Two SAS files for importing data. The files read the files for individual insects, extract the essential elements, and return a single file That has all the insects. The file has three variables: insect number, waveform, and duration. This contains all the information present, and all other variables can be calculated from these numbers. Note that this format avoids the problem with uncertainties regarding a TBF variable. TBF is time from beginning of file. It might be times from the beginning of a behavior or the end of a behavior. Both options work, but invites confusion if different people use different formats.

The dataset was collected via a combination of the following: 1. manual extraction of EHR-based data followed by entry into REDCap and then analysis and further processing in SAS 9.4; 2. Data pull of Epic EHR-based data from Clarity database using standard programming techniques, followed by processing in SAS 9.4 and merging with data from REDCap; 3. Collection of data directly from participants via telephone with entry into REDCap and further processing in SAS 9.4; 4. Collection of process measures from study team tracking records followed by entry into REDCap and further processing in SAS 9.4. One file in the dataset contains aggregate data generated following merging of Clarity data pull-origin dataset with a REDCap dataset and further manual processing. Recruitment for the randomized trial began at an epilepsy clinic visit, with EHR-embedded validated anxiety and depression instruments, followed by automated EHR-based research screening consent and eligibility assessment. Full...

This CD consists of a series of data files and SAS and SPSS code files containing the Public Use Microdata Sample L. It was produced by the U.S. Bureau of the Census under contract with the Louisiana Population Data Center, LSU Agricultural Center. PUMS-L contains a unique labor market area (LMA) geography delineated by Charles M. Tolbert (LSU) and Molly Sizer (University of Arkansas). PUMS-L is a minimum 0.25 percent sample. Like all PUMS geographic units, the labor market areas must have a population of at least 100,000 persons. To avoid having as few as 250 cases in smaller LMAs, the Bureau made an effort to supply at least 2000 person records per LMA. Inclusion of these additional person records resulted in a 0.45 percent sample. Sampling weights are included in the file that compensate for this oversampling of smaller LMAs. The resulting file contains information on 519,237 households and 1,139,142 persons. Weighted totals are: households - 101,916,857, persons - 248,709,867. This CD-ROM edition of PUMS-L was prepared and mastered by the Louisiana Population Data Center. The files on this CD-ROM are organized in several directories. These directories contain raw PUMS-L data files, equivalency files that document the labor market area geography, Atlas Graphics files that can be used to produce maps, and compressed, rectangularized SAS and SPSS-PC system files. One of the SAS files is an experienced civilian labor force extract that may facilitate research on labor market issues. Also included are SAS and SPSS programs configured for PUMS-L.

Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

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

To improve reporting transparency and research integrity, some journals have begun publishing study protocols and statistical analysis plans alongside trial publications. To determine the overall availability and characteristics of protocols and statistical analysis plans this study reviewed all randomized clinical trials (RCT) published in 2016 in the following 5 general medicine journals: Annals of Internal Medicine, BMJ, JAMA, Lancet, and NEJM. Characteristics of RCTs were extracted from the publication and clinical trial registry. A detailed assessment of protocols and statistical analysis plans was conducted in a 20% random sample of trials. Dataset contains extraction sheets (as SAS data files), code to calculate the values in the tables in the manuscript, and a supplemental file with additional notes on methods used in the study.

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

This data zip file consists of three different data sets using SAS format 1) Data of 105+ thousand adopters - 2 million and 795 thousand records 2) Data of 7+ thousand video game profiles 3) Data of 93+ thousand posts about video games Please unzip the file before using data. The data sets require at least 4 GB.

