Data Labeling Software Market Outlook

In 2023, the global market size for data labeling software was valued at approximately USD 1.2 billion and is projected to reach USD 6.5 billion by 2032, with a CAGR of 21% during the forecast period. The primary growth factor driving this market is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industry verticals, necessitating high-quality labeled data for model training and validation.

The surge in AI and ML applications is a significant growth driver for the data labeling software market. As businesses increasingly harness these advanced technologies to gain insights, optimize operations, and innovate products and services, the demand for accurately labeled data has skyrocketed. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where AI and ML applications are critical for advancements like predictive analytics, autonomous driving, and fraud detection. The growing reliance on AI and ML is propelling the market forward, as labeled data forms the backbone of effective AI model development.

Another crucial growth factor is the proliferation of big data. With the explosion of data generated from various sources, including social media, IoT devices, and enterprise systems, organizations are seeking efficient ways to manage and utilize this vast amount of information. Data labeling software enables companies to systematically organize and annotate large datasets, making them usable for AI and ML applications. The ability to handle diverse data types, including text, images, and audio, further amplifies the demand for these solutions, facilitating more comprehensive data analysis and better decision-making.

The increasing emphasis on data privacy and security is also driving the growth of the data labeling software market. With stringent regulations such as GDPR and CCPA coming into play, companies are under pressure to ensure that their data handling practices comply with legal standards. Data labeling software helps in anonymizing and protecting sensitive information during the labeling process, thus providing a layer of security and compliance. This has become particularly important as data breaches and cyber threats continue to rise, making secure data management a top priority for organizations worldwide.

Regionally, North America holds a significant share of the data labeling software market due to early adoption of AI and ML technologies, substantial investments in tech startups, and advanced IT infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is driven by the rapid digital transformation in countries like China and India, increasing investments in AI research, and the expansion of IT services. Europe and Latin America also present substantial growth opportunities, supported by technological advancements and increasing regulatory compliance needs.

Component Analysis

The data labeling software market can be segmented by component into software and services. The software segment encompasses various platforms and tools designed to label data efficiently. These software solutions offer features such as automation, integration with other AI tools, and scalability, which are critical for handling large datasets. The growing demand for automated data labeling solutions is a significant trend in this segment, driven by the need for faster and more accurate data annotation processes.

In contrast, the services segment includes human-in-the-loop solutions, consulting, and managed services. These services are essential for ensuring the quality and accuracy of labeled data, especially for complex tasks that require human judgment. Companies often turn to service providers for their expertise in specific domains, such as healthcare or automotive, where domain knowledge is crucial for effective data labeling. The services segment is also seeing growth due to the increasing need for customized solutions tailored to specific business requirements.

Moreover, hybrid approaches that combine software and human expertise are gaining traction. These solutions leverage the scalability and speed of automated software while incorporating human oversight for quality assurance. This combination is particularly useful in scenarios where data quality is paramount, such as in medical imaging or autonomous vehicle training. The hybrid model is expected to grow as companies seek to balance efficiency with accuracy in their

Global Data Labeling Software Market Report 2024 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024-2030. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

AI Assisted Annotation Tools Market Outlook

In 2023, the global AI assisted annotation tools market size was valued at approximately USD 600 million. Propelled by increasing demand for labeled data in machine learning and AI-driven applications, the market is expected to grow at a CAGR of 25% from 2024 to 2032, reaching an estimated market size of USD 3.3 billion by 2032. Factors such as advancements in AI technologies, an upsurge in data generation, and the need for accurate data labeling are fueling this growth.

The rapid proliferation of AI and machine learning (ML) has necessitated the development of robust data annotation tools. One of the key growth factors is the increasing reliance on AI for commercial and industrial applications, which require vast amounts of accurately labeled data to train AI models. Industries such as healthcare, automotive, and retail are heavily investing in AI technologies to enhance operational efficiencies, improve customer experience, and foster innovation. Consequently, the demand for AI-assisted annotation tools is expected to soar, driving market expansion.

