A database that contains sequences built from the existing primary sequence data in GenBank. The sequences and corresponding annotations are experimentally supported and have been published in a peer-reviewed scientific journal.

Reflect Data Annotation

## Overview

Reflect Data Annotation is a dataset for object detection tasks - it contains Objects annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1. Overview We provide various types of Annotated Imagery Data annotation services, including:
2. Bounding box
3. Polygon
4. Segmentation
5. Polyline
6. Key points
7. Image classification
8. Image description ...
9. Our Capacity
10. Platform: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator.It has successfully been applied to nearly 5,000 projects.

• Annotation Tools: Nexdata's platform integrates 30 sets of annotation templates, covering audio, image, video, point cloud and text.

-Secure Implementation: NDA is signed to gurantee secure implementation and Annotated Imagery Data is destroyed upon delivery.

-Quality: Multiple rounds of quality inspections ensures high quality data output, certified with ISO9001

1. About Nexdata Nexdata has global data processing centers and more than 20,000 professional annotators, supporting on-demand data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/computerVisionTraining?source=Datarade

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

The Data Annotation Tools Market Report is Segmented by Component (Software Platforms, and Services), Data Type (Text, Image, and More), Annotation Type (Manual, Semi-Supervised, and More), Deployment Mode (Cloud, On-Premise, and Hybrid), End-User Industry (IT and Telecom, BFSI, Retail and E-Commerce, and More), and Geography. The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Task 10 Data Annotation

## Overview

Task 10 Data Annotation is a dataset for object detection tasks - it contains Boxes annotations for 258 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The open-source data annotation tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market's expansion is fueled by the need for efficient and cost-effective annotation solutions, particularly for large datasets. Organizations across various sectors, including automotive, healthcare, and finance, are leveraging these tools to improve the accuracy and performance of their AI models. The availability of open-source alternatives offers a significant advantage over proprietary solutions, enabling developers and researchers to customize tools according to their specific needs and avoid vendor lock-in. Furthermore, the collaborative nature of open-source projects fosters innovation and continuous improvement, resulting in a more dynamic and rapidly evolving ecosystem. While the market is relatively nascent, it exhibits a substantial growth trajectory, attracting numerous companies and developers, as evidenced by the active participation of organizations such as Alecion, Amazon Mechanical Turk, and Appen Limited. This competitive landscape further accelerates innovation and accessibility. The open-source nature of these tools also democratizes access to advanced AI development capabilities. Smaller companies and individual researchers can now participate in the development and deployment of AI solutions, leveling the playing field and fostering wider adoption. However, the market faces challenges such as the need for ongoing community support and maintenance of these tools, ensuring their long-term viability and preventing fragmentation. Despite these challenges, the future outlook for the open-source data annotation tool market remains positive, with continued growth driven by increased adoption in various industries and advancements in AI and ML technologies. The market is predicted to maintain a healthy compound annual growth rate (CAGR) over the forecast period, reflecting the sustained demand for efficient and accessible data annotation solutions.

Discover the booming Data Annotation Tool Software market! Explore a 25% CAGR forecast to 2033, key drivers, trends, and leading companies shaping this crucial sector for AI and Machine Learning. Get insights into market size, regional analysis, and future growth potential.

Basketball Data Annotation

## Overview

Basketball Data Annotation is a dataset for object detection tasks - it contains Drive annotations for 254 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

alpr dataset for the testing part , want to download for test

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.

Traffic Data Annotation

## Overview

Traffic Data Annotation is a dataset for object detection tasks - it contains Cars Bikes Pedestrain annotations for 323 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Annotation data were generated from existing genome assemblies of Calonectria henricotiae JAC13-131 (aka P-10-5865) and C. pseudonaviculata JAC13-27 (aka CT1). Gene prediction and annotations were conducted using the Funannotate v1.8.1 pipeline (https://funannotate.readthedocs.io/en/latest/).

The automated data annotation tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of AI-powered applications across various industries, including healthcare, automotive, and finance, necessitates vast amounts of accurately annotated data. Furthermore, the ongoing advancements in deep learning algorithms and the emergence of sophisticated annotation tools are streamlining the data annotation process, making it more efficient and cost-effective. The market is segmented by tool type (text, image, and others) and application (commercial and personal use), with the commercial segment currently dominating due to the substantial investment by enterprises in AI initiatives. Geographic distribution shows a strong concentration in North America and Europe, reflecting the high adoption rate of AI technologies in these regions; however, Asia-Pacific is expected to show significant growth in the coming years due to increasing technological advancements and investments in AI development. The competitive landscape is characterized by a mix of established technology giants and specialized data annotation providers. Companies like Amazon Web Services, Google, and IBM offer integrated annotation solutions within their broader cloud platforms, competing with smaller, more agile companies focusing on niche applications or specific annotation types. The market is witnessing a trend toward automation within the annotation process itself, with AI-assisted tools increasingly employed to reduce manual effort and improve accuracy. This trend is expected to drive further market growth, even as challenges such as data security and privacy concerns, as well as the need for skilled annotators, persist. However, the overall market outlook remains positive, indicating continued strong growth potential through 2033. The increasing demand for AI and ML, coupled with technological advancements in annotation tools, is expected to overcome existing challenges and drive the market towards even greater heights.

