Explore the booming data collection and labeling market, driven by AI advancements. Discover key growth drivers, market trends, and forecasts for 2025-2033, essential for AI development across IT, automotive, and healthcare.

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.

This dataset contains synthetic review data collected from popular online learning platforms such as Coursera, Udemy, and RateMyProfessors. It is designed to support sentiment analysis research by providing structured review content labeled with sentiment classifications.

📌 Purpose The dataset aims to facilitate Natural Language Processing (NLP) tasks, especially in the context of educational feedback analysis, by enabling users to:

Train and evaluate sentiment classification models.

Analyze learner satisfaction across platforms.

Visualize sentiment trends in online education.

📂 Dataset Composition The dataset is synthetically generated and includes review texts with associated sentiment labels. It may include:

Review text: A learner's comment or review.

Sentiment label: Categories like positive, neutral, or negative.

Source indicator: Platform such as Coursera, Udemy, or RateMyProfessors.

🔍 Potential Applications Sentiment classification using machine learning (e.g., Logistic Regression, SVM, BERT, VADER).

Topic modeling to extract key concerns or highlights from reviews.

Dashboards for educational insights and user experience monitoring.

✅ Notes This dataset is synthetic and intended for academic and research purposes only.

No personally identifiable information (PII) is included.

Labeling is consistent with typical sentiment classification tasks.

Telecom Data Labeling Market Outlook

According to our latest research, the global Telecom Data Labeling market size reached USD 1.42 billion in 2024, driven by the exponential growth in data generation, increasing adoption of AI and machine learning in telecom operations, and the rising complexity of communication networks. The market is forecasted to expand at a robust CAGR of 22.8% from 2025 to 2033, reaching an estimated USD 10.09 billion by 2033. This strong momentum is underpinned by the escalating demand for high-quality labeled datasets to power advanced analytics and automation in the telecom sector.

The growth trajectory of the Telecom Data Labeling market is fundamentally propelled by the surging data volumes generated by telecom networks worldwide. With the proliferation of 5G, IoT devices, and cloud-based services, telecom operators are inundated with massive streams of structured and unstructured data. Efficient data labeling is essential to transform raw data into actionable insights, fueling AI-driven solutions for network optimization, predictive maintenance, and fraud detection. Additionally, the mounting pressure on telecom companies to enhance customer experience and operational efficiency is prompting significant investments in data labeling infrastructure and services, further accelerating market expansion.

Another critical growth factor is the rapid evolution of artificial intelligence and machine learning applications within the telecommunications industry. AI-powered tools depend on vast quantities of accurately labeled data to deliver reliable predictions and automation. As telecom companies strive to automate network management, detect anomalies, and personalize user experiences, the demand for high-quality labeled datasets has surged. The emergence of advanced labeling techniques, including semi-automated and automated labeling methods, is enabling telecom enterprises to keep pace with the growing data complexity and volume, thus fostering faster and more scalable AI deployments.

Furthermore, regulatory compliance and data privacy concerns are shaping the landscape of the Telecom Data Labeling market. As governments worldwide tighten data protection regulations, telecom operators are compelled to ensure that data used for AI and analytics is accurately labeled and anonymized. This necessity is driving the adoption of robust data labeling solutions that not only facilitate compliance but also enhance data quality and integrity. The integration of secure, privacy-centric labeling platforms is becoming a competitive differentiator, especially in regions with stringent data governance frameworks. This trend is expected to persist, reinforcing the marketÂ’s upward trajectory.

AI-Powered Product Labeling is revolutionizing the telecom industry by providing more efficient and accurate data annotation processes. This technology leverages artificial intelligence to automate the labeling of large datasets, reducing the time and costs associated with manual labeling. By utilizing AI algorithms, telecom operators can ensure that their data is consistently labeled with high precision, which is crucial for training machine learning models. This advancement not only enhances the quality of labeled data but also accelerates the deployment of AI-driven solutions across various applications, such as network optimization and customer experience management. As AI-Powered Product Labeling continues to evolve, it is expected to play a pivotal role in the telecom sector's digital transformation journey, enabling operators to harness the full potential of their data assets.

