Discover the booming Automated Data Annotation Tools market! This comprehensive analysis reveals key trends, drivers, restraints, and forecasts for 2025-2033, covering major regions & applications. Learn about leading companies and unlock opportunities in this rapidly evolving AI landscape.

The Automated Data Annotation Tools market is booming, projected to reach $3.2 Billion by 2033. Discover key market trends, growth drivers, and leading companies shaping this vital sector for AI development. Explore our in-depth analysis covering market segmentation, regional insights, and future forecasts.

The booming manual data annotation tools market is projected to reach $1045.4 million by 2025, growing at a CAGR of 14.2% through 2033. Learn about key drivers, trends, regional insights, and leading companies shaping this crucial sector for AI development. Explore market segmentation by application (IT, BFSI, Healthcare, etc.) and annotation type (image/video, text, audio).

The automated data annotation tool market is booming, projected to reach $10 billion by 2033. Learn about market trends, key players (Amazon, Google, etc.), and the driving forces behind this explosive growth in AI training data. Discover insights into regional market shares and segmentation data.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented September 15, 2017.A virtual database of annotations between databases.

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

This dataset contains 35 of 39 taxonomies that were the result of a systematic review. The systematic review was conducted with the goal of identifying taxonomies suitable for semantically annotating research data. A special focus was set on research data from the hybrid societies domain.

The following taxonomies were identified as part of the systematic review:

Filename	Taxonomy Title
acm_ccs	ACM Computing Classification System [1]
amec	A Taxonomy of Evaluation Towards Standards [2]
bibo	A BIBO Ontology Extension for Evaluation of Scientific Research Results [3]
cdt	Cross-Device Taxonomy [4]
cso	Computer Science Ontology [5]
ddbm	What Makes a Data-driven Business Model? A Consolidated Taxonomy [6]
ddi_am	DDI Aggregation Method [7]
ddi_moc	DDI Mode of Collection [8]
n/a	DemoVoc [9]
discretization	Building a New Taxonomy for Data Discretization Techniques [10]
dp	Demopaedia [11]
dsg	Data Science Glossary [12]
ease	A Taxonomy of Evaluation Approaches in Software Engineering [13]
eco	Evidence & Conclusion Ontology [14]
edam	EDAM: The Bioscientific Data Analysis Ontology [15]
n/a	European Language Social Science Thesaurus [16]
et	Evaluation Thesaurus [17]
glos_hci	The Glossary of Human Computer Interaction [18]
n/a	Humanities and Social Science Electronic Thesaurus [19]
hcio	A Core Ontology on the Human-Computer Interaction Phenomenon [20]
hft	Human-Factors Taxonomy [21]
hri	A Taxonomy to Structure and Analyze Human–Robot Interaction [22]
iim	A Taxonomy of Interaction for Instructional Multimedia [23]
interrogation	A Taxonomy of Interrogation Methods [24]
iot	Design Vocabulary for Human–IoT Systems Communication [25]
kinect	Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors [26]
maco	Thesaurus Mass Communication [27]
n/a	Thesaurus Cognitive Psychology of Human Memory [28]
mixed_initiative	Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey [29]
qos_qoe	A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction [30]
ro	The Research Object Ontology [31]
senses_sensors	A Human-Centered Taxonomy of Interaction Modalities and Devices [32]
sipat	A Taxonomy of Spatial Interaction Patterns and Techniques [33]
social_errors	A Taxonomy of Social Errors in Human-Robot Interaction [34]
sosa	Semantic Sensor Network Ontology [35]
swo	The Software Ontology [36]
tadirah	Taxonomy of Digital Research Activities in the Humanities [37]
vrs	Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions [38]
xdi	Cross-Device Interaction [39]

We converted the taxonomies into SKOS (Simple Knowledge Organisation System) representation. The following 4 taxonomies were not converted as they were already available in SKOS and were for this reason excluded from this dataset:

1) DemoVoc, cf. http://thesaurus.web.ined.fr/navigateur/
available at https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf

2) European Language Social Science Thesaurus, cf. https://thesauri.cessda.eu/elsst/en/
available at https://zenodo.org/record/5506929

3) Humanities and Social Science Electronic Thesaurus, cf. https://hasset.ukdataservice.ac.uk/hasset/en/
available at https://zenodo.org/record/7568355

4) Thesaurus Cognitive Psychology of Human Memory, cf. https://www.loterre.fr/presentation/
available at https://skosmos.loterre.fr/P66/en/

References

[1] “The 2012 ACM Computing Classification System,” ACM Digital Library, 2012. https://dl.acm.org/ccs (accessed May 08, 2023).

[2] AMEC, “A Taxonomy of Evaluation Towards Standards.” Aug. 31, 2016. Accessed: May 08, 2023. [Online]. Available: https://amecorg.com/amecframework/home/supporting-material/taxonomy/

[3] B. Dimić Surla, M. Segedinac, and D. Ivanović, “A BIBO ontology extension for evaluation of scientific research results,” in Proceedings of the Fifth Balkan Conference in Informatics, in BCI ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 275–278. doi: 10.1145/2371316.2371376.

