This repository stores synthetic datasets derived from the database of the UK Biobank (UKB) cohort.

The datasets were generated for illustrative purposes, in particular for reproducing specific analyses on the health risks associated with long-term exposure to air pollution using the UKB cohort. The code used to create the synthetic datasets is available and documented in a related GitHub repo, with details provided in the section below. These datasets can be freely used for code testing and for illustrating other examples of analyses on the UKB cohort.

The synthetic data have been used so far in two analyses described in related peer-reviewed publications, which also provide information about the original data sources:

• Vanoli J, et al. Long-term associations between time-varying exposure to ambient PM2.5 and mortality: an analysis of the UK Biobank. Epidemiology. 2025;36(1):1-10. DOI: 10.1097/EDE.0000000000001796 [freely available here, with code provided in this GitHub repo]
• Vanoli J, et al. Confounding issues in air pollution epidemiology: an empirical assessment with the UK Biobank cohort. International Journal of Epidemiology. 2025;54(5):dyaf163. DOI: 10.1093/ije/dyaf163 [freely available here, with code provided in this GitHub repo]

Note: while the synthetic versions of the datasets resemble the real ones in several aspects, the users should be aware that these data are fake and must not be used for testing and making inferences on specific research hypotheses. Even more importantly, these data cannot be considered a reliable description of the original UKB data, and they must not be presented as such.

The work was supported by the Medical Research Council-UK (Grant ID: MR/Y003330/1).

Content

The series of synthetic datasets (stored in two versions with csv and RDS formats) are the following:

• synthbdcohortinfo: basic cohort information regarding the follow-up period and birth/death dates for 502,360 participants.
• synthbdbasevar: baseline variables, mostly collected at recruitment.
• synthpmdata: annual average exposure to PM_2.5 for each participant reconstructed using their residential history.
• synthoutdeath: death records that occurred during the follow-up with date and ICD-10 code.

In addition, this repository provides these additional files:

• codebook: a pdf file with a codebook for the variables of the various datasets, including references to the fields of the original UKB database.
• asscentre: a csv file with information on the assessment centres used for recruitment of the UKB participants, including code, names, and location (as northing/easting coordinates of the British National Grid).
• Countries_December_2022_GB_BUC: a zip file including the shapefile defining the boundaries of the countries in Great Britain (England, Wales, and Scotland), used for mapping purposes [source].

Generation of the synthetic data

The datasets resemble the real data used in the analysis, and they were generated using the R package synthpop (www.synthpop.org.uk). The generation process involves two steps, namely the synthesis of the main data (cohort info, baseline variables, annual PM_2.5 exposure) and then the sampling of death events. The R scripts for performing the data synthesis are provided in the GitHub repo (subfolder Rcode/synthcode).

The first part merges all the data, including the annual PM_2.5 levels, into a single wide-format dataset (with a row for each subject), generates a synthetic version, adds fake IDs, and then extracts (and reshapes) the single datasets. In the second part, a Cox proportional hazard model is fitted on the original data to estimate risks associated with various predictors (including the main exposure represented by PM_2.5), and then these relationships are used to simulate death events in each year. Details on the modelling aspects are provided in the article.

This process guarantees that the synthetic data do not hold specific information about the original records, thus preserving confidentiality. At the same time, the multivariate distribution and correlation across variables, as well as the mortality risks, resemble those of the original data, so the results of descriptive and inferential analyses are similar to those in the original assessments. However, as noted above, the data are used only for illustrative purposes, and they must not be used to test other research hypotheses.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

If you use this dataset for your scientific work, please cite: A. Vysocky, S. Grushko, T. Spurny, R. Pastor and T. Kot, "Generating Synthetic Depth Image Dataset for Industrial Applications of Hand Localisation," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3206948.

Dataset created in CoppeliaSim 3D environment. Model of the hand, primitive shape obstacles and specific heightfield simulating noise and random depth background is captured with depth sensing vision sensor. Images are saved as single channel 320x240px PNG files.

Vision sensor in the scene is 1.0m above the ground and minimum sensing distance is set to 0.2m. 0.8m workspace is discretized to 8bit depth.

Masks are generated with a sensor capturing only the hand and the image is binarized. The mask contains whole hand with forearm.

2 sets of dataset hand_1 and hand_2 contain 135k labeled images each. Hand_1 includes images of a pointing gesture performing hand, hand_2 is a open palm hand.

