Data Labeling Service Market Outlook

The global data labeling service market size is projected to grow from $2.1 billion in 2023 to $12.8 billion by 2032, at a robust CAGR of 22.6% during the forecast period. This impressive growth is driven by the exponential increase in data generation and the rising demand for artificial intelligence (AI) and machine learning (ML) applications across various industries. The necessity for structured and labeled data to train AI models effectively is a primary growth factor that is propelling the market forward.

One of the key growth factors in the data labeling service market is the proliferation of AI and ML technologies. These technologies require vast amounts of labeled data to function accurately and efficiently. As more businesses adopt AI and ML for applications ranging from predictive analytics to autonomous vehicles, the demand for high-quality labeled data is surging. This trend is particularly evident in sectors like healthcare, automotive, retail, and finance, where AI and ML are transforming operations, improving customer experiences, and driving innovation.

Another significant factor contributing to the market growth is the increasing complexity and diversity of data. With the advent of big data, not only the volume but also the variety of data has escalated. Data now comes in multiple formats, including images, text, video, and audio, each requiring specific labeling techniques. This complexity necessitates advanced data labeling services that can handle a wide range of data types and ensure accuracy and consistency, further fueling market growth. Additionally, advancements in technology, such as automated and semi-supervised labeling solutions, are making the labeling process more efficient and scalable.

Furthermore, the growing emphasis on data privacy and security is driving the demand for professional data labeling services. With stringent regulations like GDPR and CCPA coming into play, companies are increasingly outsourcing their data labeling needs to specialized service providers who can ensure compliance and protect sensitive information. These providers offer not only labeling accuracy but also robust security measures that safeguard data throughout the labeling process. This added layer of security is becoming a critical consideration for enterprises, thereby boosting the market.

Automatic Labeling is becoming increasingly significant in the data labeling service market as it offers a solution to the challenges posed by the growing volume and complexity of data. By utilizing sophisticated algorithms, automatic labeling can process large datasets swiftly, reducing the time and cost associated with manual labeling. This technology is particularly beneficial for industries that require rapid data processing, such as autonomous vehicles and real-time analytics in finance. As AI models become more advanced, the precision and reliability of automatic labeling are continuously improving, making it a viable option for a wider range of applications. The integration of automatic labeling into existing workflows not only enhances efficiency but also allows human annotators to focus on more complex tasks that require nuanced understanding.

On a regional level, North America currently leads the data labeling service market, followed by Europe and Asia Pacific. The high concentration of AI and tech companies, combined with substantial investments in AI research and development, makes North America a dominant player in the market. Europe is also experiencing significant growth, driven by increasing AI adoption across various industries and supportive government initiatives. Meanwhile, the Asia Pacific region is poised for the highest CAGR, attributed to rapid digital transformation, a burgeoning AI ecosystem, and increasing investments in AI technologies, especially in countries like China, India, and Japan.

Type Analysis

The data labeling service market is segmented by type into image, text, video, and audio. Image labeling dominates the market due to the widespread use of computer vision applications in industries such as automotive (for autonomous driving), healthcare (for medical imaging), and retail (for visual search and recommendation systems). The demand for image labeling services is driven by the need for accurately labeled images to train sophisticated AI

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.

A curated dataset of 241,000+ English-language comments labeled for sentiment (negative, neutral, positive). Ideal for training and evaluating NLP models in sentiment analysis.

Dataset Features

1. text: Contains individual English-language comments or posts sourced from various online platforms.

2. label: Represents the sentiment classification assigned to each comment. It uses the following encoding:

0 — Negative sentiment 1 — Neutral sentiment 2 — Positive sentiment

Distribution

• Format: CSV (Comma-Separated Values)
• 2 Columns: text: The comment content label: Sentiment classification (0 = Negative, 1 = Neutral, 2 = Positive)
• File Size: Approximately 23.9 MB
• Structure: Each row contains a single comment and its corresponding sentiment label.

Usage

This dataset is ideal for a variety of applications:

• 1. Sentiment Analysis Model Training: Train machine learning or deep learning models to classify text as positive, negative, or neutral.

• 2. Text Classification Projects: Use as a labeled dataset for supervised learning in text classification tasks.

• 3. Customer Feedback Analysis: Train models to automatically interpret user reviews, support tickets, or survey responses.

Coverage

• Geographic Coverage: Primarily English-language content from global online platforms

• Time Range: The exact time range of data collection is unspecified; however, the dataset reflects contemporary online language patterns and sentiment trends typically observed in the 2010s to early 2020s.

