Data Annotation And Labeling Market Size And Forecast

Data Annotation And Labeling Market size was valued to be USD 1080.8 Million in the year 2023 and it is expected to reach USD 8851.05 Million in 2031, growing at a CAGR of 35.10% from 2024 to 2031.

Data Annotation And Labeling Market Drivers

Increased Adoption of Artificial Intelligence (AI) and Machine Learning (ML): The demand for large volumes of high-quality labeled data to effectively train these systems is being driven by the widespread adoption of AI and ML technologies across various industries, thereby fueling the growth of the Data Annotation And Labeling Market.

Advancements in Computer Vision and Natural Language Processing: A need for annotated and labeled data to develop and enhance AI models capable of understanding and interpreting visual and textual data accurately is created by the rapid progress in fields such as computer vision and natural language processing.

Growth of Cloud Computing and Big Data: The adoption of AI and ML solutions has been facilitated by the rise of cloud computing and the availability of massive amounts of data, leading to an increased demand for data annotation and labeling services to organize and prepare this data for analysis and model training.

This is a fork of Urban Knupleš's "SROIE datasetv2", with ground truth labels for invoice numbers. train/labels.json and test/test_labels.json contains the ground truth invoice numbers for the associated image.

Below is a copy + paste from his repo. Here is my dataset documentation and notes. See there as well for heuristics used to label the dataset. The version tag is data-v2.

Scanned receipts OCR and information extraction (SROIE) + LayoutLM (base)

This dataset was created for the ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction (SROIE). The dataset has receipts written in English. It also contains the pre-trained base model LayoutLM, which I use to train the model to extract information from the SROIE dataset.

Content

The dataset contains 973 scanned receipts. For each receipt you have an .jpg file of the scanned receipt, a .txt file holding OCR information and a .txt file holding the key information values.

Acknowledgements

The dataset is not my own work. I nearly grouped things in the right order and folders. The original authors are the organizers of the competition.

Dataset Description: Multi-layer Darknet Traffic Behavioral Dataset This dataset provides labeled network traffic captures across multiple anonymizing technologies and VPNs, organized by behavioral context rather than merely by protocol or port. It is designed for research in darknet behavior classification, encrypted traffic analysis, and cybersecurity anomaly detection.

Labeling and Data Collection Process All traffic sessions were manually labeled based on their known behavioral context during generation. For example, a capture session involving YouTube over Tor was explicitly labeled as video streaming. No automated or heuristic-based labeling methods were employed; instead, behavior isolation was ensured through controlled setups and traffic timing. This meticulous manual labeling ensures high-confidence ground-truth annotations.

The dataset spans five privacy-preserving technologies:

Tor

Freenet

I2P

ZeroNet

VPN

Within these environments, nine behavioral classes were captured:

Browsing

Email

Chatting

Voice over IP (VoIP)

File Transfer (FTP)

Audio Streaming

Video Streaming

Peer-to-Peer (P2P) Sharing

Normal (non-darknet traffic)

Dataset Structure The dataset is organized across three hierarchical labeling layers:

Layer 1 (Binary Labeling):

Normal: 360,358 samples

Darknet: 91,404 samples

Layer 2 (Technology-Specific Classification):

Freenet: 26,284

ZeroNet: 25,499

I2P: 22,958

Tor: 12,546

VPN: 4,117

Layer 3 (Behavioral Labeling):

Browsing: 33,586

FTP: 20,214

Video: 9,559

P2P: 9,392

Email: 7,873

Audio: 5,953

Chat: 3,489

VOIP: 1,338

Note: Not all behaviors were captured across all technologies due to availability limitations. For instance, VOIP traffic was not recorded in Freenet or Zeronet environments.

Format and Features The dataset is provided in CSV format, where each row corresponds to a single network flow. Each flow is labeled with its corresponding behavioral class. Features include:

Timestamp

Flow duration

Packet count

Byte count

Inter-arrival time metrics

TCP/UDP header statistics

Directional flow indicators

This rich feature set allows for a variety of research tasks, including but not limited to

Encrypted traffic classification

Behavioral profiling of darknet activity

Multi-class machine learning modeling

Intrusion and anomaly detection systems (IDS/ADS)

Use Cases This dataset is particularly useful for:

Building behavior-based intrusion detection systems

Evaluating classifiers under intra-class and inter-technology variations

Understanding how the same behavior (e.g., video streaming) manifests differently over various privacy-enhancing technologies

Developing real-time detection systems for anonymized or encrypted traffic

Citation and Licensing Please cite this dataset appropriately in any research work, and refer to the included license terms for usage and redistribution.

Pre Printed Wire Labels Market Outlook

The global pre printed wire labels market size was estimated at USD 1.5 billion in 2023 and is projected to reach USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.7% during the forecast period. A significant growth factor driving this market is the increasing demand for efficient wiring systems across various industries, such as telecommunications, data centers, and industrial sectors, which require precise and durable labeling solutions for better organization and maintenance.

One of the primary growth factors for the pre printed wire labels market is the rapid expansion of the telecommunications industry. With the advent of 5G and the continuous need for high-speed internet, there has been a substantial increase in the installation of new network infrastructure. Consequently, the demand for efficient labeling solutions, which ensures the easy identification and management of cables and wires, has skyrocketed. The ability of pre printed wire labels to withstand harsh environmental conditions while providing clear and durable markings makes them an indispensable tool in this sector.

