Discover the booming market for open-source data labeling tools! Learn about its $500 million valuation in 2025, projected 25% CAGR, key drivers, and top players shaping this rapidly expanding sector within the AI revolution. Explore market trends and forecasts through 2033.

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 Labeling and Artwork Management Software market has emerged as a critical component for companies across various industries, including pharmaceuticals, food and beverage, cosmetics, and consumer goods. This software streamlines the complex processes of designing, approving, and managing product labels and artwor

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

General Information

This dataset was created for the training and testing of machine learning systems for extracting information from slates/on-screen or filmed text in video productions. The data associated with each instance was acquired by observing text on the slates in the file. There are two levels of data collected, a direct transcription and contextual information. For the direct transcription if there was illegible text an approximation was derived. The information is reported by the original creator of the slates and can be assumed to be accurate.

The data was collected using a software made specifically to categorize and transcribe metadata from these instances (see file directory description). The transcription was written in a natural reading order (for a western audience), so right to left and top to bottom. If the instance was labeled “Graphical” then the reading order was also right to left and top to bottom within individual sections as well as work as a whole.

This dataset was created by Madison Courtney, in collaboration with GBH Archives staff, and in consultation with researchers in the Brandeis University Department of Computer Science.

Uniqueness and overlapping data

Some of the slates come from different episodes of the same series; therefore, some slates have data overlap. For example, the “series-title” may be common across many slates. However, each slate instance in this dataset was labeled independently of the others. No information was removed, but not every slate contains the same information.

Different “sub-types” of slates have different graphical features, and present unique challenges for interpretation. In general, sub-types H (Handwritten), G (Graphical), C (Clapperboard) are more complex than D (Simple digital text) and B (Slate over bars). Most instances in the dataset are D. Users may wish to restrict the set to only those with subtype D.

Labels and annotations were created by an expert human judge. In Version 2, labels and annotations were created only once without any measure of inter-annotator agreement. In Version 3, all data were confirmed and/or edited by a second expert human judge. The dataset is self-contained. But more information about the assets from which these slates were taken can be found at the main website of the AAPB https://www.americanarchive.org/

Data size and structure

The data is tabular. There are 7 columns and 503 rows. Each row represents a different labeled image. The image files themselves are included in the dataset directory. The columns are as follows:

• 0: filename : The name of the image file for this slate
• 1: seen : A boolean book-keeping field used during the annotation process
• 2: type-label : The type of scene pictured in the image. All images in this set have type "S" signifying "Slate"
• 3: subtype-label : The sub-type of scene pictured in the image. Possible subtypes are "H" (Handwritten), "C" (Clapperboard), "D" (Simple digital text), "B" (Slate over bars), "G" (Graphical).
• 4: modifier : A boolean value indicating whether the slate was "transitional" in the sense that the still image was captured as the slate was fading in or out of view.
• 5: note-3 : Verbatim transcription of the text appearing on the slate
• 6: note-4 : Data in key-value structure indicating important data values presented on the slate. Possible keys are "program-title", "episode-title", "series-title", "title", "episode-no", "create-date", "air-date", "date", "director", "producer", "camera". Dates were normalized as YYYY-MM-DD. Names were normalized as Last, First Middle.

Data format

The directory contains the tabular data, the image files, and a small utility for viewing and/or editing labels. The Keystroke Labeler utility is a simple, serverless HTML-based viewer/editor. You can use the Keystroke Labeler by simply opening labeler.html in your web browser. The data are also provided serialized as JSON and CSV. The exact same label data appears redundantly in these 3 files: - img_arr_prog.js - the label data loaded by the Keystroke Labeler - img_labels.csv - the label data serialized as CSV - img_labels.json - the label data serialized as JSON

This dataset includes metadata about programs in the American Archive of Public Broadcasting. Any use of programs referenced by this dataset are subject to the terms of use set by the American Archive of Public Broadcasting.

Discover the booming Label Design & Printing Software market! This comprehensive analysis reveals key trends, growth drivers, and regional market shares from 2019-2033, including insights into cloud-based solutions, enterprise adoption, and top players like Canon and Xerox. Explore the future of label printing technology!

Discover the booming label printing software market! This in-depth analysis reveals a $2.5 billion market in 2025, projected to grow at 7% CAGR through 2033. Explore key drivers, trends, and top players like Endicia, Zebra Technologies, and NiceLabel. Learn how cloud solutions, e-commerce, and regulatory compliance are shaping this dynamic sector.