From a regional perspective, Asia Pacific is emerging as a powerhouse in the Telecom Data Labeling market, fueled by rapid digitalization, expanding telecom infrastructure, and the early adoption of 5G technologies. North America remains a significant contributor, owing to its mature telecom ecosystem and high investments in AI research and development. Europe is also witnessing steady growth, driven by regulatory mandates and increasing focus on data-driven network management. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with investments in digital transformation and telecom modernization initiatives providing new growth avenues. These regional dynamics collectively underscore the global nature

This dataset contains metadata (title, abstract, date of publication, field, etc) for around 1 million academic articles. Each record contains additional information on the country of study and whether the article makes use of data. Machine learning tools were used to classify the country of study and data use.

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the year 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that are included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample.

The empirical approach employed in this project utilizes text mining with Natural Language Processing (NLP). The goal of NLP is to extract structured information from raw, unstructured text. In this project, NLP is used to extract the country of study and whether the paper makes use of data. We will discuss each of these in turn.

To determine the country or countries of study in each academic article, two approaches are employed based on information found in the title, abstract, or topic fields. The first approach uses regular expression searches based on the presence of ISO3166 country names. A defined set of country names is compiled, and the presence of these names is checked in the relevant fields. This approach is transparent, widely used in social science research, and easily extended to other languages. However, there is a potential for exclusion errors if a country’s name is spelled non-standardly.

The second approach is based on Named Entity Recognition (NER), which uses machine learning to identify objects from text, utilizing the spaCy Python library. The Named Entity Recognition algorithm splits text into named entities, and NER is used in this project to identify countries of study in the academic articles. SpaCy supports multiple languages and has been trained on multiple spellings of countries, overcoming some of the limitations of the regular expression approach. If a country is identified by either the regular expression search or NER, it is linked to the article. Note that one article can be linked to more than one country.

The second task is to classify whether the paper uses data. A supervised machine learning approach is employed, where 3500 publications were first randomly selected and manually labeled by human raters using the Mechanical Turk service (Paszke et al. 2019).[1] To make sure the human raters had a similar and appropriate definition of data in mind, they were given the following instructions before seeing their first paper:

Each of these documents is an academic article. The goal of this study is to measure whether a specific academic article is using data and from which country the data came.

There are two classification tasks in this exercise:

1. identifying whether an academic article is using data from any country

2. Identifying from which country that data came.

For task 1, we are looking specifically at the use of data. Data is any information that has been collected, observed, generated or created to produce research findings. As an example, a study that reports findings or analysis using a survey data, uses data. Some clues to indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.

After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.[2]

For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper.

We expect between 10 and 35 percent of all articles to use data.

The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labelled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. 900 articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

The model was able to predict whether an article made use of data with 87% accuracy evaluated on the set of articles held out of the model training. The correlation between the number of articles written about each country using data estimated under the two approaches is given in the figure below. The number of articles represents an aggregate total of

General Information

This dataset was created for the training and testing of machine learning systems for extracting information from slates/on-screen or filmed text in video productions. The data associated with each instance was acquired by observing text on the slates in the file. There are two levels of data collected, a direct transcription and contextual information. For the direct transcription if there was illegible text an approximation was derived. The information is reported by the original creator of the slates and can be assumed to be accurate.

The data was collected using a software made specifically to categorize and transcribe metadata from these instances (see file directory description). The transcription was written in a natural reading order (for a western audience), so right to left and top to bottom. If the instance was labeled “Graphical” then the reading order was also right to left and top to bottom within individual sections as well as work as a whole.

This dataset was created by Madison Courtney, in collaboration with GBH Archives staff, and in consultation with researchers in the Brandeis University Department of Computer Science.

Uniqueness and overlapping data

Some of the slates come from different episodes of the same series; therefore, some slates have data overlap. For example, the “series-title” may be common across many slates. However, each slate instance in this dataset was labeled independently of the others. No information was removed, but not every slate contains the same information.

Different “sub-types” of slates have different graphical features, and present unique challenges for interpretation. In general, sub-types H (Handwritten), G (Graphical), C (Clapperboard) are more complex than D (Simple digital text) and B (Slate over bars). Most instances in the dataset are D. Users may wish to restrict the set to only those with subtype D.

Labels and annotations were created by an expert human judge. In Version 2, labels and annotations were created only once without any measure of inter-annotator agreement. In Version 3, all data were confirmed and/or edited by a second expert human judge. The dataset is self-contained. But more information about the assets from which these slates were taken can be found at the main website of the AAPB https://www.americanarchive.org/

Data size and structure

The data is tabular. There are 7 columns and 503 rows. Each row represents a different labeled image. The image files themselves are included in the dataset directory. The columns are as follows:

• 0: filename : The name of the image file for this slate
• 1: seen : A boolean book-keeping field used during the annotation process
• 2: type-label : The type of scene pictured in the image. All images in this set have type "S" signifying "Slate"
• 3: subtype-label : The sub-type of scene pictured in the image. Possible subtypes are "H" (Handwritten), "C" (Clapperboard), "D" (Simple digital text), "B" (Slate over bars), "G" (Graphical).
• 4: modifier : A boolean value indicating whether the slate was "transitional" in the sense that the still image was captured as the slate was fading in or out of view.
• 5: note-3 : Verbatim transcription of the text appearing on the slate
• 6: note-4 : Data in key-value structure indicating important data values presented on the slate. Possible keys are "program-title", "episode-title", "series-title", "title", "episode-no", "create-date", "air-date", "date", "director", "producer", "camera". Dates were normalized as YYYY-MM-DD. Names were normalized as Last, First Middle.

Data format

The directory contains the tabular data, the image files, and a small utility for viewing and/or editing labels. The Keystroke Labeler utility is a simple, serverless HTML-based viewer/editor. You can use the Keystroke Labeler by simply opening labeler.html in your web browser. The data are also provided serialized as JSON and CSV. The exact same label data appears redundantly in these 3 files: - img_arr_prog.js - the label data loaded by the Keystroke Labeler - img_labels.csv - the label data serialized as CSV - img_labels.json - the label data serialized as JSON

This dataset includes metadata about programs in the American Archive of Public Broadcasting. Any use of programs referenced by this dataset are subject to the terms of use set by the American Archive of Public Broadcasting.

AI in Semi-supervised Learning Market Outlook

According to our latest research, the AI in Semi-supervised Learning market size reached USD 1.82 billion in 2024 globally, driven by rapid advancements in artificial intelligence and machine learning applications across diverse industries. The market is expected to expand at a robust CAGR of 28.1% from 2025 to 2033, reaching a projected value of USD 17.17 billion by 2033. This exponential growth is primarily fueled by the increasing need for efficient data labeling, the proliferation of unstructured data, and the growing adoption of AI-driven solutions in both large enterprises and small and medium businesses. As per the latest research, the surging demand for automation, accuracy, and cost-efficiency in data processing is significantly accelerating the adoption of semi-supervised learning models worldwide.

One of the most significant growth factors for the AI in Semi-supervised Learning market is the explosive increase in data generation across industries such as healthcare, finance, retail, and automotive. Organizations are continually collecting vast amounts of structured and unstructured data, but the process of labeling this data for supervised learning remains time-consuming and expensive. Semi-supervised learning offers a compelling solution by leveraging small amounts of labeled data alongside large volumes of unlabeled data, thus reducing the dependency on extensive manual annotation. This approach not only accelerates the deployment of AI models but also enhances their accuracy and scalability, making it highly attractive for enterprises seeking to maximize the value of their data assets while minimizing operational costs.

Another critical driver propelling the growth of the AI in Semi-supervised Learning market is the increasing sophistication of AI algorithms and the integration of advanced technologies such as deep learning, natural language processing, and computer vision. These advancements have enabled semi-supervised learning models to achieve remarkable performance in complex tasks like image and speech recognition, medical diagnostics, and fraud detection. The ability to process and interpret vast datasets with minimal supervision is particularly valuable in sectors where labeled data is scarce or expensive to obtain. Furthermore, the ongoing investments in research and development by leading technology companies and academic institutions are fostering innovation, resulting in more robust and scalable semi-supervised learning frameworks that can be seamlessly integrated into enterprise workflows.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments are also contributing significantly to the expansion of the AI in Semi-supervised Learning market. Cloud-based deployment offers unparalleled scalability, flexibility, and cost-efficiency, allowing organizations of all sizes to access cutting-edge AI tools and infrastructure without the need for substantial upfront investments. This democratization of AI technology is empowering small and medium enterprises to leverage semi-supervised learning for competitive advantage, driving widespread adoption across regions and industries. Additionally, the emergence of AI-as-a-Service (AIaaS) platforms is further simplifying the integration and management of semi-supervised learning models, enabling businesses to accelerate their digital transformation initiatives and unlock new growth opportunities.