Another pivotal growth driver is the burgeoning data center industry. As the world becomes increasingly digitized, the proliferation of data centers has become inevitable to handle the massive amounts of data generated daily. Data centers require meticulously organized cabling systems to ensure optimal performance and minimize downtime. Pre printed wire labels offer an efficient solution by aiding quick identification of cables, reducing errors, and improving overall operational efficiency. The growing investments in data center infrastructure globally are, therefore, anticipated to bolster the market growth significantly.

Furthermore, the industrial sector's evolution, driven by automation and smart manufacturing technologies, has amplified the need for sophisticated labeling solutions. Modern industrial machinery and control systems are now more interconnected, necessitating accurate and durable wire labeling to facilitate maintenance and reduce operational mishaps. The integration of Industry 4.0 technologies in manufacturing processes is expected to further stimulate the demand for pre printed wire labels, as these labels play a crucial role in the seamless operation of automated systems.

The use of Cable And Wire Markers is becoming increasingly essential in various industries to ensure efficient management and organization of complex wiring systems. These markers provide a simple yet effective solution for labeling and identifying cables, which is crucial for maintaining order and preventing errors in environments with extensive wiring networks. As industries continue to advance technologically, the demand for reliable cable and wire identification solutions, such as markers, is expected to rise. This is particularly true in sectors like telecommunications and data centers, where precise cable management is vital for operational efficiency. The integration of Cable And Wire Markers into existing systems can significantly enhance the ease of maintenance and troubleshooting, thereby supporting the overall performance and reliability of the infrastructure.

Regionally, North America holds a significant share of the pre printed wire labels market, driven by the presence of substantial telecommunications and data center infrastructures. The rapid technological advancements and stringent regulatory standards regarding wire and cable management in this region also contribute to the high demand for pre printed wire labels. Moreover, the continuous support from government initiatives to enhance digital infrastructure is likely to sustain market growth in North America.

Product Type Analysis

The pre printed wire labels market is categorized into various product types, including heat shrink labels, self-laminating labels, wrap-around labels, and others. Heat shrink labels are especially notable due to their unique property of shrinking when heated, which ensures a snug fit around wires and cables. This makes them highly preferred in environments where durability and resistance to abrasion are paramount. Their ability to withstand extreme temperature variations and chemical exposure further enhances their application across multiple industries, particularly in harsh industrial settings.

Self-laminating labels, on the other hand, offer the advantage of an addit

Context

As my final project in Data Analytics Bootcamp I decided to build cocktail generator. Dataset contains of almost 500 alcoholic cocktails where each ingredient has asigned label in column 'Alc-type' or 'Basic-taste' and defined volume in ml or grams (columns 'Value-ml' & 'Value-gr').

Content

Dataset prepared by AIFirst has been used as a basis for this dataset: Cocktails Ingredients

Inspiration

I hope this cleaned and organized dataset will become useful for analysis or modeling.

A text classification dataset consists of a curated collection of textual data organized into categories or labels, designed for training and evaluating machine learning models. These datasets are crucial for tasks such as sentiment analysis, topic categorization, and spam detection, among others.

Key Components: Textual Data: Includes documents, articles, reviews, tweets, or any other form of text, often accompanied by labels or categories. Labels or Categories: Each piece of text is assigned a label that indicates its classification, such as positive/negative sentiment, topic category (e.g., sports, politics, technology), or spam/non-spam. Metadata: Additional information related to the text, such as timestamps, authors, sources, or any relevant metadata that may aid in analysis. Applications: Sentiment Analysis: Analyzing the sentiment expressed in text (positive, negative, neutral). Topic Classification: Categorizing text into predefined topics or themes. Spam Detection: Identifying and filtering out unsolicited or unwanted messages. Language Identification: Determining the language of the text. Intent Detection: Understanding the purpose or intent behind a user's text input. Usage: Model Training: Used to train machine learning models, particularly supervised learning algorithms like Naive Bayes, Support Vector Machines (SVM), and deep learning models such as Recurrent Neural Networks (RNNs) and Transformers. Evaluation: Assessing the performance of text classification models through metrics such as accuracy, precision, recall, and F1-score. Research and Development: Supporting research in natural language processing (NLP), text mining, and computational linguistics. Availability: Public Datasets: Many text classification datasets are publicly available and can be accessed through repositories like Kaggle, UCI Machine Learning Repository, or academic websites. Custom Datasets: Organizations often curate their own datasets specific to their domain or application, which may include proprietary or specialized data. Ethical Considerations: Data Privacy: Ensuring that sensitive information in text datasets is handled securely and anonymized where necessary. It was taken from https://keras.io/examples/ Bias and Fairness: Mitigating biases in training data that may affect the performance and fairness of classification models. Conclusion: Text classification datasets serve as foundational resources for developing and evaluating models that automate the analysis and categorization of textual data. They play a crucial role in advancing research, improving applications, and enhancing understanding in various fields related to natural language processing and machine learning.

Network Patch Panel Label Market Outlook

According to our latest research, the global network patch panel label market size reached USD 546 million in 2024, reflecting a robust demand for efficient cable management solutions across various industries. The market is projected to grow at a CAGR of 6.2% from 2025 to 2033, reaching an estimated USD 938 million by 2033. This growth trajectory is primarily driven by the increasing complexity of network infrastructures, heightened demand for organized data centers, and the critical need for precise identification and traceability of network connections.

