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.

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.

The Data Annotation and Labeling (DAL) solutions market is experiencing robust growth, fueled by the escalating demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market's expansion is driven by the increasing adoption of AI across diverse sectors, including automotive, healthcare, and finance. The need for accurate and reliable data annotation to train sophisticated algorithms is paramount, pushing the demand for specialized DAL services and tools. While precise market sizing data is unavailable, a conservative estimate based on industry reports and the mentioned CAGR suggests a 2025 market value of approximately $5 billion, projecting to $8 billion by 2030, given a moderate annual growth of 8-10%. Key trends include the rise of automated annotation tools, a growing preference for hybrid annotation models combining human expertise with automated systems, and an increasing focus on data privacy and security. Despite the positive outlook, the market faces certain constraints. The high cost of data annotation, the need for specialized skills and expertise, and challenges in maintaining data quality across large datasets present hurdles to widespread adoption. However, advancements in technology and the emergence of innovative solutions are continually mitigating these challenges. The competitive landscape is characterized by a mix of established players like Appen and Telus International, alongside smaller, specialized firms like Centific and Akkodis, suggesting a dynamic and evolving market structure. The geographical distribution is expected to be dominated by North America and Europe initially, with increasing participation from Asia-Pacific regions due to growing AI adoption in those markets.

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

Curated image privacy data sets

In the article, we trained and evaluated models on the Image Privacy Dataset (IPD) and the PrivacyAlert dataset. The datasets are originally provided by other sources and have been re-organised and curated for this work.

Our curation organises the datasets in a common structure. We updated the annotations and labelled the splits of the data in the annotation file. This avoids having separated folders of images for each data split (training, validation, testing) and allows a flexible handling of new splits, e.g. created with a stratified K-Fold cross-validation procedure. As for the original datasets (PicAlert and PrivacyAlert), we provide the link to the images in bash scripts to download the images. Another bash script re-organises the images in sub-folders with maximum 1000 images in each folder.

Both datasets refer to images publicly available on Flickr. These images have a large variety of content, including sensitive content, seminude people, vehicle plates, documents, private events. Images were annotated with a binary label denoting if the content was deemed to be public or private. As the images are publicly available, their label is mostly public. These datasets have therefore a high imbalance towards the public class. Note that IPD combines two other existing datasets, PicAlert and part of VISPR, to increase the number of private images already limited in PicAlert. Further details in our corresponding https://doi.org/10.48550/arXiv.2503.12464" target="_blank" rel="noopener">publication.

List of datasets and their original source:

• PicAlert [Images occupy 2.4 GB]
• VISPR [Images occupy 49.7 GB]
• PrivacyAlert [Images occupy 1 GB]

Notes:

• For PicAlert and PrivacyAlert, only urls to the original locations in Flickr are available in the Zenodo record
• Collector and authors of the PrivacyAlert dataset selected the images from Flickr under Public Domain license
• Owners of the photos on Flick could have removed the photos from the social media platform
• Running the bash scripts to download the images can incur in the "429 Too Many Requests" status code

Pre-computed visual entitities

Some of the models run their pipeline end-to-end with the images as input, whereas other models require different or additional inputs. These inputs include the pre-computed visual entities (scene types and object types) represented in a graph format, e.g. for a Graph Neural Network. Re-using these pre-computed visual entities allows other researcher to build new models based on these features while avoiding re-computing the same on their own or for each epoch during the training of a model (faster training).

For each image of each dataset, namely PrivacyAlert, PicAlert, and VISPR, we provide the predicted scene probabilities as a .csv file , the detected objects as a .json file in COCO data format, and the node features (visual entities already organised in graph format with their features) as a .json file. For consistency, all the files are already organised in batches following the structure of the images in the datasets folder. For each dataset, we also provide the pre-computed adjacency matrix for the graph data.

Note: IPD is based on PicAlert and VISPR and therefore IPD refers to the scene probabilities and object detections of the other two datasets. Both PicAlert and VISPR must be downloaded and prepared to use IPD for training and testing.

Further details on downloading and organising data can be found in our GitHub repository: https://github.com/graphnex/privacy-from-visual-entities (see ARTIFACT-EVALUATION.md#pre-computed-visual-entitities-)

Enquiries, questions and comments

If you have any enquiries, question, or comments, or you would like to file a bug report or a feature request, use the issue tracker of our GitHub repository.

We present Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle.