Another significant growth factor is the growing complexity and volume of data generated across various sectors. With the exponential increase in data, the manual annotation process becomes impractical, necessitating automated or semi-automated tools to handle large datasets efficiently. AI-assisted annotation tools offer a solution by improving the speed and accuracy of data labeling, thereby enabling businesses to leverage AI capabilities more effectively. This trend is particularly pronounced in sectors like IT and telecommunications, where data volumes are immense.

Furthermore, the rise of personalized and precision medicine in healthcare is boosting the demand for AI-assisted annotation tools. Accurate data labeling is crucial for developing advanced diagnostic tools, treatment planning systems, and patient management solutions. AI-assisted annotation tools help in labeling complex medical data sets, such as MRI scans and histopathological images, ensuring high accuracy and consistency. This demand is further amplified by regulatory requirements for data accuracy and reliability in medical applications, thereby driving market growth.

The evolution of the Image Annotation Tool has been pivotal in addressing the challenges posed by the increasing complexity of data. These tools have transformed the way industries handle data, enabling more efficient and accurate labeling processes. By automating the annotation of images, these tools reduce the time and effort required to prepare data for AI models, particularly in fields like healthcare and automotive, where precision is paramount. The integration of AI technologies within these tools allows for continuous learning and improvement, ensuring that they can adapt to the ever-changing demands of data annotation. As a result, businesses can focus on leveraging AI capabilities to drive innovation and enhance operational efficiencies.

From a regional perspective, North America remains the dominant player in the AI-assisted annotation tools market, primarily due to the early adoption of AI technologies and significant investments in AI research and development. The presence of major technology companies and a robust infrastructure for AI implementation further bolster this dominance. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by increasing digital transformation initiatives, growing investments in AI, and expanding IT infrastructure.

Component Analysis

The AI-assisted annotation tools market is segmented into software and services based on components. The software segment holds a significant share of the market, primarily due to the extensive deployment of annotation software across various industries. These software solutions are designed to handle diverse data types, including text, image, audio, and video, providing a comprehensive suite of tools for data labeling. The continuous advancements in AI algorithms and machine learning models are driving the development of more sophisticated annotation software, further enhancing their accuracy and efficiency.

Within the software segment, there is a growing trend towards the integration of AI and machine learning capabilities to automate the annotation process. This integration reduces the dependency on manual efforts, significantly improving the speed and s

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

The size of the Data Annotation Tool Market market was valued at USD 3.9 USD billion in 2023 and is projected to reach USD 6.64 USD billion by 2032, with an expected CAGR of 7.9% during the forecast period. A Data Annotation Tool is defined as the software that can be employed to make annotations to data hence helping a learning computer model learn patterns. These tools provide a way of segregating the data types to include images, texts, and audio, as well as videos. Some of the subcategories of annotation include images such as bounding boxes, segmentation, text such as entity recognition, sentiment analysis, audio such as transcription, sound labeling, and video such as object tracking. Other common features depend on the case but they commonly consist of interfaces, cooperation with others, suggestion of labels, and quality assurance. It can be used in the automotive industry (object detection for self-driving cars), text processing (classification of text), healthcare (medical imaging), and retail (recommendation). These tools get applied in training good quality, accurately labeled data sets for the engineering of efficient AI systems. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

AI in Human-in-the-Loop AI Market Outlook

According to our latest research, the AI in Human-in-the-Loop AI market size reached USD 4.1 billion in 2024, reflecting robust expansion driven by the rising demand for high-quality, reliable AI systems across industries. The market is poised for significant growth, projected to achieve a value of USD 15.6 billion by 2033, registering a compelling CAGR of 15.8% over the forecast period. The surge in adoption is primarily fueled by the necessity for human intervention in critical AI processes, ensuring accuracy, compliance, and ethical outcomes in machine learning applications, as per the latest research findings.

