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

Market Analysis: The AI Data Labeling Solution market is anticipated to grow at a substantial CAGR of XX% during the forecast period of 2025-2033. This growth is driven by the increasing adoption of AI and ML technologies, along with the demand for high-quality annotated data for model training. The market is segmented by application (IT, automotive, healthcare, financial, etc.), type (cloud-based, on-premise), and region (North America, Europe, Asia Pacific, etc.). The cloud-based segment is expected to hold a dominant share due to its flexibility, scalability, and cost-effectiveness. North America is expected to lead the market due to the early adoption of AI technologies. Key Trends and Challenges: One of the key trends in the AI Data Labeling Solution market is the rise of automated and semi-automated data labeling tools. These tools utilize AI algorithms to streamline the process, reducing the cost and time required to label large datasets. Another notable trend is the increasing demand for AI-labeled data in sectors such as autonomous driving, healthcare, and finance. However, the market also faces challenges, including the lack of standardized data labeling practices and regulations, as well as concerns over data privacy and security.

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

Regulatory Documents - Stationery Goods Labeling Standards

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Enterprise Labeling Software Market Size 2024-2028

The enterprise labeling software market size is forecast to increase by USD 133.9 mn at a CAGR of 6.59% between 2023 and 2028.

The market is witnessing significant growth due to several key trends. The adoption of enterprise labeling solutions is increasing as businesses seek to streamline their labeling processes and improve efficiency. Dynamic labeling, which allows for real-time updates to labels, is gaining popularity as it enables companies to quickly respond to changing regulations or product information. The market is experiencing growth as companies leverage data integration and analytics to streamline labeling processes, ensuring greater accuracy, compliance, and operational efficiency. Moreover, stringent government regulations mandating accurate and compliant labeling are driving the need for enterprise labeling software. These factors are expected to fuel market growth In the coming years. The market landscape is constantly evolving, and staying abreast of these trends is essential for businesses looking to remain competitive.

What will be the Size of the Enterprise Labeling Software Market During the Forecast Period?

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The market encompasses solutions designed for creating, managing, and printing labels in various industries. Compliance with regulations and ensuring labeling accuracy are key drivers for this market. Real-time updates and customizable templates enable businesses to maintain consistency and adapt to changing requirements. Integration capabilities with enterprise systems, data management planning, and the printing process are essential for streamlining workflows and improving efficiency. Innovative technology, such as automation and machine learning, enhances labeling quality and speed, providing a competitive edge.
A user-friendly interface and economic conditions influence market demand. Urbanization and the growing need for packaging solutions, branding, and on-premises-based software further expand the market's reach. Overall, the market continues to grow, offering significant benefits to businesses seeking to optimize their labeling processes.

How is this Enterprise Labeling Software Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premise
  Cloud


End-user

  FMCG
  Retail and e-commerce
  Healthcare
  Warehousing and logistics
  Others


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  Middle East and Africa



  South America

By Deployment Insights

The on-premise segment is estimated to witness significant growth during the forecast period.

The market is driven by the need for compliance, creation, management, printing, and real-time updates of labels in various industries. Large enterprises require unique labeling solutions to meet diverse industry standards and traceability regulations, ensuring product quality and customer satisfaction. On-premise and cloud-based enterprise labeling software offer agility, scalability, and flexibility, optimizing operations and enhancing resilience and adaptability. Compliance management, seamless collaboration, contactless processes, safety measures, and predictive analytics are key features. Driving factors include digitalization, automation, and evolving challenges in logistics and e-commerce. However, varying industry standards, implementation costs, legacy systems, and integration challenges pose restraining factors. Enterprise labeling software solutions offer customizable templates, integration capabilities, and language support, catering to the economic condition, urbanization, and packaging solutions.

Brands prioritize a data-driven approach and regulatory requirements In their labeling strategy. The market is expected to grow, with key players catering to enterprise sizes and time to market.

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The On-premise segment was valued at USD 163.80 mn in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 41% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The market in APAC is projected to experience significant growth due to the increasing number of end-users in sectors such as food and beverage, personal care products, and pharmaceuticals.