• Demographics: Specific demographic information (e.g., age, gender, location, industry) is not included in the dataset, as the focus is purely on textual sentiment rather than user profiling.

License

CC0

Who Can Use It

• Data Scientists: For training machine learning models.
• Researchers: For academic or scientific studies.
• Businesses: For analysis, insights, or AI development.

The AI Training Dataset Market size was valued at USD 2124.0 million in 2023 and is projected to reach USD 8593.38 million by 2032, exhibiting a CAGR of 22.1 % during the forecasts period. An AI training dataset is a collection of data used to train machine learning models. It typically includes labeled examples, where each data point has an associated output label or target value. The quality and quantity of this data are crucial for the model's performance. A well-curated dataset ensures the model learns relevant features and patterns, enabling it to generalize effectively to new, unseen data. Training datasets can encompass various data types, including text, images, audio, and structured data. The driving forces behind this growth include:

Hydroxyl radical protein footprinting (HRPF) coupled with mass spectrometry yields information about residue solvent exposure and protein topology. However, data from these experiments are sparse and require computational interpretation to generate useful structural insight. We previously implemented a Rosetta algorithm that uses experimental HRPF data to improve protein structure prediction. Modern structure prediction methods, such as AlphaFold2 (AF2), use machine learning (ML) to generate their predictions. Implementation of an HRPF-guided version of AF2 is challenging due to the substantial amount of training data required and the inherently abstract nature of ML networks. Thus, here we present a hybrid method that uses a light gradient boosting machine to predict residue solvent accessibility from experimental HRPF data. These predictions were subsequently used to improve Rosetta structure prediction. Our hybrid approach identified models with atomic-level detail for all four proteins in our benchmark set. These results illustrate that it is possible to successfully use ML in combination with HRPF data to accurately predict protein structures.

Data Labeling Tools Market Outlook

The global data labeling tools market size was valued at approximately USD 1.6 billion in 2023, and it is anticipated to reach around USD 8.5 billion by 2032, growing at a robust CAGR of 20.3% over the forecast period. The rapid expansion of the data labeling tools market can be attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industries, coupled with the growing need for annotated data to train AI models accurately.

One of the primary growth factors driving the data labeling tools market is the exponential increase in data generation across industries. As organizations collect vast amounts of data, the need for structured and annotated data becomes paramount to derive actionable insights. Data labeling tools play a crucial role in categorizing and tagging this data, thus enabling more effective data utilization in AI and ML applications. Furthermore, the rising investments in AI technologies by both private and public sectors have significantly boosted the demand for data labeling solutions.

Another significant growth factor is the advancements in natural language processing (NLP) and computer vision technologies. These advancements have heightened the demand for high-quality labeled data, particularly in sectors like healthcare, retail, and automotive. For instance, in the healthcare sector, data labeling is essential for developing AI models that can assist in diagnostics and treatment planning. Similarly, in the automotive industry, labeled data is crucial for enhancing autonomous driving technologies. The ongoing advancements in these areas continue to fuel the market growth for data labeling tools.

Additionally, the increasing trend of remote work and the emergence of digital platforms have also contributed to the market's growth. With more businesses shifting to online operations and remote work environments, the need for AI-driven tools to manage and analyze data has become more critical. Data labeling tools have emerged as vital components in this digital transformation, enabling organizations to maintain productivity and efficiency. The growing reliance on digital platforms further accentuates the necessity for accurate data annotation, thereby propelling the market forward.

Data Annotation Tools are pivotal in the realm of AI and ML, serving as the backbone for creating high-quality labeled datasets. These tools streamline the process of annotating data, making it more efficient and less prone to human error. With the rise of AI applications across various sectors, the demand for sophisticated data annotation tools has surged. They not only enhance the accuracy of AI models but also significantly reduce the time required for data preparation. As organizations strive to harness the full potential of AI, the role of data annotation tools becomes increasingly crucial, ensuring that the data fed into AI systems is both accurate and reliable.

From a regional perspective, North America holds the largest share in the data labeling tools market due to the early adoption of AI and ML technologies and the presence of major technology companies. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digitalization, increasing investments in AI research, and the growing presence of AI startups. Europe, Latin America, and the Middle East & Africa are also witnessing significant growth, albeit at a slower pace, due to the rising awareness and adoption of data labeling solutions.

