For the TIMSS 2015 fourth grade assessment, the database includes student mathematics and science achievement data as well as the student, parent, teacher, school, and curricular background data for the 47 participating countries and 6 benchmarking entities. For the TIMSS 2015 eighth grade assessment, the database includes student mathematics and science achievement data as well as the student, teacher, school, and curricular background data for the 39 participating countries and 6 benchmarking entities. The TIMSS 2015 International Database also includes data from the TIMSS Numeracy 2015 assessment, with the participation of 7 countries and 1 benchmarking entity. The student, parent, teacher, and school data files are in SAS and SPSS formats. The entire database and its supporting documents are described in the TIMSS 2015 User Guide (Foy, 2017) and its three supplements. The data can be analyzed using the downloadable IEA IDB Analyzer (version 4.0), an application developed by the IEA Data Processing and Research Center to facilitate the analysis of the TIMSS data. A restricted use version of the TIMSS 2015 International Database is available to users who require access to variables removed from the public use version (see Chapter 4 of the User Guide). Users who require access to the restricted use version of the International Database to conduct their analyses should contact the IEA through its Study Data Repository.

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

The TIMSS Advanced 2015 International Database is available to all individuals interested in the data collected and analyzed as part of TIMSS Advanced 2015. The aim is to support and promote the use of these data by researchers, analysts, and others interested in improving education. A public use version of the database is available for download using the links below. For the TIMSS Advanced 2015 assessment, the database includes student achievement data for two subjects, advanced mathematics and physics, as well as the student, teacher, school, and curricular background data for the 9 participating countries. The student, teacher, and school data files are in SAS and SPSS formats. The entire database and its supporting documents are described in the TIMSS Advanced 2015 User Guide for the International Database (Foy, 2017) and its three supplements. The data can be analyzed using the downloadable IEA IDB Analyzer (version 4.0), an application developed by the IEA Data Processing and Research Center to facilitate the analysis of the TIMSS data. A restricted use version of the TIMSS Advanced 2015 International Database is available to users who require access to variables removed from the public use version (see Chapter 4 of the User Guide). Users who require access to the restricted use version of the International Database to conduct their analyses should contact the IEA through its Study Data Repository.

Functional Annotation of Variants - Online Resource (FAVOR, https://favor.genohub.org) is a comprehensive whole genome variant annotation database and a variant browser, providing hundreds of functional annotation scores from a variety of aspects of variant biological function. This FAVOR Full Database is comprised of a collection of full annotation scores for all possible SNVs (8,812,917,339) and observed indels (79,997,898) in Build GRCh38/hg38, including VARIANT INFO, CHROMOSOME, POSITION, REFERENCE ALLELE, ALTERNATIVE ALLELE, VARIANT-ANNOVAR, CHROMOSOME, START-POSITION, END-POSITION, REF-ANNOVAR, ALT-ANNOVAR, POSITION, REF-VCF, ALT-VCF, ALOFT-VALUE, ALOFT-DESCRIPTION, APC-CONSERVATION, APC-CONSERVATION-V2, APC-EPIGENETICS-ACTIVE, APC-EPIGENETICS, APC-EPIGENETICS-REPRESSED, APC-EPIGENETICS-TRANSCRIPTION, APC-LOCAL-NUCLEOTIDE-DIVERSITY, APC-LOCAL-NUCLEOTIDE-DIVERSITY-V2, APC-LOCAL-NUCLEOTIDE-DIVERSITY-V3, APC-MAPPABILITY, APC-MICRO-RNA, APC-MUTATION-DENSITY, APC-PROTEIN-FUNCTION, APC-PROTEIN-FUNCTION-V2, APC-PROTEIN-FUNCTION-V3, APC-PROXIMITY-TO-CODING, APC-PROXIMITY-TO-CODING-V2, APC-PROXIMITY-TO-TSSTES, APC-TRANSCRIPTION-FACTOR, BRAVO-AN, BRAVO-AF, FILTER-STATUS, CAGE-ENHANCER, CAGE-PROMOTER, CAGE-TC, CLNSIG, CLNSIGINCL, CLNDN, CLNDNINCL, CLNREVSTAT, ORIGIN, CLNDISDB, CLNDISDBINCL, GENEINFO, POLYPHEN2-HDIV-SCORE, POLYPHEN2-HVAR-SCORE, MUTATION-TASTER-SCORE, MUTATION-ASSESSOR-SCORE, METASVM-PRED, FATHMM-XF, FUNSEQ-VALUE, FUNSEQ-DESCRIPTION, GENECODE-COMPREHENSIVE-CATEGORY, GENECODE-COMPREHENSIVE-INFO, GENECODE-COMPREHENSIVE-EXONIC-CATEGORY, GENECODE-COMPREHENSIVE-EXONIC-INFO, GENEHANCER, AF-TOTAL, AF-ASJ-FEMALE, AF-EAS-FEMALE, AF-AFR-MALE, AF-FEMALE, AF-FIN-MALE, AF-OTH-FEMALE, AF-AMI, AF-OTH, AF-MALE, AF-AMI-FEMALE, AF-AFR, AF-EAS-MALE, AF-SAS, AF-NFE-FEMALE, AF-ASJ-MALE, AF-RAW, AF-OTH-MALE, AF-NFE-MALE, AF-ASJ, AF-AMR-MALE, AF-AMR-FEMALE, AF-SAS-FEMALE, AF-FIN, AF-AFR-FEMALE, AF-SAS-MALE, AF-AMR, AF-NFE, AF-EAS, AF-AMI-MALE, AF-FIN-FEMALE, LINSIGHT, GC, CPG, MIN-DIST-TSS, MIN-DIST-TSE, SIFT-CAT, SIFT-VAL, POLYPHEN-CAT, POLYPHEN-VAL, PRIPHCONS, MAMPHCONS, VERPHCONS, PRIPHYLOP, MAMPHYLOP, VERPHYLOP, BSTATISTIC, CHMM-E1, CHMM-E2, CHMM-E3, CHMM-E4, CHMM-E5, CHMM-E6, CHMM-E7, CHMM-E8, CHMM-E9, CHMM-E10, CHMM-E11, CHMM-E12, CHMM-E13, CHMM-E14, CHMM-E15, CHMM-E16, CHMM-E17, CHMM-E18, CHMM-E19, CHMM-E20, CHMM-E21, CHMM-E22, CHMM-E23, CHMM-E24, CHMM-E25, GERP-RS, GERP-RS-PVAL, GERP-N, GERP-S, ENCODEH3K4ME1-SUM, ENCODEH3K4ME2-SUM, ENCODEH3K4ME3-SUM, ENCODEH3K9AC-SUM, ENCODEH3K9ME3-SUM, ENCODEH3K27AC-SUM, ENCODEH3K27ME3-SUM, ENCODEH3K36ME3-SUM, ENCODEH3K79ME2-SUM, ENCODEH4K20ME1-SUM, ENCODEH2AFZ-SUM, ENCODE-DNASE-SUM, ENCODETOTAL-RNA-SUM, GRANTHAM, FREQ100BP, RARE100BP, SNGL100BP, FREQ1000BP, RARE1000BP, SNGL1000BP, FREQ10000BP, RARE10000BP, SNGL10000BP, REMAP-OVERLAP-TF, REMAP-OVERLAP-CL, CADD-RAWSCORE, CADD-PHRED, K24-BISMAP, K24-UMAP, K36-BISMAP, K36-UMAP, K50-BISMAP, K50-UMAP, K100-BISMAP, K100-UMAP, NUCDIV, RDHS, RECOMBINATION-RATE, REFSEQ-CATEGORY, REFSEQ-INFO, REFSEQ-EXONIC-CATEGORY, REFSEQ-EXONIC-INFO, SUPER-ENHANCER, TG-AFR, TG-ALL, TG-AMR, TG-EAS, TG-EUR, TG-SAS, UCSC-CATEGORY, UCSC-INFO, UCSC-EXONIC-CATEGORY, UCSC-EXONIC-INFO. These annotation scores can be integrated into FAVORannotator (https://github.com/zhouhufeng/FAVORannotator) to create an annotated GDS (aGDS) file by storing the genotype data and their functional annotation data in an all-in-one file. The aGDS file can then facilitate a wide range of functionally-informed downstream analyses.

