The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

This repository is associated with the GitHub repository: https://github.com/jacquelynzhy/Statistical_Superradiance, which includes the code to reproduce the findings of Zhu et al. (2025). Specifically, the file "Output1.dat" here represents the output of running ZEVN with the initial conditions outlined in Section 3.1 of Zhu et al. (2025), which only included BH-BH binaries. For the values generated by a ZEVN run and instructions on how to select the type of remnants you are interested in, please refer to Spera et al. (2019) and the ZEVN GitHub page at: https://gitlab.com/sevncodes/sevn.

NOTE ON GLAS BINARY DATA: Access to all ICESat/GLAS binary data products at NSIDC DAAC was removed 01 August 2017.The Binary Data Subsetter also has been decommissioned. ICESat/GLAS data remain available in HDF5 format (GLAH05).

The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and Land Elevation Satellite (ICESat) provides global measurements of polar ice sheet elevation to discern changes in ice volume (mass balance) over time. Secondary objectives of GLAS are to measure sea ice roughness and thickness, cloud and atmospheric properties, land topography, vegetation canopy heights, ocean surface topography, and surface reflectivity.

GLAS has a 1064 nm laser channel for surface altimetry and dense cloud heights, and a 532 nm lidar channel for the vertical distribution of clouds and aerosols.

Level-1B waveform parameterization data (GLA05) include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics.

Each data granule has an associated browse product that users can quickly view to determine the general quality of the data in the granule. Browse products consist of image plots of key parameters and statistics. Data are in scaled integer binary format, big-endian (Unix) byte order.

Median (IQR) proportion of samples in which the model did not converge overall and by data generation parameters. (DOC)

Replication materials for Esarey and Pierce, "Assessing Fit Quality and Testing for Misspecification in Binary Dependent Variable Models," Political Analysis 2012.

This dataset, derived from the Palmer's Penguins dataset, is intended to be a valuable resource for beginners in logistic regression.

The idea is to classify Gentoo and Adelie penguins using lgistic regression but you can learn something about them too!! 🙂:

Gentoo Penguins (Pygoscelis papua)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3880940%2F544281eed175e6f70787192cdf2c90ac%2Fgentoo.jpeg?generation=1701019777180956&alt=media" alt=""> - Habitat: Predominantly found on the Antarctic Peninsula and nearby islands. - Physical Characteristics: Recognizable by their bright orange-red bills and white stripe extending across the top of their heads. They are among the larger penguin species. - Diet: Primarily feed on krill, though their diet also includes fish and squid. - Behavior: Known for their fast swimming ability and long, deep dives while foraging.

Adelie Penguins (Pygoscelis adeliae)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3880940%2Faddfdad600743788f1cd44d3f474a5f3%2Fa-pair-of-adelie-penguins-photo-from-june-1984-all-hands-magazine-d14147-1024.jpeg?generation=1701019849646979&alt=media" alt=""> - Habitat: Widely distributed along the entire Antarctic coast. - Physical Characteristics: Smaller than Gentoo penguins, with distinctive black and white plumage and a blue-black bill. - Diet: Mainly eat krill, along with some fish and squid. - Behavior: Highly social and known for their loud calls and aggressive behavior during the breeding season.

The dataset was collected in Palmer Archipelago: A group of islands off the northwestern coast of the Antarctic Peninsula. Home to a diverse range of wildlife, including several species of penguins, seals, and seabirds. This archipielago is characterized by cold temperatures, strong winds, and significant ice cover, although it's one of the most rapidly warming areas on Earth.

Usage Scenarios for the Dataset

Educational Use: Ideal for teaching logistic regression in data science and statistics courses. Students can learn how to handle categorical and numerical data, as well as binary classification.

Ecological Research: Researchers can use the dataset to study penguin population dynamics, diet preferences, and the impact of climate change on habitat and species distribution.

Conservation Efforts: Conservationists could analyze trends in penguin populations and health indicators (like body mass) to inform protection strategies.

Data Visualization Projects: The dataset is suitable for creating visual representations of data, such as scatter plots or heat maps, to illustrate differences between species or changes over time.

Machine Learning Model Development: Beginners in machine learning can use this dataset to build and validate logistic regression models, before moving on to more complex algorithms.

Statistical Analysis: The dataset can be used to perform statistical tests to understand correlations between different physical characteristics of penguins and their environment or diet.

