Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanisms as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data.

This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024).Please see README for the details of the datasets.

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041

This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of 1216 instances, 888 positives, and 328 negatives, in 1152 jpg images with a resolution of 1280x720 pixels. These are divided into 576 real images and 576 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

The folder structure of the dataset is as follows:

• vms_dataset/
  • data.csv
  • real_images/
    • imgs/
    • annotations/
  • data-augmentation/
    • imgs/
    • annotations/

In which:

• data.csv: Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.
• real_images: Images extracted directly from the videos.
• data-augmentation: Images created using data-augmentation
• imgs: Image files in .jpg format.
• annotations: Annotation files in .xml format.

The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision.

Data Augmentation Tools Market Outlook

As per our latest research, the global Data Augmentation Tools market size reached USD 1.47 billion in 2024, reflecting the rapidly increasing adoption of artificial intelligence and machine learning across diverse sectors. The market is experiencing robust momentum, registering a CAGR of 25.3% from 2025 to 2033. By the end of 2033, the Data Augmentation Tools market is forecasted to reach a substantial value of USD 11.6 billion. This impressive growth is primarily driven by the escalating need for high-quality, diverse datasets to train advanced AI models, coupled with the proliferation of digital transformation initiatives across industries.

The primary growth factor fueling the Data Augmentation Tools market is the exponential rise in AI and machine learning applications, which require vast amounts of labeled data for effective training. As organizations strive to develop more accurate and robust models, the demand for data augmentation solutions that can synthetically expand and diversify datasets has surged. This trend is particularly pronounced in sectors such as healthcare, automotive, and retail, where the quality and quantity of data directly impact the performance and reliability of AI systems. The market is further propelled by the increasing complexity of data types, including images, text, audio, and video, necessitating sophisticated augmentation tools capable of handling multimodal data.

Another significant driver is the growing focus on reducing model bias and improving generalization capabilities. Data augmentation tools enable organizations to generate synthetic samples that account for various real-world scenarios, thereby minimizing overfitting and enhancing the robustness of AI models. This capability is critical in regulated industries like BFSI and healthcare, where the consequences of biased or inaccurate models can be severe. Furthermore, the rise of edge computing and IoT devices has expanded the scope of data augmentation, as organizations seek to deploy AI solutions in resource-constrained environments that require optimized and diverse training datasets.

The proliferation of cloud-based solutions has also played a pivotal role in shaping the trajectory of the Data Augmentation Tools market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations of all sizes to access advanced augmentation capabilities without significant infrastructure investments. Additionally, the integration of data augmentation tools with popular machine learning frameworks and platforms has streamlined adoption, enabling seamless workflow integration and accelerating time-to-market for AI-driven products and services. These factors collectively contribute to the sustained growth and dynamism of the global Data Augmentation Tools market.

From a regional perspective, North America currently dominates the Data Augmentation Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and early adoption of digital transformation initiatives have established North America as a key hub for data augmentation innovation. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by the rapid expansion of the IT and telecommunications sector, burgeoning e-commerce industry, and increasing government initiatives to promote AI adoption. Europe also maintains a significant market presence, supported by stringent data privacy regulations and a strong focus on ethical AI development.

Component Analysis

The Component segment of the Data Augmentation Tools market is bifurcated into Software and Services, each playing a critical role in enabling organizations to leverage data augmentation for AI and machine learning initiatives. The software sub-segment comprises

IntroductionMachine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.MethodsWe utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.ResultsThe combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set.DiscussionsOur work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth—often overlooked factors– can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy, escalating data security concerns, and the rising demand for high-quality training data for AI and machine learning models. The market's expansion is fueled by several key factors: the growing adoption of AI across various industries, the limitations of real-world data availability due to privacy regulations like GDPR and CCPA, and the cost-effectiveness and efficiency of synthetic data generation. We project a market size of approximately $2 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033). This rapid expansion is expected to continue, reaching an estimated market value of over $10 billion by 2033. The market is segmented based on deployment models (cloud, on-premise), data types (image, text, tabular), and industry verticals (healthcare, finance, automotive). Major players are actively investing in research and development, fostering innovation in synthetic data generation techniques and expanding their product offerings to cater to diverse industry needs. Competition is intense, with companies like AI.Reverie, Deep Vision Data, and Synthesis AI leading the charge with innovative solutions. However, several challenges remain, including ensuring the quality and fidelity of synthetic data, addressing the ethical concerns surrounding its use, and the need for standardization across platforms. Despite these challenges, the market is poised for significant growth, driven by the ever-increasing need for large, high-quality datasets to fuel advancements in artificial intelligence and machine learning. The strategic partnerships and acquisitions in the market further accelerate the innovation and adoption of synthetic data platforms. The ability to generate synthetic data tailored to specific business problems, combined with the increasing awareness of data privacy issues, is firmly establishing synthetic data as a key component of the future of data management and AI development.

