Abstract: Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.

The "People - Segmentation" dataset is a high-quality polygon annotation dataset containing 1000 publicly available images of people in various settings and environments. The dataset comprises a total of 1035 labels across one class, capturing people in different poses, expressions, and backgrounds. It is released under the CC BY-SA 4.0 license, providing researchers, data scientists, and enthusiasts with the ability to gain valuable insights into human activities and enabling object-level understanding. This makes it an indispensable tool for a range of applications, including but not limited to object detection, facial recognition, and human-computer interaction systems. With annotations, researchers can analyze and gain insights into the development of accurate person detection algorithms.

Dataset Name - People - Segmentation Data Asset Type - Image Data Asset Volume - 1000 images Data Asset Content - People in various settings and environments Data Asset Source - Publicly available on the web Annotation Type - Polygon Annotation Format - COCO Platform Used - Supervisely

This dataset is created by Quantigo AI, as a part of our commitment towards advancing the fields of AI and machine learning. If you have any queries about our datasets, please contact us at datasets@quantigo.ai.

Visit our website at https://quantigo.ai/ to learn more about our services and commitment to advancing the fields of AI and machine learning.

AI-Powered Medical Imaging Annotation Market Outlook

According to our latest research, the global AI-powered medical imaging annotation market size reached USD 1.24 billion in 2024, demonstrating robust traction across healthcare and life sciences sectors. The market is projected to expand at a compound annual growth rate (CAGR) of 23.7% from 2025 to 2033, reaching an estimated USD 9.31 billion by 2033. This significant growth is primarily driven by the increasing adoption of artificial intelligence (AI) in medical diagnostics, the rising prevalence of chronic diseases necessitating advanced imaging techniques, and the urgent need for high-quality annotated datasets to train sophisticated AI algorithms for clinical applications.

A pivotal growth factor for the AI-powered medical imaging annotation market is the escalating demand for precision medicine and personalized healthcare. As healthcare providers and researchers strive for tailored treatment plans, the need for accurate and detailed medical image annotation becomes paramount. AI-driven annotation platforms enable rapid, consistent, and scalable labeling of complex imaging data such as CT, MRI, and X-ray scans, facilitating the development of advanced diagnostic tools. Furthermore, the integration of AI in annotation workflows reduces human error, improves annotation speed, and enhances the quality of datasets, all of which are essential for training reliable machine learning models used in disease detection, prognosis, and treatment planning.

Another significant driver is the exponential growth in medical imaging data generated globally. With the proliferation of advanced imaging modalities and the increasing use of digital health records, healthcare systems are inundated with vast quantities of imaging data. Manual annotation of such data is time-consuming, labor-intensive, and prone to inconsistencies. AI-powered annotation solutions address these challenges by automating the labeling process, ensuring uniformity, and enabling real-time collaboration among radiologists, data scientists, and clinicians. This not only accelerates the deployment of AI-powered diagnostic tools but also supports large-scale clinical research initiatives aimed at uncovering novel biomarkers and improving patient outcomes.

The growing emphasis on regulatory compliance and data standardization also fuels market expansion. Regulatory bodies such as the FDA and EMA increasingly mandate the use of annotated datasets for the validation and approval of AI-driven diagnostic devices. As a result, healthcare organizations and medical device manufacturers are investing heavily in AI-powered annotation platforms that comply with stringent data privacy and security standards. Moreover, the emergence of cloud-based annotation solutions enhances accessibility and scalability, allowing stakeholders from diverse geographies to collaborate seamlessly on large annotation projects, thereby accelerating innovation and commercialization in the medical imaging domain.

Regionally, North America dominates the AI-powered medical imaging annotation market due to its advanced healthcare infrastructure, high adoption of AI technologies, and substantial investments in medical research. Europe follows closely, benefiting from strong regulatory support and a well-established healthcare ecosystem. The Asia Pacific region is poised for the fastest growth, driven by increasing healthcare expenditure, rapid digitalization, and government initiatives promoting AI adoption in healthcare. Latin America and the Middle East & Africa are emerging markets, gradually embracing AI-powered solutions to address gaps in diagnostic capabilities and improve healthcare access. This regional diversification underscores the global relevance and transformative potential of AI-powered medical imaging annotation.

Component Analysis

The component segment of the AI-powered medical imaging annotation market is bifurcated into software and services, each pla

As of 2022, the size of the machine learning platform industry in China reached roughly *** billion yuan and was estimated to surpass *** billion yuan by the end of 2023. The machine learning platform facilitates the training of machine learning models for data scientists, algorithm developers, and annotation specialists.

Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in each case, an annotation needs to clearly identify its scope and its own terminology. However, electronic annotation of data differs from annotation of documents: the content of the annotations, including expectations and supporting evidence, is more often shared among members of networks. Any consequent actions taken by the holders of the annotated data could be shared as well. But even those current annotation systems that admit data as their subject often make it difficult or impossible to annotate at fine-enough granularity to use the results in this way for data quality control. We address these kinds of issues by offering simple extensions to an existing annotation ontology and describe how the results support an interest-based distribution of annotations. We are using the result to design and deploy a platform that supports annotation services overlaid on networks of distributed data, with particular application to data quality control. Our initial instance supports a set of natural science collection metadata services. An important application is the support for data quality control and provision of missing data. A previous proof of concept demonstrated such use based on data annotations modeled with XML-Schema.

AI Data Management Market size was valued at USD 34.7 Billion in 2024 and is projected to reach USD 120.15 Billion by 2032, growing at a CAGR of 16.2% from 2025 to 2032.

AI Data Management Market Drivers

Data Explosion: The exponential growth of data generated from various sources (IoT devices, social media, etc.) necessitates efficient and intelligent data management solutions.

AI/ML Model Development: High-quality data is crucial for training and validating AI/ML models. AI data management tools help prepare, clean, and optimize data for optimal model performance.

Improved Data Quality: AI algorithms can automate data cleaning, identification, and correction of inconsistencies, leading to higher data quality and more accurate insights.

Enhanced Data Governance: AI-powered tools can help organizations comply with data privacy regulations (e.g., GDPR, CCPA) by automating data discovery, classification, and access control.

Increased Operational Efficiency: Automating data management tasks with AI frees up data scientists and analysts to focus on more strategic activities, such as model development and analysis.

Segmentation and Key Points of Human Body (MD-Image-053)

Introduction

The "Segmentation and Key Points of Human Body Dataset" is designed for the apparel and visual entertainment sectors, featuring a collection of internet-collected images with resolutions ranging from 1280 x 960 to 5184 x 3456 pixels. This dataset is comprehensive, including instance and semantic segmentation of 27 categories of body parts along with 24 key points annotations, providing detailed data for human body analysis and applications.

If you has interested in the full version of the datasets, featuring 6.6k annotated images, please visit our website maadaa.ai and leave a request.

Specification

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22149246%2F7dc65763d846e1ce51d51de554889b40%2Fsegmentation%20keypoint.jpg?generation=1724923513996127&alt=media" alt="">

About maadaa.ai

Since 2015, maadaa.ai has been dedicated to delivering specialized AI data services. Our key offerings include:

• Data Collection: Comprehensive data gathering tailored to your needs.

• Data Annotation: High-quality annotation services for precise data labeling.

• Off-the-Shelf Datasets: Ready-to-use datasets to accelerate your projects.

• Annotation Platform: Maid-X is our data annotation platform built for efficient data annotation.

We cater to various sectors, including automotive, healthcare, retail, and more, ensuring our clients receive the best data solutions for their AI initiatives.

Marine imaging has evolved from small, narrowly focussed applications to large-scale applications covering areas of several hundred square kilometers or time series covering observation periods of several months. The analysis and interpretation of the accumulating large volume of digital images or videos will continue to challenge the marine science community to keep this process efficient and effective. It is safe to say that any strategy will rely on some software platform supporting manual image and video annotation, either for a direct manual annotation-based analysis or for collecting training data to deploy a machine learning–based approach for (semi-)automatic annotation. This paper describes how computer-assisted manual full-frame image and video annotation is currently performed in marine science and how it can evolve to keep up with the increasing demand for image and video annotation and the growing volume of imaging data. As an example, observations are presented how the image and video annotation tool BIIGLE 2.0 has been used by an international community of more than one thousand users in the last 4 years. In addition, new features and tools are presented to show how BIIGLE 2.0 has evolved over the same time period: video annotation, support for large images in the gigapixel range, machine learning assisted image annotation, improved mobility and affordability, application instance federation and enhanced label tree collaboration. The observations indicate that, despite novel concepts and tools introduced by BIIGLE 2.0, full-frame image and video annotation is still mostly done in the same way as two decades ago, where single users annotated subsets of image collections or single video frames with limited computational support. We encourage researchers to review their protocols for education and annotation, making use of newer technologies and tools to improve the efficiency and effectivity of image and video annotation in marine science.