The various standards and requirements for environmentally friendly labeled products

Data Collection and Labeling Market size was valued at USD 18.18 Billion in 2023 and is projected to reach USD 93.37 Billion by 2031 growing at a CAGR of 25.03% from 2024 to 2031.

Key Market Drivers:
• Increasing Reliance on Artificial Intelligence and Machine Learning: As AI and machine learning become more prevalent in numerous industries, the necessity for reliable data gathering and categorization grows. By 2025, the AI business is estimated to be worth $126 billion, emphasizing the significance of high-quality datasets for effective modeling.
• Increasing Emphasis on Data Privacy and Compliance: With stronger requirements such as GDPR and CCPA, enterprises must prioritize data collection methods that assure privacy and compliance. The global data privacy industry is expected to grow to USD $6.7 Bbillion by 2023, highlighting the need for responsible data handling methods in labeling processes.
• Emergence Of Advanced Data Annotation Tools: The emergence of enhanced data annotation tools is being driven by technological improvements, which are improving efficiency and lowering costs. Global Data Annotation tools market is expected to grow significantly, facilitating faster and more accurate labeling of data, essential for meeting the increasing demands of AI applications.

The drug labels and other drug-specific information on this Web site represent the most recent drug listing information companies have submitted to the Food and Drug Administration (FDA). (See 21 CFR part 207.) The drug labeling and other information has been reformatted to make it easier to read but its content has neither been altered nor verified by FDA. The drug labeling on this Web site may not be the labeling on currently distributed products or identical to the labeling that is approved. Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies described in monographs. Drugs marked 'OTC monograph final' or OTC monograph not final' are not checked for conformance to the monograph. Drugs marked 'unapproved medical gas', 'unapproved homeopathic' or 'unapproved drug other' on this Web site have not been evaluated by FDA for safety and efficacy and their labeling has not been approved. In addition, FDA is not aware of scientific evidence to support homeopathy as effective.

Label Printer Market size was valued at USD 5.7 Billion in 2023 and is projected to reach USD 9.5 Billion by 2030, growing at a CAGR of 6% during the forecasted period 2024 to 2030.

Global Label Printer Market Drivers

The market drivers for the Label Printer Market can be influenced by various factors. These may include:

Growing Need for Packaged Goods: The need for label printers to produce product labels and packaging materials is driven by the growing demand for packaged goods across a number of industries, including food and beverage, medicines, cosmetics, and consumer goods.
Strict Labeling rules: Manufacturers are forced to invest in cutting-edge labeling technologies in order to comply with strict government and regulatory body rules pertaining to product labeling, traceability, and safety standards. This has increased demand for label printers that can comply with these regulations.
Expanding E-commerce Sector: To manage the large amount of orders and shipments, the e-commerce sector’s rapid global expansion calls for effective labeling and packaging solutions. For online retail businesses, label printers are essential for producing shipping labels, barcodes, and tracking data.
Technological developments in printing: Label printer usage is fueled by improvements in print quality, speed, and customizability brought about by technological developments like digital printing, RFID (Radio Frequency Identification), and UV printing.
Need for On-demand label printing: High-speed label printers with variable data printing (VDP) for short print runs and fast turnaround times are becoming more and more popular as a result of the trend toward on-demand printing to satisfy customized labeling requirements and lower inventory costs.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

Regulatory Document - Stroller Product Labeling Standards

Table S1. The entire MedDRA PT corpus for Boxed Warning sections among selected 367 drugs; (XLS 62 kb)

This repository contains synthetic log data suitable for evaluation of intrusion detection systems. The logs were collected from a testbed that was built at the Austrian Institute of Technology (AIT) following the approaches by [1], [2], and [3]. Please refer to these papers for more detailed information on the dataset and cite them if the data is used for academic publications. Other than the related AIT-LDSv1.1, this dataset involves a more complex network structure, makes use of a different attack scenario, and collects log data from multiple hosts in the network. In brief, the testbed simulates a small enterprise network including mail server, file share, WordPress server, VPN, firewall, etc. Normal user behavior is simulated to generate background noise. After some days, two attack scenarios are launched against the network. Note that the AIT-LDSv2.0 extends this dataset with additional attack cases and variations of attack parameters.