One of the principal growth factors in the AI in Human-in-the-Loop AI market is the increasing complexity and scale of AI models, which necessitate human oversight to maintain accuracy and fairness. As organizations across sectors deploy AI solutions for mission-critical tasks, the need to mitigate algorithmic bias and ensure compliance with evolving regulatory frameworks has become paramount. Human-in-the-loop (HITL) approaches allow experts to validate, correct, and annotate data, improving both the performance and trustworthiness of AI models. This trend is particularly evident in sectors such as healthcare, autonomous vehicles, and financial services, where the cost of error is high and explainability is crucial.

Another significant driver is the proliferation of data-intensive applications, which require extensive data labeling, annotation, and continuous model training. The rise of generative AI, conversational agents, and computer vision systems has exponentially increased the volume of data that needs to be processed. HITL frameworks enable organizations to leverage human expertise for nuanced tasks such as sentiment analysis, object recognition, and content moderation, which are challenging for fully automated systems. As businesses strive for higher model accuracy and reduced time-to-market, the integration of human feedback loops into AI workflows has emerged as a best practice, further accelerating market growth.

Furthermore, the adoption of AI in Human-in-the-Loop AI solutions is being bolstered by the growing emphasis on ethical AI and responsible innovation. Enterprises are increasingly held accountable for the societal impacts of their AI systems, prompting investments in transparent, auditable, and human-centric AI development processes. The convergence of AI with regulatory requirements such as GDPR, HIPAA, and emerging AI Acts in various regions underscores the necessity for HITL mechanisms. This alignment between business objectives and regulatory compliance is creating a virtuous cycle, driving sustained demand for HITL solutions across diverse industry verticals.

From a regional perspective, North America continues to dominate the AI in Human-in-the-Loop AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced AI research ecosystem, significant investments by tech giants, and a mature regulatory landscape. Europe is witnessing steady growth driven by stringent data protection laws and a strong focus on ethical AI. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid digitalization, government initiatives, and the expansion of AI-driven industries in countries such as China, Japan, and India. These regional dynamics are expected to shape the competitive landscape and innovation trajectories in the years ahead.

Component Analysis

The Component segment of the AI in Human-in-the-Loop AI market is categorized into Software, Hardware, and Services, each playing a crucial role in the ecosystem. Software solutions form the backbone of HITL systems, encompassing data annotation platforms, model management tools, and workflow automation suites. These tools enable seamless collaboration between human experts and AI models, facilitating efficient data labeling, validation, and feedback integration. The demand for advanced software platforms is surging as organizations seek scalable, user-friendly, and secure solutions to manage complex HITL workflows. Innovations in user interface design, integration capabilities, and automation features are further enhancing the value proposition of software offerings in this segment.

Hardware components, while representing a smaller share compared to sof

The Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

The dataset contains 1689 train data, 243 test data and 483 valid data.

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

The Australian Animal Tagging And Monitoring System (AATAMS) is a coordinated marine animal tagging project. Satellite Relay Data Loggers (SRDL) (most with CTDs, and some also with fluorometers) are used to explore how marine mammal behaviour relates to their oceanic environment. Loggers developed at the University of St Andrews Sea Mammal Research Unit transmit data in near real time via the Argo satellite system. The Satellite Relay Data Loggers are deployed on marine mammals, including Elephant Seals, Weddell Seals, Australian Fur Seals, Australian Sea Lions, New Zealand Fur Seals. Data is being collected in the Southern Ocean, the Great Australian Bight, and off the South-East Coast of Australia. Data parameters measured by the instruments include time, conductivity (salinity), temperature, speed, fluorescence (available in the future) and depth. The data represented by this record are presented in delayed mode.

This dataset contains data related to the experiment conducted in the paper Towards the Systematic Testing of Virtual Reality Programs.

It contains an implementation of an approach for predicting defect proneness on unlabeled datasets- Average Clustering and Labeling (ACL).

