NSnot statistically-significant difference versus right SAS.

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

Matching is frequently used in observational studies, especially in medical research. However, only a small number of articles with matching programs for the SAS software (SAS Institute Inc., Cary, NC, USA) are available, even less are usable for inexperienced users of SAS software. This article presents a matching program for the SAS software and links to an online repository for examples and test data. The program enables matching on several variables and includes in-depth explanation of the expressions used and how to customize the program. The selection of controls is randomized and automated, minimizing the risk of selection bias. Also, the program provides means for the researcher to test for incomplete matching.

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

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

List of SAS and bike tour-to-right for the territory of the city of Angers * * * * Definition of SAS: Layout, inscribed in the road code since 1998, the bicycle or bicycle lock (the regulatory term) is a demarcated space, which, at a crossroads with a traffic light, allows bicycles to be placed between the stop line of the cars and the pedestrian crossing, and to the right or left of the airlock if they want to turn. Definition of turn-to-right: Signage entirely dedicated to cyclists. Placed at a red light, it allows them to continue their path without having to mark the stop even if the light is red. This authorisation is permitted only for the direction(s) indicated by an arrow on the sign (conditional crossing authorisation sign M12), and the cyclist must always give priority to other users, including pedestrians. * * * * Description of certain fields in the dataset: VEL_TAD: Presence of a tour-to-right cyclist VEL_SAS: Presence of a bicycle lock POST: panel support > FEUX (automotive tricolor lamp); Velo (tricolor fire on bike layout); Tram (Tricolor light on Tram line) ID_VOIE: track ID. This identifier makes it possible to join with the referential Tronçons des voie d’Angers Loire Métropole

The mean value of changes in SAS by switching position from back- to abdominal-lying using the MRI method (calculated from data in Table 3).

The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing Unit, value of the Unit, tenure and occupancy status in housing Unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

This SAS macro generates childhood mortality estimates (neonatal, post-neonatal, infant (1q0), child (4q1) and under-five (5q0) mortality) and standard errors based on birth histories reported by women during a household survey. We have made the SAS macro flexible enough to accommodate a range of calculation specifications including multi-stage sampling frames, and simple random samples or censuses. Childhood mortality rates are the component death probabilities of dying before a specific age. This SAS macro is based on a macro built by Keith Purvis at MeasureDHS. His method is described in Estimating Sampling Errors of Means, Total Fertility, and Childhood Mortality Rates Using SAS (www.measuredhs.com/pubs/pdf/OD17/OD17.pdf, section 4). More information about Childhood Mortality Estimation can also be found in the Guide to DHS Statistics (www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf, page 93). We allow the user to specify whether childhood mortality calculations should be based on 5 or 10 years of birth histories, when the birth history window ends, and how to handle age of death with it is reported in whole months (rather than days). The user can also calculate mortality rates within sub-populations, and take account of a complex survey design (unequal probability and cluster samples). Finally, this SAS program is designed to read data in a number of different formats.

The global market size of Mini-SAS and Mini-SAS HD is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Mini-SAS and Mini-SAS HD Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Mini-SAS and Mini-SAS HD industry. The key insights of the report:
1.The report provides key statistics on the market status of the Mini-SAS and Mini-SAS HD manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Mini-SAS and Mini-SAS HD industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Mini-SAS and Mini-SAS HD Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Mini-SAS and Mini-SAS HD as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Mini-SAS and Mini-SAS HD market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

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...

In the fiscal year of 2024, SAS Scandinavian Airlines, based in Sweden’s capital Stockholm, generated a revenue of approximately ** billion Swedish krona. This represents a **** percent increase compared to the previous year. There was a substantial increase in revenue after the coronavirus pandemic, but still remained below the pre-pandemic level in 2019. Passenger revenue accounted for the highest value of the airline, at more than ** billion Swedish krona, ** percent of total revenue. Which are the most valuable segments of SAS’ passenger flights? European flights of SAS particularly generated the highest amount of passenger revenue in the FY 2024. The airline’s total revenue in this region was more than ** billion Swedish kronor. The second and third most valuable areas were intercontinental, as well as domestic flights. How many people travel by SAS’ planes? In the mentioned financial year, SAS handled **** million passengers in total, included scheduled and charter traffic. The passenger load factor, meaning the airline’s capacity utilization, amounted to ** percent.