The data is organized in a table structure. Code4ML includes several main objects: competitions information, raw code blocks collected form Kaggle and manually marked up snippets. Each table has a .csv format.

Each competition has the text description and metadata, reflecting competition and used dataset characteristics as well as evaluation metrics (competitions.csv). The corresponding datasets can be loaded using Kaggle API and data sources.

The code blocks themselves and their metadata are collected to the data frames concerning the publishing year of the initial kernels. The current version of the corpus includes two code blocks files: snippets from kernels up to the 2020 year (сode_blocks_upto_20.csv) and those from the 2021 year (сode_blocks_21.csv) with corresponding metadata. The corpus consists of 2 743 615 ML code blocks collected from 107 524 Jupyter notebooks.

Marked up code blocks have the following metadata: anonymized id, the format of the used data (for example, table or audio), the id of the semantic type, a flag for the code errors, the estimated relevance to the semantic class (from 1 to 5), the id of the parent notebook, and the name of the competition. The current version of the corpus has ~12 000 labeled snippets (markup_data_20220415.csv).

As marked up code blocks data contains the numeric id of the code block semantic type, we also provide a mapping from this number to semantic type and subclass (actual_graph_2022-06-01.csv).

The dataset can help solve various problems, including code synthesis from a prompt in natural language, code autocompletion, and semantic code classification.

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:

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

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Data Labeling And Annotation Tools Market Size 2025-2029

The data labeling and annotation tools market size is forecast to increase by USD 2.69 billion at a CAGR of 28% between 2024 and 2029.

The market is experiencing significant growth, driven by the explosive expansion of generative AI applications. As AI models become increasingly complex, there is a pressing need for specialized platforms to manage and label the vast amounts of data required for training. This trend is further fueled by the emergence of generative AI, which demands unique data pipelines for effective training. However, this market's growth trajectory is not without challenges. Maintaining data quality and managing escalating complexity pose significant obstacles. ML models are being applied across various sectors, from fraud detection and sales forecasting to speech recognition and image recognition.
Ensuring the accuracy and consistency of annotated data is crucial for AI model performance, necessitating robust quality control measures. Moreover, the growing complexity of AI systems requires advanced tools to handle intricate data structures and diverse data types. The market continues to evolve, driven by advancements in machine learning (ML), computer vision, and natural language processing. Companies seeking to capitalize on market opportunities must address these challenges effectively, investing in innovative solutions to streamline data labeling and annotation processes while maintaining high data quality.

What will be the Size of the Data Labeling And Annotation Tools Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant activity and trends, with a focus on enhancing annotation efficiency, ensuring data privacy, and improving model performance. Annotation task delegation and remote workflows enable teams to collaborate effectively, while version control systems facilitate model deployment pipelines and error rate reduction. Label inter-annotator agreement and quality control checks are crucial for maintaining data consistency and accuracy. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Data privacy concerns are addressed through secure data handling practices and access controls. Model retraining strategies and cost optimization techniques are essential for adapting to evolving datasets and budgets. Dataset bias mitigation and accuracy improvement methods are key to producing high-quality annotated data.

Training data preparation involves data preprocessing steps and annotation guidelines creation, while human-in-the-loop systems allow for real-time feedback and model fine-tuning. Data validation techniques and team collaboration tools are essential for maintaining data integrity and reducing errors. Scalable annotation processes and annotation project management tools streamline workflows and ensure a consistent output. Model performance evaluation and annotation tool comparison are ongoing efforts to optimize processes and select the best tools for specific use cases. Data security measures and dataset bias mitigation strategies are essential for maintaining trust and reliability in annotated data.

How is this Data Labeling And Annotation Tools Industry segmented?

The data labeling and annotation tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Text
  Video
  Image
  Audio


Technique

  Manual labeling
  Semi-supervised labeling
  Automatic labeling


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The Text segment is estimated to witness significant growth during the forecast period. The data labeling market is witnessing significant growth and advancements, primarily driven by the increasing adoption of generative artificial intelligence and large language models (LLMs). This segment encompasses various annotation techniques, including text annotation, which involves adding structured metadata to unstructured text. Text annotation is crucial for machine learning models to understand and learn from raw data. Core text annotation tasks range from fundamental natural language processing (NLP) techniques, such as Named Entity Recognition (NER), where entities like persons, organizations, and locations are identified and tagged, to complex requirements of modern AI.