ACL models get good prediction performance and are comparable to typical supervised learning models in terms of F-measure. ACL offers a viable choice for defect prediction on unlabeled dataset.

This dataset also contains analyzes related to code smells on C# repositories. Please check the paper to get futher information.

The Australian Animal Tagging And Monitoring System (AATAMS) is a coordinated marine animal tagging project. Satellite Relay Data Loggers (SRDL) (most with CTDs, and some also with fluorometers) are used to explore how marine mammal behaviour relates to their oceanic environment. Loggers developed at the University of St Andrews Sea Mammal Research Unit transmit data in near real time via the Argo satellite system. The Satellite Relay Data Loggers are deployed on marine mammals, including Elephant Seals, Weddell Seals, Australian Fur Seals, Australian Sea Lions, New Zealand Fur Seals. Data is being collected in the Southern Ocean, the Great Australian Bight, and off the South-East Coast of Australia. Data parameters measured by the instruments include time, conductivity (salinity), temperature, speed, fluorescence (available in the future) and depth. The data represented by this record are presented in delayed mode.

B. subtilis and E. coli cell segmentation dataset consisting of

test data annotated by three experts (test),

data annotated manually by a single microbeSEG user within 30 minutes (30min-man),

data annotated manually by a single microbeSEG user within 30 minutes and data annotated with microbeSEG pre-labeling with 15 minutes manual correction time (30min-man_15min-pre, includes the 30min-man dataset).

Images, instance segmentation masks and image-segmentation overlays are provided. All images are crops of size 320px x 320px. Annotations were made with ObiWan-Microbi.

Data acquisition

The phase contrast images of growing B. subtilis and E. coli colonies were acquired with a fully automated time-lapse microscope setup (TI Eclipse, Nikon, Germany) using a 100x oil immersion objective (Plan Apochromat λ Oil, N.A. 1.45, WD 170 µm, Nikon Microscopy). Time-lapse images were taken every 15 minutes for B. subtilis and every 20 minutes for E. coli. Cultivation took place inside a special microfluidic cultivation device. Resolution: 0.07μm/px for B. subtilis und 0.09μm/px for E. coli.

microbeSEG import

For the use with microbeSEG, create or select a new training set within the software and use the training data import functionality. Best import train data with the "train" checkbox checked, validation data with the "val" checkbox checked, and test data with the "test" checkbox checked. Since the images are already normalized, the "keep normalization" functionality can be used.

Modern mass spectrometry setups used in today’s proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNPxl for UV-induced peptide–RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark (Ramus, C.; J. Proteomics 2016, 132, 51–62). The second workflow, RNPxl, represents the first software solution to date for identification of peptide–RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results.