Type Analysis

The data labeling tools market is segmented into various types, including image, text, audio, and video labeling tools. Image labeling tools hold a significant market share owing to the extensive use of computer vision applications in various industries such as healthcare, automotive, and retail. These tools are essential for training AI models to recognize and categorize visual data, making them indispensable for applications like medical imaging, autonomous vehicles, and facial recognition. The growing demand for high-quality labeled images is a key driver for this segment.

Text labeling tools are another critical segment, driven by the increasing adoption of NLP technologies. Text data labeling is vital for applications such as sentiment analysis, chatbots, and language translation services. With the proliferation of text-based d

Data Annotation Tools Market Outlook

According to our latest research, the global Data Annotation Tools market size reached USD 2.1 billion in 2024. The market is set to expand at a robust CAGR of 26.7% from 2025 to 2033, projecting a remarkable value of USD 18.1 billion by 2033. The primary growth driver for this market is the escalating adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which necessitates high-quality labeled data for model training and validation.

One of the most significant growth factors propelling the data annotation tools market is the exponential rise in AI-powered applications across sectors such as healthcare, automotive, retail, and BFSI. As organizations increasingly integrate AI and ML into their core operations, the demand for accurately annotated data has surged. Data annotation tools play a crucial role in transforming raw, unstructured data into structured, labeled datasets that can be efficiently used to train sophisticated algorithms. The proliferation of deep learning and natural language processing technologies further amplifies the need for comprehensive data labeling solutions. This trend is particularly evident in industries like healthcare, where annotated medical images are vital for diagnostic algorithms, and in automotive, where labeled sensor data supports the evolution of autonomous vehicles.

Another prominent driver is the shift toward automation and digital transformation, which has accelerated the deployment of data annotation tools. Enterprises are increasingly adopting automated and semi-automated annotation platforms to enhance productivity, reduce manual errors, and streamline the data preparation process. The emergence of cloud-based annotation solutions has also contributed to market growth by enabling remote collaboration, scalability, and integration with advanced AI development pipelines. Furthermore, the growing complexity and variety of data types, including text, audio, image, and video, necessitate versatile annotation tools capable of handling multimodal datasets, thus broadening the market's scope and applications.

The market is also benefiting from a surge in government and private investments aimed at fostering AI innovation and digital infrastructure. Several governments across North America, Europe, and Asia Pacific have launched initiatives and funding programs to support AI research and development, including the creation of high-quality, annotated datasets. These efforts are complemented by strategic partnerships between technology vendors, research institutions, and enterprises, which are collectively advancing the capabilities of data annotation tools. As regulatory standards for data privacy and security become more stringent, there is an increasing emphasis on secure, compliant annotation solutions, further driving innovation and market demand.

From a regional perspective, North America currently dominates the data annotation tools market, driven by the presence of major technology companies, well-established AI research ecosystems, and significant investments in digital transformation. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid industrialization, expanding IT infrastructure, and a burgeoning startup ecosystem focused on AI and data science. Europe also holds a substantial market share, supported by robust regulatory frameworks and active participation in AI research. Latin America and the Middle East & Africa are gradually catching up, with increasing adoption in sectors such as retail, automotive, and government. The global landscape is characterized by dynamic regional trends, with each market contributing uniquely to the overall growth trajectory.

Component Analysis

The data annotation tools market is segmented by component into software and services, each playing a pivotal role in the market's overall ecosystem. Software solutions form the backbone of the market, providing the technical infrastructure for auto

Manual Data Annotation Tools Market Outlook

In 2023, the global market size for manual data annotation tools is estimated at USD 1.2 billion, and it is projected to reach approximately USD 5.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 18.3%. The burgeoning demand for high-quality annotated data to train machine learning models and enhance AI capabilities is a significant growth factor driving this market. As industries increasingly adopt AI and machine learning technologies, the need for accurate and comprehensive data annotation tools has become paramount, propelling the market to unprecedented heights.

The rapid expansion of artificial intelligence and machine learning applications across various industries is one of the primary growth drivers for the manual data annotation tools market. High-quality labeled data is crucial for training sophisticated AI models, which in turn fuels the demand for efficient and effective annotation tools. Industries such as healthcare, automotive, and retail are leveraging AI to enhance operational efficiency and customer experience, further amplifying the need for advanced data annotation solutions.

Technological advancements in data annotation tools are also significantly contributing to market growth. Innovations such as AI-assisted annotation, improved user interfaces, and integration capabilities with other data management platforms have made these tools more user-friendly and efficient. As a result, even organizations with limited technical expertise can now leverage these tools to annotate large datasets accurately, thereby accelerating the adoption and expansion of data annotation tools globally.