One of the key growth factors propelling the network patch panel label market is the rapid expansion of data centers worldwide. As enterprises and cloud service providers continue to build and upgrade large-scale data centers to accommodate rising data traffic, the need for efficient cable management and labeling solutions becomes paramount. Network patch panel labels play a crucial role in ensuring seamless identification, reducing downtime during maintenance, and minimizing the risk of human error. The proliferation of edge computing and the deployment of 5G networks are further intensifying the demand for high-density cabling solutions, which, in turn, boosts the adoption of advanced labeling systems. The integration of smart labeling technologies, such as QR codes and RFID-enabled labels, is also enhancing traceability and operational efficiency, further driving market growth.

Another significant driver for the network patch panel label market is the increasing emphasis on regulatory compliance and industry standards. Various sectors, particularly telecommunications, healthcare, and financial services, are subject to stringent regulations regarding network documentation and traceability. Accurate labeling of network infrastructure is essential for meeting compliance requirements, facilitating audits, and ensuring business continuity. Organizations are investing in high-quality, durable labels that can withstand environmental stressors such as heat, humidity, and chemical exposure. The growing trend of digital transformation and the adoption of smart building technologies are also contributing to the demand for reliable labeling solutions, as modern commercial and industrial facilities require robust network infrastructure with clearly marked connections for efficient operation and maintenance.

The network patch panel label market is also benefiting from advancements in material science and printing technologies. Manufacturers are introducing innovative label materials and printing methods that offer enhanced durability, legibility, and ease of installation. The shift towards eco-friendly and recyclable materials is gaining traction, aligning with global sustainability initiatives. Furthermore, the increasing availability of customizable and pre-printed labels is streamlining installation processes, reducing labor costs, and improving overall network management efficiency. The rising penetration of e-commerce platforms and the expansion of distribution networks are making these products more accessible to end-users across diverse geographic regions, further fueling market expansion.

From a regional perspective, North America continues to dominate the network patch panel label market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of a well-established IT infrastructure, widespread adoption of advanced networking technologies, and a strong focus on data center modernization. Europe follows closely, driven by increasing investments in smart cities and industrial automation. The Asia Pacific region is emerging as the fastest-growing market, propelled by rapid urbanization, the expansion of telecommunications networks, and rising demand for commercial and industrial connectivity solutions. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing infrastructure development and digital transformation initiatives.

In the context of evolving data center environments, Rack Labeling Systems have emerged as a critical component for efficient cable management. These systems provide a structured approach to labeling, ensuring that each cable and connection point is easily identifiable. This not only aids in reducing the time required

Data Strip Label Holder Market Outlook

According to our latest research, the global data strip label holder market size reached USD 1.24 billion in 2024, driven by the increasing need for efficient inventory management and product identification across retail and warehousing sectors. The market is projected to grow at a CAGR of 5.7% during the forecast period, with the market size expected to reach USD 2.06 billion by 2033. The primary growth factor is the rapid expansion of organized retail and the growing adoption of automation and digitalization in supply chain management, which is fueling demand for advanced labeling solutions.

The data strip label holder market is experiencing robust growth due to the increasing emphasis on operational efficiency and accuracy in inventory tracking. Retailers and warehouse operators are seeking solutions that enable quick product identification, price updates, and shelf management. The proliferation of supermarkets, hypermarkets, and large retail chains, especially in emerging economies, is significantly contributing to market expansion. Additionally, the integration of automation technologies in logistics and inventory management has magnified the need for reliable and durable label holders, further propelling market growth.

Another critical growth driver is the heightened focus on customer experience and store aesthetics. Retailers are investing in high-quality data strip label holders to ensure that product information is clearly displayed and shelves are well-organized, which directly impacts consumer purchasing decisions. The advent of customizable and visually appealing label holders, made from materials such as plastic and metal, is enabling retailers to enhance their brand image and improve in-store navigation. Furthermore, the trend toward sustainability is encouraging the adoption of eco-friendly and recyclable label holder materials, adding a new dimension to market growth.

Technological advancements and the rise of e-commerce are also reshaping the data strip label holder market. The increasing demand for real-time inventory management and the need for seamless integration between physical and digital retail environments are prompting manufacturers to develop innovative label holder solutions compatible with RFID tags and digital shelf labels. In addition, the surge in online grocery shopping and omnichannel retailing is driving investments in efficient labeling systems that can support dynamic pricing and rapid product turnover, thereby boosting the overall market outlook.

Regionally, Asia Pacific is emerging as the fastest-growing market, fueled by rapid urbanization, expanding retail infrastructure, and the growing adoption of automation in logistics and supply chains. North America and Europe continue to hold significant market shares due to established retail sectors and advanced warehousing practices, while Latin America and the Middle East & Africa are witnessing steady growth, supported by increasing investments in retail modernization and infrastructure development.

Product Type Analysis

The product type segment of the data strip label holder market is highly diversified, comprising adhesive, magnetic, clip-on, snap-on, and other specialized holders. Adhesive data strip label holders dominate the market, primarily due to their ease of installation and versatility across various shelving surfaces. Retailers favor adhesive holders for their ability to provide a secure and tamper-resistant solution, ensuring that labels remain intact even in high-traffic environments. The widespread adoption of adhesive holders in supermarkets, hypermarkets, and convenience stores underscores their importance in streamlining shelf management and price labeling.

