SAS script and input files for calculations of sensitivity and specificity based on different model settings and weather data in the weather data file supplied here.

A SAS program for inputting data and estimating parameters under the model presented in Expression 2, and an interpretation of the output of the program.

Output from programming code written to summarize 2018 monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MM_SOP2_for_SAS.csv' and is stored in ServCat reference 136485. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

Output from programming code written to summarize immature monarch butterfly, milkweed and nectar plant abundance from monitoring data acquired using a grid of 1 square-meter quadrats at custom 2017 GRTS draw sites within select monitoring areas (see SOP 3 in ServCat reference 103368 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MonMonSOP3DS1_forSAS.csv' and is stored in ServCat reference 137698. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

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 repository contains the SAS model input data and the model results that can be used to reproduce the water age results for the three study tiles presented in Yu et al. Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field

File 1: SAS_model_input_TileX.csv

The input data for the SAS model calibration for the three tiles.

File 2: behavioral_parameter_sets_Model#1_TileX.csv

The behavioral parameter sets obtained from the calibration of SAS model 1 (i.e., time-invariant kQ).

File 3: behavioral_parameter_sets_Model#2_TileX.csv

The behavioral parameter sets obtained from the calibration of SAS model 2 (i.e., time-variant kQ).

File 4: Cl_simulation_results_Model#1_TileX.csv

Simulated chloride concentration based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).

File 5: Cl_simulation_results_Model#2_TileX.csv

Simulated chloride concentration based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).

File 6: median_water_age_Model#1_TileX.csv

Median water age of tile discharge based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).

File 7: median_water_age_Model#2_TileX.csv

Median water age of tile discharge based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).

File 8: File_column_names.txt

A text file that explains the column names for each file

These data were gathered to provide information on Kahn and Antonucci's life-span developmental model, "convoys of social support," which explores interpersonal relationships over time. Older adults (aged 50+) were interviewed on their health status, labor force status, and other demographic characteristics, and on the composition and degree of closeness of members of their current support network (e.g., spouses, children, friends). Three concentric circles of closeness were defined, varying in terms of transcendence of the relationship beyond role requirements, stability over the life span, and exchange of many different types of support (confiding, reassurance, respect, care when ill, discussion when upset, and talk about health). The principal respondents named a total of 6,341 network members, ranging in age from 18 to 96 years. Detailed structural and functional characteristics were collected from the principal respondents on the first ten named members of each support network. Similar interviews were then conducted with one to three network members of those 259 principal respondents who were 70+ years old. Two data files are provided: Part 1 contains merged data from the interviews of both the principal respondents aged 70+ and their network members, and Part 2 contains data from the principal respondents aged 50+. Datasets: DS0: Study-Level Files DS1: Principals, Aged 70+/Network Data DS2: Principals, Aged 50+ Data DS3: SAS Proc Format Statements for Principals, Aged 70+/Network Data DS4: SAS Input Statements for Principals, Aged 70+/Network Data DS5: SAS Format Statements for Principals, Aged 70+/Network Data DS6: SAS Label Statements for Principals, Aged 70+/Network Data DS7: SAS Missing Value Statements for Principals, Aged 70+/Network Data DS8: SPSS Data List Statements for Principals, Aged 70+/Network Data DS9: SPSS Variable Label Statements for Principals, Aged 70+/Network Data DS10: SPSS Value Label Statements for Principals, Aged 70+/Network Data DS11: SPSS Missing Value Statements for Principals, Aged 70+/Network Data DS12: SAS Proc Format Statements for Principals, Aged 50+ Data DS13: SAS Input Statements for Principals, Aged 50+ Data DS14: SAS Format Statements for Principals, Aged 50+ Data DS15: SAS Label Statements for Principals, Aged 50+ Data DS16: SAS Missing Value Statements for Principals, Aged 50+ Data DS17: SPSS Data List Statements for Principals, Aged 50+ Data DS18: SPSS Variable Label Statements for Principals, Aged 50+ Data DS19: SPSS Value Label Statements for Principals, Aged 50+ Data DS20: SPSS Missing Value Statements for Principals, Aged 50 Data Multistage national probability sample of households with at least one member aged 50 years or older and an oversampling of all household members aged 70 years or older. Additionally, up to three network members were interviewed for each of the respondents aged 70+ (as well as one child and one grandchild if not already named), for a total of 497 network members. There was some overlap between principal respondents and network members: 102 network members were also principal respondents, and 40 were named by more than one principal respondent. The age distribution of the 718 principal respondents was 50-64 years (N = 333), 65-74 years (N = 227), and 75-95 years (N = 158). Persons 50 years and older in households of the United States.

Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms. Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time. Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months. Methods 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. Fully eligible individuals later completed telephone consent, enrollment and randomization. Thirty total participants were randomized 1:1 to EHR portal versus telephone outcome assessment, and patient-reported and process outcomes were collected at 3- and 6-months, with primary outcome 6-month retention in EHR arm(feasibility target: ≥11 participants retained). Variables in this dataset include recruitment flow diagram data, baseline participant sociodemographic and clinical characteristics, retention (successful PROM collection at 6 months), and process measures. The process measures included research staff time to collect outcomes, research staff time to collect outcomes and enter data, time from initial outcome collection reminder to outcome collection, and number of reminders sent to participants for outcome collection. PROMs were collected via the randomized method only at 3 months. At 6 months, if the criteria for retention was not met by the randomized method (failure to return outcomes by 1 week after 5 post-due date reminders for outcome collection), up to 3 additional attempts were made to collect outcomes by the alternative method, and process measures were also collected during this hybrid outcome collection method approach.

The input macro parameters in the SAS macro %n_gssur.

This dataset contains input structures and parameters for coarse-grained molecular dynamics simulation of SAS-6 protein oligomers as well as post-processing files and analysis scripts. Abstract of related publication: Discovering mechanisms governing organelle assembly is a fundamental pursuit in the life sciences. The centriole is an evolutionarily conserved organelle with a signature 9-fold symmetrical chiral arrangement of microtubules imparted onto the cilium it templates. The first structure in nascent centrioles is a cartwheel, which comprises stacked 9-fold symmetrical SAS-6 ring polymers and emerging orthogonal to a surface surrounding resident centrioles. The mechanisms through which SAS-6 polymerization ensures centriole organelle architecture remain elusive. We deployed photothermally-actuated off-resonance tapping high-speed atomic force microscopy (PORT-HS-AFM) to decipher surface SAS-6 self-assembly mechanisms. We discovered that the surface shifts the reaction equilibrium by ~104 compared to solution. Moreover, coarse-grained molecular dynamics simulations and PORT-HS-AFM revealed that the surface converts the inherently helical propensity of SAS-6 polymers into 9-fold rings with residual asymmetry, which may guide ring stacking and impart chiral features to centrioles and cilia. Overall, our work reveals fundamental design principles governing centriole assembly.

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Do you need to accelerate the performance of your data-intensive application workloads? HPE Server Mixed Use (MU) Solid State Drives (SSDs) deliver high performance and endurance while reducing power consumption for customers with applications requiring high random read and write IOPs performance. HPE MU SSDs offer the highest performance applications that need a balance of strong read and write and they typically have an endurance of between High Performance, Exceptional Reliability and Efficiency for Faster Business ResultsHPE Mixed Use (MU) Solid State Drives (SSD) are ideal for Big Data Analytics, Cloud Computing, Active Archiving, Database Applications and Data Warehousing.Achieve higher Input / Output per Second (IOPs) to enhance the performance of your data center.Maintains data accuracy with full data-path error detection.A broad portfolio of optimized solutions in capacities up to 1.6 TB and growing.12 GB SAS, 6 GB SATA and NVMe.

Abstract

The main objective of the new agricultural statistics program is to provide timely, accurate, credible and comprehensive agricultural statistics to describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock; which can be used for food and agriculture policy formulation and planning, and for the compilation of national accounts statistics.