This dataset contains synthetic video samples generated from a 10-class subset of Tiny ImageNet using Stable Video Diffusion (SVD). It is designed to evaluate the impact of generative temporal augmentation on image classification performance.

Each training and validation video corresponds to a single image augmented into a sequence of frames.

Videos are stored in .mp4 format and labeled via train.csv and val.csv.

Sources:

Tiny ImageNet: Stanford CS231n

SVD model: Stable Video Diffusion

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.

Deep learning-based text classification models need abundant labeled data to obtain competitive performance. To tackle this, multiple researches try to use data augmentation to expand the corpus size.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Data Augmentation Tools Market Outlook

According to our latest research, the global Data Augmentation Tools market size reached USD 1.62 billion in 2024, with a robust year-on-year growth trajectory. The market is poised for accelerated expansion, projected to achieve a CAGR of 26.4% from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 12.34 billion. This dynamic growth is primarily driven by the rising demand for artificial intelligence (AI) and machine learning (ML) applications across diverse industry verticals, which necessitate vast quantities of high-quality training data. The proliferation of data-centric AI models and the increasing complexity of real-world datasets are compelling enterprises to invest in advanced data augmentation tools to enhance data diversity and model robustness, as per the latest research insights.

One of the principal growth factors fueling the Data Augmentation Tools market is the intensifying adoption of AI-driven solutions across industries such as healthcare, automotive, retail, and finance. Organizations are increasingly leveraging data augmentation to overcome the challenges posed by limited or imbalanced datasets, which are often a bottleneck in developing accurate and reliable AI models. By synthetically expanding training datasets through augmentation techniques, enterprises can significantly improve the generalization capabilities of their models, leading to enhanced performance and reduced risk of overfitting. Furthermore, the surge in computer vision, natural language processing, and speech recognition applications is creating a fertile environment for the adoption of specialized augmentation tools tailored to image, text, and audio data.

Another significant factor contributing to market growth is the rapid evolution of augmentation technologies themselves. Innovations such as Generative Adversarial Networks (GANs), automated data labeling, and domain-specific augmentation pipelines are making it easier for organizations to deploy and scale data augmentation strategies. These advancements are not only reducing the manual effort and expertise required but also enabling the generation of highly realistic synthetic data that closely mimics real-world scenarios. As a result, businesses across sectors are able to accelerate their AI/ML development cycles, reduce costs associated with data collection and labeling, and maintain compliance with stringent data privacy regulations by minimizing the need to use sensitive real-world data.

The growing integration of data augmentation tools within cloud-based AI development platforms is also acting as a major catalyst for market expansion. Cloud deployment offers unparalleled scalability, accessibility, and collaboration capabilities, allowing organizations of all sizes to harness the power of data augmentation without significant upfront infrastructure investments. This democratization of advanced data engineering tools is especially beneficial for small and medium enterprises (SMEs) and academic research institutes, which often face resource constraints. The proliferation of cloud-native augmentation solutions is further supported by strategic partnerships between technology vendors and cloud service providers, driving broader market penetration and innovation.

From a regional perspective, North America continues to dominate the Data Augmentation Tools market, driven by the presence of leading AI technology companies, a mature digital infrastructure, and substantial investments in research and development. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digital transformation initiatives, a burgeoning startup ecosystem, and increasing government support for AI innovation. Europe also holds a significant share, underpinned by strong regulatory frameworks and a focus on ethical AI development. Meanwhile, Latin America and the Middle East & Africa are witnessing steady adoption, particularly in sectors such as BFSI and healthcare, where data-driven insights are becoming increasingly critical.

Component Analysis

The Data Augmentation Tools market by component is bifurcated into Software and Services. The software segment currently accounts for the largest share of the market, owing to the widespread deployment of standalone and integrated augmentation solutions across enterprises and research institutions. These software plat

This dataset supports the publication: James A. Grant-Jacob, Matthew Praeger, Matthew Loxham, Robert W. Eason and Ben Mills. Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation. IOP Environmental Research Communications. DOI: 10.1088/2515-7620/abed94

🩺 Dataset Description

This dataset is an augmented version of an ECG image dataset created to balance and enrich the original classes for deep learning–based cardiovascular disease classification.

The original dataset consisted of unbalanced image counts per class in the training set: - ABH: 233 images - MI: 239 images - HMI: 172 images - NORM: 284 images

To improve class balance and model generalization, each class in the training set was expanded to 500 images using a combination of morphological, noise-based, and geometric data augmentation techniques. Additionally, the test set includes 112 images per class.