Version 2.1 (2/4/2025)The Uniform Appraisal Dataset (UAD) Appraisal-Level Public Use File (PUF) is the nation’s first publicly available appraisal-level dataset of appraisal records, giving the public new access to a selected set of data fields found in appraisal reports. The UAD Appraisal-Level PUF are provided separately for the Enterprises (Fannie Mae and Freddie Mac) and Federal Housing Administration (FHA) appraisals:The Enterprise UAD Appraisal-Level PUF is based on a five percent nationally representative random sample of appraisals for single-family mortgages acquired by the Enterprises. The current release includes appraisals from 2013 through 2022.The FHA UAD Appraisal-Level PUF is based on a five percent nationally representative random sample of appraisals for single-family mortgages insured by FHA. The current release includes appraisals from 2017 through 2022.The UAD Appraisal-Level PUF is a resource for users capable of using statistical software to extract and analyze data. Users can download annual or combined files in CSV, R, SAS and Stata formats. All files are zipped for ease with download.The UAD Appraisal-Level PUF provides the public and policymakers with meaningful data about appraisals to identify trends, conduct research, and promote compliance. Public, transparent data about appraisals promotes a liquid, efficient, competitive, and resilient national housing market.​DocumentationUAD PUF Dat​a Dictionary Version 2.1 (2/4/2025)UAD PUF Version History Version 2.1 (2/4/2025)UAD PUF D​ata Documentation (2/4/2025)

Contains our extraction sheets (as SAS data files), code to calculate the values in the tables in our manuscript, and a supplemental file with additional notes on methods used in our study.

This online supplement contains data files and computer code, enabling the public to reproduce the results of the analysis described in the report titled “Thrifty Food Plan Cost Estimates for Alaska and Hawaii” published by USDA FNS in July 2023. The report is available at: https://www.fns.usda.gov/cnpp/tfp-akhi. The online supplement contains a user guide, which describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis; a data file in CSV format, which contains the most detailed information on food price differentials between the mainland U.S. and Alaska and Hawaii derived from Circana (formerly Information Resources Inc) retail scanner data as could be released without disclosing proprietary information; SAS and R code, which use the provided data file to reproduce the results of the report; and an excel spreadsheet containing the reproduced results from the SAS or R code. For technical inquiries, contact: FNS.FoodPlans@usda.gov. Resources in this dataset:

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement User Guide File name: TFPCostEstimatesForAlaskaAndHawaii-UserGuide.pdf Resource description: The online supplement user guide describes the contents of the online supplement in detail, provides a data dictionary, and outlines the methodology used in the analysis.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Data File File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementDataFile.csv Resource description: The online supplement data file contains food price differentials between the mainland United States and Anchorage and Honolulu derived from Circana (formerly Information Resources Inc) retail scanner data. The data was aggregated to prevent disclosing proprietary information.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement R Code File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementRCode.R Resource description: The online supplement R code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the R programming language.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement SAS Code (zipped) File name: TFPCostEstimatesforAlaskaandHawaii-OnlineSupplementSASCode.zip Resource description: The online supplement SAS code enables users to read in the online supplement data file and reproduce the results of the analysis as described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report using the SAS programming language. This SAS file is provided in zip format for compatibility with Ag Data Commons; users will need to unzip the file prior to its use.

Resource title: Thrifty Food Plan Cost Estimates for Alaska and Hawaii Online Supplement Reproduced Results File name: TFPCostEstimatesforAlaskaandHawaii-ReproducedResults.xlsx Resource description: The online supplement reproduced results are output from either the online supplement R or SAS code and contain the results of the analysis described in the Thrifty Food Plan Cost Estimates for Alaska and Hawaii report.

InfoGroup’s Historical Business Backfile consists of geo-coded records of millions of US businesses and other organizations that contain basic information on each entity, such as: contact information, industry description, annual revenues, number of employees, year established, and other data. Each annual file consists of a “snapshot” of InfoGroup’s data as of the last day of each year, creating a time series of data 1997-2019. Access is restricted to current Harvard University community members. Use of Infogroup US Historical Business Data is subject to the terms and conditions of a license agreement (effective March 16, 2016) between Harvard and Infogroup Inc. and subject to applicable laws. Most data files are available in either .csv or .sas format. All data files are compressed into an archive in .gz, or GZIP, format. Extraction software such as 7-Zip is required to unzip these archives.