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

In-orbit satellite image datasets

Several training and test datasets of realistic image data from in-orbit proximity operations. The data attempts to facilitate research into applications of machine learning to the problem of visual guidance in proximity operations in space. The simulation software used has been released at https://Ben-Guthrie.github.io/satvis.

Datasets

The datasets have the following format:

dataset_name - data - all_data.txt - bounding_box.txt - rotations.txt - depth - 000000.exr, 000001.exr, ... - img - 000000.png, 000001.png, ... - labels - body - 000000.png, 000001.png, ... - solar panel - 000000.png, 000001.png, ... - ... - subsets - image_subsets.txt - pair_subsets.txt

Note that in the datasets, the image index is skipped after each simulation ends. For example, if a simulation generates 50 image files, labelled 000000.png to 000049.png, then index 50 will be skipped before inserting the data from the next simulation, which will instead start with 000051.png.

The subsets/ directory contains a subset label, either 0, 1 or 2 for "train", "valid" and "test". In image_subsets.txt, this is given for each image file, whereas pair_subsets.txt collects the files into image pairs with different separations between images, and assigns each to a subset. This latter option is relevant when looking at the change in positions or attitudes over time.

Data labels

In data/all_data.txt, the images are each labelled, in the following order, with the relative position (x, y, z) of the chaser, quaternion describing the attitude of the target (x, y, z, w), quaternion of the chaser (x, y, z, w) and time (t). The line number corresponds to the image number, where the first line contains the data for image 000000.png.

If present, the file data/bounding_box.txt contains the name and bounding box about the satellite. The bounding box is defined as (x, y, w, h), where x and y are the pixel coordinates of the top-left of the box, and w and h are the width and height in pixels.

The labels/ directory contains a separate directory for all surface labels provided in the simulation software, in the format labels/label_name/img_number. This label is a black and white image, where a pixel with intensity greater than 0.5 corresponds to the specific surface.

If the option is selected during simulation, the data is collected into pairs of images, with different separations between the image files. For each pair of images, the file data/rotations.txt contains the index of the "before" and "after" images, the observed rotation quaternion (x, y, z, w) which is a combination of rotations of both target and chaser, and the scalar distance to the target before and after the timestep.

Generating new data

The simulation software used to construct the datasets is provided at https://ben-guthrie.github.io/satvis/ for generating new data under specific conditions. Please see the webpage for further instructions on using the software.

GS1 Digital Link for DC Labels Market Outlook

According to our latest research, the GS1 Digital Link for DC Labels market size reached USD 1.42 billion in 2024, exhibiting robust growth propelled by surging demand for digitized supply chain solutions and enhanced traceability. The market is forecasted to grow at a CAGR of 14.8% from 2025 to 2033, reaching a projected value of USD 4.58 billion by 2033. This expansion is driven by the increasing adoption of digital transformation initiatives across industries, rising regulatory requirements for product authentication, and the need for real-time data visibility in logistics and retail environments.

One of the primary growth factors for the GS1 Digital Link for DC Labels market is the escalating need for transparency and traceability across global supply chains. Modern consumers are demanding more information regarding the origin, authenticity, and journey of products, especially in sectors such as food & beverage and healthcare. GS1 Digital Link technology enables the encoding of standardized data into digital labels, allowing stakeholders to access detailed product information through a simple scan. This not only enhances consumer trust but also helps businesses comply with stringent regulatory standards, reduce the risk of counterfeiting, and streamline product recalls. The growing emphasis on sustainability and ethical sourcing further amplifies the adoption of digital labeling solutions, as they provide a reliable means to communicate product provenance and environmental impact.

Another significant driver is the rapid digitization and automation of logistics and retail operations. The integration of GS1 Digital Link-enabled DC labels with enterprise resource planning (ERP) and warehouse management systems (WMS) allows for seamless tracking and management of products throughout the supply chain. This digital transformation is further accelerated by the proliferation of the Internet of Things (IoT), where smart labels equipped with RFID and QR code technologies facilitate real-time inventory monitoring, predictive analytics, and automated replenishment. As businesses strive to optimize operational efficiency and reduce manual errors, the demand for advanced digital labeling solutions continues to surge. The increasing prevalence of omnichannel retailing and e-commerce platforms also necessitates robust labeling systems to ensure accurate order fulfillment and customer satisfaction.