This dataset provides a unique opportunity for a wide range of users, from educators and students to researchers and conservationists, to explore and understand the fascinating world of these Antarctic penguins.

NOTE ON GLAS BINARY DATA: Access to all ICESat/GLAS binary data products at NSIDC DAAC was removed 01 August 2017.The Binary Data Subsetter also has been decommissioned.ICESat/GLAS data remain available in HDF5 format.

The Geoscience Laser Altimeter System (GLAS) instrument on the Ice, Cloud, and Land Elevation Satellite (ICESat) provides global measurements of polar ice sheet elevation to discern changes in ice volume (mass balance) over time. Secondary objectives of GLAS are to measure sea ice roughness and thickness, cloud and atmospheric properties, land topography, vegetation canopy heights, ocean surface topography, and surface reflectivity.

GLAS has a 1064 nm laser channel for surface altimetry and dense cloud heights, and a 532 nm lidar channel for the vertical distribution of clouds and aerosols.

Level-2 cloud heights for multi-layer clouds (GLA09) contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.

Each data granule has an associated browse product that users can quickly view to determine the general quality of the data in the granule. Browse products consist of image plots of key parameters and statistics. Data are in scaled integer binary format, big-endian (Unix) byte order.

Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

Elliott, Graham, Ghanem, Dalia, Krüger, Fabian, (2016) "Forecasting Conditional Probabilities of Binary Outcomes under Misspecification." Review of Economics and Statistics 98:4, 742-755.

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://github.com/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

The digital binary drivers market is an essential component within the broader spectrum of electronic components and automation technology, focusing on advanced systems that manage the operation of various digital devices. These drivers play a crucial role in converting binary signals into actionable electronic impu

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Data on two tomographic studies on Berea sandstone as supplemental information of the publication "Flow field tomography of reactive transport: comparison of β⁺ tracers ¹⁸F, ⁷⁶Br & ¹²⁴I" by Jann Schöngart, Johannes Kulenkampff, and Cornelius Fischer.

Part of the data published here was used for prior works by Schabernack et al. (2025). Therefore, the the presented dataset has overlap withthe dataset published in Kulenkampff et al. (2024). This overlap is limited to the µCT data, and the PET data for analysis D_B and D_C.

The data in this publication consists of:

µCT data

Core_D_after_dissolution_2496x2496x1615.raw: µCT of the inlet section of berea sandstone core D before dissolution as normalized graylevel data, voxel size = 10.032 µm. Format: 3D-array of uInt16, x=1:2496, y=1:2496, z=1:1615.

Core_D_before_dissolution_2307x2329x1452_uint16.raw: µCT of the inlet section of berea sandstone core D after dissolution as normalized graylevel data, voxel size = 10.032 µm. Format: 3D-array of uInt16, x=1:2307, y=1:2329, z=1:1452.

Positron emission tomography data
All PET data is stored as three-dimensional binary arrays of floats, with a voxel size of 1.15 mm.

Stored in [subset]_PET_raw.zip:

Uncalibrated positron emission tomography time series (decay corrected). Each image consists of two files - a header file (.hv) and the binary image file (.v). The header file contains information on how to read the binary file, as well as additional information.
Please note that not all of the metadata given in the header file (like timestamps, etc.) are generated automatically and not neccessarily accurate.

Stored in [subset]_PET_err.zip:

Relative errors of the PET_raw data, calculated from count rates using poisson statistics. A value of 1 equals 100% error. The volumes are cut to the ROI. The data structure is identical to [samplename]_PET_raw.zip.

Stored in [subset]_PET_corrected.zip:

Positron emission tomography time series, corrected for tracer activity and detector sensitivity fluctuations. Values are in in Bq/voxel. Voxels with relative errors above 100% are discarded. The volumes are cut to the ROI. The data structure is identical to [samplename]_PET_raw.zip.

Flow field data
stored in [subset]_flowfield.zip:
Flow Direction_[X]x[Y]x[Z]x1_vec3_double.raw: Flow direction vectors as binary data of the shape [x,y,z,[3]], a three dimensional array of vectors which are stored as double (float64), voxel size = 1.15 mm.

Flow Rate_[X]x[Y]x[Z]x1_double.raw: Flow rates (uncalibrated) as binary data of the shape [x,y,z], a three dimensional array of doubles (float64), voxel size = 1.15 mm.