The CDFW Owned and Operated Lands and Conservation Easements dataset is a subset of the CDFW Lands dataset. It contains lands owned (fee title), some operated (wildlife areas, ecological reserves, and public/fishing access properties that are leases/agreements with other agencies that may be publicly accessible) and conservation easements held by CDFW. CDFW Owned and Operated Lands and Conservation Easements replaces the prior dataset, DFG Owned and Operated Lands, which included only fee title lands and some operated lands (wildlife areas, ecological reserves, and public/fishing access properties that are leases/agreements with other agencies and that may be publicly accessible). This is a generalized version dataset that has a shorter attribute table than the original and also has been dissolved based on the fields included. Please note that some lands may not be accessible due to the protection of resources and habitat. It is recommended that users contact the appropriate regional office for access information and consult regulations for CDFW lands in Sections 550, 550.1, 551, 552, 630 and 702. The CDFW Lands dataset is a digitized geographical inventory of selected lands owned and/or administered by the California Department of Fish and Wildlife. Properties such as ecological reserves, wildlife areas, undesignated lands containing biological resource values, public and fishing access lands, and CDFW fish hatcheries are among those lands included in this inventory. Types of properties owned or administered by CDFW which may not be included in this dataset are parcels less than 1 acre in size, such as fishing piers, fish spawning grounds, fish barriers, and other minor parcels. Physical boundaries of individual parcels are determined by the descriptions contained in legal documents and assessor parcel maps relating to that parcel. The approximate parcel boundaries are drawn onto U.S. Geological Survey 7.5'-series topographic maps, then digitized and attributed before being added to the dataset. In some cases, assessor parcel or best available datasets are used to digitize the boundary. Using parcel data to adjust the boundaries is a work in progress and will be incorporated in the future. Township, range, and section lines were based on the U.S. Geological Survey 7.5' series topographic maps (1:24,000 - scale). In some areas, the boundaries will not align with the Bureau of Land Management's Public Lands Survey System (PLSS). See the "SOURCE" field for data used to digitize boundary.This dataset is intended to provide information on the location of lands owned and/or administered by the California Department of Fish and Wildlife (CDFW) and for general conservation planning within the state. This dataset is not intended for navigational use. Users should contact the CDFW, Wildlife Branch, Lands Program or CDFW Regional offices for access information to a particular property. These datasets do not provide legal determination of parcel acreages or boundaries. Legal parcel acreages are based on County Assessor records. Users should contact the Wildlife Branch, Lands Program for this information and related data. When labeling or displaying properties on any map, use the provided field named "MAPLABEL" or use a generic label such as "conservation lands", "restricted lands", or some other similiar generalized label. All conservation easements are closed to public access.This dataset is not a surveyed product and is not a legal record of original survey measurements. They are representations or reproductions of information using various sources, scales, and precision of boundary data. As such, the data do not carry legal authority to determine a boundary, the location of fixed works nor is it suitable for navigational purposes. The California Department of Fish and Wildlife shall not be held liable for any use or misuse of the data. Users are responsible for ensuring the appropriate use of the data . It is strongly recommended that users acquire this dataset directly from the California Department of Fish and Wildlife and not indirectly through other sources which may have outdated or misinterpreted information.

The Australian Animal Tracking And Monitoring System (AATAMS) is a coordinated marine animal tagging project.

Satellite Relay Data Loggers (SRDL) (most with CTDs, and some also with fluorometers) are used to explore how marine mammal behaviour relates to their oceanic environment. Loggers developed at the University of St Andrews Sea Mammal Research Unit transmit data in near real time via the Argo satellite system.

The Satellite Relay Data Loggers are deployed on marine mammals, including Elephant Seals, Weddell Seals, Australian Fur Seals, Australian Sea Lions, New Zealand Fur Seals. Data parameters measured by the instruments include time, conductivity (salinity), temperature, speed, fluorescence (available in the future) and depth.

Data is being collected in the Southern Ocean, the Great Australian Bight, and off the South-East Coast of Australia.

This dataset has excluded the data from Antarctic waters as it is expected that data would be published via the OBIS Antarctic node AntBIF.

Each species has been linked to the World Register of Marine Species (WoRMS https://www.marinespecies.org).

Context

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."

Zalando seeks to replace the original MNIST dataset

Content

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (see above), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.

To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix. For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.

Labels

Each training and test example is assigned to one of the following labels:

0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot

TL;DR

Each row is a separate image Column 1 is the class label. Remaining columns are pixel numbers (784 total). Each value is the darkness of the pixel (1 to 255) Acknowledgements

Original dataset was downloaded from https://github.com/zalandoresearch/fashion-mnist Dataset was converted to CSV with this script: https://pjreddie.com/projects/mnist-in-csv/ License

The MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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

Airport Synthetic Data Generation Market Outlook

According to the latest research, the global airport synthetic data generation market size in 2024 is valued at USD 1.42 billion. The market is experiencing robust growth, driven by the increasing adoption of artificial intelligence and machine learning in airport operations. The market is projected to reach USD 6.81 billion by 2033, expanding at a remarkable CAGR of 18.9% from 2025 to 2033. One of the primary growth factors is the escalating need for high-quality, diverse datasets to train AI models for security, passenger management, and operational efficiency within airport environments.