Another 2 sets of dataset hand1_robot and hand2_robot contain 45k labeled images each. In this dataset real workspace with robot and the operator is simulated.

Position coded in the name of files is a position of the index finger in the workplace where zero position is in the center of the image 1 meter below the camera. Names of depth image and corresponding mask are identical.

If you use this dataset for your scientific work, please cite: A. Vysocky, S. Grushko, T. Spurny, R. Pastor and T. Kot, "Generating Synthetic Depth Image Dataset for Industrial Applications of Hand Localisation," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3206948.

Synthetic data generated for the ECHILD training course (code available at https://github.com/UCL-CHIG/ECHILD_Synthetic), along with suggested solutions for practical exercises in R.

Abstract

Developing robust deep learning models for fetal ultrasound image analysis requires comprehensive, high-quality datasets to effectively learn informative data representations within the domain. However, the scarcity of labelled ultrasound images poses substantial challenges, especially in low-resource settings. To tackle this challenge, we leverage synthetic data to enhance the generalizability of deep learning models. This study proposes a diffusion-based method, Fetal Ultrasound LoRA (FU-LoRA), which involves fine-tuning latent diffusion models using the LoRA technique to generate synthetic fetal ultrasound images. These synthetic images are integrated into a hybrid dataset that combines real-world and synthetic images to improve the performance of zero-shot classifiers in low-resource settings. Our experimental results on fetal ultrasound images from African cohorts demonstrate that FU-LoRA outperforms the baseline method by a 13.73% increase in zero-shot classification accuracy. Furthermore, FU-LoRA achieves the highest accuracy of 82.40%, the highest F-score of 86.54%, and the highest AUC of 89.78%. It demonstrates that the FU-LoRA method is effective in the zero-shot classification of fetal ultrasound images in low-resource settings. Our code and data are publicly accessible on GitHub.

Method

Our FU-LoRA method: Fine-tuning the pre-trained latent diffusion model (LDM) [2] using the LoRA method on a small fetal ultrasound dataset from high-resource settings (HRS). This approach integrates synthetic images to enhance generalization and performance of deep learning models. We conduct three fine-tuning sessions for the diffusion model to generate three LoRA models with different hyper-parameters: alpha in [8, 32, 128], and r in [8, 32, 128]. The merging rate alpha/r is fixed to 1. The purpose of this operation is to delve deeper into LoRA to uncover optimizations that can improve the model's performance and evaluate the effectiveness of parameter r in generating synthetic images.

Datasets

The Spanish dataset (URL) in HRS includes 1,792 patient records in Spain [1]. All images are acquired during screening in the second and third trimesters of pregnancy using six different machines operated by operators with similar expertise. We randomly selected 20 Spanish ultrasound images from each of the five maternal–fetal planes (Abdomen, Brain, Femur, Thorax, and Other) to fine-tune the LDM using LoRA technique, and 1150 Spanish images (230 x 5 planes) to create the hybrid dataset. In summary, fine-tuning the LDM utilizes 100 images including 85 patients. Training downstream classifiers uses 6148 images from 612 patients. Within the 6148 images used for training, a subset of 200 images is randomly selected for validation purposes. The hybrid dataset employed in this study has a total of 1150 Spanish images, representing 486 patients.

We create the synthetic dataset comprising 5000 fetal ultrasound images (500 x 2 samplers x 5 planes) accessible to the open-source community. The generation process utilizes our LoRA model Rank r = 128 with Euler and UniPC samplers known for their efficiency. Subsequently, we integrate this synthetic dataset with a small amount of Spanish data to create a hybrid dataset.

Implementation Details

The hyper-parameters of LoRA models are defined as follows: batch size to 2; LoRA learning rate to 1e-4; total training steps to 10000; LoRA dimension to 128; mixed precision selection to fp16; learning scheduler to constant; and input size (resolution) to 512. The model is trained on a single NVIDIA RTX A5000, 24 GB with 8-bit Adam optimizer on PyTorch.

If you use this dataset for your work, please cite the related papers: A. Vysocky, S. Grushko, T. Spurny, R. Pastor and T. Kot, Generating Synthetic Depth Image Dataset for Industrial Applications of Hand Localisation, in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3206948.