Magnetic data strip label holders are gaining traction in environments where frequent label changes are necessary, such as warehouses and distribution centers. Their reusability and ability to attach to metal shelving units make them a cost-effective and flexible option for dynamic inventory settings. As warehouse automation and just-in-time inventory practices become more prevalent, the demand for magnetic label holders is expected to rise steadily, especially in regions with advanced logistics infrastructure.

Clip-on and snap-on data strip label holders cater to retailers and

The CHIRLA dataset (Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis) is designed for long-term person re-identification (Re-ID) in real-world scenarios. The dataset consists of multi-camera video recordings captured over seven months in an indoor office environment. This dataset aims to facilitate the development and evaluation of Re-ID algorithms capable of handling significant variations in individuals’ appearances, including changes in clothing and physical characteristics. The dataset includes 22 individuals with 963,554 bounding box annotations across 596,345 frames.For more details refers to CHIRLA paper: https://arxiv.org/pdf/2502.06681Data Generation ProceduresThe dataset was recorded at the Robotics, Vision, and Intelligent Systems Research Group headquarters at the University of Alicante, Spain. Seven strategically placed Reolink RLC-410W cameras were used to capture videos in a typical office setting, covering areas such as laboratories, hallways, and shared workspaces. Each camera features a 1/2.7" CMOS image sensor with a 5.0-megapixel resolution and an 80° horizontal field of view. The cameras were connected via Ethernet and WiFi to ensure stable streaming and synchronization.A ROS-based interconnection framework was used to synchronize and retrieve images from all cameras. The dataset includes video recordings at a resolution of 1080×720 pixels, with a consistent frame rate of 30 fps, stored in AVI format with DivX MPEG-4 encoding.Data Processing Methods and StepsData processing involved a semi-automatic labeling procedure:Detection: YOLOv8x was used to detect individuals in video frames and extract bounding boxes.Tracking: The Deep SORT algorithm was employed to generate tracklets and assign unique IDs to detected individuals.Manual Verification: A custom graphical user interface (GUI) was developed to facilitate manual verification and correction of the automatically generated labels.Bounding boxes and IDs were assigned consistently across different cameras and sequences to maintain identity coherence.Data Structure and FormatThe dataset comprises:Video Files: 70 videos, each corresponding to a specific camera view in a sequence, stored in AVI format.Annotation Files: JSON files containing frame-wise annotations, including bounding box coordinates and identity labels.Benchmark Data: Processed image crops organized for ReID and tracking evaluationThe dataset is structured as follows:videos/seq_XXX/camera_Y.avi: Video files for each camera view.annotations/seq_XXX/camera_Y.json: Annotation files providing labeled bounding boxes and IDs.benchmark: Train and test data to use in two benchmarks proposed for tracking and Re-ID tasks in different scenarios.Datail data directory struture:CHIRLA_dataset/ ├── videos/ # Raw video files │ └── seq_XXX/ │ └── camera_Y.avi # Video files for each camera view ├── annotations/ # Frame-level annotations │ └── seq_XXX/ │ └── camera_Y.json # Bounding boxes and IDs └── benchmark/ # Processed benchmark data ├── reid/ # Person Re-Identification │ ├── long_term/ # Long-term ReID scenario │ │ ├── train/ │ │ │ ├── train_0/ │ │ │ │ └── seq_XXX/ │ │ │ └── train_1/ │ │ └── test/ │ │ ├── test_0/ # Validation subset │ │ └── test_1/ # Test subset │ ├── multi_camera/ # Multi-camera ReID │ ├── multi_camera_long_term/ # Combined scenario │ └── reappearance/ # Reappearance detection └── tracking/ # Person Tracking ├── brief_occlusions/ # Short-term occlusions └── multiple_people_occlusions/ # Multi-person scenarios For more information on how to use the benchmark data refers to CHIRLA github repository: https://github.com/bdager/CHIRLA and paper: https://arxiv.org/pdf/2502.06681 .Use Cases and ReusabilityThe CHIRLA dataset is suitable for:Long-term person re-identificationMulti-camera tracking and re-identificationSingle-camera tracking and re-identificationCitationIf you use CHIRLA dataset and benchmark, please cite the work as:@article{bdager2025chirla,title={CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis},author={Dominguez-Dager, Bessie and Escalona, Felix and Gomez-Donoso, Fran and Cazorla, Miguel},journal={arXiv preprint arXiv:2502.06681},year={2025},}

Quantitative analysis depends on pure-substance primary calibrators with known mass fractions of impurity. The datasets included were generated using mass spectrometry for the evaluation of label-free quantification (LFQ) as a method for determining the mass fraction of host-cell proteins (HCPs) in bioengineered proteins. For this purpose, hemoglobin-A2 (HbA2) was used, as obtained by overexpression in E.coli. Two different materials had been produced: natural, and U-15N-labeled HbA2. To quantify impurity, precursor ion (MS1-) intensities were integrated over all E.coli-proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E.coli-cell lysate, that had been spiked at known mass-ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and were found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of nine proteins.