The increasing prevalence of big data analytics is another critical factor driving market growth. Organizations are generating and collecting vast amounts of data daily, and the ability to annotate and analyze this data effectively is essential for extracting actionable insights. Manual data annotation tools play a crucial role in this process by providing the necessary infrastructure to label and categorize data accurately, enabling organizations to harness the full potential of their data assets.

Data Collection And Labelling are foundational processes in the realm of AI and machine learning. As the volume of data generated by businesses and individuals continues to grow exponentially, the need for effective data collection and labeling becomes increasingly critical. This process involves gathering raw data and meticulously annotating it to create structured datasets that can be used to train machine learning models. The accuracy of data labeling directly impacts the performance of AI systems, making it a crucial step in developing reliable and efficient AI solutions. In sectors like healthcare and automotive, where precision is paramount, the demand for robust data collection and labeling practices is particularly high, driving innovation and investment in this area.

From a regional perspective, North America currently holds the largest market share, driven by the high adoption rates of AI and machine learning technologies, significant investment in research and development, and the presence of key market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid digital transformation, increased investment in AI technologies, and the growing need for data annotation services in emerging economies such as China and India.

Type Analysis

Text annotation tools are a critical segment within the manual data annotation tools market. These tools enable the labeling of text data, which is essential for applications such as natural language processing (NLP), sentiment analysis, and chatbots. As the demand for NLP applications grows, so does the need for efficient text annotation tools. Companies are increasingly leveraging these tools to improve their customer service, automate responses, and enhance user experience, thereby driving the segment's growth.

Image annotation tools form another significant segment in the market. These tools are used to label and categorize images, which is vital for training computer vision models. The automotive industry heavily relies on image annotation for developing autonomous driving systems, which need accurately labeled images to recognize objects and make decisions in real time. Additionally, sectors such

Annotation Software Market Outlook

The global annotation software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.2 billion by 2032, growing at a CAGR of 12% during the forecast period. The market growth is driven by the escalating need for data labeling in machine learning models and the increasing adoption of AI across various industries.

The annotation software market is experiencing robust growth due to the burgeoning demand for annotated data in machine learning and artificial intelligence applications. As industries increasingly integrate AI and machine learning into their operations, the necessity for accurately labeled data has never been higher. This surge is particularly notable in sectors such as healthcare, where annotated data is pivotal for training diagnostic algorithms, and in autonomous driving technology, which requires extensive data labeling for object recognition and decision-making processes. Consequently, the annotation software market is poised for significant expansion, fueled by these technological advancements and the growing reliance on AI-driven solutions.

Additionally, the proliferation of big data and the escalating volume of unstructured data are further propelling the demand for annotation software. Organizations are recognizing the value of harnessing this data to gain actionable insights and enhance decision-making processes. Annotation software plays a crucial role in transforming raw data into structured, labeled datasets that can be effectively utilized for various analytical and predictive purposes. This trend is particularly prominent in industries such as finance and retail, where accurate data labeling is essential for tasks such as fraud detection, customer sentiment analysis, and personalized marketing strategies. As a result, the annotation software market is witnessing substantial growth as businesses strive to leverage the potential of big data for competitive advantage.

Moreover, the increasing emphasis on automation and efficiency in data processing workflows is driving the adoption of annotation software solutions. Manual data labeling is a time-consuming and labor-intensive process, leading organizations to seek automated annotation tools that can streamline and expedite the labeling process. These software solutions offer advanced features such as machine learning-assisted labeling, collaborative annotation capabilities, and integration with existing data management systems, enabling organizations to achieve higher productivity and accuracy in their data annotation efforts. As the demand for efficient data processing continues to rise, the annotation software market is expected to witness sustained growth, driven by the need for automation and improved operational efficiency.

Regionally, North America is expected to dominate the annotation software market, owing to its strong technological infrastructure and the presence of key market players. The region's advanced IT ecosystem and high adoption rate of AI and machine learning technologies contribute significantly to market growth. Additionally, the Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, driven by rapid industrialization, increasing investments in AI research and development, and the growing focus on digital transformation across various sectors. Europe, Latin America, and the Middle East & Africa also present substantial growth opportunities, supported by favorable government initiatives, expanding AI adoption, and increasing awareness of the benefits of data annotation in these regions.