The archives have the following structure. The gather directory contains the raw log data from each host in the network, as well as their system configurations. The labels directory contains the ground truth for those log files that are labeled. The processing directory contains configurations for the labeling procedure and the rules directory contains the labeling rules. Labeling of events that are related to the attacks is carried out with the Kyoushi Labeling Framework.

Each dataset contains traces of a specific attack scenario:

Scenario 1 (see gather/attacker_0/logs/sm.log for detailed attack log):

nmap scan

WPScan

dirb scan

webshell upload through wpDiscuz exploit (CVE-2020-24186)

privilege escalation

Scenario 2 (see gather/attacker_0/logs/dnsteal.log for detailed attack log):

DNSteal data exfiltration

The log data collected from the servers includes

Apache access and error logs (labeled)

audit logs (labeled)

auth logs (labeled)

VPN logs (labeled)

DNS logs (labeled)

syslog

suricata logs

exim logs

horde logs

mail logs

Note that only log files from affected servers are labeled. Label files and the directories in which they are located have the same name as their corresponding log file in the gather directory. Labels are in JSON format and comprise the following attributes: line (number of line in corresponding log file), labels (list of labels assigned to that log line), rules (names of labeling rules matching that log line). Note that not all attack traces are labeled in all log files; please refer to the labeling rules in case that some labels are not clear.

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 publications:

[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.

[2] M. Landauer, M. Frank, F. Skopik, W. Hotwagner, M. Wurzenberger, and A. Rauber, "A Framework for Automatic Labeling of Log Datasets from Model-driven Testbeds for HIDS Evaluation". ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (ACM SaT-CPS 2022), April 27, 2022, Baltimore, MD, USA. ACM.

[3] M. Frank, "Quality improvement of labels for model-driven benchmark data generation for intrusion detection systems", Master's Thesis, Vienna University of Technology, 2021.

Snapshot img

Labels Market Size 2024-2028

The labels market size is forecast to increase by USD 13.17 billion at a CAGR of 5.97% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing demand for sleeve labels in various industries, particularly food packaging. Sleeve labels, including shrink sleeves and stretch sleeves, have gained popularity due to their ability to provide full-coverage branding and product information. The implementation of barcode technology is another trend driving market growth, enabling efficient inventory management and supply chain optimization. However, the market faces challenges such as rising raw material prices, with key materials like polypropylene (PP), polyethylene (PE), polybutylene terephthalate (PBT), and PET experiencing price fluctuations. Manufacturers are responding by exploring alternative materials and production methods to mitigate these costs.
Overall, the market is expected to continue its growth trajectory, fueled by these trends and challenges.

What will be the Size of the Labels Market during the Forecast Period?

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The market encompasses a diverse range of materials, including glass, metals, wood, natural rubber, plastics, and various plastic resins such as polystyrene (PS), polyethylene (PE), polypropylene (PP), polybutylene terephthalate (PBT), polyphenylene oxide (PPO), polyurethane (PU), polyvinyl chloride (PVC), polyethylene terephthalate (PET), polycarbonate (PC), polysulfone (PSU), polyamide (PA), polyphenylsulfone (PPSU), and others. 
This market exhibits robust growth, driven by the increasing demand for labels in various sectors, particularly packaging. Plastics dominate the market due to their versatility, cost-effectiveness, and durability. Key trends include the growing preference for sustainable and eco-friendly labels, the adoption of digital printing technologies, and the increasing use of smart labels with RFID and NFC capabilities.
The market is expected to continue its expansion, driven by these trends and the ever-evolving needs of industries worldwide.

How is this Labels Industry segmented and which is the largest segment?