Data-Centric AI Platform Market Outlook

According to our latest research, the global Data-Centric AI Platform market size reached USD 5.8 billion in 2024, demonstrating robust adoption across key industries. The market is poised to expand at a remarkable CAGR of 23.2% from 2025 to 2033, propelled by the increasing prioritization of data quality, governance, and automation in artificial intelligence initiatives. By 2033, the Data-Centric AI Platform market size is projected to reach approximately USD 44.2 billion, reflecting the transformative impact of data-centric strategies on AI model development and deployment. This surge is primarily fueled by the growing need for scalable, reliable, and explainable AI solutions that place data at the core of model performance and decision-making processes.

The primary growth factor for the Data-Centric AI Platform market is the paradigm shift from model-centric to data-centric AI development. Enterprises have recognized that the quality, consistency, and representativeness of data are more crucial than merely optimizing algorithms. This realization has led to increased investments in platforms that facilitate data labeling, annotation, curation, and governance. Organizations across sectors such as healthcare, finance, and retail are leveraging these platforms to ensure AI systems are trained on robust, unbiased, and high-quality datasets. The ability to automate data pipeline management, detect anomalies, and improve data diversity directly enhances model accuracy, reliability, and fairness—driving widespread adoption of data-centric AI solutions.

A second significant driver is the rapid proliferation of AI applications across a broad spectrum of industries, necessitating scalable and collaborative data management capabilities. As AI use cases become more complex, organizations face mounting challenges in managing large volumes of heterogeneous data. Data-centric AI platforms address this challenge by offering integrated tools for data versioning, lineage tracking, and collaborative data workflows. These platforms enable data scientists, engineers, and business analysts to work together seamlessly, accelerating the development and deployment of AI models. The rise of regulations emphasizing data transparency and accountability, such as GDPR and CCPA, further incentivizes enterprises to adopt platforms that embed data governance and compliance features at their core.

Moreover, the increasing adoption of cloud-based deployment models is catalyzing market growth by making data-centric AI platforms more accessible and scalable. Cloud-native platforms offer flexibility, lower upfront costs, and rapid provisioning of resources, making it easier for organizations of all sizes to experiment with and scale AI initiatives. The ability to integrate with diverse data sources, automate data preparation, and leverage cloud-based compute power accelerates time-to-value for AI projects. This trend is particularly significant for small and medium enterprises (SMEs), which can now access advanced data-centric AI capabilities without heavy infrastructure investments. As a result, the democratization of AI development is becoming a reality, further boosting the momentum of the Data-Centric AI Platform market.

From a regional perspective, North America currently leads the global Data-Centric AI Platform market, accounting for the largest share in 2024. This dominance is attributed to the presence of major technology providers, early enterprise adoption, and substantial investments in AI research and development. Europe follows closely, driven by stringent data privacy regulations and a strong focus on ethical AI. The Asia Pacific region is emerging as a high-growth market, fueled by digital transformation initiatives and expanding AI ecosystems in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing increased interest, particularly in sectors like finance and telecommunications, albeit at a relatively nascent stage. As regional disparities in digital infrastructure and regulatory environments narrow, the global market is expected to witness even more balanced growth across all major geographies.

Component Analysis

The Data-Centric AI Platform market is segmented by component into Software and Services, each playing a pivotal role in driving the market’s expansion. The software segment encompasses comprehensiv

Single-person Portrait Matting Dataset (MD-Image-003)

Introduction

Our "Single-person Portrait Matting Dataset" is a pivotal resource for the fashion, media, and social media industries, providing finely labeled portrait images that capture a wide range of postures and hairstyles from various countries. With a focus on high-resolution images exceeding 1080 x 1080 pixels, this dataset is tailored for applications requiring detailed segmentation, including hair, ears, fingers, and other intricate portrait features.

If you has interested in the full version of the datasets, featuring 50k annotated images, please visit our website maadaa.ai and leave a request.

Specification

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22149246%2F67fcd50d42041ab8fcc41486dbf664bd%2Fsingle%20person.png?generation=1724923229039129&alt=media" alt="">

About maadaa.ai

Since 2015, maadaa.ai has been dedicated to delivering specialized AI data services. Our key offerings include:

• Data Collection: Comprehensive data gathering tailored to your needs.

• Data Annotation: High-quality annotation services for precise data labeling.

• Off-the-Shelf Datasets: Ready-to-use datasets to accelerate your projects.

• Annotation Platform: Maid-X is our data annotation platform built for efficient data annotation.

We cater to various sectors, including automotive, healthcare, retail, and more, ensuring our clients receive the best data solutions for their AI initiatives.