Car-Highway Data Annotation Project

Introduction

In this project, we aim to annotate car images captured on highways. The annotated data will be used to train machine learning models for various computer vision tasks, such as object detection and classification.

Project Goals

• Collect a diverse dataset of car images from highway scenes.
• Annotate the dataset to identify and label cars within each image.
• Organize and format the annotated data for machine learning model training.

Tools and Technologies

For this project, we will be using Roboflow, a powerful platform for data annotation and preprocessing. Roboflow simplifies the annotation process and provides tools for data augmentation and transformation.

Annotation Process

1. Upload the raw car images to the Roboflow platform.
2. Use the annotation tools in Roboflow to draw bounding boxes around each car in the images.
3. Label each bounding box with the corresponding class (e.g., car).
4. Review and validate the annotations for accuracy.

Data Augmentation

Roboflow offers data augmentation capabilities, such as rotation, flipping, and resizing. These augmentations can help improve the model's robustness.

Data Export

Once the data is annotated and augmented, Roboflow allows us to export the dataset in various formats suitable for training machine learning models, such as YOLO, COCO, or TensorFlow Record.

Milestones

1. Data Collection and Preprocessing
2. Annotation of Car Images
3. Data Augmentation
4. Data Export
5. Model Training

Conclusion

By completing this project, we will have a well-annotated dataset ready for training machine learning models. This dataset can be used for a wide range of applications in computer vision, including car detection and tracking on highways.

Magnetic Label Holders Market Outlook

According to our latest research, the global magnetic label holders market size reached USD 1.29 billion in 2024, supported by a robust demand across warehousing, retail, and industrial sectors. The market is expanding at a steady CAGR of 4.7% and is projected to achieve a value of USD 1.98 billion by 2033. The primary growth factor fueling this expansion is the increasing emphasis on efficient inventory management and the rising adoption of flexible organizational solutions in commercial and industrial environments.

One of the key growth drivers for the magnetic label holders market is the rapid transformation of supply chain and warehouse management practices globally. As businesses strive for higher efficiency and accuracy in inventory tracking, the demand for easily updatable and reusable labeling solutions such as magnetic label holders is surging. These products enable seamless reorganization of storage spaces, quick identification of goods, and facilitate compliance with evolving regulatory standards for traceability. Technological advancements in materials and product design, such as enhanced magnetic strength and robust protective coatings, are further augmenting product durability and appeal, thereby broadening their acceptance in diverse industrial settings.

Another significant factor propelling the market is the growing penetration of organized retail and e-commerce sectors. Retailers and logistics companies are increasingly investing in scalable labeling systems to manage ever-changing product assortments and promotional activities. Magnetic label holders offer a cost-effective and flexible solution, allowing for frequent changes without the need for adhesive labels or permanent fixtures. Their utility extends beyond warehouses to retail shelves, office environments, and even residential applications where dynamic organization is required. The shift towards automation and digital inventory systems is also complementing the adoption of magnetic labeling solutions, as these holders integrate seamlessly with barcode and RFID technologies for real-time tracking.

Sustainability concerns and the push for eco-friendly organizational tools have also contributed to the marketÂ’s positive outlook. Magnetic label holders, being reusable and durable, align with green initiatives by reducing single-use plastics and paper waste. Manufacturers are responding by developing holders from recycled materials and offering customizable solutions to meet specific end-user requirements. This trend is particularly pronounced in developed regions, where regulatory frameworks and corporate sustainability goals are more stringent. As businesses and consumers become more environmentally conscious, the preference for sustainable labeling options is expected to further stimulate market growth.

From a regional perspective, Asia Pacific is emerging as a dominant force in the magnetic label holders market, driven by rapid industrialization, expansion of organized retail, and the proliferation of e-commerce fulfillment centers. North America and Europe continue to hold substantial market shares due to their advanced logistics infrastructure and early adoption of innovative storage solutions. Meanwhile, the Middle East & Africa and Latin America are witnessing gradual growth, propelled by infrastructural development and increasing foreign investments in logistics and retail sectors. The interplay of these regional dynamics is shaping the competitive landscape and influencing the market trajectory through 2033.

Product Type Analysis

The magnetic label holders market is segmented by product type into Flexible Magnetic Label Holders, Rigid Magnetic Label Holders, Magnetic Data Card Holders, Magnetic Pockets, and Others. Among these, flexible magnetic label holders account for the largest market share, owing to their adaptability and ease of use across diverse shelving systems. These holders are favored in dynamic environments such as warehouses and retail outlets, wher

AIT Log Data Sets

This repository contains synthetic log data suitable for evaluation of intrusion detection systems. The logs were collected from four independent testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by Landauer et al. (2020) [1]. Please refer to the paper for more detailed information on automatic testbed generation and cite it if the data is used for academic publications. In brief, each testbed simulates user accesses to a webserver that runs Horde Webmail and OkayCMS. The duration of the simulation is six days. On the fifth day (2020-03-04) two attacks are launched against each web server.

The archive AIT-LDS-v1_0.zip contains the directories "data" and "labels".

The data directory is structured as follows. Each directory mail.

Setup details of the web servers:

• OS: Debian Stretch 9.11.6
• Services:
  • Apache2
  • PHP7
  • Exim 4.89
  • Horde 5.2.22
  • OkayCMS 2.3.4
  • Suricata
  • ClamAV
  • MariaDB

Setup details of user machines:

• OS: Ubuntu Bionic
• Services:
  • Chromium
  • Firefox

User host machines are assigned to web servers in the following way:

• mail.cup.com is accessed by users from host machines user-{0, 1, 2, 6}
• mail.spiral.com is accessed by users from host machines user-{3, 5, 8}
• mail.insect.com is accessed by users from host machines user-{4, 9}
• mail.onion.com is accessed by users from host machines user-{7, 10}

The following attacks are launched against the web servers (different starting times for each web server, please check the labels for exact attack times):

• Attack 1: multi-step attack with sequential execution of the following attacks:
  • nmap scan
  • nikto scan
  • smtp-user-enum tool for account enumeration
  • hydra brute force login
  • webshell upload through Horde exploit (CVE-2019-9858)
  • privilege escalation through Exim exploit (CVE-2019-10149)
• Attack 2: webshell injection through malicious cookie (CVE-2019-16885)

Attacks are launched from the following user host machines. In each of the corresponding directories user-

• user-6 attacks mail.cup.com
• user-5 attacks mail.spiral.com
• user-4 attacks mail.insect.com
• user-7 attacks mail.onion.com

The log data collected from the web servers includes

• Apache access and error logs
• syscall logs collected with the Linux audit daemon
• suricata logs
• exim logs
• auth logs
• daemon logs
• mail logs
• syslogs
• user logs

Note that due to their large size, the audit/audit.log files of each server were compressed in a .zip-archive. In case that these logs are needed for analysis, they must first be unzipped.

Labels are organized in the same directory structure as logs. Each file contains two labels for each log line separated by a comma, the first one based on the occurrence time, the second one based on similarity and ordering. Note that this does not guarantee correct labeling for all lines and that no manual corrections were conducted.

Version history and related data sets:

• AIT-LDS-v1.0: Four datasets, logs from single host, fine-granular audit logs, mail/CMS.
  • AIT-LDS-v1.1: Removed carriage return of line endings in audit.log files.
• AIT-LDS-v2.0: Eight datasets, logs from all hosts, system logs and network traffic, mail/CMS/cloud/web.

Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU project GUARD (833456).

If you use the dataset, please cite the following publication:

[1] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, "Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed," in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [PDF]

Magnetic Shelf Label Holder Market Outlook

According to our latest research, the Global Magnetic Shelf Label Holder market size was valued at $1.2 billion in 2024 and is projected to reach $2.1 billion by 2033, expanding at a CAGR of 6.3% during 2024–2033. The primary growth driver for the Magnetic Shelf Label Holder market is the rising demand for efficient inventory management and product identification solutions across retail, warehousing, and logistics sectors worldwide. As businesses increasingly prioritize operational efficiency and accuracy in inventory tracking, the adoption of magnetic shelf label holders is accelerating, supported by advancements in materials, ease of installation, and compatibility with digital labeling systems. This trend is further fueled by the rapid expansion of organized retail and e-commerce, which necessitates clear and flexible labeling for dynamic inventory environments.

Regional Outlook

North America currently holds the largest share of the global Magnetic Shelf Label Holder market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature retail infrastructure, early adoption of automation technologies, and robust investments in supply chain optimization. The presence of leading retail chains, advanced warehousing facilities, and a strong culture of technological innovation further reinforce North America’s leadership. Additionally, the region benefits from stringent regulatory compliance regarding product labeling and traceability, compelling businesses to adopt reliable and standardized labeling solutions such as magnetic shelf label holders. The United States, in particular, is witnessing substantial demand from both large-scale supermarkets and specialized retail outlets, driving continuous market growth.

Asia Pacific is emerging as the fastest-growing region in the Magnetic Shelf Label Holder market, with a projected CAGR of 8.4% from 2024 to 2033. This rapid growth is driven by the booming retail and e-commerce sectors in countries like China, India, and Southeast Asia, where organized retail formats and modern warehousing are expanding at an unprecedented pace. Increasing investments in logistics infrastructure, coupled with the digital transformation of supply chains, are boosting the adoption of magnetic labeling solutions. Governments in this region are also supporting smart warehouse initiatives and retail modernization, creating fertile ground for innovative labeling products. The shift towards automation and the need for flexible, reusable labeling systems in high-volume environments are further propelling market expansion across Asia Pacific.

In emerging markets such as Latin America, the Middle East, and Africa, the adoption of magnetic shelf label holders is gradually gaining momentum, albeit at a slower pace compared to developed regions. These markets face unique challenges, including limited access to advanced supply chain technologies, fluctuating economic conditions, and variable regulatory frameworks. However, localized demand is rising as retail chains expand and international brands establish their presence, necessitating modern inventory management practices. Policy reforms aimed at improving trade logistics and warehousing standards are expected to stimulate further growth, although market penetration remains constrained by cost sensitivities and the need for greater awareness about the benefits of magnetic labeling solutions.

