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).

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology). Topics: most important political problems of the country; political interest; party inclination; behavior at the polls in the Federal Parliament election 1972; political participation and willingness to participate in political protests. Demography: age; sex; marital status; religious denomination; school education; interest in politics; party preference. Übungsdatensatz zum SAS-Buch von Uehlinger. Auswahl einzelner Variablen und Fälle aus dem Datensatz der ZA-Studie 0757 (Politische Ideologie). Themen: Wichtigste politische Probleme des Landes; politisches Interesse; Parteineigung; Wahlverhalten bei der Bundestagswahl 1972; politische Partizipation und Teilnahmebereitschaft an politischen Protesten. Demographie: Alter; Geschlecht; Familienstand; Konfession; Schulbildung; Politikinteresse; Parteipräferenz. Random selection Zufallsauswahl Oral survey with standardized questionnaire

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

The table presents the percentage of problems where SAS-Pro performed better than, or at par with CE, SSM, and STSA. In addition, the table presents the average improvement in the RMSD, SI, SAS scores for these problems when SAS-Pro is used instead of other solvers.

The Risk Prevention Plans (PPR) were established by the Act of 2 February 1995 on strengthening the protection of the environment. They are the key instrument of the State in the field of risk prevention. Their objective is to monitor development in areas exposed to a major risk. The PPRs are approved by the prefects and generally carried out by the departmental directorates of the territories (DDT). These plans regulate land use or land use through building prohibitions or requirements on existing or future buildings (constructive provisions, vulnerability reduction work, restrictions on agricultural use or practices, etc.). These plans may be under development (prescribed), implemented in advance or approved. The RPP file contains a presentation note, a regulatory zoning plan and a regulation. Other graphic documents that are useful for understanding the approach (e.g. hazards, issues, etc.) can be attached. Each PPR is identified by a polygon that corresponds to the set of affected municipalities within the scope of the prescription when it is in the prescribed state; and the envelope of restricted areas when it is in the approved state. This geographical table allows mapping existing PPRNs or PPRTs on the department.

This SAS script is written for the manuscript "Do Nonfinancial Firms Use Financial Assets to Take Risk" (Chen and Duchin, RCFS, 2023). It needs to use the sas data set,cashholding_list_maindata, in folder of data, and sas macros in the folder of macros

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 Synoptic Arctic Survey 2021 was a research expedition on board the Swedish icebreaker Oden to the High Arctic. The data presented here are the meteorological, oceanographical and ship data collected onboard Oden during the expedition.

This data set contains meteorological, oceanographical and ship data collected onboard IB Oden during the expedition Synoptic Arctic Survey 2021, which was an international research cruise to the Arctic Ocean.

Meteorological variables: Air temperature, Humidity, Wind direction/speed, Atmospheric pressure, Cloud height/cloudiness, Photosynthetic Active Radiation (PAR).

Oceanographical variables: Sea water temperature, Conductivity, Salinity and Sound velocity.

Ship data: Position, Speed, Course, Heading, Water depth.

A "Parameter list" which describes the dataset is attached.

Quality Information: No quality check or other processing of the data has been undertaken. Users should be aware of this in further data handling and analysis.

Macro variables, input, and output parameters for %polynova_2way SAS macro.

The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) routinely releases radiosondes two times per day (00 and 12 UTC) and does occasional special releases at other times of day (usually 18 UTC) from sites across the United States. This data set includes the quality controlled NWS soundings released from 16 stations located within the Southeast Atmosphere Study (SAS) domain for the SAS field phase (1 June to 15 July 2013). A total of 1438 quality controlled, high resolution (1-second) soundings are contained in the final SAS data set. These data were converted into the EOL Sounding Composite (ESC) format (columnar ASCII).

This repository contains the input data for SAS and nitrate transport modeling and the model results that can be used to reproduce the water age and nitrate reactive transport results presented in Sha et al. Coupled hydrologic and biogeochemical responses of nitrate export in a tile-drained agricultural watershed revealed by SAS functions and nitrate isotopes.

File 1: Hydrometric_data.xlsx

Input hydrometric data from 1994 to 2023 water years for SAS-based water age modeling.

File 2: Concentration_data.xlsx

Input chloride concentration data from 1994 to 2023 water years for SAS-based water age modeling. Nitrate concentration data are also included.

File 3: Isotope_data.xlsx

Nitrate isotope data measured during 2021-2023 water years.

File 4: SAS_model_behavioral_parameter_sets.xlsx

The behavioral parameter sets obtained from the calibration of the SAS model.

File 5: Nitrate_transport_model_behavioral_parameter_sets_DynamicModel.xlsx

The behavioral parameter sets obtained from the calibration of the dynamic nitrate transport model.

File 6: Nitrate_transport_model_behavioral_parameter_sets_StaticModel.xlsx

The behavioral parameter sets obtained from the calibration of the static nitrate transport model.

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid:

hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);

hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).

The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (here).

The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.

Exposure estimates were obtained with SAS® software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).

Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.

The input and output data for the exposure assessment are reported in the following annexes:

Annex A.1 – Input data for the exposure assessment of CAG-TCP

Annex A.2 – Input data for the exposure assessment of CAG-TCF

Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP

Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF

Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP

Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF

Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software (here).

The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (here). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.

This data set follows from the "weight.csv" data set. Primarily based on the work by Sarria, as shown in the Sarria workbook https://www.sciencedirect.com/science/article/pii/S0168169909000283 for aphids, the new variables examine durations, frequencies, and the time to specific behavioral events. These values are calculated and saved by the SAS program, and afterwards the insect size data were added to complete the dataset. This intermediate step was necessary because the size data needed to be included.

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to monitor development in areas at risk.The development of a risk prevention plan generates a set of spatial data organised into several data sets. This dataset describes the restricted areas of the plan once approved. RPP regulations generally distinguish between ‘construction ban areas’, so-called ‘red areas’, where the hazard level is high and the general rule is the construction ban; ‘areas subject to requirements’, ‘blue zones’ where the hazard level is medium and projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or prescriptions.

Statistics of the experimental data set.

Attenuation histogram property analysis and BMI-to-(SAT/VAT-volume/area) correlative analysis with PCC (and SCC in parenthesis).