Global Data Annotation Tools Market size at US$ 102.38 Billion in 2023, set to reach US$ 908.57 Billion by 2032 at a CAGR of 24.4% from 2024 to 2032.

The Data Annotation Tools Market Report is Segmented by Component (Text, Image, Other Types), by Type (Manual, Semi-Supervised, Automatic), by End-User (BFSI, IT and Telecom, Retail, Healthcare, Government, Other End-Users), by Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Global Automated Data Annotation Tool market size 2025 was XX Million. Automated Data Annotation Tool Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The data collection and labeling 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, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033), reaching approximately $75 billion by 2033. This expansion is primarily driven by the increasing adoption of AI across diverse sectors, including healthcare (medical image analysis, drug discovery), automotive (autonomous driving systems), finance (fraud detection, risk assessment), and retail (personalized recommendations, inventory management). The rising complexity of AI models and the need for more diverse and nuanced datasets are significant contributing factors to this growth. Furthermore, advancements in data annotation tools and techniques, such as active learning and synthetic data generation, are streamlining the data labeling process and making it more cost-effective. However, challenges remain. Data privacy concerns and regulations like GDPR necessitate robust data security measures, adding to the cost and complexity of data collection and labeling. The shortage of skilled data annotators also hinders market growth, necessitating investments in training and upskilling programs. Despite these restraints, the market’s inherent potential, coupled with ongoing technological advancements and increased industry investments, ensures sustained expansion in the coming years. Geographic distribution shows strong concentration in North America and Europe initially, but Asia-Pacific is poised for rapid growth due to increasing AI adoption and the availability of a large workforce. This makes strategic partnerships and global expansion crucial for market players aiming for long-term success.

Historical treebanks tend to be manually annotated, which is not surprising, since state-of-the-art parsers are not accurate enough to ensure high-quality annotation for historical texts. We show that automatic parsing can be an efficient pre-annotation tool for Old East Slavic texts.

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several factors: the rising adoption of AI across various sectors (including IT, automotive, healthcare, and finance), the need for cost-effective data annotation solutions, and the inherent flexibility and customization offered by open-source tools. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant, particularly for organizations with stringent data security requirements. The market's growth is further propelled by advancements in automation and semi-supervised learning techniques within data labeling, leading to increased efficiency and reduced annotation costs. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher adoption of AI technologies in these regions; however, Asia-Pacific is emerging as a rapidly growing market due to increasing investment in AI and the availability of a large workforce for data annotation. Despite the promising outlook, certain challenges restrain market growth. The complexity of implementing and maintaining open-source tools, along with the need for specialized technical expertise, can pose barriers to entry for smaller organizations. Furthermore, the quality control and data governance aspects of open-source annotation require careful consideration. The potential for data bias and the need for robust validation processes necessitate a strategic approach to ensure data accuracy and reliability. Competition is intensifying with both established and emerging players vying for market share, forcing companies to focus on differentiation through innovation and specialized functionalities within their tools. The market is anticipated to maintain a healthy growth trajectory in the coming years, with increasing adoption across diverse sectors and geographical regions. The continued advancements in automation and the growing emphasis on data quality will be key drivers of future market expansion.

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system. Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations)

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a computational tool for manual annotation of language corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system.

Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI).

Video Annotation Services Market Analysis The global video annotation services market size was valued at USD 475.6 million in 2025 and is projected to reach USD 843.2 million by 2033, exhibiting a compound annual growth rate (CAGR) of 7.4% over the forecast period. The increasing demand for video data in various industries such as healthcare, transportation, retail, and entertainment, coupled with the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies, is driving the market growth. Moreover, the emergence of new annotation techniques and the increasing adoption of cloud-based annotation solutions are further contributing to the market expansion. Key market trends include the integration of AI and ML capabilities to enhance annotation accuracy and efficiency, the increasing adoption of remote and hybrid work models leading to the demand for automated video annotation tools, and the focus on ethical and responsible data annotation practices to ensure data privacy and protection. Major companies operating in the market include Acclivis, Ai-workspace, GTS, HabileData, iMerit, Keymakr, LXT, Mindy Support, Sama, Shaip, SunTec, TaskUs, Tasq, and Triyock. North America holds a dominant share in the market, followed by Europe and Asia Pacific.

The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a computational tool for manual annotation of language corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system.

Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments.

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.