From a regional perspective, North America currently dominates the AI in Semi-supervised Learning market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI vendors, robust technological infrastructure, and high investments in AI research and development are key factors driving market growth in these regions. Asia Pacific is expected to witness the fastest CAGR during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing government initiatives to promote AI adoption. Meanwhile, Latin America and the Middle East & Africa are also showing promising growth potential, supported by rising awareness of AI benefits and growing investments in digital transformation projects across various sectors.

Component Analysis

The component segment of the AI in Semi-supervised Learning market is divided into software, hardware, and services, each playing a pivotal role in the adoption and implementation of semi-s

FDA Monoclonal Antibody Regulatory Dataset

About the Dataset

This dataset aggregates comprehensive regulatory documentation and resources from the U.S. Food and Drug Administration (FDA), specifically related to monoclonal antibodies (mAbs). It provides structured access to critical FDA filings, clinical trial documentation, and drug labels, serving as an essential resource for regulatory analysis, clinical research, and AI-driven applications.

Contents

The dataset comprises:

• FDA Documentation

  • New Drug Applications (NDA) submissions and approval summaries.
  • Investigational New Drug (IND) filings, including clinical and preclinical data.
  • International Council for Harmonisation (ICH) guidance documents relevant to monoclonal antibody regulation.

• Clinical Trial Documentation

  • Protocols, study designs, and outcome reports from clinical trials.
  • Regulatory correspondence and approval notices.

• Drug Labels

  • Structured drug labeling information for 180 approved monoclonal antibodies, detailing indications, dosages, adverse reactions, warnings, and clinical pharmacology.

Potential Use Cases

This dataset supports various research and analytical tasks, including:

• Regulatory compliance analysis: Identify key elements and benchmarks for successful FDA approvals.
• Clinical trial design optimization: Inform trial protocols using historical approval data.
• Natural Language Processing (NLP) applications: Enable text classification, information extraction, summarization, and entity recognition tasks.
• Safety and efficacy research: Facilitate comparative analysis of drug labels and clinical outcomes.

Intended Audience

• Regulatory professionals and pharmaceutical industry researchers.
• Biomedical data scientists and informaticians.
• NLP and machine learning practitioners focused on biomedical applications.

Data Format

• All documents and labels are provided in machine-readable PDF format that can be parsed using PyPDF, but some drug labels may be a faxed document in a PDF, which may require OCR to parse via Tesseract.

Acknowledgments

This dataset utilizes publicly available information provided by the FDA and other regulatory bodies.

Citation

If you use this dataset in your research or applications, please provide an appropriate citation referencing this dataset.

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 ])}

Relation extraction (RE) is concerned with developing methods and models that automatically detect and retrieve relational information from unstructured data. It is crucial to information extraction (IE) applications that aim to leverage the vast amount of knowledge contained in unstructured natural language text, for example, in web pages, online news, and social media; and simultaneously require the powerful and clean semantics of structured databases instead of searching, querying, and analyzing unstructured text directly. In practical applications, however, relation extraction is often characterized by limited availability of labeled data, due to the cost of annotation or scarcity of domain-specific resources. In such scenarios it is difficult to create models that perform well on the task. It therefore is desired to develop methods that learn more efficiently from limited labeled data and also exhibit better overall relation extraction performance, especially in domains with complex relational structure. In this thesis, I propose to use transfer learning to address this problem, i.e., to reuse knowledge from related tasks to improve models, in particular, their performance and efficiency to learn from limited labeled data. I show how sequential transfer learning, specifically unsupervised language model pre-training, can improve performance and sample efficiency in supervised and distantly supervised relation extraction. In the light of improved modeling abilities, I observe that better understanding neural network-based relation extraction methods is crucial to gain insights that further improve their performance. I therefore present an approach to uncover the linguistic features of the input that neural RE models encode and use for relation prediction. I further complement this with a semi-automated analysis approach focused on model errors, datasets, and annotations. It effectively highlights controversial examples in the data for manual evaluation and allows to specify error hypotheses that can be verified automatically. Together, the researched approaches allow us to build better performing, more sample efficient relation extraction models, and advance our understanding despite their complexity. Further, it facilitates more comprehensive analyses of model errors and datasets in the future.