The labels industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Consumer goods
  Cosmetics and personal care
  Pharmaceuticals
  Others


Type

  Pressure-sensitive label
  Glue-applied label
  Sleeve label
  In-mold labels


Geography

  APAC

    China
    Japan


  Europe

    Germany
    France


  North America

    US


  South America



  Middle East and Africa

By End-user Insights

The consumer goods segment is estimated to witness significant growth during the forecast period. In the consumer goods sector, labels serve essential functions such as providing product information, differentiating offerings, and adhering to regulatory requirements. Labeling applications span across various product categories including food packaging, beverages, clothing, toiletries, and kitchenware. Companies In the Fast-Moving Consumer Goods (FMCG) industry frequently update labels to align with evolving consumer preferences. Regulatory bodies like China and Brazil impose specific labeling guidelines for various product types. In the packaging sector, plastics, such as polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polyamide (PA), dominate label production due to their versatility and cost-effectiveness. Building & construction and medical devices segments also utilize engineering plastics like Polycarbonate (PC), Polysulfone (PSU), Polypropylene (PP), and Polyurethane (PU) for labels. Labeling trends extend to sectors like electric vehicles (EVs) and the Clean Seas campaign, which promote eco-friendly alternatives to traditional plastics.

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The consumer goods segment was valued at USD 16.37 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 44% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in APAC is experiencing significant growth due to increasing demand for packaged food and beverages, driven by lifestyle and demographic changes In the middle-class population. This trend is particularly prominent in developing countries like India and China. Additionally, the rise in exports from ASEAN countries and the increasing demand for packaged personal care products are further fuel

The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models," submitted for publication to Integrated Materials and Manufacturing Innovations. Machine learning and autonomy are increasingly prevalent in materials science, but existing models are often trained or tuned using idealized data as absolute ground truths. In actual materials science, "ground truth" is often a matter of interpretation and is more readily determined by consensus. Here we present the data, software, and other files for a study using as-obtained diffraction data as a test case for evaluating the performance of machine learning models in the presence of differing expert opinions. We demonstrate that experts with similar backgrounds can disagree greatly even for something as intuitive as using diffraction to identify the start and end of a phase transformation. We then use a logarithmic likelihood method to evaluate the performance of machine learning models in relation to the consensus expert labels and their variance. We further illustrate this method's efficacy in ranking a number of state-of-the-art phase mapping algorithms. We propose a materials data challenge centered around the problem of evaluating models based on consensus with uncertainty. The data, labels, and code used in this study are all available online at data.gov, and the interested reader is encouraged to replicate and improve the existing models or to propose alternative methods for evaluating algorithmic performance.

According to Cognitive Market Research, the global Print and Apply Labeling market will be USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.30% from 2024 to 2031.

North America Print and Apply Labeling market held 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.5% from 2024 to 2031.
Europe Print and Apply Labeling Market is projected to expand at a compound annual growth rate (CAGR) of 2.8% from 2024 to 2031. Europe accounted for a share of over 30% of the global
Asia Pacific Print and Apply Labeling market held 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.3% from 2024 to 2031.
Latin America's Print and Apply Labeling market held 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
Middle East and Africa Print and Apply Labeling market held 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.0% from 2024 to 2031.
The primary labeling segment is set to rise in industries such as food, pharmaceuticals, and cosmetics, where consumer safety, information, and regulatory compliance are of utmost importance since primary labeling provides essential product information directly on the product packaging, propelling the market growth.
Conversely, secondary labeling plays a vital role in logistics, inventory management, and retail operations for efficient supply chain management, inventory tracking, and retail sales. 

Technological Advancements in Labeling Solutions to Increase the Demand Globally

Technological advancements in labeling solutions have revolutionized the industry and are poised to increase global demand significantly. Innovations such as digital printing, RFID labeling, smart labels, and automation have transformed traditional labeling processes, offering enhanced efficiency, flexibility, and customization capabilities. Digital printing technologies enable high-quality, on-demand printing of labels with minimal setup time and waste, catering to the growing need for quick turnaround times and customized labeling solutions. Radiofrequency identification labeling allows for real-time tracking and tracing of products throughout the supply chain, improving inventory management and enhancing product visibility. Smart labels equipped with sensors and data storage capabilities provide valuable insights into product integrity, freshness, and authenticity, driving demand in industries such as food and pharmaceuticals. Further, automation in labeling systems streamlines production processes, reduces labor costs, and improves accuracy, appealing to industries seeking operational efficiency and scalability. As businesses across various sectors recognize the benefits of these technological advancements in labeling solutions, the global demand is expected to surge, driven by the pursuit of improved efficiency, product differentiation, and compliance with evolving regulatory requirements.