Report Scope

The study of protein function and dynamics in their native cellular environment is essential for progressing fundamental science. To overcome the requirement of genetic modification of the protein or the limitations of dissociable fluorescent ligands, ligand-directed (LD) chemistry has most recently emerged as a complementary, bioorthogonal approach for labeling native proteins. Here, we describe the rational design, development, and application of the first ligand-directed chemistry approach for labeling the A1AR in living cells. We pharmacologically demonstrate covalent labeling of A1AR expressed in living cells while the orthosteric binding site remains available. The probes were imaged using confocal microscopy and fluorescence correlation spectroscopy to study A1AR localization and dynamics in living cells. Additionally, the probes allowed visualization of the specific localization of A1ARs endogenously expressed in dorsal root ganglion (DRG) neurons. LD probes developed here hold promise for illuminating ligand-binding, receptor signaling, and trafficking of the A1AR in more physiologically relevant environments.

Data Description

This dataset is linked to the publication "Recursive classification of satellite imaging time-series: An application to land cover mapping". In this paper, we introduce the recursive Bayesian classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. To reproduce the results presented in the paper, the RBC-SatImg folder and the code in the GitHub repository RBC-SatImg are required.

The RBC-SatImg folder contains:

• Sentinel-2 time-series imagery from three key regions: Oroville Dam (CA, USA) and Charles River (Boston, MA, USA) for water mapping, and the Amazon Rainforest (Brazil) for deforestation detection.
• The RBC-WatData dataset with manually generated water mapping labels for the Oroville Dam and Charles River regions. This dataset is well-suited for multitemporal land cover and water mapping research, as it accounts for the dynamic evolution of true class labels over time.
• Pickle files with output to reproduce the results in the paper, including:
  • Instantaneous classification results for GMM, LR, SIC, WN, DWM
  • Posterior results obtained with the RBC framework

The Sentinel-2 images and forest labels used in the deforestation detection experiment for the Amazon Rainforest have been obtained from the MultiEarth Challenge dataset.

Folder Structure

The following paths can be changed in the configuration file from the GitHub repository as desired. The RBC-SatImg is organized as follows:

• `./log/` (EMPTY): Default path for storing log files generated during code execution.
• `./evaluation_results/`: Contains the results to reproduce the findings in the paper, including two sub-folders:
  • `./classification/`: For each test site, four sub-folders are included as:
    • `./accuracy/`: Each sub-folder corresponding to an experimental configuration contains pickle files with balanced classification accuracy results and information about the models. The default configuration used in the paper is "conf_00."
    • `./figures/`: Includes result figures from the manuscript in SVG format.
    • `./likelihoods/`: Contains pickle files with instantaneous classification results.
    • `./posteriors/`: Contains pickle files with posterior results generated by the RBC framework.
  • `./sensitivity_analysis/`: Contains sensitivity analysis results, organized by different test sites and epsilon values.
• `./Sentinel2_data/`: Contains Sentinel-2 images used for training and evaluation, organized by scenarios (Oroville Dam, Charles River, Amazon Rainforest). Selected images have been filtered and processed as explained in the manuscript. The Amazon Rainforest images and labels have been obtained from the MultiEarth dataset, and consequently, the labels are included in this folder instead of the RBC-WatData folder.
• `./RBC-WatData/`: Contains the water labels that we manually generated with the LabelStudio tool.

Cable Management Label Printer Tape Market Outlook

According to our latest research, the global cable management label printer tape market size reached USD 1.47 billion in 2024, demonstrating steady expansion across multiple industry verticals. The market is poised for significant growth, with a projected CAGR of 6.8% from 2025 to 2033. By the end of the forecast period, the market is expected to attain a value of approximately USD 2.69 billion by 2033. This robust growth trajectory is driven by the increasing demand for efficient cable management solutions in industries such as IT & telecom, manufacturing, and data centers, where clear identification and organization of cables are critical for operational efficiency and safety.

One of the primary growth factors for the cable management label printer tape market is the rapid proliferation of data centers and the digitization of enterprises worldwide. As organizations continue to invest in digital transformation initiatives, the need for highly organized and clearly labeled cable infrastructures has become paramount. The complexity of network architectures and the exponential growth in data traffic have necessitated the deployment of advanced cable labeling solutions to prevent downtime, enhance troubleshooting, and ensure compliance with stringent industry standards. The ongoing evolution of smart buildings and the increasing adoption of IoT devices further amplify the demand for reliable cable management practices, thereby fueling the adoption of label printer tapes.

Another significant driver accelerating market growth is the stringent regulatory landscape governing cable identification and safety standards, particularly in sectors such as healthcare, construction, and manufacturing. Regulatory bodies across North America, Europe, and Asia Pacific have established guidelines mandating proper cable labeling to mitigate risks associated with electrical faults, fire hazards, and operational failures. This has compelled organizations to invest in high-quality, durable label printer tapes that can withstand harsh environmental conditions, including extreme temperatures, moisture, and chemical exposure. The shift towards automation and the integration of Industry 4.0 technologies in manufacturing facilities have also contributed to the increased adoption of sophisticated cable labeling solutions for enhanced traceability and process optimization.

The market is further bolstered by continuous technological advancements in label printer tape materials and printing technologies. Manufacturers are focusing on developing tapes with superior adhesive properties, resistance to abrasion, and compatibility with a wide range of surfaces. The introduction of eco-friendly and recyclable materials aligns with the growing emphasis on sustainability and environmental responsibility across industries. Additionally, the emergence of user-friendly, portable, and wireless label printing devices has simplified the cable labeling process, enabling end-users to achieve greater efficiency and accuracy. These innovations, coupled with the expansion of e-commerce channels and the availability of customized solutions, are expected to sustain the positive momentum in the cable management label printer tape market.