The global data marketplace market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across diverse sectors. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This expansion is fueled by several key factors, including the rise of big data analytics, the proliferation of connected devices generating massive datasets, and the growing need for efficient data monetization strategies. Businesses are increasingly recognizing the value of high-quality, readily accessible data for improving operational efficiency, enhancing customer experiences, and gaining a competitive edge. Key segments driving this growth are finance, e-commerce, and healthcare, where data insights are crucial for risk management, personalized marketing, and improved patient care respectively. The emergence of advanced technologies like AI and machine learning further amplifies the market’s potential, enabling more sophisticated data analysis and valuable insights extraction. While data privacy and security concerns represent a significant restraint, ongoing regulatory developments and the adoption of robust security measures are helping to mitigate these risks. The geographical distribution of the data marketplace market reveals a significant concentration in North America and Europe, driven by robust digital infrastructure, high levels of data literacy, and established data-driven business practices. However, developing economies in Asia-Pacific are showcasing promising growth potential, owing to rising internet penetration, increasing smartphone usage, and a burgeoning tech sector. Major players such as Microsoft, Amazon, and other established technology firms are heavily invested in developing and expanding data marketplace platforms, leading to intense competition and further innovation within the sector. The future of the data marketplace market looks incredibly bright, with the continued expansion of data volumes, technological advancements, and a rising understanding of the strategic value of data expected to propel substantial growth in the coming years. This growth is anticipated to be further bolstered by the increasing adoption of data sharing agreements, improved data quality, and efficient data governance frameworks.

Thirteen young adults (YA group, mean age 25.5, SD 5.3, 4F/9M) and eleven elderly adults (EA group, mean age 64.2, SD 7.1, 4F/7M) were recruited for the study. All subjects had no medical history of the neural pathological conditions, i.e., stroke, head trauma or tumors.Each subject underwent the sensorimotor integration training (SIT) procedure which consisted in N repetitions of elementary sensorimotor integration task. The task required the participants to classify the duration of presented audio stimulus and execute of one of the motor actions (ME) depending on its duration. Specifically, the participant should clench left or right hand into a fist in response to short (SAS) or long (LAS) audio stimulus. The durations of SAS and LAS were 300 ms and 750 ms, respectively. During ME the hand should be held clenched until the next audio stimulus of the same duration, which informed the participant about the end of ME. The time interval required for SI and ME within single trail was chosen randomly in the range 4--5 s. The pause between the trials was also picked randomly within the range 6--8 s. See Fig. 1A for a single trial timeline. The audio stimuli were presented via audio speakers located on the table in front of the participant. During SIT each participant performed N=60 SI task repetitions (30 per stimulus). The overall duration of the experimental session was approximately 10 minutes per participant.The raw EEG recordings were filtered using the 50 Hz Notch filter. Additionally, the data were filtered using the 5th-order Butterworth filter in the range 1-100 Hz to remove low-frequency artifacts. The ocular and cardiac artifacts were removed using the independent component analysis (ICA). The filtered time-series were segmented into 60 epochs 6 s long each according to the experiment protocol. Each epoch included 3 s of prestimulus EEG and 3 s of poststimulus EEG centered at the presentation of the first audio stimulus. To evaluate the effect of SIT on cortical activation with trial progression the timeline of experimental session (N=60 epochs total, 30 per stimulus) was divided into four equal Intervals: T1 (epochs 1-15); T2 (epochs 16-30); T3 (epochs 31-45); T4 (epochs 46-60). Thus, the interval T1 represented early phase, while the interval T4 represented the last phase of the experiment. The data was then inspected manually and the epochs with the remaining artifacts were rejected. Finally, each interval contained 10 artifact-free epochs (5 epochs per stimulus), 40 total. See Fig. 1B for sampling epochs into sets.Individual epochs for each subject are stored in the attached ._epo.fif files for Python MNE package for M/EEG analysis. Prefix EA or YA in the name of the file identifies the age group, which subject belongs to. Postfix T1, T2, T3 or T4 in the name of the file indicates the interval of SIT.