⚖️ Final Dataset Composition

• Training set: 4 classes × 500 images each → 2,000 images total
• Test set: 4 classes × 112 images each → 448 images total

🔬 Data Augmentation Techniques

1. Morphological Alterations - Erosion - Dilation - None (original preserved)

2. Noise Introduction - augment_noise_black_rain — simulates black streaks - augment_noise_pixel_dropout_black — random black pixel dropout - augment_noise_white_rain — simulates white streaks - augment_noise_pixel_dropout_white — random white pixel dropout

3. Geometric Transformations - Shift — small translations in all directions - Scale — random zoom-in/zoom-out between 0.9× and 1.1× - Rotate — small random rotation between -5° and +5°

These transformations were applied with balanced proportions to ensure diversity and realism while preserving diagnostic features of ECG signals.

💡 Intended Use

This dataset is designed for: - Training and evaluating deep learning models (CNNs, ViTs) for ECG image classification - Research in medical image augmentation, imbalanced data learning, and cardiovascular disease prediction

📘 License

This dataset is released under the CC0 1.0 License, allowing free use and distribution for research and educational purposes.

Medical image analysis is critical to biological studies, health research, computer- aided diagnoses, and clinical applications. Recently, deep learning (DL) techniques have achieved remarkable successes in medical image analysis applications. However, these techniques typically require large amounts of annotations to achieve satisfactory performance. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for medical image analysis while reducing annotation efforts? To address this problem, we have outlined two specific aims: (A1) Utilize existing annotations effectively from advanced models; (A2) extract generic knowledge directly from unannotated images.

To achieve the aim (A1): First, we introduce a new data representation called TopoImages, which encodes the local topology of all the image pixels. TopoImages can be complemented with the original images to improve medical image analysis tasks. Second, we propose a new augmentation method, SAMAug-C, that lever- ages the Segment Anything Model (SAM) to augment raw image input and enhance medical image classification. Third, we propose two advanced DL architectures, kCBAC-Net and ConvFormer, to enhance the performance of 2D and 3D medical image segmentation. We also present a gate-regularized network training (GrNT) approach to improve multi-scale fusion in medical image segmentation. To achieve the aim (A2), we propose a novel extension of known Masked Autoencoders (MAEs) for self pre-training, i.e., models pre-trained on the same target dataset, specifically for 3D medical image segmentation.

Scientific visualization is a powerful approach for understanding and analyzing various physical or natural phenomena, such as climate change or chemical reactions. However, the cost of scientific simulations is high when factors like time, ensemble, and multivariate analyses are involved. Additionally, scientists can only afford to sparsely store the simulation outputs (e.g., scalar field data) or visual representations (e.g., streamlines) or visualization images due to limited I/O bandwidths and storage space. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for scientific data generation and compression while reducing simulation and storage costs?

To tackle this problem: First, we propose a DL framework that generates un- steady vector fields data from a set of streamlines. Based on this method, domain scientists only need to store representative streamlines at simulation time and recon- struct vector fields during post-processing. Second, we design a novel DL method that translates scalar fields to vector fields. Using this approach, domain scientists only need to store scalar field data at simulation time and generate vector fields from their scalar field counterparts afterward. Third, we present a new DL approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results.

Result of 10-Fold cross-validation on augmented dataset.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

The purified dataset for data augmentation for DAISM-DNNXMBD can be downloaded from this repository.

The pbmc8k dataset downloaded from 10X Genomics were processed and uesd for data augmentation to create training datasets for training DAISM-DNN models. pbmc8k.h5ad contains 5 cell types (B.cells, CD4.T.cells, CD8.T.cells, monocytic.lineage, NK.cells), and pbmc8k_fine.h5ad cantains 7 cell types (naive.B.cells, memory.B.cells, naive.CD4.T.cells, memory.CD4.T.cells,naive.CD8.T.cells, memory.CD8.T.cells, regulatory.T.cells, monocytes, macrophages, myeloid.dendritic.cells, NK.cells).

For RNA-seq dataset, it contains 5 cell types (B.cells, CD4.T.cells, CD8.T.cells, monocytic.lineage, NK.cells). Raw FASTQ reads were downloaded from the NCBI website, and transcription and gene-level expression quantification were performed using Salmon (version 0.11.3) with Gencode v29 after quality control of FASTQ reads using fastp. All tools were used with default parameters.

Overview

This is the data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators". It contains the paper’s data archive with model outputs (see results folder) and the Singularity image for (optionally) re-running experiments.

For the Python tool used to generate synthetic data, please refer to Synthia.

Requirements

• Singularity >= 3
• Portable Batch System (PBS) job scheduler*
• Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM )

*Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the hpc directory (e.g. #PBS -lwalltime=72:00:00).

Usage

To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with:

qsub hpc/fit.sh

then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use:

qsub hpc/stats.sh
qsub hpc/ml_control.sh
qsub hpc/ml_synth.sh

Finally, to plot all artifacts included in the paper use:

qsub hpc/plot.sh

Licence

Code released under MIT license. Data from the reduced NWP-SAF dataset released under CC BY 4.0.