Screen Writing and Annotation Software have become increasingly intertwined, especially as the demand for multimedia content grows. Screenwriters and content creators are leveraging annotation software to enhance their scripts and storyboards with detailed notes and visual cues. This integration allows for a more dynamic and interactive approach to storytelling, enabling writers to collaborate more effectively with directors, producers, and other team members. By utilizing annotation tools, screenwriters can ensure that their creative vision is accurately conveyed and understood by all stakeholders involved in the production process. This trend is particularly evident in the film and television industry, where the need for precise communication and collaboration is paramount to the success of any project.

Component Analysis

The a

CBeamXP: Continuous Beam Cross-section Predictors datasetThe CBeamXP (Continuous Beam Cross-section (X) Predictors) is a dataset containing 1,000,000 data-points to be used for machine learning research. Each data-point represents an Ultimate Limit State (ULS) compliant beam from a continuous system consisting out of 11 members with utilisation ratios between 0.97 to 1.00. The predictors include span and uniformly distributed loads (UDLs) which can be used to predict the cross-sectional properties of each beam contained within the dataset. This dataset is publicly available on a CC-BY-4.0 licence and was used within the Gallet et al. (2024) journal article "Machine learning for structural design models of continuous beam systems via influence zones" (doi.org/10.1088/1361-6420/ad3334). Publications making use of the CBeamXP dataset are requested to cite the aforementioned journal article.In addition to the dataset, a training script, environment YAML file and a collection of saved models developed in the Gallet et al. (2024) study are available. These can be used to quickly generate user defined neural networks, compare performances and verify the results achieved by the Gallet et al. (2024) investigation.There are 5 files in this directory:CBeamXP_dataset.csvGallet_2024_training_script.pyGallet_2024_environment.ymlREADME.txtsaved_models.zipClick "Download all" (button at the top) to download the files and and look at the README.txt file for further details on the dataset and how to use the training script.

Description

This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman.

The data consists of the following files:

• ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format
• design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format
• enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format

Details on dataset construction can be found in our paper and dataloaders can be found in our Github repo.

Reference

A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.

Dataset References

Datasets used were derived from the following works:

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In Proteins: Structure, Function and Bioinformatics (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823

Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). Generative Models for Graph-Based Protein Design. https://openreview.net/pdf?id=SJgxrLLKOE

Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. Nucleic Acids Research, 42 (Database issue), D485–D489.

Data DescriptionThe DIPSER dataset is designed to assess student attention and emotion in in-person classroom settings, consisting of RGB camera data, smartwatch sensor data, and labeled attention and emotion metrics. It includes multiple camera angles per student to capture posture and facial expressions, complemented by smartwatch data for inertial and biometric metrics. Attention and emotion labels are derived from self-reports and expert evaluations. The dataset includes diverse demographic groups, with data collected in real-world classroom environments, facilitating the training of machine learning models for predicting attention and correlating it with emotional states.Data Collection and Generation ProceduresThe dataset was collected in a natural classroom environment at the University of Alicante, Spain. The recording setup consisted of six general cameras positioned to capture the overall classroom context and individual cameras placed at each student’s desk. Additionally, smartwatches were used to collect biometric data, such as heart rate, accelerometer, and gyroscope readings.Experimental SessionsNine distinct educational activities were designed to ensure a comprehensive range of engagement scenarios:News Reading – Students read projected or device-displayed news.Brainstorming Session – Idea generation for problem-solving.Lecture – Passive listening to an instructor-led session.Information Organization – Synthesizing information from different sources.Lecture Test – Assessment of lecture content via mobile devices.Individual Presentations – Students present their projects.Knowledge Test – Conducted using Kahoot.Robotics Experimentation – Hands-on session with robotics.MTINY Activity Design – Development of educational activities with computational thinking.Technical SpecificationsRGB Cameras: Individual cameras recorded at 640×480 pixels, while context cameras captured at 1280×720 pixels.Frame Rate: 9-10 FPS depending on the setup.Smartwatch Sensors: Collected heart rate, accelerometer, gyroscope, rotation vector, and light sensor data at a frequency of 1–100 Hz.Data Organization and FormatsThe dataset follows a structured directory format:/groupX/experimentY/subjectZ.zip Each subject-specific folder contains:images/ (individual facial images)watch_sensors/ (sensor readings in JSON format)labels/ (engagement & emotion annotations)metadata/ (subject demographics & session details)Annotations and LabelingEach data entry includes engagement levels (1-5) and emotional states (9 categories) based on both self-reported labels and evaluations by four independent experts. A custom annotation tool was developed to ensure consistency across evaluations.Missing Data and Data QualitySynchronization: A centralized server ensured time alignment across devices. Brightness changes were used to verify synchronization.Completeness: No major missing data, except for occasional random frame drops due to embedded device performance.Data Consistency: Uniform collection methodology across sessions, ensuring high reliability.Data Processing MethodsTo enhance usability, the dataset includes preprocessed bounding boxes for face, body, and hands, along with gaze estimation and head pose annotations. These were generated using YOLO, MediaPipe, and DeepFace.File Formats and AccessibilityImages: Stored in standard JPEG format.Sensor Data: Provided as structured JSON files.Labels: Available as CSV files with timestamps.The dataset is publicly available under the CC-BY license and can be accessed along with the necessary processing scripts via the DIPSER GitHub repository.Potential Errors and LimitationsDue to camera angles, some student movements may be out of frame in collaborative sessions.Lighting conditions vary slightly across experiments.Sensor latency variations are minimal but exist due to embedded device constraints.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025dipserdatasetinpersonstudent1, title={DIPSER: A Dataset for In-Person Student Engagement Recognition in the Wild}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Carolina Lorenzo Álvarez and Jorge Fernandez-Herrero and Diego Viejo and Rosabel Roig-Vila and Miguel Cazorla}, year={2025}, eprint={2502.20209}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2502.20209}, } Usage and ReproducibilityResearchers can utilize standard tools like OpenCV, TensorFlow, and PyTorch for analysis. The dataset supports research in machine learning, affective computing, and education analytics, offering a unique resource for engagement and attention studies in real-world classroom environments.