[4] F. Brudy et al., “Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, in CHI ’19. New York, NY, USA: Association for Computing Machinery, Mai 2019, pp. 1–28. doi: 10.1145/3290605.3300792.

[5] A. A. Salatino, T. Thanapalasingam, A. Mannocci, F. Osborne, and E. Motta, “The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas,” in Lecture Notes in Computer Science 1137, D. Vrandečić, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., Monterey, California, USA: Springer, Oct. 2018, pp. 187–205. Accessed: May 08, 2023. [Online]. Available: http://oro.open.ac.uk/55484/

[6] M. Dehnert, A. Gleiss, and F. Reiss, “What makes a data-driven business model? A consolidated taxonomy,” presented at the European Conference on Information Systems, 2021.

[7] DDI Alliance, “DDI Controlled Vocabulary for Aggregation Method,” 2014. https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html (accessed May 08, 2023).

[8] DDI Alliance, “DDI Controlled Vocabulary for Mode Of Collection,” 2015. https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html (accessed May 08, 2023).

[9] INED - French Institute for Demographic Studies, “Thésaurus DemoVoc,” Feb. 26, 2020. https://thesaurus.web.ined.fr/navigateur/en/about (accessed May 08, 2023).

[10] A. A. Bakar, Z. A. Othman, and N. L. M. Shuib, “Building a new taxonomy for data discretization techniques,” in 2009 2nd Conference on Data Mining and Optimization, Oct. 2009, pp. 132–140. doi: 10.1109/DMO.2009.5341896.

[11] N. Brouard and C. Giudici, “Unified second edition of the Multilingual Demographic Dictionary

Abstract: Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.

This dataset consists of web annotation data that originates from the CRC 1475 "Metaphors of Religion" (https://w3id.org/MoRe-SFB1475/) in a collaborative effort by researchers from Ruhr University Bochum (RUB) and Karlsruhe Institute of Technology (KIT). It includes metaphor analyses that have been created by scholars from the subproject C03 "Metaphors of Everyday Life" during the CRC's first funding period from 2022 to late 2025.

This dataset consists of web annotation data that originates from the CRC 1475 "Metaphors of Religion" (https://w3id.org/MoRe-SFB1475/) in a collaborative effort by researchers from Ruhr University Bochum (RUB) and Karlsruhe Institute of Technology (KIT). It includes metaphor analyses that have been created by scholars from the subproject C04 "Metaphor and Social Positioning in Religious Online Forums" during the CRC's first funding period from 2022 to late 2025.

This dataset consists of tabular data of observed streamflow, URL links to timelapse images, and deep learning model predictions for 11 sites in western Massachusetts. The dataset also includes a record of annotation data used to train the deep learning models. This data release is supporting information for an associated journal article describing the data collection, development of the deep learning models, and the interpretation of estimated relative streamflow produced by the models.

Discover the booming premium annotation tools market! Explore a comprehensive analysis revealing a $1115.9 million market size in 2025, projected to grow at a 7.8% CAGR. Learn about key drivers, trends, and regional insights impacting this crucial sector for AI and machine learning development.

This dataset consists of web annotation data that originates from the CRC 1475 "Metaphors of Religion" (https://w3id.org/MoRe-SFB1475/) in a collaborative effort by researchers from Ruhr University Bochum (RUB) and Karlsruhe Institute of Technology (KIT). It includes metaphor analyses that have been created by scholars from the subproject A02 "The Kinesis of Immortality. Spatial-kinetic Metaphors and Daoist Salvation" during the CRC's first funding period from 2022 to late 2025.

The annotating software market is booming, projected to reach over $1 billion by 2033. Discover key trends, regional insights, and leading companies driving this growth in our comprehensive market analysis. Explore web-based vs. on-premise solutions and their applications in education, business, and machine learning.

The "People - Segmentation" dataset is a high-quality polygon annotation dataset containing 1000 publicly available images of people in various settings and environments. The dataset comprises a total of 1035 labels across one class, capturing people in different poses, expressions, and backgrounds. It is released under the CC BY-SA 4.0 license, providing researchers, data scientists, and enthusiasts with the ability to gain valuable insights into human activities and enabling object-level understanding. This makes it an indispensable tool for a range of applications, including but not limited to object detection, facial recognition, and human-computer interaction systems. With annotations, researchers can analyze and gain insights into the development of accurate person detection algorithms.

Dataset Name - People - Segmentation Data Asset Type - Image Data Asset Volume - 1000 images Data Asset Content - People in various settings and environments Data Asset Source - Publicly available on the web Annotation Type - Polygon Annotation Format - COCO Platform Used - Supervisely

This dataset is created by Quantigo AI, as a part of our commitment towards advancing the fields of AI and machine learning. If you have any queries about our datasets, please contact us at datasets@quantigo.ai.

Visit our website at https://quantigo.ai/ to learn more about our services and commitment to advancing the fields of AI and machine learning.