S. Grushko, A. Vysocký, J. Chlebek, P. Prokop, HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial Environments. preprint in arXiv, 2023, https://doi.org/10.48550/arXiv.2304.05826

The HaDR dataset is a multimodal dataset designed for human-robot gesture-based interaction research, consisting of RGB and Depth frames, with binary masks for each hand instance (i1, i2, single class data). The dataset is entirely synthetic, generated using Domain Randomization technique in CoppeliaSim 3D. The dataset can be used to train Deep Learning models to recognize hands using either a single modality (RGB or depth) or both simultaneously. The training-validation split comprises 95K and 22K samples, respectively, with annotations provided in COCO format. The instances are uniformly distributed across the image boundaries. The vision sensor captures depth and color images of the scene, with the depth pixel values scaled into a single channel 8-bit grayscale image in the range [0.2, 1.0] m. The following aspects of the scene were randomly varied during generation of dataset: • Number, colors, textures, scales and types of distractor objects selected from a set of 3D models of general tools and geometric primitives. A special type of distractor – an articulated dummy without hands (for instance-free samples) • Hand gestures (9 options). • Hand models’ positions and orientations. • Texture and surface properties (diffuse, specular and emissive properties) and number (from none to 2) of the object of interest, as well as its background. • Number and locations of directional lights sources (from 1 to 4), in addition to a planar light for ambient illumination. The sample resolution is set to 320×256, encoded in lossless PNG format, and contains only right hand meshes (we suggest using Flip augmentations during training), with a maximum of two instances per sample.

Test dataset (real camera images): Test dataset containing 706 images was captured using a real RGB-D camera (RealSense L515) in a cluttered and unstructured industrial environment. The dataset comprises various scenarios with diverse lighting conditions, backgrounds, obstacles, number of hands, and different types of work gloves (red, green, white, yellow, no gloves) with varying sleeve lengths. The dataset is assumed to have only one user, and the maximum number of hand instances per sample was limited to two. The dataset was manually labelled, and we provide hand instance segmentation COCO annotations in instances_hands_full.json (separately for train and val) and full arm instance annotations in instances_arms_full.json. The sample resolution was set to 640×480, and depth images were encoded in the same way as those of the synthetic dataset.

Channel-wise normalization and standardization parameters for datasets

If you use this dataset for your work, please cite the related papers: A. Vysocky, S. Grushko, T. Spurny, R. Pastor and T. Kot, Generating Synthetic Depth Image Dataset for Industrial Applications of Hand Localisation, in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3206948.

S. Grushko, A. Vysocký, J. Chlebek, P. Prokop, HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial Environments. preprint in arXiv, 2023, https://doi.org/10.48550/arXiv.2304.05826

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).

The dataset used in this study is publicly available for research purposes. If you are using this dataset, please cite the following paper, which outlines the complete details of the dataset and the methodology used for its generation:

Amit Karamchandani, Javier Núñez, Luis de-la-Cal, Yenny Moreno, Alberto Mozo, Antonio Pastor, "On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination," under submission.

This dataset contains a synthetic dataset generated to differentiate between benign and malicious heavy hitter flows within complex network environments. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.

To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.

The feature set includes flow statistics commonly used in network analysis, such as:

• Traffic protocol type,
• Flow duration (the time between the initial and final packet in both directions),
• Total count of payload packets transmitted in both directions,
• Cumulative bytes transmitted in both directions,
• Time discrepancy between the first packet observations at the source and destination,
• Packet and byte transmission rates per second within each interval, and
• Total packet and byte counts within each interval in both directions.

This synthetic dataset contains 5,000 student records exploring the relationship between study hours and academic performance.

Dataset Features

• student_id: Unique identifier for each student (1-5000)
• study_hours: Hours spent studying (0-12 hours, continuous)
• grade: Final exam score (0-100 points, continuous)

Potential Use Cases

• Linear regression modeling and practice
• Data visualization exercises
• Statistical analysis tutorials
• Machine learning for beginners
• Educational research simulations

Data Quality

• No missing values
• Normally distributed residuals
• Realistic educational scenario
• Ready for immediate analysis

Data Generation Code

This dataset was generated using R.

R Code

# Set seed for reproducibility
set.seed(42)

# Define number of observations (students)
n <- 5000

# Generate study hours (independent variable)
# Uniform distribution between 0 and 12 hours
study_hours <- runif(n, min = 0, max = 12)

# Create relationship between study hours and grade
# Base grade: 40 points
# Each study hour adds an average of 5 points
# Add normal noise (standard deviation = 10)
theoretical_grade <- 40 + 5 * study_hours

# Add normal noise to make it realistic
noise <- rnorm(n, mean = 0, sd = 10)

# Calculate final grade
grade <- theoretical_grade + noise

# Limit grades between 0 and 100
grade <- pmin(pmax(grade, 0), 100)

# Create the dataframe
dataset <- data.frame(
 student_id = 1:n,
 study_hours = round(study_hours, 2),
 grade = round(grade, 2)
)

Synthetic data for DCASE 2019 task 4

Freesound dataset [1,2]: A subset of FSD is used as foreground sound events for the synthetic subset of the dataset for DCASE 2019 task 4. FSD is a large-scale, general-purpose audio dataset composed of Freesound content annotated with labels from the AudioSet Ontology [3].

SINS dataset [4]: The derivative of the SINS dataset used for DCASE2018 task 5 is used as background for the synthetic subset of the dataset for DCASE 2019 task 4. The SINS dataset contains a continuous recording of one person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.

The synthetic set is composed of 10 sec audio clips generated with Scaper [5]. The foreground events are obtained from FSD. Each event audio clip was verified manually to ensure that the sound quality and the event-to-background ratio were sufficient to be used an isolated event. We also verified that the event was actually dominant in the clip and we controlled if the event onset and offset are present in the clip. Each selected clip was then segmented when needed to remove silences before and after the event and between events when the file contained multiple occurrences of the event class.

License:

All sounds comming from FSD are released under Creative Commons licences. Synthetic sounds can only be used for competition purposes until the full CC license list is made available at the end of the competition.

Further information on dcase website.

References:

[1] F. Font, G. Roma & X. Serra. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013.

[2] E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter & X. Serra. Freesound Datasets: A Platform for the Creation of Open Audio Datasets. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.

[3] Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter. Audio Set: An ontology and human-labeled dataset for audio events. In Proceedings IEEE ICASSP 2017, New Orleans, LA, 2017.

[4] Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers. The SINS database for detection of daily activities in a home environment using an acoustic sensor network. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 32–36. November 2017.

[5] J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello. Scaper: A library for soundscape synthesis and augmentation In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017.

We measure the DEO of models using real test data—DEO(R) and synthetic test data DEO(S). DEO delta quantifies the difference between DEO(R) and DEO(S). All synthetic data where generated using privacy-loss parameter ϵ = 5.0.

This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020. Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technology, pp.1-22. [2] Wang, J., Tam, W.C., Jia, Y., Peacock, R., Reneke, P., Fu, E.Y. and Cleary, T., 2021. P-Flash - A machine learning-based model for flashover prediction using recovered temperature data. Fire Safety Journal, 122, p.103341.

Synthetic datasets were generated as benchmarks capturing the intrinsic characteristics of original data to investigate the performance of additive feature attribution methods for regression tasks. The synthetic datasets were generated based on 2, 6 and 8 clusters formed with the original data. The 6-cluster dataset was used for primary analysis and the other two were used for sensitivity analysis. The synthetic dataset was generated from the original data acquired from Aviation Data for Research Repository, which was collected and processed by EUROCONTROL from the Enhanced Tactical Flow Management System (ETFMS) flight data messages containing all flights in Europe throughout the year 2019, from May to October. The original dataset consisted of fundamental details of the flights, flight status, preceding flight legs, ATFM regulations, weather conditions, calendar information, etc. A brief description of the columns in the synthetic data files is presented in the file 'data_description.pdf' and a more detailed discussion on features can be found in the works of Koolen and Coliban [1] and Dalmau et al. [2].

References[1] H. Koolen and I. Coliban, Flight Progress Messages Document, EUROCONTROL, Brussels, Belgium, Tech. Rep., 2020.[2] R. Dalmau, F. Ballerini, H. Naessens, S. Belkoura, and S. Wangnick, An Explainable Machine Learning Approach to Improve Take-off Time Predictions, Journal of Air Transport Management, vol. 95, p. 102 090, Aug. 2021. doi: 10.1016/j.jairtraman.2021.102090.

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).

We measure the DSP of models using real test data—DSP(R) and synthetic test data DSP(S). DEO delta quantifies the difference between DSP(R) and DSP(S). All synthetic data where generated using privacy-loss parameter ϵ = 5.0.