This package contains datasets derived from experimental data from two studies. Both studies employed a mixed-methods approach with university participants using an industrial VR application for training in electrical maintenance tasks. The first dataset corresponds to a study that used an experimental design with 60 participants divided into two groups: the interactive VR group (labeled as 'VR') and the passive monitor viewing group (labeled as 'Monitor'). This data was used to perform various analytical methods to examine learning outcomes and self-efficacy. The second dataset comes from a study that increased the number of participants in the VR group by 27, bringing the total to 57 participants. This study used a quantitative research design and the data was used to implement a Structural Equation Modelling (SEM) approach. This analysis was conducted to investigate the different factors affecting learning in VR. The experimental design and data management plan received approval from the Tilburg University ethics committee (REDC # 20201035).

Developments in Artificial Intelligence (AI) have had an enormous impact on scientific research in recent years. Yet, relatively few robust methods have been reported in the field of structure-based drug discovery. To train AI models to abstract from structural data, highly curated and precise biomolecule-ligand interaction datasets are urgently needed. We present MISATO, a curated dataset of almost 20000 experimental structures of protein-ligand complexes, associated molecular dynamics traces, and electronic properties. Semi-empirical quantum mechanics was used to systematically refine protonation states of proteins and small molecule ligands. Molecular dynamics traces for protein-ligand complexes were obtained in explicit water. The dataset is made readily available to the scientific community via simple python data-loaders. AI baseline models are provided for dynamical and electronic properties. This highly curated dataset is expected to enable the next-generation of AI models for structure-based drug discovery. Our vision is to make MISATO the first step of a vibrant community project for the development of powerful AI-based drug discovery tools.

The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.

Usage Notes

The datasets are formatted in the HDF5 format developed by the HDF5 Group. We utilized and thus recommend the usage of Python bindings pyHDF to handle the datasets.

The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets:

raw/raw-0
label/label-0
landmark/landmark-0
landmark/landmark-1
landmark/landmark-2

Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets.

Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows:

{'coordsys': 'LPS',
 'id': 1,
 'ijk_position': array([181, 188, 100]),
 'label': 'CochleaTop',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}

{'coordsys': 'LPS',
 'id': 2,
 'ijk_position': array([222, 182, 145]),
 'label': 'OvalWindow',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}

{'coordsys': 'LPS',
 'id': 3,
 'ijk_position': array([223, 209, 147]),
 'label': 'RoundWindow',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}