Porosity_[X]x[Y]x[Z]x1_double.raw: Porosities (uncalibrated) as binary data of the shape [x,y,z], a three dimensional array of doubles (float64), voxel size = 1.15 mm.

Transport Error_[X]x[Y]x[Z]x1_double.raw: A measure of error quantifying the ratio of computed in- and outflow to each voxel. Values close to 0 are better. Stored as binary data of the shape [x,y,z], a three dimensional array of doubles (float64), voxel size = 1.15 mm.

Velocity_[X]x[Y]x[Z]x1_double.raw: Velocities (uncalibrated) as binary data of the shape [x,y,z], a three dimensional array of doubles (float64), voxel size = 1.15 mm.

The Binary Decoders market plays a pivotal role in modern electronics, facilitating the conversion of binary information into a more readable and usable format suitable for various electronic devices. These components are integral to data processing, communication systems, and digital circuits, enabling efficient da

Data sets as described in "Goodbye to Chi-by-Eye: A Bayesian Analysis of Photometric Binaries in Six Open Clusters", Childs et al. 2023 https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract

Method comparison studies are essential for development in medical and clinical fields. These studies often compare a cheaper, faster, or less invasive measuring method with a widely used one to see if they have sufficient agreement for interchangeable use. Moreover, unlike simply reading measurements from devices, e.g., reading body temperature from a thermometer, the response measurement in many clinical and medical assessments is impacted not only by the measuring device but also by the rater. For example, widespread inconsistencies are commonly observed among raters in psychological or cognitive assessment studies due to different characteristics such as rater training and experience, especially in large-scale assessment studies when many raters are employed. This paper proposes a model-based approach to assess agreement of two measuring methods for paired repeated binary measurements under the scenario where the agreement between two measuring methods and the agreement among raters are required to be studied simultaneously. Based upon the generalized linear mixed models (GLMM), the decision on the adequacy of interchangeable use is made by testing the equality of fixed effects of methods. Approaches for assessing method agreement, such as the Bland-Altman diagram and Cohen's kappa, are also developed for repeated binary measurements based upon the latent variables in GLMMs. We assess our novel model-based approach by simulation studies and a real clinical application, in which patients are evaluated repeatedly for delirium with two validated screening methods. Both the simulation studies and the real data analyses demonstrate that our proposed approach can effectively assess method agreement.

The Antioxidant Binary Blends market has gained significant traction in recent years, driven by an increasing awareness of the health benefits associated with antioxidants. These blends are widely utilized across various industries, including food and beverage, cosmetics, and pharmaceuticals, primarily for their abi

1) Data Introduction • The Palmer Penguins Dataset focuses on two species of penguins, Gentoo and Adelie, collected from the Palmer Archipelago. It includes physical attributes such as bill length, bill depth, flipper length, body mass, and the year of observation. This dataset is ideal for binary classification problems, like logistic regression, making it perfect for beginners in data science and statistics.

2) Data Utilization (1) Palmer Penguins data has characteristics that: • It includes measurements of physical characteristics and habitat information, useful for differentiating species and studying ecological patterns. (2) Palmer Penguins data applications: • Educational Use: Ideal for teaching logistic regression and other machine learning techniques, allowing students to handle categorical and numerical data for binary classification. • Ecological Research: Useful for studying penguin population dynamics, diet preferences, and the impact of climate change on habitat and species distribution.

Attached file provides supplementary data for linked article.

In this related article, the researchers focus on a pseudo-coefficient of determination for generalized linear models with binary outcome. Although there are numerous coefficients of determination proposed in the literature, none of them is identified as the best in terms of estimation accuracy, or incorporates all desired characteristics of a precise coefficient of determination. Considering this, we propose a new coefficient of determination by using a computational Monte Carlo approach, and exhibit main characteristics of the proposed coefficient of determination both analytically and numerically. We evaluate and compare performances of the proposed and nine existing coefficients of determination by a comprehensive Monte Carlo simulation study. The proposed measure is found superior to the existent measures when dependent variable is balanced or moderately unbalanced for probit, logit, and complementary log-log link functions and a wide range of sample sizes. Due to the extensive design space of our simulation study, we identify new conditions in which previously recommended coefficients of determination should be used carefully.