Protein-Protein, Genetic, and Chemical Interactions for SAS (Drosophila melanogaster) curated by BioGRID (https://thebiogrid.org); DEFINITION: stranded at second

MOSES study data definition programs for SAS to read-in the study data

HD Mini SAS Cable Market Outlook

The global HD Mini SAS Cable market size is expected to witness significant growth, with projections estimating a rise from USD 1.2 billion in 2023 to USD 2.5 billion by 2032, reflecting a CAGR of 8.1%. The growth of this market is driven by the increasing demand for high-speed data transfer solutions in various industries.

The surge in data consumption and the exponential growth of data centers worldwide are primary growth factors for the HD Mini SAS Cable market. As businesses increasingly rely on big data analytics, cloud computing, and IoT, the need for efficient and reliable data transfer becomes paramount. HD Mini SAS Cables, known for their high-speed data transfer capabilities and robust performance, are essential components in modern data infrastructure. Consequently, the proliferation of data centers and the continuous advancement of data-driven technologies significantly bolster market expansion.

Another crucial growth driver is the escalating demand for high-performance computing and real-time data processing in telecommunications and consumer electronics. Telecommunications companies are rapidly upgrading their network infrastructure to support the growing demand for bandwidth-intensive applications like video streaming and online gaming. Similarly, the consumer electronics sector is witnessing an upsurge in the adoption of devices that require high-speed data transfer, such as gaming consoles, high-definition televisions, and storage devices. These trends are expected to propel the demand for HD Mini SAS Cables in the coming years.

The automotive industry's shift towards advanced driver-assistance systems (ADAS) and autonomous vehicles also contributes to market growth. Modern vehicles are increasingly equipped with sophisticated electronics systems that necessitate high-speed data communication for functions such as navigation, infotainment, and safety features. HD Mini SAS Cables play a critical role in ensuring reliable and swift data transmission within these systems, thereby supporting the automotive segment's expansion within the market.

From a regional perspective, North America is anticipated to dominate the HD Mini SAS Cable market, owing to its early adoption of advanced technologies and the presence of major data centers and IT companies. Asia Pacific is expected to exhibit the highest growth rate, driven by rapid technological advancements, increasing investment in data infrastructure, and the booming consumer electronics market in countries like China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also poised to contribute to the market's growth, although their impact may be relatively lower compared to North America and Asia Pacific.

Product Type Analysis

The HD Mini SAS Cable market is segmented by product type into Internal HD Mini SAS Cables and External HD Mini SAS Cables. Internal HD Mini SAS Cables are designed for use within a computer or server, providing connectivity between internal components such as hard drives, SSDs, and motherboards. These cables are crucial in data centers and high-performance computing environments where efficient and reliable internal data transfer is essential. The demand for Internal HD Mini SAS Cables is expected to grow steadily, driven by the increasing deployment of high-density servers and storage solutions.

External HD Mini SAS Cables, on the other hand, are used for connections between external devices and systems. They are commonly employed in scenarios where data needs to be transferred between different hardware units, such as connecting external storage devices to a network or linking multiple servers. External HD Mini SAS Cables are particularly favored in data centers and enterprise environments due to their ability to support high-speed data transfer over longer distances. The growth of cloud computing and the increasing complexity of IT infrastructure are expected to fuel the demand for these cables.

Both internal and external HD Mini SAS Cables are designed to handle large volumes of data with minimal latency and high reliability. However, the choice between the two depends on the specific application requirements and the physical layout of the system. The market for both types of cables is expected to witness robust growth, driven by the ongoing expansion of data centers and the rising demand for high-speed data transfer solutions in various industries.

Manufacturers of HD Mini SAS Cables are continuously innovating to enha

Abstract The Scott-Knott cluster analysis is an alternative approach to mean comparisons with high power and no subset overlapping. It is well suited for the statistical challenges in agronomy associated with testing new cultivars, crop treatments, or methods. The original Scott-Knott test was developed to be used under balanced designs; therefore, the loss of a single plot can significantly increase the rate of type I error. In order to avoid type I error inflation from missing plots, we propose an adjustment that maintains power similar to the original test while adding error protection. The proposed adjustment was validated from more than 40 million simulated experiments following the Monte Carlo method. The results indicate a minimal loss of power with a satisfactory type I error control, while keeping the features of the original procedure. A user-friendly SAS macro is provided for this analysis.