In this regard, the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) from November 2015 to October 2016 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Second Economic Development and Poverty Reduction Strategy (EDPRS II) and Vision 2020. This 2016 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipments, use of crop production by agricultural operators and by large scale farmers.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

The 2016 RSAS targeted agricultural operators and large scale farmers operating in Rwanda.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Seasonal Agriculture Survey (SAS) sample is composed of two categories of respondents: agricultural operators1 and large-scale farmers (LSF).

For the 2016 SAS, NISR used as the sampling method a dual frame sampling design combining selected area frame sample3 segments and a list of large-scale farmers.

NISR used also imagery from RNRA with a very high resolution of 25 centimeters to divide the total land of the country into twelve strata. A total number of 540 segments were spread throughout the country as coverage of the survey with 25,346 and 23,286 agricultural operators in Season A and Season B respectively. From these numbers of agricultural operators, sub-samples were selected during the second phases of Seasons A and B.

It is important to note that in each of agricultural season A and B, data collection was undertaken in two phases. Phase I was mainly used to collect data on demographic and social characteristics of interviewees, area under crops, crops planted, rainfall, livestock, etc. Phase II was mainly devoted to the collection of data on yield and production of crops.

Phase I serves at collecting data on area under different types of crops in the screening process, whereas the Phase II is mainly devoted to the collection of data on demographic, social characteristics of interviewees, together with yields of the different crops produced. Enumerated large-scale farmers (LSF) were 558 in both 2015 Season A and B. The LSF were engaged in either crop farming activities only, livestock farming activities only, or both crop and livestock farming activities.

Agricultural operators are the small scale farmers within the sample segments. Every selected segment was firstly screened using the appropriate materials such as the segment maps, GIS devices and the screening form. Using these devices, the enumerators accounted for every plot inside the sample segments. All Tracts6 were classified as either agricultural (cultivated land, pasture, and fallow land) or non-agricultural land (water, forests, roads, rocky and bare soils, and buildings).

During Phase I, a complete enumeration of all farmers having agricultural land and operating within the 540 selected segments was undertaken and a total of 25,495 and 24,911 agricultural operators were enumerated respectively in Seasons A and B. Season C considered only 152 segments, involving 3,445 agricultural operators.

In phase II, 50% of the large-scale farmers were undertaking crop farming activities only and 50% of the large-scale farmers were undertaking both crop and livestock farming and were selected for interview. A sample of 199 and 194 large-scale farmers were interviewed in Seasons A and B, respectively, using a farm questionnaire.

From the agricultural operators enumerated in the sample segments during Phase I, a sample of the agricultural operators was designed for Phase II as follows: 5,502 for Season A, 5,337 for Season B and 644 for Season C. The method of probability proportional to size (PPS) sampling at the national level was used. Furthermore, the total number of enumerated large-scale farmers was 774 in 2016 Season A and 622 in Season B.

The Season C considered 152 segments counting 8,987 agricultural operators from which 963 agricultural operators were selected for survey interviews.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

There were two types of questionnaires used for this survey namely Screening questionnaire and farm questionnaires.

A Screening Questionnaire was used to collect information that enabled identification of an Agricultural Operator or Large Scale Farmer and his or her land use.
Farm questionnaires were of two types: a) Phase I Farm Questionnaire was used to collect data on characteristics of Agricultural Operators, crop identification and area, inputs (seeds, fertilizers, labor, …) for Agricultural Operators and large scale farmers. b) Phase 2 Farm questionnaire was used in the collection of data on crop production and use of production.

It is important to mention that all these Farm Questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. For season C, after screening, an interview was conducted for each selected tract/Agricultural Operator using one consolidated Farm questionnaire. All the surveys questionnaires used were published in both English and Kinyarwanda languages.

Cleaning operations

Data editing took place at different stage. Firstly, the filled questionnaires were repatriated at NISR for office editing and coding before data entry started. Data entry of the completed and checked questionnaires was undertaken at the NISR office by 20 staff trained in using the CSPro software. To ensure appropriate matching of data in the completed questionnaires and plot area measurements from the GIS unit, a LOOKUP file was integrated in the CSPro data entry program to confirm the identification of each agricultural operator or LSF before starting data entry. Thereafter, data were entered in computers, edited and summarized in tables using SPSS and Excel.

