and patterns observed were reported on in 2003 for the 1997-2002 data, and a report summarizing changes and patterns from 1997-2017 will be reported on at this time. Several variables are recorded each survey year and placed into an Excel spreadsheet. The spreadsheets are read with a SAS program to generate a SAS dataset used in SAS programs to determine rates, depth loss, and associations between depth and change through regression.

2013, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format. This dataset can be joined with city-level spatial data in a geographic information system (GIS) to produce maps of 27 measures at the city-level.
Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

Periodicity: Annual

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...

Track the SAS 7 in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 341396001, IMO: 7925209

Patient appointment information is obtained from the Veterans Health Information Systems and Technology Architecture Scheduling module. The Patient Appointment Information application gathers appointment data to be loaded into a national database for statistical reporting. Patient appointments are scanned from September 1, 2002 to the present, and appointment data meeting specified criteria are transmitted to the Austin Information Technology Center Patient Appointment Information Transmission (PAIT) national database. Subsequent transmissions (bi-monthly) update PAIT bi-monthly via Health Level Seven message transmissions through Vitria Interface Engine (VIE) connections. A Statistical Analysis Software (SAS) program in Austin utilizes PAIT data to create a bi-monthly SAS dataset on the Austin mainframe. This additional data is used to supplement the existing Clinic Appointment Wait Time and Clinic Utilization extracts created by the Veterans Health Administration Support Service Center (VSSC).

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.