Highly reproducible interaction data in the Yeast Interacting Proteins Database with the "IST hit" (to be described in the table below) of 3 or more. Annotation (gene name and description) is updated by the SGD (Saccharomyces Genome Database;http://www.yeastgenome.org/, August 15, 2009). The number of data is 841. The data are given in a CSV format text file.

Data Annotation Platforms for Computer Vision Market Outlook

According to our latest research, the global data annotation platforms for computer vision market size stood at USD 1.67 billion in 2024, reflecting robust demand across diverse industries. The market is projected to expand at a CAGR of 24.8% from 2025 to 2033, reaching a forecasted value of USD 13.19 billion by 2033. This remarkable growth is primarily driven by the accelerating adoption of artificial intelligence (AI) and machine learning (ML) technologies, which require high-quality annotated datasets to improve the accuracy and efficiency of computer vision applications. As organizations increasingly leverage automation and intelligent systems, the need for scalable, reliable, and efficient data annotation platforms continues to surge globally.

A key growth factor fueling the data annotation platforms for computer vision market is the rapid proliferation of AI-powered solutions across sectors such as automotive, healthcare, retail, and agriculture. These industries heavily depend on annotated data to train computer vision algorithms for tasks like object detection, image segmentation, and facial recognition. The evolution of autonomous vehicles, for instance, has significantly increased the demand for precisely labeled image and video datasets, as these are critical for the safe and reliable operation of self-driving systems. Similarly, in healthcare, annotated medical images are integral to diagnostic tools powered by AI, enhancing early disease detection and personalized treatment. As the complexity and volume of visual data continue to grow, organizations are prioritizing investments in advanced data annotation platforms to maintain a competitive edge and ensure the highest standards of model performance.

Another prominent driver is the increasing integration of cloud-based data annotation platforms, which offer scalability, flexibility, and cost-effectiveness. Cloud deployment models enable organizations to access annotation tools and collaborate with distributed teams in real time, streamlining the annotation workflow and accelerating project timelines. This trend is particularly notable among enterprises with large-scale computer vision projects that require rapid data processing and quality control. Moreover, cloud-based platforms often incorporate features such as automated annotation, quality assurance, and data security, further enhancing their appeal to businesses aiming to optimize their AI development cycles. As the adoption of cloud computing continues to rise, the demand for cloud-enabled data annotation platforms is expected to witness substantial growth over the forecast period.

The increasing emphasis on data privacy and regulatory compliance is also shaping the trajectory of the data annotation platforms for computer vision market. With stringent data protection laws coming into force worldwide, organizations are seeking annotation solutions that ensure data confidentiality, secure storage, and transparent audit trails. This has spurred innovation among platform providers, who are now offering robust security features and customizable access controls to address the unique compliance requirements of different industries. Additionally, partnerships with specialized annotation service providers are enabling companies to access domain-specific expertise and high-quality labeled datasets, further driving market expansion. As regulatory landscapes evolve, the demand for compliant and secure data annotation platforms is anticipated to remain strong, fostering sustained market growth.

From a regional perspective, North America currently dominates the data annotation platforms for computer vision market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology companies, a mature AI ecosystem, and significant investments in research and development. However, the Asia Pacific region is expected to exhibit the fastest growth rate during the forecast period, driven by rapid digital transformation, increasing adoption of AI technologies, and the expanding footprint of global tech giants in countries such as China, India, and Japan. Europe also represents a significant market, supported by robust regulatory frameworks and growing adoption of computer vision applications across industries. As these regions continue to invest in AI infrastructure and talent development, the global data annotation platforms for computer vision market is poised for sustained expansion

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

Data Annotation Tools Market size was valued at USD 0.03 Billion in 2024 and is projected to reach USD 4.04 Billion by 2032, growing at a CAGR of 25.5% during the forecasted period 2026 to 2032.Global Data Annotation Tools Market DriversThe market drivers for the Data Annotation Tools Market can be influenced by various factors. These may include:Rapid Growth in AI and Machine Learning: The demand for data annotation tools to label massive datasets for training and validation purposes is driven by the rapid growth of AI and machine learning applications across a variety of industries, including healthcare, automotive, retail, and finance.Increasing Data Complexity: As data kinds like photos, videos, text, and sensor data become more complex, more sophisticated annotation tools are needed to handle a variety of data formats, annotations, and labeling needs. This will spur market adoption and innovation.Quality and Accuracy Requirements: Training accurate and dependable AI models requires high-quality annotated data. Organizations can attain enhanced annotation accuracy and consistency by utilizing data annotation technologies that come with sophisticated annotation algorithms, quality control measures, and human-in-the-loop capabilities.Applications Specific to Industries: The development of specialized annotation tools for particular industries, like autonomous vehicles, medical imaging, satellite imagery analysis, and natural language processing, is prompted by their distinct regulatory standards and data annotation requirements.

Transcriptome annotations of Coptotermes formosanus

The names of the annotation files reflect the region and signer pair number (e.g. ber_01 corresponds to signer pair 1 from the region Berlin) to mirror the names used on ling.meine-dgs.de. Since there may sometimes be multiple videos available for one and the same signer pair, I added an additional seven-number code which corresponds to the code displayed on the transcript page when hovering over the transcript names shown in the leftmost column. The procedure for the data annotations created for this project is described in detail in Chapter 2 of my dissertation entitled "Iconicity as a mediator between verb semantics and morphosyntactic structure: A corpus-based study on verbs in German Sign Language", to be made publicly available at the beginning of 2020.