Response rate

The response rate for Seasonal Agriculture Survey is 98%.

Data appraisal

All Farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. And in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the Team leader.

Simulation input files for gromacs to reproduce the data from the manuscript "Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data" (submitted to Comm. Chem.)

This dataset contains each SAS syntax file, input data file and output file used in completion of the Livestock Science Submission Manuscript titled “Associations of cardiovascular function and structure with feed efficiency and carcass composition in beef cattle” along with a step by step guide to work through all files in the correct order to complete the statistical analysis and reproduce the results detailed in the manuscript.

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs. This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform. The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560 Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options. The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply. Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson. See included README file for operational details and further description of the measured data signals. Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

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The Maxtor Atlas 15K II SAS combines a 16MB cache, 98MB/s sustained data transfer rates and as fast as 3.0 msec seek times with the latest enterprise interface technology Serial Attached SCSI (SAS). Atlas 15K II drive seek times deliver the required IOPS (input/output per second) performance to support transactional and IO-intensive applications. Its sustained data rate makes it the ideal drive for high-bandwidth applications. Serial Attached SCSI brings together the best features of other enterprise interfaces to deliver higher system availability, scalability, end-user flexibility, performance and SCSI investment protection.

"The Youth Risk Behavior Surveillance System (YRBSS) is a set of surveys that track behaviors that can lead to poor health in students grades 9 through 12. The surveys are administered every other year. Some of the health-related behaviors and experiences monitored are:* Student demographics: sex, sexual identity, race and ethnicity, and grade* Youth health behaviors and conditions: sexual, injury and violence, bullying, diet and physical activity, obesity, and mental health, including suicide* Substance use behaviors: electronic vapor product and tobacco product use, alcohol use, and other drug use* Student experiences: parental monitoring, school connectedness, unstable housing, and exposure to community violence"(Taken from https://web.archive.org/web/20231011104407/https://www.cdc.gov/healthyyouth/data/yrbs/overview.htm)This deposit consists of ASCII datasets, SAS input syntax, and .csv datasets for the National YRBS/YRBSS datasets for 1991-2021. The data were downloaded in October of 2023 for a student who wished to use the data for a thesis.

METADATA OUTLINE SHEET 1 STUDY INFORMATION SHEET 2 RANDOM ASSISNMENT OF PREGNANT RATS TO TREATMENT GROUPS SHEER 3 MATERNAL WEIGHT AND WEIGHT GAIN DURING DOSING and fetal DATA RAW DATA RESULTS PREDICTIONS OF DOSE ADDITIVITY SHEET 4 TESTOSTERONE (T PROD) DATA RAW DATA SAS INPUT FILES TREATMENT EFFECTS SHEET 5 CUSTOM GENE (mRNA) SAS INPUT FILE WITH SAS STATEMENTS AND RQW DATA SHEET 6 RESULTS FROM STATISTICAL ANALYSIS OF CUSTOM ARRAY mRNA DATA CUSTOM ARRAY: LIST OF GENES AND GENE DESCRIPTIONS SHEET 7 PREDICTION MODELS OF TESTOSTERONE PRODUCTION REDUCTIONS AND REPRODUCTIVE EFFECTS OF IN UTERO PHTHALATE EXPOSURE SHEET 8 TREATMENT EFFECTS PREDICTED FROM THE TESTOSTERONE PREDICTION MODELS COMPARISON OF THE PREDICTED EFFECTS OF THE DBP+DINP MIXTURE WITH OBSERVED EFFECTS OF THE REFERENCE CHEMICAL (DBP) AT EQUIVALENT DOSES, ASSUMING DOSE ADDITIVITY. This dataset is associated with the following publication: Gray, L., C. Lambright, N. Evans, J. Ford, and J. Conley. Using Targeted Fetal Rat Testis Genomic and Endocrine Alterations to Predict the Effects of a Phthalate Mixture on the Male Reproductive Tract.. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 100180, (2024).