Moreover,

Collective behaviour such as the flocks of birds and schools of fish is inspired by computer-based systems and is widely used in agents’ formation. The human could easily recognise these behaviours; however, it is hard for a computer system to recognise these behaviours. Since humans could easily recognise these behaviours, ground truth data on human perception of collective behaviour could enable machine learning methods to mimic this human perception. Hence ground truth data has been collected from human perception of collective behaviour recognition by running an online survey. Specific collective motions considered in this online survey include 16 structured and unstructured behaviours. The defined structured collective motions include boids’ movements with an identifiable embedded pattern. Unstructured collective motions consist of random movement of boids with no patterns. The participants are from diverse levels of knowledge, all over the world, and are over 18 years old. Each question contains a short video (around 10 seconds), captured from one of the 16 simulated movements. The videos are shown in a randomized order to the participants. Then they were asked to label each structured motion of boids as ‘flocking’, ‘aligned’, or ‘grouped’ and others as ‘not flocking’, ‘not aligned’, or ‘not grouped’. By averaging human perceptions, three binary labelled datasets of these motions are created. The data could be trained by machine learning methods, which enabled them to automatically recognise collective behaviour.

These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. The dataset contains images from a variety of imaging modalities, at different resolutions, using difference sources of contrast and featuring different organs/ pathologies. This data was use to train, test and validated a foundational model for 3D vessel segmentation, tUbeNet, which can be found on github. The paper descripting the training and validation of the model can be found here. Filenames are structured as follows: Data - [Modality]_[species Organ]_[resolution].tif Labels - [Modality]_[species Organ]_[resolution]_labels.tif Sub-volumes of larger dataset - [Modality]_[species Organ]_subvolume[dimensions in pixels].tif Manual labelling of blood vessels was carried out using Amira (2020.2, Thermo-Fisher, UK). Training data: opticalHREM_murineLiver_2.26x2.26x1.75um.tif: A high resolution episcopic microscopy (HREM) dataset, acquired in house by staining a healthy mouse liver with Eosin B and imaged using a standard HREM protocol. NB: 25% of this image volume was withheld from training, for use as test data. CT_murineTumour_20x20x20um.tif: X-ray microCT images of a microvascular cast, taken from a subcutaneous mouse model of colorectal cancer (acquired in house). NB: 25% of this image volume was withheld from training, for use as test data. RSOM_murineTumour_20x20um.tif: Raster-Scanning Optoacoustic Mesoscopy (RSOM) data from a subcutaneous tumour model (provided by Emma Brown, Bohndiek Group, University of Cambridge). The image data has undergone filtering to reduce the background ​(Brown et al., 2019)​. OCTA_humanRetina_24x24um.tif: retinal angiography data obtained using Optical Coherence Tomography Angiography (OCT-A) (provided by Dr Ranjan Rajendram, Moorfields Eye Hospital). Test data: MRI_porcineLiver_0.9x0.9x5mm.tif: T1-weighted Balanced Turbo Field Echo Magnetic Resonance Imaging (MRI) data from a machine-perfused porcine liver, acquired in-house. Test Data MFHREM_murineTumourLectin_2.76x2.76x2.61um.tif: a subcutaneous colorectal tumour mouse model was imaged in house using Multi-fluorescence HREM in house, with Dylight 647 conjugated lectin staining the vasculature ​(Walsh et al., 2021)​. The image data has been processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 480x480x640 voxels was manually labelled (MFHREM_murineTumourLectin_subvolume480x480x640.tif). MFHREM_murineBrainLectin_0.85x0.85x0.86um.tif: an MF-HREM image of the cortex of a mouse brain, stained with Dylight-647 conjugated lectin, was acquired in house ​(Walsh et al., 2021)​. The image data has been downsampled and processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 1000x1000x99 voxels was manually labelled. This sub-volume is provided at full resolution and without preprocessing (MFHREM_murineBrainLectin_subvol_0.57x0.57x0.86um.tif). 2Photon_murineOlfactoryBulbLectin_0.2x0.46x5.2um.tif: two-photon data of mouse olfactory bulb blood vessels, labelled with sulforhodamine 101, was kindly provided by Yuxin Zhang at the Sensory Circuits and Neurotechnology Lab, the Francis Crick Institute ​(Bosch et al., 2022)​. NB: A sub-volume of 500x500x79 voxel was manually labelled (2Photon_murineOlfactoryBulbLectin_subvolume500x500x79.tif). References: ​​Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M. C., Whiteley, I., Storm, M., Bonnin, A., Rau, C., Margrie, T., Collinson, L., & Schaefer, A. T. (2022). Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nature Communications 2022 13:1, 13(1), 1–16. https://doi.org/10.1038/s41467-022-30199-6 ​Brown, E., Brunker, J., & Bohndiek, S. E. (2019). Photoacoustic imaging as a tool to probe the tumour microenvironment. DMM Disease Models and Mechanisms, 12(7). https://doi.org/10.1242/DMM.039636 ​Walsh, C., Holroyd, N. A., Finnerty, E., Ryan, S. G., Sweeney, P. W., Shipley, R. J., & Walker-Samuel, S. (2021). Multifluorescence High-Resolution Episcopic Microscopy for 3D Imaging of Adult Murine Organs. Advanced Photonics Research, 2(10), 2100110. https://doi.org/10.1002/ADPR.202100110 ​Walsh, C., Holroyd, N., Shipley, R., & Walker-Samuel, S. (2020). Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging. Communications in Computer and Information Science, 1248 CCIS, 235–249. https://doi.org/10.1007/978-3-030-52791-4_19 ​ 