Single-Cell Annotation with AI Market Outlook

According to our latest research, the global single-cell annotation with AI market size reached USD 412 million in 2024, reflecting robust adoption across the life sciences and healthcare industries. The market is expected to expand at a CAGR of 22.6% during the forecast period, propelling the total market value to nearly USD 2.93 billion by 2033. This impressive growth is primarily driven by the integration of advanced artificial intelligence technologies in single-cell analysis, accelerating breakthroughs in disease diagnosis, drug discovery, and personalized medicine.

One of the key growth factors for the single-cell annotation with AI market is the surging demand for high-resolution cellular data in biomedical research. Traditional bulk sequencing techniques often mask the heterogeneity of cell populations, while single-cell technologies, augmented by AI, enable researchers to dissect complex tissues at a granular level. The proliferation of single-cell RNA sequencing (scRNA-seq) and other omics platforms has generated massive datasets, necessitating sophisticated AI-driven annotation tools for accurate cell type identification and functional analysis. This convergence of big data and AI is revolutionizing our understanding of cellular biology and disease mechanisms, fueling market growth.

Another major driver is the increasing collaboration between pharmaceutical companies, academic institutes, and technology providers. Pharmaceutical and biotechnology firms are leveraging AI-powered single-cell annotation platforms to accelerate drug discovery pipelines and identify novel therapeutic targets. Academic and research institutions, on the other hand, are utilizing these technologies to unravel the cellular basis of diseases and develop precision medicine strategies. The growing availability of open-source AI frameworks and cloud-based analytics solutions further lowers the barriers to adoption, making advanced single-cell annotation accessible to a broader scientific community and thereby expanding the market footprint.

The rapid advancements in machine learning, deep learning, and natural language processing are also propelling the single-cell annotation with AI market forward. AI algorithms can efficiently handle the high dimensionality and complexity of single-cell datasets, automating the annotation process and reducing human error. Deep learning models, in particular, are being trained on vast repositories of annotated single-cell data, enabling more accurate and scalable cell type classification. Furthermore, the integration of natural language processing allows for the extraction of relevant biological insights from scientific literature, enhancing the interpretability and utility of single-cell data. These technological innovations are expected to remain at the forefront of market expansion over the forecast period.

Regionally, North America continues to dominate the single-cell annotation with AI market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of leading biotechnology companies, robust research infrastructure, and significant investments in AI-driven life sciences solutions. However, Asia Pacific is emerging as the fastest-growing region, supported by rising government funding for genomics research, expanding pharmaceutical sectors, and increasing adoption of digital health technologies. Europe also holds a substantial market share, driven by collaborative research initiatives and strong regulatory support for precision medicine. Latin America and the Middle East & Africa are witnessing steady growth, though at a comparatively slower pace, as these regions gradually strengthen their bioinformatics and healthcare capabilities.

Technology Analysis

The technology segment of the single-cell annotation with AI market encompasses machine learning, deep learning, natural language processing (NLP), and other emerging AI methodologies. Machine learning remains the foundational technology, enabling the automated classification and clustering of single-cell data. Supervised and unsupervised learning algorithms are widely used to identify patterns in gene expression profiles, facilitating the annotation of cell types and states. The scalability and adaptability of machine learning models make them highly suitable for handling large, heterogeneous single-cell datasets, which are increasingly generated by high-throughput sequencing platforms.<br /&g

This dataset was produced through a study funded by the Open Societal Challenges programme and using tools developed through the Pelagios Network (https://pelagios.org/) to annotate and visualise object itineraries in museum collections data - in this case, a sample of textual data about navigational instruments held at National Museums Scotland (NMS). The term ‘object itinerary’ describes the journey an object takes through space and time, including its interactions between the people, organisations and other objects it encounters.Data was annotated using the Recogito Studio platform (https://recogitostudio.org/) using a data model based on selected terms from the CIDOC CRM (https://www.cidoc-crm.org/) and Linked Art (https://linked.art/) ontologies. Recogito Studio's Geo-Tagger plugin was used to align places mentioned in the text to their equivalents in Wikidata (https://www.wikidata.org/), and the exported geo-tags were further processed to enable visualisation in Peripleo (the main repository for Peripleo is at https://github.com/britishlibrary/peripleo; the visualisation of this data can be found at https://sarahmiddle.github.io/Peripleo_PelagiosOSC/).The following files are included:Object Itineraries Data Model (ObjectItinerariesDataModel_20241029.owl): OWL 2 ontology, developed using Protege (https://protege.stanford.edu/), which represents key classes and properties (entities and relationships) in the description of object itineraries.Annotation Protocol (NMSDataAnnotationProtocol_20241028.pdf): document describing how the original data was annotated in accordance with the data model, which resulted in the CSV and GeoJSON export files.Annotations (NMSDataAnnotations_20241028.csv): CSV export from Recogito Studio, containing all annotations, including the annotated text, its position in the main document, and associated tags.Geo-Tags (NMSDataGeoTags_20241028.geojson): GeoJSON export from Recogito Studio, containing all geo-tags, including the co-ordinates of each annotated place and the identifiers of their equivalents in Wikidata.Enhanced Geo-Tags (NMSDataGeoTags_TransformedEnhanced_20241028.json): JSON-LD file containing a transformed and enhanced version of the GeoJSON export, used to visualise the data in Peripleo and provide additional contextual information, including links to the relevant NMS catalogue records.