Deep learning (DL) techniques have demonstrated exceptional success in developing high-performing models for medical imaging applications. However, their effectiveness largely depends on access to extensive, high-quality labeled datasets, which are challenging to obtain in the medical field due to the high cost of annotation and privacy constraints. This dissertation introduces several novel deep-learning approaches aimed at addressing challenges associated with imperfect medical datasets, with the goal of reducing annotation efforts and enhancing the generalization capabilities of DL models. Specifically, two imperfect data challenges are studied in this dissertation. (1) Scarce annotation, where only a limited amount of labeled data is available for training. We propose several novel self-supervised learning techniques that leverage the inherent structure of medical images to improve representation learning. In addition, data augmentation with synthetic models is explored to generate synthetic images to improve self-supervised learning performance. (2) Weak annotation, in which the training data has only image-level annotation, noisy annotation, sparse annotation, or inconsistent annotation. We first introduce a novel self-supervised learning-based approach to better utilize the image level label for medical image semantic segmentation. Motivated by the large inter-observer variation in myocardial annotations for ultrasound images, we further propose an extended dice metric that integrates multiple annotations into the loss function, allowing the model to focus on learning generalizable features while minimizing variations caused by individual annotators.

Context, Sources, and Inspirations Behind the Dataset When developing a hybrid model that combines human-like reasoning with neural network precision, the choice of dataset is crucial. The datasets used in training such a model were selected and curated based on specific goals and requirements, drawing inspiration from a variety of contexts. Below is a breakdown of the datasets, their origins, sources, and the inspirations behind selecting them:

1. Context of the Dataset Selection Objective: To create a model capable of generalizing across diverse tasks, including classification, regression, language understanding, and visual recognition. The model is designed to tackle challenges involving unseen data, complex reasoning, and multi-modal inputs. Approach: A combination of publicly available benchmark datasets and proprietary datasets from specific domains was used. The data sources aimed to provide comprehensive coverage of real-world scenarios and diverse input types to enhance the model's robustness.
2. Data Sources Public Benchmark Datasets ImageNet and COCO (Common Objects in Context):

Inspiration: Widely recognized for image classification and object detection tasks. They provide a large and varied set of labeled images, covering thousands of object categories. Source: Open datasets maintained by research communities. Usage: Used for training and testing the vision component of the hybrid model, focusing on object recognition and scene understanding. MultiWOZ (Multi-Domain Wizard-of-Oz):

Inspiration: A comprehensive dialogue dataset covering multiple domains (e.g., restaurant booking, hotel reservations). Source: Created by dialogue researchers, it provides annotated conversations mimicking real-world human interactions. Usage: Leveraged for training the language understanding and dialogue generation capabilities of the model. ConceptNet:

Inspiration: Designed to provide commonsense knowledge, helping models reason beyond factual information by understanding relationships and contexts. Source: An open-source project that aggregates data from various crowdsourced resources like Wikipedia, WordNet, and Open Mind Common Sense. Usage: Integrated into the reasoning module to improve multi-hop and commonsense reasoning. UCI Machine Learning Repository:

Inspiration: A well-known repository containing diverse datasets for various machine learning tasks, such as loan approval and medical diagnosis. Source: Academic research and publicly available datasets contributed by the research community. Usage: Used for structured data tasks, particularly in financial and healthcare analytics. B. Proprietary and Domain-Specific Datasets Healthcare Records Dataset:

Inspiration: The increasing demand for predictive analytics in healthcare motivated the use of patient records to predict health outcomes. Source: Anonymized data collected from healthcare providers, including patient demographics, medical history, and diagnostic information. Usage: Trained and tested the model's ability to handle regression tasks, such as predicting patient recovery rates and health risks. Financial Transactions and Loan Application Data:

Inspiration: To address risk analytics in financial services, loan application datasets containing applicant profiles, credit scores, and financial history were used. Source: Collaboration with financial institutions provided access to anonymized loan application data. Usage: Focused on classification tasks for loan approval predictions and credit scoring. C. Synthesized Data and Augmented Datasets Synthetic Dialogue Scenarios: Inspiration: To test the model's performance on hypothetical scenarios and rare cases not covered in standard datasets. Source: Generated using rule-based models and simulations to create additional training samples, especially for edge cases in dialogue tasks. Usage: Improved model robustness by exposing it to challenging and less common dialogue interactions. 3. Inspirations Behind the Dataset Choice Diverse Task Requirements: The hybrid model was designed to handle multiple types of tasks (classification, regression, reasoning), necessitating diverse datasets covering different input formats (images, text, structured data). Real-World Relevance: The selected datasets were inspired by real-world use cases in healthcare, finance, and customer service, reflecting common scenarios where such a hybrid model could be applied. Challenging Scenarios: To test the model's reasoning capabilities, datasets like ConceptNet and synthetic scenarios were included, inspired by the need to handle complex logical reasoning and inferencing tasks. Inclusivity and Fairness: Public datasets were chosen to ensure coverage across various demographic groups, reducing bias and improving fairness in predictions. 4. Pre-Processing and Data Preparation Standardization and Normalization: Structured data were ...