The GS1 Digital Link for DC Labels market is also witnessing substantial growth due to advancements in printing and data encoding technologies. Innovations in direct thermal and thermal transfer printing, coupled with the emergence of cloud-based label management platforms, have made it easier for organizations to deploy and scale digital labeling solutions. These technological advancements enable the customization of labels for specific applications, support multi-language and multi-format data, and facilitate integration with mobile and web-based applications. Additionally, the growing collaboration between technology providers, industry associations, and regulatory bodies is fostering the development of interoperable standards and best practices, further driving market adoption across diverse sectors.

From a regional perspective, North America and Europe currently dominate the GS1 Digital Link for DC Labels market, accounting for a combined market share of over 60% in 2024. This dominance is attributed to the early adoption of digital supply chain technologies, strong regulatory frameworks, and the presence of leading market players in these regions. Asia Pacific, however, is poised to witness the highest growth rate during the forecast period, driven by rapid industrialization, expanding retail and e-commerce sectors, and increasing government initiatives to promote product safety and traceability. Latin America and the Middle East & Africa are also emerging as promising markets, supported by rising investments in logistics infrastructure and the growing awareness of digital labeling benefits among manufacturers and distributors.

Component Analysis

The GS1 Digital Link for DC Labels market is segmented by component into software, hardware, and services. The software segment encompasses label management systems, data encoding platforms, and integration tools that facilitate the creation, customization, and deployment of digital labels. This segm

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.

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.

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.

AI Dataset Search Platform Market Outlook

According to our latest research, the global AI Dataset Search Platform market size reached USD 1.87 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 27.6% during the forecast period, reaching an estimated USD 16.17 billion by 2033. This remarkable growth is primarily attributed to the escalating demand for high-quality, diverse, and scalable datasets required to train advanced artificial intelligence and machine learning models across various industries. The proliferation of AI-driven applications and the increasing emphasis on data-centric AI development are key growth factors propelling the adoption of AI dataset search platforms globally.

The surge in AI adoption across sectors such as healthcare, BFSI, retail, automotive, and education is fueling the need for efficient and reliable dataset discovery solutions. Organizations are increasingly recognizing that the success of AI models hinges on the quality and relevance of the training data, leading to a surge in investments in dataset search platforms that offer advanced filtering, metadata tagging, and data governance capabilities. The integration of AI dataset search platforms with cloud infrastructures further streamlines data access, collaboration, and compliance, making them indispensable tools for enterprises aiming to accelerate AI innovation. The growing complexity of AI projects, coupled with the exponential growth in data volumes, is compelling organizations to seek platforms that can automate and optimize the process of dataset discovery and curation.

Another significant growth factor is the rapid evolution of AI regulations and data privacy frameworks worldwide. As data governance becomes a top priority, AI dataset search platforms are evolving to include robust features for data lineage tracking, access control, and compliance with regulations such as GDPR, HIPAA, and CCPA. The ability to ensure ethical sourcing and transparent usage of datasets is increasingly valued by enterprises and academic institutions alike. This regulatory landscape is driving the adoption of platforms that not only facilitate efficient dataset search but also enable organizations to demonstrate accountability and compliance in their AI initiatives.

The expanding ecosystem of AI developers, data scientists, and machine learning engineers is also contributing to the market's growth. The democratization of AI development, supported by open-source frameworks and cloud-based collaboration tools, has increased the demand for platforms that can aggregate, index, and provide easy access to diverse datasets. AI dataset search platforms are becoming central to fostering innovation, reducing development cycles, and enabling cross-domain research. As organizations strive to stay ahead in the competitive AI landscape, the ability to quickly identify and utilize optimal datasets is emerging as a critical differentiator.

From a regional perspective, North America currently dominates the AI dataset search platform market, accounting for over 38% of global revenue in 2024, driven by the strong presence of leading AI technology companies, active research communities, and significant investments in digital transformation. Europe and Asia Pacific are also witnessing rapid adoption, with Asia Pacific expected to exhibit the highest CAGR of 29.3% during the forecast period, fueled by government initiatives, burgeoning AI startups, and increasing digitalization across industries. Latin America and the Middle East & Africa are gradually embracing AI dataset search platforms, supported by growing awareness and investments in AI research and infrastructure.