Focus on Sustainability and Eco-friendly Labeling to Propel Market Growth

Many leading companies are innovating to create eco-friendly labels that resonate with environmentally conscious consumers in response to growing environmental awareness among consumers and regulatory demands to reduce packaging waste. Sustainable labeling practices encompass various strategies, including recyclable materials, biodegradable substrates, and labels from renewable resources. Further, advancements in eco-friendly printing technologies, such as water-based inks and energy-efficient printing processes, contribute to reducing the environmental footprint of labeling operations. By embracing eco-friendly labeling practices, companies can enhance their brand image, meet consumer preferences for environmentally responsible products, and comply with evolving regulatory requirements to promote sustainability in packaging and labeling.

Additionally, the government establishes standards and guidelines that mandate or incentivize companies to adopt sustainable labeling practices, such as using recyclable materials, reducing packaging waste, implementing eco-friendly printing technologies, and promoting transparency in labeling practi...

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Animal Welfare Standards - A Comparison of Industry Guidelines and Independent LabelsItem Type: PDFSummary: Table outlining certification guidelines for various prominent labels including: American Humane Certified, Certified Humane Program, Animal Welfare Approved, Global Animal Partnership 5-Step Animal Welfare Rating Program, & Certified Organic for various food products.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: Animal Welfare Institute website, https://awionline.org/content/consumers-guide-food-labels-and-animal-welfareFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=ca39998e2c974f9ebe2a436c2258eec3UID: 24Data Requested: Current regulations: who qualifies and who doesnt, who can we help qualify GAP certs, procedure rules, etc.)Method of Acquisition: downloaded from the Animal Welfare Institute website, https://awionline.org/content/consumers-guide-food-labels-and-animal-welfareDate Acquired: 6/23/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 5Tags: PENDING

This experiment focuses on the analysis of green tea aroma using a set of gas sensors. Specifically, the gas sensors selected for this research are the TGS822, TGS2602, TGS2620, MQ138, MQ5, and MQ3. The experiment involved testing a total of 78 different tea samples (chops), with each sample being observed three times. To conduct the experiment, a tea chamber was utilized, capable of accommodating 125 grams of dry green tea. The tea chamber was connected to a sensor chamber through a hose and intake micro air pump. During data acquisition, air from the tea chamber flowed into the sensor chamber for a duration of 60 seconds. Once the airflow from the tea sample was complete, the gas sensors recorded the aroma data for 60 seconds, resulting in 60 data records. These records were then saved into a CSV file for further processing and labeling. The labeling process involved referencing the Indonesian National Standard (SNI) 3945:2016, which defines the quality parameters for green tea according to ISO 11287 Green tea - definition and basic requirements. The SNI 3945:2016 standard specifies both special requirements and general requirements for green tea quality assessment. Special requirements encompass aspects such as water content, soluble ash, ash alkalinity, crude fiber, polyphenols, metal contamination, and microbial contamination. On the other hand, general requirements cover the physical and organoleptic characteristics of the tea, including dryness, steeping water, and steeping dregs. To evaluate the quality of Gambung green tea, an organoleptic test was conducted by a tea tester. The results of this test were used to label the data set obtained from the e-nose. The data set had two labels: quality standard ("good" and "quality defect") for the discrete classification task, and organoleptic score, which combined ratings for dry appearance, brew color, taste, aroma, and dregs of brewing, for the continuous regression task. In summary, this study aimed to predict the quality standard and organoleptic score of green tea samples using gas sensor data. The gas sensors were selected based on their suitability for analyzing the tea's aroma. The labeled data set, obtained through experimentation and organoleptic testing, would serve as the basis for training models for classification and regression tasks.

A nonprescription drug labelling standard outlines the permissible conditions of use and labelling requirements, such as dose, intended use, directions for use, warnings, active ingredients and combinations thereof. Labelling standards are developed for drugs that have a well characterized safety and efficacy profile under specific conditions of use.

Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability metrics and other summaries used to asses reliability after each trial.

The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.