The aim of this project was to create a synthetic dataset without using the original (secure, controlled) dataset to do so, and instead using only publicly available analytical output (i.e. output that was cleared for publication) to create the synthetic data. Such synthetic data may allow users to gain familiarity with and practise on data that is like the original before they gain access to the original data (where time in a secure setting may be limited).

The Annual Survey of Hours and Earnings 2011 and Census 2011: Synthetic Data was created without access to the original ASHE-2011 Census dataset (which is only available in a secure setting via the ONS Secure Research Service: "Annual Survey of Hours and Earnings linked to 2011 Census - England and Wales"). It was created as a teaching aid to support a training course "An Introduction to the linked ASHE-2011 Census dataset" organised by Administrative Data Research UK and the National Centre for Research Methods. The synthetic dataset contains a subset of the variables in the original dataset and was designed to reproduce the analytical output contained in the ASHE-Census 2011 Data Linkage User Guide.

Subtitle:

Simulated Dataset of Customer Purchase Behavior

Description:

This dataset contains simulated data representing customer purchase behavior. It includes various features such as age, gender, income, education, region, loyalty status, purchase frequency, purchase amount, product category, promotion usage, and satisfaction score.

File Information:

• File Format: CSV
• Number of Rows: 100000
• Number of Columns: 12

Column Descriptors:

• age: Age of the customer.
• gender: Gender of the customer (0 for Male, 1 for Female).
• income: Annual income of the customer.
• education: Education level of the customer.
• region: Region where the customer resides.
• loyalty_status: Loyalty status of the customer.
• purchase_frequency: Frequency of purchases made by the customer.
• purchase_amount: Amount spent by the customer in each purchase.
• product_category: Category of the purchased product.
• promotion_usage: Indicates whether the customer used promotional offers (0 for No, 1 for Yes).
• satisfaction_score: Satisfaction score of the customer.

Provenance:

The dataset was simulated using the simstudy package in R. Various distributions and formulas were used to generate synthetic data representing customer purchase behavior. The data is organized to mimic real-world scenarios, but it does not represent actual customer data.

This is a synthetic dataset that can be used by users that are interested in benchmarking methods of explainable artificial intelligence (XAI) for geoscientific applications. The dataset is specifically inspired from a climate forecasting setting (seasonal timescales) where the task is to predict regional climate variability given global climate information lagged in time. The dataset consists of a synthetic input X (series of 2D arrays of random fields drawn from a multivariate normal distribution) and a synthetic output Y (scalar series) generated by using a nonlinear function F: R^d -> R.

The synthetic input aims to represent temporally independent realizations of anomalous global fields of sea surface temperature, the synthetic output series represents some type of regional climate variability that is of interest (temperature, precipitation totals, etc.) and the function F is a simplification of the climate system.

Since the nonlinear function F that is used to generate the output given the input is known, we also derive and provide the attribution of each output value to the corresponding input features. Using this synthetic dataset users can train any AI model to predict Y given X and then implement XAI methods to interpret it. Based on the “ground truth” of attribution of F the user can assess the faithfulness of any XAI method.

NOTE: the spatial configuration of the observations in the NetCDF database file conform to the planetocentric coordinate system (89.5N - 89.5S, 0.5E - 359.5E), where longitude is measured in the positive heading east from the prime meridian.

We release a human-centric synthetic data generator PeopleSansPeople which contains simulation-ready 3D human assets, a parameterized lighting and camera system, and generates 2D and 3D bounding box, instance and semantic segmentation, and COCO pose labels. Using PeopleSansPeople, we performed benchmark synthetic data training using a Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network using synthetic data and fine-tuning on target real-world data (few-shot transfer to limited subsets of COCO-person train [2]) resulted in a keypoint AP of 60.37±0.48 (COCO test-dev2017) outperforming models trained with the same real data alone (keypoint AP of 55.80) and pre-trained with ImageNet (keypoint AP of 57.50). This freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.

This repository contains scripts, input files, and some example output files for the Residential Population Generator, an R-based tool to generate synthetic human residental populations to use in making estimates of near-field chemical exposures. This tool is most readily adapted for using in the workflow for CHEM, the Combined Human Exposure Model, avaialable in two other GitHub repositories in the HumanExposure project, including ProductUseScheduler and source2dose. CHEM is currently best suited to estimating exposure to product use. Outputs from RPGen are translated into ProductUseScheduler, which with subsequent outputs used in source2dose.