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.

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.

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.

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

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.

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

The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for B3PPs, their performance has often been hampered by issue of limited positive data.In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set.This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

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

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.

The data included here within is the associated model training results from the correlated paper "Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning". This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally

This dataset is an augmented version of the FER2013 dataset, designed for improving emotion recognition tasks in machine learning and deep learning models. FER2013 was introduced during the ICML 2013 Workshop on Challenges in Representation Learning and contains grayscale facial expression images classified into seven emotion categories: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.

Enhancements in this Dataset: 1. Preprocessing: - Applied histogram equalization to improve image contrast. - Denoising using Gaussian smoothing to reduce noise. 2. Augmentation: - Augmented the dataset by applying transformations such as rotation, flipping, zooming, and shifting to enhance diversity.

Applications: - Facial Expression Recognition. - Emotion-based Human-Computer Interaction (HCI). - Mental health analysis through automated emotion detection.

Source: The original FER2013 dataset was obtained from the ICML 2013 Workshop and is publicly available on Kaggle.

The popularity of Deep Learning (DL) methods used in business process management research and practice is constantly increasing. One important factor that hinders the adoption of DL in certain areas is the availability of sufficiently large training datasets, particularly affecting domains where process models are mainly defined manually with a high knowledge-acquisition effort. In this paper, we examine process model augmentation in combination with semi-supervised transfer learning to enlarge existing datasets and train DL models effectively. The use case of similarity learning between manufacturing process models is discussed. Based on a literature study of existing augmentation techniques, a concept is presented with different categories of augmentation from knowledge-light approaches to knowledge-intensive ones, e. g. based on automated planning. Specifically, the impacts of augmentation approaches on the syntactic and semantic correctness of the augmented process models are considered. The concept also proposes a semi-supervised transfer learning approach to integrate augmented and non-augmented process model datasets in a two-phased training procedure. The experimental evaluation investigates augmented process model datasets regarding their quality for model training in the context of similarity learning between manufacturing process models. The results indicate a large potential with a reduction of the prediction error of up to 53%.

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.

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)

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.

🩺 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.

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.

Audiomentations

A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.

official : https://github.com/iver56/audiomentations

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

The Synthetic Data Generation market is booming, projected to reach $11.9 billion by 2033 with a 25% CAGR. Learn about key drivers, trends, and top companies shaping this rapidly expanding sector, addressing data privacy and AI model training needs. Explore market segmentation and regional analysis for a comprehensive overview.