Component Analysis

The AI Dataset Search Platform market is segmented by component into Software and Services. Software solutions constitute the backbone of this market, providing the core functionalities required for dataset discovery, indexing, metadata management, and integration with existing AI workflows. The software segment is witnessing robust growth as organizations seek advanced platforms capable of handling large-scale, multi-source datasets with sophisticated search capabilities powered by natural language processing and machine learning algorithms. These platforms are increasingly incorporating features such as semantic search, automated data labeling, and customizable data pipelines, enabling users to eff

Context

I would like to write a quest scraper. A Tool that takes a look at an image of a Heroquest quest map and can derive all symbols with their positions correctly; turning the "dead" image once again into an editable quest file. On Heroscribe.org a great java-based tool for editing quest files can be downloaded. In ideal case, my tool can take an image and output the Heroscribe format.

That's a task for later. Today, we just want to do the recognition.

I took around 100 Maps from the ancient game Heroquest, cut them down to single square images and used them as training data set for a neural net. The incredible imbalance in the data set made it necessary that I made 100 more maps, to boost the underrepresented symbol appearances. All of the maps have been made in Heroscribe (downloadable at Heroscribe.org) and exported as png; like that they have the same size.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F9050fb998965fcf24ef4b76d4c9fe4d7%2F11-BastionofChaos_EU.png?generation=1570256920345210&alt=media" alt="EU format Heroquest map">

Now I have 13 thousand snippets of Heroquest Quest Maps, in three cut out factors (78, 42 and 34 pixel). In each sample, there can be one or more of the following things: Monsters, Furniture, Doors, and rooms. For each snippet, the position information is already preserved in the data set: It was taken during the cropping process. You know where you cut the image right now, so why not keeping that information right away?

In the easiest case, there is just one symbol in a square. In some cases there are two or three of them at the same time; like there can be one or more door, one monster, and the square itself is discolored because the room is a special room. So here we have do recognize several symbols at the same time.

The first (roughly half) of the dataset contains real data from real maps, in the second half I've made up data to fill gaps in the data coverage.

The Y-Labels

Y-Data is provided in an excel-formatted spreadsheet. One column is for single-square-items and furniture; four for doors and one for rooms. If there were too many items in one square, or sometimes when I was tired from labelling all the data, it could happen that I was putting a label in the wrong column or even put the wrong label. I guess that currently, around 0.5% of the data is mislabelled; except for the room symbol column; which is not at all well labeled.

I tried to train a resnet to recognize the Y-Data given and it was surprisingly difficult. The current best working solution has four convolutional layers and one dense layer; has nothing to do with the current state-of-the-art deep learning. The advantage is, it is trainable under an hour on any laptop; the disadvantage is does not yet always work as intended.

See some examples for the images and the difficulties: The "center pic" of a "table" symbol: It is difficult to recognize anything here.

https://i.imgur.com/yCP4pF9.png" alt="Table, small cutout">

And the same square in the "pic" cutout:

https://i.imgur.com/9a9scVN.png" alt="Table, big cutout">

"center pic" of a Treasure Chest: Sufficient to recognize it; easily!

https://i.imgur.com/KjX1QUV.png" alt="Treasure Chest, small cutout">

Big cutout of the same Treasure Chest: Distracting details in the surrounding.

https://i.imgur.com/OPBlWHV.png" alt="Treasure Chest, big cutout">

For each symbol, I also extracted the two main colors. There are maps in the EU format, which are completely black and white (see above picture). The other half of the maps is in US format: Monsters are green, furniture is dark red, traps and trapped furniture have a orange or turquoise background instead of white; Hero symbols are bright red. There is real information in those colors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F3900bd109f86618a48e619ec00ce892d%2F11-BastionofChaos_US.png?generation=1570257035192555&alt=media" alt="US format Heroquest map">

The symbols in the data set are black and white, all of them. The columns 'min_color' and 'max_color' preserve the color information. I planned to give it as an auxiliary input to the neural net, but didn't yet get round to do it. The color information can be distracting, too: In the US map format, sometimes otherwise normal furniture symbols are marked with trap colors when they thought about some special event for it.

Target acceptance rates

Those are quite easy images on one side. Noiseless, size-fixed, no skew or zoom coming from photography... I even bootstrapped my data set by using K-Means to bulk-label some images. Yes, K-Means. It is easy to classify this data beyond the 95% recognition. So what's the catch?

First of all, the number of classes. It's not a single-class recognition problem; in this data set we have around 100 class...

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.

For more details about the dataset and its applications, please refer to our GitHub repository.

The BMO-GNN Dataset is curated to facilitate research on Bayesian Mesh Optimization and Graph Neural Networks for engineering performance prediction, specifically targeting applications in road wheel design. This dataset consists of multiple 3D wheel CAD models converted into graph structures, capturing the geometry (node coordinates) and connectivity (adjacency) necessary for GNN-based surrogate modeling.

High-fidelity finite element analyses (FEA) were performed to obtain mass, rim stiffness, and disk stiffness labels, which serve as ground truth for training and evaluating GNNs. By leveraging re-meshing and clustering techniques, each wheel geometry is represented in a graph form, allowing researchers to explore mesh-resolution effects on predictive accuracy through Bayesian Optimization.

Key Features

Graph Representations of 3D Wheels: - Each 3D CAD wheel is converted into a graph (via subdividing and clustering), resulting in a node–edge structure rather than traditional voxel, point cloud, or B-Rep data.

Label Data from FEA: - Mass (kg)
- Rim Stiffness (kgf/mm)
- Disk Stiffness (kgf/mm)

Diverse Geometric Variations: - Over 900 distinct wheel designs, each having unique shapes and structural properties.

Mesh Quality Variation: - Subdivision and clustering parameters (e.g., num_subdivide, num_cluster) are varied to produce different mesh qualities—valuable for studying the trade-off between model accuracy and computational cost.

Designed for Bayesian Optimization + GNN: - The dataset structure (graphs.pkl) supports iterative mesh-resolution optimization, making it ideal for advanced surrogate modeling, hyperparameter tuning, and robust performance prediction in automotive or mechanical contexts.

Dataset Generation

Geometry Acquisition - We collected 3D CAD wheel models reflecting a broad range of shapes and design parameters.
- CAD files were processed using Python/Open3D to create initial polygon meshes.

FEA-based Label Computation
- Altair SimLab (or comparable CAE tools) performed modal or structural analyses.
- For each wheel, finite element solutions yielded the mass, rim stiffness, and disk stiffness.
- Tetrahedral mesh convergence was verified for accuracy in labeling.

Mesh to Graph Conversion
- Polygon meshes were subdivided (to refine detail) and clustered (to control node count) through pyacvd or a similar library, creating consistent mesh resolutions.
- Resulting re-meshed data were then converted into adjacency matrices (edge connections) and node-coordinate matrices (XYZ) for GNN input.

Dataset Packaging (graphs.pkl)
- All graph data and corresponding normalized labels (mass, rim stiffness, disk stiffness) are compiled into a single serialized file.
- Graph elements include node coordinates, adjacency matrices, and shape IDs to trace back to original wheels if needed.

Data Preprocessing

(*) “Faces” here refer to the triangular faces in the polygon mesh before conversion. Depending on subdivision/clustering parameters, these numbers vary significantly.

Train–Test Split
- Typically, an 80–10–10 split (train–validation–test) is used.
- Min–Max scaling is applied to both node features (XYZ) and labels (mass, rim stiffness, disk stiffness).

File: graphs.pkl
- Contains a list of graph objects, each with: - Node feature matrix (N × 3 for XYZ coordinates, normalized)
- Adjacency matrix (N × N, storing edge weights or connectivity)
- Label (mass, rim stiffness, or disk stiffness, normalized)

Usage Examples

GNN Surrogate Modeling - Researchers can feed graphs.pkl directly into frameworks like Spektral or PyTorch Geometric to train or evaluate a GNN for predicting mechanical performance.

Mesh Resolution Studies
- By comparing re-meshed versions, one can analyze how node count and clustering influence prediction accuracy and computational time.

Bayesian Optimization Experiments
- Ideal for iterative search of “best” subdivision/clustering parameters, balancing accuracy vs. training cost.

References

1. Altair SimLab – used for CAE automation and finite element analyses.

Citation

If you find the BMO-GNN Dataset useful, please cite:

@article{pa...

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 continuou

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.