Whole slide images (WSIs) are digitized histopathology images. WSIs are stored in a pyramidal data structure that contains the same images at multiple magnification levels. In digital pathology, most algorithmic approaches to analyze WSIs use a single magnification level. However, images at different magnification levels may reveal relevant and distinct properties in the image, such as global context or detailed spatial arrangement. Given their high resolution, WSIs cannot be processed as a whole and are broken down into smaller pieces called tiles. Then, a prediction at the tile-level is made for each tile in the larger image. As many classification problems require a prediction at a slide-level, there exist common strategies to integrate the tile-level insights into a slide-level prediction. We explore two approaches to tackle this problem, namely a multiple instance learning framework and a representation learning algorithm (the so-called “barcode approach”) based on clustering. In this work, we apply both approaches in a single- and multi-scale setting and compare the results in a multi-label histopathology classification task to show the promises and pitfalls of multi-scale analysis. Our work shows a consistent improvement in performance of the multi-scale models over single-scale ones. Using multiple instance learning and the barcode approach we achieved a 0.06 and 0.06 improvement in F1 score, respectively, highlighting the importance of combining multiple scales to integrate contextual and detailed information.

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

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 ])}

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

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.

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 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

The dataset used in this study is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, originally provided by the University of Wisconsin and obtained via Kaggle. It consists of 569 observations, each corresponding to a digitized image of a fine needle aspirate (FNA) of a breast mass. The dataset contains 32 attributes: one identifier column (discarded during preprocessing), one diagnosis label (malignant or benign), and 30 continuous real-valued features that describe the morphology of cell nuclei. These features are grouped into three statistical descriptors—mean, standard error (SE), and worst (mean of the three largest values)—for ten morphological properties including radius, perimeter, area, concavity, and fractal dimension. All feature values were normalized using z-score standardization to ensure uniform scale across models sensitive to input ranges. No missing values were present in the original dataset. Label encoding was applied to the diagnosis column, assigning 1 to malignant and 0 to benign cases. The dataset was split into training (80%) and testing (20%) sets while preserving class balance via stratified sampling. The accompanying Python source code (breast_cancer_classification_models.py) performs data loading, preprocessing, model training, evaluation, and result visualization. Four lightweight classifiers—Decision Tree, Naïve Bayes, Perceptron, and K-Nearest Neighbors (KNN)—were implemented using the scikit-learn library (version 1.2 or later). Performance metrics including Accuracy, Precision, Recall, F1-score, and ROC-AUC were calculated for each model. Confusion matrices and ROC curves were generated and saved as PNG files for interpretability. All results are saved in a structured CSV file (classification_results.csv) that contains the performance metrics for each model. Supplementary visualizations include all_feature_histograms.png (distribution plots for all standardized features), model_comparison.png (metric-wise bar plot), and feature_correlation_heatmap.png (Pearson correlation matrix of all 30 features). The data files are in standard CSV and PNG formats and can be opened using any spreadsheet or image viewer, respectively. No rare file types are used, and all scripts are compatible with any Python 3.x environment. This data package enables reproducibility and offers a transparent overview of how baseline machine learning models perform in the domain of breast cancer diagnosis using a clinically-relevant dataset.