Growth in the airport synthetic data generation market is primarily fueled by the aviation industry’s rapid digital transformation. Airports worldwide are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as privacy concerns, data scarcity, and high labeling costs. The ability to generate vast amounts of representative, bias-free, and customizable data is empowering airports to develop and test AI-driven solutions for security, baggage handling, and passenger flow management. As airports strive to enhance operational efficiency and passenger experience, the demand for synthetic data generation solutions is expected to surge further, especially as regulatory frameworks around data privacy become more stringent.

Another significant driver is the growing sophistication of cyber threats and the need for advanced security and surveillance systems in airport environments. Synthetic data generation technologies enable the creation of diverse and complex scenarios that are difficult to capture in real-world datasets. This capability is crucial for training robust AI models for facial recognition, anomaly detection, and predictive maintenance, without compromising passenger privacy. The integration of synthetic data with real-time sensor and video feeds is also facilitating more accurate and adaptive security protocols, which is a top priority for airport authorities and government agencies worldwide.

Moreover, the increasing adoption of cloud-based solutions and the evolution of AI-as-a-Service (AIaaS) platforms are accelerating the deployment of synthetic data generation tools across airports of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling airports to access advanced synthetic data capabilities without significant upfront investments in infrastructure. Additionally, the collaboration between technology providers, airlines, and regulatory bodies is fostering innovation and standardization in synthetic data generation practices. This collaborative ecosystem is expected to drive further market growth by enabling seamless integration of synthetic data into existing airport management systems.

From a regional perspective, North America currently leads the airport synthetic data generation market, accounting for the largest share in 2024. This dominance is attributed to the presence of major technology vendors, high airport traffic, and early adoption of AI-driven solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid infrastructure development, increased air travel demand, and government initiatives to modernize airport operations. Europe, Latin America, and the Middle East & Africa are also exhibiting steady growth, supported by investments in smart airport projects and digital transformation strategies.

Component Analysis

The airport synthetic data generation market by component is segmented into software and services. Software solutions dominate the market, as they form the backbone of synthetic data generation, offering customizable platforms for data simulation, annotation, and validation. These solutions are crucial for generating large-scale, high-fidelity datasets tailored to specific airport applications, such as security, baggage handling, and passenger analytics. Leading software providers are continuously enh

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Dipeptides have recently attracted considerable attention due to their newly found biological functions and potential biomarkers of diseases. Global analysis of dipeptides (400 common dipeptides in total number) in samples of complex matrices would enable functional studies of dipeptides and biomarker discovery. In this work, we report a method for high-coverage detection and accurate relative quantification of dipeptides. This method is based on differential chemical isotope labeling (CIL) of dipeptides with dansylation and liquid chromatography Orbitrap tandem mass spectrometry (LC-Orbitrap-MS). An optimized LC gradient ensured the separation of dansyl-dipeptides, including positional isomers (e.g., leucine- and isoleucine-containing dipeptides). MS/MS collision energy in Orbitrap MS was optimized to provide characteristic fragment ion information to sequence dansyl-dipeptides. Using the optimized conditions, a CIL standard library consisting of retention time, MS, and MS/MS information of a whole set of 400 dansyl-dipeptides was constructed to facilitate rapid dipeptide identification. For qualitative analysis of dipeptides in real samples, IsoMS data processing software’s parameters were tuned to improve the coverage of dipeptide annotation. Data-dependent acquisition was also carried out to improve the reliability of dipeptide identification. As examples of applications, we successfully identified a total of 321 dipeptides in rice wines and 105 dipeptides in human serum samples. For quantitative analysis, we demonstrated that the intensity ratios of the peak pairs from 96% of the dansyl-dipeptides detectable in a 1:1 mixture of 12C- and 13C-labeled rice wine samples were within ±20% of an expected value of 1.0. More than 90% of dipeptides were detected with a relative standard deviation of less than 10%, showing good performance of relative quantification.

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