Site-specific proteolytic processing is an important, irreversible post-translational protein modification with implications in many diseases. Enrichment of protein N-terminal peptides followed by mass spectrometry-based identification and quantification enables proteome-wide characterization of proteolytic processes and protease substrates but is challenged by the lack of specific annotation tools. A common problem is, for example, ambiguous matches of identified peptides to multiple protein entries in the databases used for identification. We developed MaxQuant Advanced N-termini Interpreter (MANTI), a standalone Perl software with an optional graphical user interface that validates and annotates N-terminal peptides identified by database searches with the popular MaxQuant software package by integrating information from multiple data sources. MANTI utilizes diverse annotation information in a multistep decision process to assign a conservative preferred protein entry for each N-terminal peptide, enabling automated classification according to the likely origin and determines significant changes in N-terminal peptide abundance. Auxiliary R scripts included in the software package summarize and visualize key aspects of the data. To showcase the utility of MANTI, we generated two large-scale TAILS N-terminome data sets from two different animal models of chemically and genetically induced kidney disease, puromycin adenonucleoside-treated rats (PAN), and heterozygous Wilms Tumor protein 1 mice (WT1). MANTI enabled rapid validation and autonomous annotation of >10 000 identified terminal peptides, revealing novel proteolytic proteoforms in 905 and 644 proteins, respectively. Quantitative analysis indicated that proteolytic activities with similar sequence specificity are involved in the pathogenesis of kidney injury and proteinuria in both models, whereas coagulation processes and complement activation were specifically induced after chemical injury.

AdA Project Public Data Release

This repository holds public data provided by the AdA project (Affektrhetoriken des Audiovisuellen - BMBF eHumanities Research Group Audio-Visual Rhetorics of Affect).

See: http://www.ada.cinepoetics.fu-berlin.de/en/index.html The data is made accessible under the terms of the Creative Commons Attribution-ShareAlike 3.0 License. The data can be accessed also at the project's public GitHub repository: https://github.com/ProjectAdA/public

Further explanations of the data can be found on our AdA project website: https://projectada.github.io/. See also the peer-reviewed data paper for this dataset that is in review to be published in NECSUS_European Journal of Media Studies, and will be available from https://necsus-ejms.org/ and https://mediarep.org

The data currently includes:

AdA Filmontology

The latest public release of the AdA Filmontology: https://github.com/ProjectAdA/public/tree/master/ontology

A vocabulary of film-analytical terms and concepts for fine-grained semantic video annotation.

The vocabulary is also available online in our triplestore: https://ada.cinepoetics.org/resource/2021/05/19/eMAEXannotationMethod.html

Advene Annotation Template

The latest public release of the template for the Advene annotation software: https://github.com/ProjectAdA/public/tree/master/advene_template

The template provides the developed semantic vocabulary in the Advene software with ready-to-use annotation tracks and predefined values.

In order to use the template you have to install and use Advene: https://www.advene.org/

Annotation Data

The latest public releases of our annotation datasets based on the AdA vocabulary: https://github.com/ProjectAdA/public/tree/master/annotations

The dataset of news reports, documentaries and feature films on the topic of "financial crisis" contains more than 92.000 manual & semi-automatic annotations authored in the open-source-software Advene (Aubert/Prié 2005) by expert annotators as well as more than 400.000 automatically generated annotations for wider corpus exploration. The annotations are published as Linked Open Data under the CC BY-SA 3.0 licence and available as rdf triples in turtle exports (ttl files) and in Advene's non-proprietary azp-file format, which allows instant access through the graphical interface of the software.

The annotation data can also be queried at our public SPARQL Endpoint: http://ada.filmontology.org/sparql

• The dataset contains furthermore sample files for two different export capabilities of the web application AdA Annotation explorer: 1) all manual annotations of the type "field size" throughout the corpus as csv files. 2) static html exports of different queries conducted in the AdA Annotation Explorer.

Manuals

The data set includes different manuals and documentations in German and English: https://github.com/ProjectAdA/public/tree/master/manuals

"AdA Filmontology – Levels, Types, Values": an overview over all analytical concepts and their definitions.

"Manual: Annotating with Advene and the AdA Filmontology". A manual on the usage of Advene and the AdA Annotation Explorer that provides the basics for annotating audiovisual aesthetics and visualizing them.

"Notes on collaborative annotation with the AdA Filmontology"

自动数据注释工具市场的市场规模基于应用程序（文本注释，图像注释，视频注释，音频注释）和 product （AI培训，数据标记，机器学习模型，自治系统，NLP）和地理区域（北美，欧洲，亚太地区，南美和中东以及中东以及非洲）。

本报告提供了有关市场规模的见解，并预测了这些定义的细分市场以百万美元表示的市场价值。

Additional file 2: Table S1. Simulated validation in canonical datasets.

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system.

Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations) Version 1.5 supports listening to audio recordings (provided in the # sound_url comment line in CONLL-U)

Pacific Labeled Corals is an aggregate dataset containing 2318 coral reef survey images from four Pacific monitoring projects in Moorea (French Polynesia), the northern Line Islands, Nanwan Bay (Taiwan) and Heron Reef (Australia). Pacific Labeled Corals contain a total of 318828 expert annotations across 4 pacific reef locations, and can be used as a benchmark dataset for evaluating object recognition methods and texture descriptors as well as for domain transfer learning research. The images have all been annotated using a random point annotation tool by a coral reef expert. In addition, 200 images from each location have been cross-annotatoed by 6 experts, for a total of 7 sets of annotations for each image. These data will be published in Beijbom O., et al., 'Transforming benthic surveys through automated image annotation' (in submission). These data are a subset of the raw data from which knb-lter-mcr.4 is derived.

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a computational tool for manual annotation of language corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system.

Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags.