The data in this table are based on the Labour Force Survey (EBB).The EBB is an investigation carried out by CBS to

gather information on the relationship between people and the labour market. The characteristics of individuals are associated with their current or future position on the labour market.

The data in this table refer to unemployment duration.

Duration of unemployment refers to the number of months a person is unemployed. On the basis of the Labour Force Survey, which persons have been identified belonging to the unemployed labour force and since which month these persons being unemployed. The month when a person becomes unemployed is based on the month of the last job (of 12 hours a week or more of more than one year) or the month of job search (from 12 hours or more) per week) or the time of school leaving. The figures on the number of long-term unemployed are the result of a first study of the possibilities to arrive at a demarcation of

long-term unemployment. Of the unemployed in the Occupational Population Survey

(EBB) is known when they have stopped in the last job, when they are

start looking for work and the moment of school leaving. With this data

can be found out when someone has become unemployed; this is the

start date of unemployment. The unemployment rate is the number of months

between the start date of unemployment to the enqûete date. This one

study is part of a longer ongoing study on

unemployment duration in the Netherlands. These figures therefore have a provisional

character and can be adjusted at a later stage.

Due to a new weighing method of the EBB, all EBB are

tables stopped and moved to the archive. In place

new tables are created from this.

In these new tables, the figures are with a

new weighing method corrected until 2001. Since 2001

it is also possible for a limited set of

variables to publish quarterly figures. The years before 2001

have not been corrected and concern the previously published

figures. A detailed description of the new weighing method

the EBB can be found on the theme page.

Data available from: 2001

Frequency: discontinued

Status of the figures

The figures in this publication are provisional.

When are new figures coming?

Stop it.

File List

   Miller_and_Mitchell_Power_Analysis_Code.r (md5: e0161858aaaeac3e81a2c755640b9feb)
   Tree_BA.csv (md5:  e7e52c80094e578260cc7d4bade00208) 
  Description
    Miller_and_Mitchell_Power_Analysis_Code.R - This script runs a bootstrap power analysis based on a mixed effects model of sample data (plot is the random effect, time and site (park) are fixed effects). The simulation determines power to detect a uniform percentage per sampling cycle change in the value of a metric as a linear trend in a mixed-effects model. The sample sizes tested by the script do not have to be the same as the number of samples in the data file; any desired number of samples will be bootstrapped from the actual data.
    The script will report power for a uniform trend across all parks (model with no interaction), as well as power for a trend that occurs only at one park (model with an interaction effect, where simulated effect occurs and power is tested for each park in turn).
    This script requires a comma delimited (.csv) file with the following headings:
    ID (unique alphanumeric value for each row of data; does not need to be called "ID")
    Plot (text, not numeric only, e.g.: "ACAD1" not "1")
    Park (text)
    Year (year of sample, numeric)
    Metric (metric to be evaluated, numeric)
   The data in the file must have two measurements for each plot, with the initial measurement of all plots collected prior to any second measurements (separate data collection cycles).
    Tree_BA.csv – This is an example of the data sets used for the power analysis in this article. Data sets must be formatted as demonstrated in this data set for the simulation to work properly.

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

The data set contains links to geophysical data sets in sgy-format, given for eight profiles which are defined by START lat/long/datetime and END lat/long/datetime. The links in column point to data sets describing the profile in detail by lat/long of ships track and lat/long (2) of the remote operated vehicle.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.