Data Annotation for Computer Vision using Web Scraping and CVAT

Introduction

This project demonstrates the process of creating a labeled dataset for computer vision tasks using web scraping and the CVAT annotation tool. Web scraping was employed to gather images from the web, and CVAT was utilized to annotate these images with bounding boxes around objects of interest. This dataset can then be used to train object detection models.

Dataset Creation

1. Web Scraping: Images of 142 bird species were collected using web scraping techniques. Libraries such as requests and Beautiful Soup were likely used for this task.
2. CVAT Annotation: The collected images were uploaded to CVAT, where bounding boxes were manually drawn around each bird instance in the images. This created a labeled dataset ready for training computer vision models.

Usage

This dataset can be used to train object detection models for bird species identification. It can also be used to evaluate the performance of existing object detection models on a specific dataset.

Code

The code used for this project is available in the attached notebook. It demonstrates how to perform the following tasks:

• Download the dataset.
• Install necessary libraries.
• Upload the dataset to Kaggle.
• Create a dataset in Kaggle and upload the data.

Conclusion

This project provides a comprehensive guide to data annotation for computer vision tasks. By combining web scraping and CVAT, we were able to create a high-quality labeled dataset for training object detection models. Sources github.com/cvat-ai/cvat opencv.org/blog/data-annotation/

Sample manifest.jsonl metadata

{"version":"1.1"}
{"type":"images"}
{"name":"Spot-billed_Pelican_-_Pelecanus_philippensis_-_Media_Search_-_Macaulay_Library_and_eBirdMacaulay_Library_logoMacaulay_Library_lo/10001","extension":".jpg","width":480,"height":360,"meta":{"related_images":[]}}
{"name":"Spot-billed_Pelican_-_Pelecanus_philippensis_-_Media_Search_-_Macaulay_Library_and_eBirdMacaulay_Library_logoMacaulay_Library_lo/10002","extension":".jpg","width":480,"height":320,"meta":{"related_images":[]}}

Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.

Key Features:

LLM-generated annotations: Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.

Improved multi-label classification: Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.

Multilingual applicability: The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.

Dataset Composition:

curlie-gpt3.5-10k: 10,000 websites labeled using GPT-3.5, context 2 and 1-shot

curlie-gpt4-10k: 10,000 websites labeled using GPT-4, context 2 and zero-shot

Intended Use:

Fine-tuning and advancing Homepage2Vec or similar website classification models

Research on LLM-generated datasets for text classification tasks

Exploration of multilingual website classification

Additional Information:

Project and report repository: https://github.com/CS-433/ml-project-2-mlp

Acknowledgments:

This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with Prof. Robert West and Tiziano Piccardi.

The booming AI training data market is projected for explosive growth, reaching significant value by 2033. Learn about key market drivers, trends, restraints, and leading companies shaping this rapidly expanding sector. Explore regional breakdowns and application segments in this comprehensive market analysis.

Audio Tags for TAU Urban Scenes

Web-Based Annotations for Airport, Public Square, and Park Scenes

By [source]

About this dataset

This Multi-Annotator Tagged Soundscapes (MATS) dataset provides thoughtful audio tags describing a unique collection of airport, public square, and park scenes from TAU Urban Acoustic Scenes 2019. Annotations are provided in both raw and processed formats, with 133 annotators providing their opinions on each audio file. From providing an understanding of current sound levels to assisting in the development of noise-reduction algorithms, this dataset has something for everyone who wants to explore soundscapes from around the world. So whether you're looking for insight into urban sounds or are just interested in what you'll hear when visiting different locations around the world, this is your perfect resource!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides metadata for the TAU Urban Scenes 2019 development dataset. It can be used to explore and analyze soundscapes from urban environments, in order to better understand the acoustic environment of an urban setting.

To use this dataset, start by exploring the audio tags associated with each audio file. This will give you an overview of the type of sounds present in each scene. Then, use the provided annotations files (MATS_labels_mace100_competence06 and MATS_labels_majority_vote) to study how annotations differ between annotators and how different methods handle multi-annotator data. You can also take a closer look at individual audio files by downloading them directly from zenodo or using audio players such as Audacity or SoX to open them up.

You can then use this information to develop analyses that deep dive into various aspects of soundscapes in cities such as sound sources, noise levels, and temporal trends across different sites within these cities. This dataset provides a platform for researchers who wish to identify features that distinguish one scene from another or identify changes between time periods for specific locations!

Research Ideas

• Using a majority vote to determine the 'consensus' tags of an audio file, or to measure the agreement between multiple annotators on specific labels.
• Training a machine learning model on the MACE100 processed annotations and using it to accurately detect audio tags for new sets of audio files.
• Combining different annotation methods (MACE100/Competence06) for more robust analysis and comparison of results from multiple annotators

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: MATS_labels_mace100_competence06.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | filename | The name of the audio file. (String) | | tags | The audio tags associated with the audio file. (String) |

File: MATS_labels_majority_vote.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | filename | The name of the audio file. (String) | | tags | The audio tags associated with the audio file. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.

For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/

For 2024 edition, see: https://sem-tab-challenge.github.io/2024/

Note on License: This data includes data from the following sources. Refer to each source for license details:- Wikidata https://www.wikidata.org/

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.