Snapshot img { margin: 10px !important; } Generative AI In Data Labeling Solution And Services Market Size 2025-2029

The generative ai in data labeling solution and services market size is forecast to increase by USD 31.7 billion, at a CAGR of 24.2% between 2024 and 2029.

The global generative AI in data labeling solution and services market is shaped by the escalating demand for high-quality, large-scale datasets. Traditional manual data labeling methods create a significant bottleneck in the ai development lifecycle, which is addressed by the proliferation of synthetic data generation for robust model training. This strategic shift allows organizations to create limitless volumes of perfectly labeled data on demand, covering a comprehensive spectrum of scenarios. This capability is particularly transformative for generative ai in automotive applications and in the development of data labeling and annotation tools, enabling more resilient and accurate systems.However, a paramount challenge confronting the market is ensuring accuracy, quality control, and mitigation of inherent model bias. Generative models can produce plausible but incorrect labels, a phenomenon known as hallucination, which can introduce systemic errors into training datasets. This makes ai in data quality a critical concern, necessitating robust human-in-the-loop verification processes to maintain the integrity of generative ai in healthcare data. The market's long-term viability depends on developing sophisticated frameworks for bias detection and creating reliable generative artificial intelligence (AI) that can be trusted for foundational tasks.

What will be the Size of the Generative AI In Data Labeling Solution And Services Market during the forecast period?

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The global generative AI in data labeling solution and services market is witnessing a transformation driven by advancements in generative adversarial networks and diffusion models. These techniques are central to synthetic data generation, augmenting AI model training data and redefining the machine learning pipeline. This evolution supports a move toward more sophisticated data-centric AI workflows, which integrate automated data labeling with human-in-the-loop annotation for enhanced accuracy. The scope of application is broadening from simple text-based data annotation to complex image-based data annotation and audio-based data annotation, creating a demand for robust multimodal data labeling capabilities. This shift across the AI development lifecycle is significant, with projections indicating a 35% rise in the use of AI-assisted labeling for specialized computer vision systems.Building upon this foundation, the focus intensifies on annotation quality control and AI-powered quality assurance within modern data annotation platforms. Methods like zero-shot learning and few-shot learning are becoming more viable, reducing dependency on massive datasets. The process of foundation model fine-tuning is increasingly guided by reinforcement learning from human feedback, ensuring outputs align with specific operational needs. Key considerations such as model bias mitigation and data privacy compliance are being addressed through AI-assisted labeling and semi-supervised learning. This impacts diverse sectors, from medical imaging analysis and predictive maintenance models to securing network traffic patterns against cybersecurity threat signatures and improving autonomous vehicle sensors for robotics training simulation and smart city solutions.

How is this Generative AI In Data Labeling Solution And Services Market segmented?

The generative ai in data labeling solution and services market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. End-userIT dataHealthcareRetailFinancial servicesOthersTypeSemi-supervisedAutomaticManualProductImage or video basedText basedAudio basedGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaSouth KoreaJapanAustraliaIndonesiaEuropeGermanyUKFranceItalyThe NetherlandsSpainSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

By End-user Insights

The it data segment is estimated to witness significant growth during the forecast period.

In the IT data segment, generative AI is transforming the creation of training data for software development, cybersecurity, and network management. It addresses the need for realistic, non-sensitive data at scale by producing synthetic code, structured log files, and diverse threat signatures. This is crucial for training AI-powered developer tools and intrusion detection systems. With South America representing an 8.1% market opportunity, the demand for localized and specia

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 ​

Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate food genres can be challenging due to a lack of adequate labeled data. In this study, we present a dataset named the “Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking Recipe Dataset” that contains two million culinary recipes labeled in respective categories with extended named entities extracted from recipe descriptions. This collection of data includes various features such as title, NER, directions, and extended NER, as well as nine different labels representing genres including bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides, and fusions. The proposed pipeline named 3A2M+ extends the size of the Named Entity Recognition (NER) list to address missing named entities like heat, time or process from the recipe directions using two NER extraction tools. 3A2M+ dataset provides a comprehensive solution to the various challenging recipe-related tasks, including classification, named entity recognition, and recipe generation. Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6%. Our investigation indicates that the title feature played a more significant role in classifying the genre.

The Data Annotation Service Market size was valued at USD 1.89 Billion in 2024 and is projected to reach USD 10.07 Billion by 2032, growing at a CAGR of 23% from 2026 to 2032.Global Data Annotation Service Market DriversThe data annotation service market is experiencing robust growth, propelled by the ever-increasing demand for high-quality, labeled data to train sophisticated artificial intelligence (AI) and machine learning (ML) models. As AI continues to permeate various industries, the need for accurate and diverse datasets becomes paramount, making data annotation a critical component of successful AI development. This article explores the key drivers fueling the expansion of the data annotation service market.Rising Demand for Artificial Intelligence (AI) and Machine Learning (ML) Applications: One of the most influential drivers of the data annotation service market is the surging adoption of artificial intelligence (AI) and machine learning (ML) across industries. Data annotation plays a critical role in training AI algorithms to recognize, categorize, and interpret real-world data accurately. From autonomous vehicles to medical diagnostics, annotated datasets are essential for improving model accuracy and performance. As enterprises expand their AI initiatives, they increasingly rely on professional annotation services to handle large, complex, and diverse datasets. This trend is expected to accelerate as AI continues to penetrate industries such as healthcare, finance, automotive, and retail, driving steady market growth.Expansion of Autonomous Vehicle Development: The growing focus on autonomous vehicle technology is a major catalyst for the data annotation service industry. Self-driving cars require immense volumes of labeled image and video data to identify pedestrians, road signs, vehicles, and lane markings with precision.

Handwritten Digits Pixel Dataset - Documentation

Overview

The Handwritten Digits Pixel Dataset is a collection of numerical data representing handwritten digits from 0 to 9. Unlike image datasets that store actual image files, this dataset contains pixel intensity values arranged in a structured tabular format, making it ideal for machine learning and data analysis applications.

Dataset Description

Basic Information

• Format: CSV (Comma-Separated Values)
• Total Samples: [Number of rows based on your dataset]
• Features: 784 pixel columns (28×28 pixels) + 1 label column
• Label Range: Digits 0-9
• Pixel Value Range: 0-255 (grayscale intensity)

File Structure

Column Description

• label: The target variable representing the digit (0-9)
• pixel columns: 784 columns named in format [row]xcolumn
• Each pixel column contains integer values from 0-255 representing grayscale intensity

Data Characteristics

Label Distribution

The dataset contains handwritten digit samples with the following distribution:

• Digit 0: [X] samples
• Digit 1: [X] samples
• Digit 2: [X] samples
• Digit 3: [X] samples
• Digit 4: [X] samples
• Digit 5: [X] samples
• Digit 6: [X] samples
• Digit 7: [X] samples
• Digit 8: [X] samples
• Digit 9: [X] samples

(Note: Actual distribution counts would be calculated from your specific dataset)

Data Quality

• Missing Values: No missing values detected
• Data Type: All values are integers
• Normalization: Pixel values range from 0-255 (can be normalized to 0-1 for ML models)
• Consistency: Uniform 28×28 grid structure across all samples

Technical Specifications

Data Preprocessing Requirements

• Normalization: Scale pixel values from 0-255 to 0-1 range
• Reshaping: Convert 1D pixel arrays to 2D 28×28 matrices for visualization
• Train-Test Split: Recommended 80-20 or 70-30 split for model development

Recommended Machine Learning Approaches

Classification Algorithms:

• Random Forest
• Support Vector Machines (SVM)
• Neural Networks
• K-Nearest Neighbors (KNN)

Deep Learning Architectures:

• Convolutional Neural Networks (CNNs)
• Multi-layer Perceptrons (MLPs)

Dimensionality Reduction:

• PCA (Principal Component Analysis)
• t-SNE for visualization

Usage Examples

Loading the Dataset

import pandas as pd

# Load the dataset
df = pd.read_csv('/kaggle/input/handwritten_digits_pixel_dataset/mnist.csv')

# Separate features and labels
X = df.drop('label', axis=1)
y = df['label']

# Normalize pixel values
X_normalized = X / 255.0

Telecom Data Labeling Market Outlook

According to our latest research, the global Telecom Data Labeling market size reached USD 1.32 billion in 2024, demonstrating robust expansion driven by the rapid adoption of artificial intelligence and machine learning across the telecommunications sector. The market is expected to grow at a CAGR of 22.8% during the forecast period, with the market size forecasted to reach USD 9.98 billion by 2033. This exceptional growth trajectory is primarily attributed to the increasing need for high-quality, labeled data to train advanced AI models for network optimization, fraud detection, and customer experience management within telecom operations.

One of the primary growth factors fueling the Telecom Data Labeling market is the exponential surge in data generated by telecom networks, devices, and users. With the proliferation of IoT devices, 5G rollouts, and the expansion of cloud-based telecom services, telecom operators are inundated with massive volumes of structured and unstructured data. To extract actionable insights and automate critical processes, these organizations are increasingly relying on labeled datasets to train and validate AI-driven algorithms. The demand for accurate and scalable data labeling solutions has thus skyrocketed, as telecom companies seek to enhance network efficiency, reduce operational costs, and deliver personalized services to their customers. Additionally, the integration of AI-powered analytics with telecom infrastructure further amplifies the necessity for precise data annotation, ensuring that predictive models and automation tools function with optimal accuracy.

Another significant driver for the Telecom Data Labeling market is the intensifying focus on customer experience management and fraud detection. Telecom providers are leveraging AI and machine learning to proactively identify and mitigate fraudulent activities, optimize network performance, and deliver seamless user experiences. These applications demand large volumes of accurately labeled data, encompassing text, audio, image, and video formats, to train sophisticated algorithms capable of real-time decision-making. The growing complexity of telecom networks, coupled with the need for advanced analytics to interpret customer interactions and network anomalies, underscores the critical role of data labeling in achieving business objectives. As telecom operators invest heavily in digital transformation, the adoption of automated and semi-supervised labeling solutions is expected to accelerate, further propelling market growth.

Furthermore, the emergence of regulatory frameworks and data privacy mandates across different regions has spurred telecom companies to adopt more robust data labeling practices. Compliance with international standards such as GDPR, CCPA, and other local data protection laws requires telecom operators to maintain high standards of data accuracy, transparency, and accountability. This regulatory landscape is prompting the adoption of advanced data labeling platforms that offer end-to-end traceability, auditability, and security. The integration of data labeling solutions with existing telecom workflows not only enhances regulatory compliance but also supports the deployment of ethical and bias-free AI models. As a result, the demand for secure, scalable, and customizable data labeling services continues to rise, positioning the market for sustained growth throughout the forecast period.

From a regional perspective, Asia Pacific is emerging as a dominant force in the Telecom Data Labeling market, driven by rapid digitalization, large-scale 5G deployments, and the presence of leading telecom operators. North America and Europe also contribute significantly to market expansion, owing to advanced telecom infrastructure, high AI adoption rates, and a strong focus on innovation. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in telecom modernization and AI-driven solutions, albeit from a smaller base. This regional diversification not only underscores the global nature of the market but also highlights the varying adoption patterns and growth opportunities across different geographies.

Data Type Analysis

The Data Type segment in the Telecom Data Labeling market is categorized into text, image, audio, and video data. Among these, text data labeling holds a substantial share due to the extensive use of natural languag

Weed Detection (Unsupervised + Supervised Learning)

Overview

This dataset is designed to support both supervised and unsupervised learning for the task of weed detection in crop fields. It provides labeled data in YOLO format suitable for training object detection models, unlabeled data for semi-supervised or unsupervised learning, and a separate test set for evaluation. The objective is to detect and distinguish between weed and crop instances using deep learning models like YOLOv5 or YOLOv8.

Dataset Structure

│ ├── labeled/ │ ├── images/ # Labeled images for training │ └── labels/ # YOLO-format annotations │ ├── unlabeled/ # Unlabeled images for unsupervised or semi-supervised learning │ └── test/ ├── images/ # Test images └── labels/ # Ground truth annotations in YOLO format

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.

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.