Citations are an important part of scientific papers, and the proper handling of them is indispensable for the science of science. Citation field extraction is the task of parsing citations: given a citation string, extract authors, title, venue, doi etc. Since the number of citations is counted by hundreds millions, efficient computer based methods for this task are very important. The development of machine learning methods for citation field extraction requires ground truth: a large corpus of labeled citations. This dataset provides a very large (41M) corpus of labeled data obtained by the reverse process: we took structured citation lists and used BibTeX to generate labeled citation strings.

The dataset simulates student learning behavior during educational sessions, specifically capturing physiological, emotional, and activity-related data. It integrates data collected from multiple IoT sensors, including wearable devices (for tracking movement and physiological states), cameras (for analyzing facial expressions), and motion sensors (for activity tracking). The dataset contains 1,200 student-session records and is structured to represent diverse learning environments, capturing various engagement levels and emotional states.

Here’s a breakdown of the dataset and its features:

Session_ID:

Unique identifier for each session. Type: Integer Student_ID:

A unique identifier for each student participating in the session. Type: Integer HRV (Heart Rate Variability):

A physiological measure of heart rate variability, indicating the variability between consecutive heartbeats, which can provide insights into stress or engagement levels. Type: Continuous (normalized values) Skin_Temperature:

Skin temperature during the session, used to infer physiological responses to learning (such as stress or excitement). Type: Continuous (normalized values) Expression_Joy:

A feature extracted from facial expression analysis, representing the level of joy detected on the student’s face. Type: Continuous (value between 0 and 1) Expression_Confusion:

A feature extracted from facial expression analysis, representing the level of confusion detected on the student’s face. Type: Continuous (value between 0 and 1) Steps:

The number of steps the student has taken during the session, serving as an indicator of activity level. Type: Integer Emotion:

Categorized emotional state of the student during the session, derived from facial expression and engagement analysis. Values: Interest, Boredom, Confusion, Happiness Type: Categorical Engagement_Level:

A rating scale from 1 to 5 that measures the level of engagement of the student during the session. Type: Integer (1 to 5) Session_Duration:

The total duration of the session in minutes, capturing how long the student was engaged in the learning activity. Type: Integer (15 to 60 minutes) Learning_Phase:

The phase of the learning session (e.g., Introduction, Practice, Conclusion). Values: Introduction, Practice, Conclusion Type: Categorical Start_Time:

The timestamp of when the learning session started. Type: DateTime End_Time:

The timestamp of when the learning session ended. Type: DateTime Learning_Outcome:

The result of the learning session, based on the student's engagement level and session duration. Values: Successful, Unsuccessful, Partially Successful Type: Categorical HRV_Frequency_Feature:

A frequency-domain feature derived from the Fourier Transform of the HRV signal, capturing periodic fluctuations in heart rate during the session. Type: Continuous Skin_Temperature_Frequency_Feature:

A frequency-domain feature derived from the Fourier Transform of the skin temperature signal, capturing periodic variations in temperature. Type: Continuous Emotion_Label:

A numeric label corresponding to the Emotion column, used for machine learning model training. Values: 0 to 3 (corresponding to Interest, Boredom, Confusion, Happiness) Type: Integer Learning_Phase_Label:

A numeric label corresponding to the Learning_Phase column, used for machine learning model training. Values: 0 to 2 (corresponding to Introduction, Practice, Conclusion) Type: Integer

The dataset contains information about appeal cases heard at the Supreme Court of Nigeria (SCN) between the years 1962 to 2022. The dataset was extracted from case files that were provided by The Prison Law Pavillion; a data archiving firm in Nigeria. The dataset originally consisted of documentation of the various appeal cases alongside the outcome of the judgment of the SCN. Feature extraction techniques were used to generate a structured dataset containing information about a number of annotated features. Some of the features were stored as string values while some of the features were stored as numeric values. The dataset consists of information about 14 features including the outcome of the judgment. 13 features are the input variables among which 4 are stored as strings while the remaining 9 were stored as numeric values. Missing values among the numeric values were represented using the value -1. Unsupervised and Supervised machine learning algorithms can be applied to the dataset for the purpose of extracting important information required for gaining a better understanding of the relationship that exists among the features and with respect to predicting the target class which is the outcome of the SCN judgment.