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]

Abstract (en): The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 94 district and 12 appellate court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant. 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.; Standardized missing values.; Checked for undocumented or out-of-range codes.. All federal court cases, 1970-2000. 2012-05-22 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 The codebook files in Parts 57, 94, and 95 have undergone minor edits and been incorporated with their respective datasets. The SAS files in Parts 90, 91, 227, and 229-231 have undergone minor edits and been incorporated with their respective datasets. The SPSS files in Parts 92, 93, 226, and 228 have undergone minor edits and been incorporated with their respective datasets. Parts 15-28, 34-56, 61-66, 70-75, 82-89, 96-105, 107, 108, and 115-121 have had identifying information removed from the public use file and restricted data files that still include that information have been created. These parts have had their SPSS, SAS, and PDF codebook files updated to reflect the change. The data, SPSS, and SAS files for Parts 34-37 have been updated from OSIRIS to LRECL format. The codebook files for Parts 109-113 have been updated. The case counts for Parts 61-66 and 71-75 have been corrected in the study description. The LRECL for Parts 82, 100-102, and 105 have been corrected in the study description.2003-04-03 A codebook was created for Part 105, Civil Pending, 1997. Parts 232-233, SAS and SPSS setup files for Civil Data, 1996-1997, were removed from the collection since the civil data files for those years have corresponding SAS and SPSS setup files.2002-04-25 Criminal data files for Parts 109-113 have all been replaced with updated files. The updated files contain Criminal Terminations and Criminal Pending data in one file for the years 1996-2000. Part 114, originally Criminal Pending 2000, has been removed from the study and the 2000 pending data are now included in Part 113.2001-08-13 The following data files were revised to include plaintiff and defendant information: Appellate Terminations, 2000 (Part 107), Appellate Pending, 2000 (Part 108), Civil Terminations, 1996-2000 (Parts 103, 104, 115-117), and Civil Pending, 2000 (Part 118). The corresponding SAS and SPSS setup files and PDF codebooks have also been edited.2001-04-12 Criminal Terminations (Parts 109-113) data for 1996-2000 and Criminal Pending (Part 114) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.2001-03-26 Appellate Terminations (Part 107) and Appellate Pending (Part 108) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.1997-07-16 The data for 18 of the Criminal Data files were matched to the wrong part numbers and names, and now have been corrected. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Several, but not all, of these record counts include a final blank record. Researchers may want to detect this occurrence and eliminate this record before analysis. (2) In July 1984, a major change in the recording and disposition of an appeal occurred, and several data fields dealing with disposition were restructured or replaced. The new structure more clearly delineates mutually exclusive dispositions. Researchers must exercise care in using these fields for comparisons. (3) In 1992, the Administrative Office of the United States Courts changed the reporting period for statistical data. Up to 1992, the reporting period...

Comparison of the allele frequencies recorded for the NUDT15 variants in the two populations analyzed in the present study (NAM and BAP) with those of five continental populations (AFR, AMR, EAS, EUR and SAS) described in the 1000 genomes database.

Comparison of medication adherence between visually impaired and non-disabled patients with dyslipidemia.