Abstract (en): This data collection provides comparable measures of state appellate and trial court caseloads by type of case for the 50 states, the District of Columbia, and Puerto Rico. Court caseloads are tabulated according to generic reporting categories developed by the Court Statistics Project Committee of the Conference of State Court Administrators. These categories describe differences in the unit of count and the point of count when compiling each court's caseload. Major areas of investigation include (1) case filings in state appellate and trial courts, (2) case processing and dispositions in state appellate and trial courts, and (3) appellate opinions. Within each of these areas of state government investigation, cases are separated by main case type, including civil cases, capital punishment cases, other criminal cases, juvenile cases, and administrative agency appeals. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Checked for undocumented or out-of-range codes.. State appellate and trial court cases in the United States. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2003-08-27 Part 45, Appellate Court Data, 2001, and Part 46, Trial Court Data, 2001, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2002-08-13 Part 43, Appellate Court Data, 2000, and Part 44, Trial Court Data, 2000, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2001-10-31 Part 41, Appellate Court Data, 1999, and Part 42, Trial Court Data, 1999, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.2000-03-23 Part 39, Appellate Court Data, 1998, and Part 40, Trial Court Data, 1998, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks.1999-07-16 Part 37, Appellate Court Data, 1997, and Part 38, Trial Court Data, 1997, have been added to the data collection, along with corresponding SAS and SPSS data definition statements and PDF codebooks. Funding insitution(s): State Justice Institute (SJI-91-N-007-001-1). United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. The Court Statistics Project Web page is: http://www.ncsconline.org/D_Research/csp/CSP_Main_Page.html.A user guide containing court codes and variable descriptions for the 1987 data and the codebooks for the 1995-2001 data are provided as Portable Document Format (PDF) files, and the codebooks for the 1988-1992 data are available in both ASCII text and PDF versions.

This database is a compilation of nightside, high-latitude ionosphere meso-scale flow characteristics built on those used in Gabrielse et al. 2018. It is the most complete version. If you would like to use the database, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com, and/or christine.gabrielse@aero.org). Depending on how the results are used, the main authors request co-authorship on publications that utilize this database.

The methodology and selection criteria can be found in Gabrielse et al. 2018.

The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt The first three letters (RNK or SAS) designate the station used (Rankin Inlet or Saskatoon). Files named ***_FLOW-DATA-PCvsAO_poleward_YYYY.txt are for poleward-directed flows. Each text file is for a different year (YYYY).

AO=Auroral Oval for Rankin Inlet; equatorward of the auroral oval for Saskatoon (not used) PC=Polar Cap for Rankin Inlet; Auroral Oval for Saskatoon

(Note: the data files for RNK and SAS have the same format, so the PC designator means flows above the pertinent boundary (polar cap boundary for RNK, auroral oval equatorward boundary at SAS) and the AO designator means flows below the pertinent boundary.)

 time [YYYYMMDDhhmmss]
 flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]
 flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]
 FWHMavg_AO [degrees]
 FWHMkmavg_AO=[km]
 longtestranges=[ignore]
 Velmaxavg_AO=[m/s, actual average of max V in each range gate used]
 VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]
 FWHMavg_PC=[degrees]
 FWHMkmavg_PC=[km]
 Velmaxavg_PC=[m/s, actual average of max V in each range gate used]
 VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not those used in the paper. They were not found with the strict selection criteria. Please do not use. mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]
gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] ;;;;;;;;;;;;;;; minlatAO=[degrees, min geographic latitude of the flow] maxlatAO=[degrees, max geographic latitude of the flow] minlatPC=[degrees, min geographic latitude of the flow] maxlatPC=[degrees, max geographic latitude of the flow] mltAO=[degrees (MLT)] mltPC=[degrees (MLT)] AE=[nT] AL=[nT] SYMH=[nT] IMFBy=[nT] IMFBz=[nT]
F107=[sfu]

The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt Files named ***_orientation_poleward_YYYY.txt are for poleward-directed flows. Each text file is for a different year (YYYY). The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. https://doi.org/10.1029/2018JA025440 AO=auroral oval PC=polar cap

 time [YYYYMMDDhhmmss]
 mbearingAO [degrees clockwise from magnetic North]
 gbearingAO [degrees clockwise from geographic North]
 mbearingPC [degrees clockwise from magnetic North]
 gbearingPC [degrees clockwise from geographic North]

The following list describes the columns in each data file labeled, _SPEC_TEST__noRG1-2.txt

 time [YYYYMMDDhhmmss]
 RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]

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