From a regional perspective, North America continues to dominate the global market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of a well-established IT & telecom infrastructure, coupled with substantial investments in data center expansion and modernization, has positioned North America as a key growth engine. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid industrialization, urbanization, and the proliferation of smart city projects in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also emerging as promising markets, supported by infrastructural development and increasing awareness of cable management best practices.

Product Type Analysis

The cable management label printer tape market is segmented by product type into thermal transfer, direct thermal, inkjet, laser, and others. Thermal transfer tapes lead the segment, owing to their exceptional durability, resistance to smudging, and ability to withstand extreme environmental conditions. These tapes are widely preferred in industrial, electrical, and data center applicati

MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results. This dataset was introduced in the paper "Learning Features of Music from Scratch." [1]

This repository consists of 3 top-level files:

musicnet.tar.gz - This file contains the MusicNet dataset itself, consisting of PCM-encoded audio wave files (.wav) and corresponding CSV-encoded note label files (.csv). The data is organized according to the train/test split described and used in "Invariances and Data Augmentation for Supervised Music Transcription". [2]

musicnet_metadata.csv - This file contains track-level information about recordings contained in MusicNet. The data and label files are named with MusicNet ids, which you can use to cross-index the data and labels with this metadata file.

musicnet_midis.tar.gz - This file contains the reference MIDI files used to construct the MusicNet labels.

A PyTorch interface for accessing the MusicNet dataset is available on GitHub. For an audio/visual introduction and summary of this dataset, see the MusicNet inspector, created by Jong Wook Kim. The audio recordings in MusicNet consist of Creative Commons licensed and Public Domain performances, sourced from the Isabella Stewart Gardner Museum, the European Archive Foundation, and Musopen. The provenance of specific recordings and midis are described in the metadata file.

[1] Learning Features of Music from Scratch. John Thickstun, Zaid Harchaoui, and Sham M. Kakade. In International Conference on Learning Representations (ICLR), 2017. ArXiv Report.

@inproceedings{thickstun2017learning, title={Learning Features of Music from Scratch}, author = {John Thickstun and Zaid Harchaoui and Sham M. Kakade}, year={2017}, booktitle = {International Conference on Learning Representations (ICLR)} }

[2] Invariances and Data Augmentation for Supervised Music Transcription. John Thickstun, Zaid Harchaoui, Dean P. Foster, and Sham M. Kakade. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018. ArXiv Report.

@inproceedings{thickstun2018invariances, title={Invariances and Data Augmentation for Supervised Music Transcription}, author = {John Thickstun and Zaid Harchaoui and Dean P. Foster and Sham M. Kakade}, year={2018}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)} }

With fast growth, synthetic biology powers us with the capability to produce high commercial value products in an efficient resource/energy-consuming manner. Comprehensive knowledge of the protein regulatory network of a bacterial host chassis, e.g., the actual amount of the given proteins, is the key to building cell factories for certain target hyperproduction. Many talent methods have been introduced for absolute quantitative proteomics. However, for most cases, a set of reference peptides with isotopic labeling (e.g., SIL, AQUA, QconCAT) or a set of reference proteins (e.g., commercial UPS2 kit) needs to be prepared. The higher cost hinders these methods for large sample research. In this work, we proposed a novel metabolic labeling-based absolute quantification approach (termed nMAQ). The reference Corynebacterium glutamicum strain is metabolically labeled with 15N, and a set of endogenous anchor proteins of the reference proteome is quantified by chemically synthesized light (14N) peptides. The prequantified reference proteome was then utilized as an internal standard (IS) and spiked into the target (14N) samples. SWATH-MS analysis is performed to obtain the absolute expression levels of the proteins from the target cells. The cost for nMAQ is estimated to be less than 10 dollars per sample. We have benchmarked the quantitative performance of the novel method. We believe this method will help with the deep understanding of the intrinsic regulatory mechanism of C. glutamicum during bioengineering and will promote the process of building cell factories for synthetic biology.

A collection of tweets about the international computer technology business Dell can be found in the "Preprocessed Dell Tweets" dataset. To make sentiment analysis and natural language processing jobs easier, these tweets have undergone meticulous preprocessing. The dataset was first made up of tweets that were scraped from Twitter; preprocessing was done to clean and organize the data.

Important characteristics:

Text: The preprocessed text of the tweets is contained in this column, making it appropriate for natural language processing and text analysis.

Sentiment: To facilitate machine learning activities, the sentiment column has been converted into numeric values. The following is how sentiment labels have been encoded: 0: Neutral 1: Positive 2: Negative

Possible Applications: The dataset is perfect for machine learning, sentiment analysis, and sentiment classification tasks. This dataset can be used by researchers and data scientists to test and refine sentiment analysis methods. Efficient sentiment models for prediction can be developed thanks to the numerical sentiment labels.

Data Preprocessing: To prepare the dataset for analysis, the following preprocessing steps were applied:

• Punctuation and special characters were removed from the text.
• URLs and hyperlinks were stripped from the text.
• Text was converted to lowercase for uniformity.
• Stopwords (common words with limited analytical value) were removed.
• Tokenization, stemming, and lemmatization were performed to normalize the text data.