FAIR Data Management Platforms for Life Sciences Market Outlook

According to our latest research, the global FAIR Data Management Platforms for Life Sciences market size has reached USD 1.25 billion in 2024, with a robust year-on-year growth trajectory. The market is experiencing a significant push due to the increasing demand for data-driven decision-making in life sciences, and it is expected to expand at a CAGR of 14.8% during the forecast period. By 2033, the market is forecasted to reach USD 4.05 billion, highlighting the accelerating adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles across the sector. This remarkable growth is fueled by the need for better research reproducibility, regulatory compliance, and efficient data sharing within the life sciences industry, as organizations strive to leverage large-scale data for innovation and competitive advantage.

One of the primary growth factors driving the FAIR Data Management Platforms for Life Sciences market is the increasing complexity and volume of biomedical data. With advancements in genomics, proteomics, and medical imaging, life sciences organizations are generating unprecedented amounts of data that require robust management solutions. The adoption of FAIR data principles ensures that this data is not only stored securely but is also easily discoverable and usable by both humans and machines. As a result, pharmaceutical and biotechnology companies, as well as academic institutions, are prioritizing investments in platforms that enable seamless data integration, annotation, and sharing, thereby accelerating research and development timelines and reducing costs associated with data silos and duplication.

Another significant driver is the stringent regulatory landscape governing data management in the life sciences sector. Regulatory authorities across North America, Europe, and other regions are increasingly mandating transparent, auditable, and reproducible data practices for clinical trials and drug development processes. FAIR Data Management Platforms are uniquely positioned to address these requirements by providing traceability, provenance tracking, and standardized metadata frameworks. This not only enhances compliance with frameworks such as GDPR, HIPAA, and FDA 21 CFR Part 11 but also fosters greater collaboration among stakeholders, including contract research organizations (CROs), healthcare providers, and governmental agencies. The ability to demonstrate data integrity and lineage is becoming a key differentiator for organizations seeking regulatory approvals and public trust.

Furthermore, the growing emphasis on collaborative research and open science initiatives is propelling the adoption of FAIR Data Management Platforms. Life sciences research is increasingly conducted in multi-institutional and cross-border settings, necessitating interoperable data infrastructures that support seamless data exchange and joint analysis. FAIR-aligned platforms enable researchers to contribute, access, and reuse datasets efficiently, thereby driving scientific discovery and innovation. The proliferation of artificial intelligence and machine learning applications in drug discovery, genomics, and precision medicine also relies heavily on high-quality, well-annotated data, further underlining the importance of robust data management solutions. As more organizations recognize the strategic value of FAIR data, market growth is expected to accelerate.

From a regional perspective, North America currently dominates the FAIR Data Management Platforms for Life Sciences market, accounting for the largest share in 2024. This leadership position is attributed to the presence of leading pharmaceutical and biotechnology companies, advanced healthcare infrastructure, and a proactive regulatory environment. Europe follows closely, driven by strong public and private investments in life sciences research and a well-established culture of data stewardship. The Asia Pacific region is emerging as a high-growth market, supported by expanding biomedical research capabilities, increasing government initiatives to promote data standardization, and rising adoption of digital health technologies. Latin America and the Middle East & Africa are also witnessing gradual uptake, albeit at a slower pace, as local stakeholders recognize the long-term benefits of FAIR data principles for research efficiency and innovation.

Component Analysis

The FAIR Data Manage

Vehicle Detection Dataset

This dataset is designed for vehicle detection tasks, featuring a comprehensive collection of images annotated for object detection. This dataset, originally sourced from Roboflow (https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system), was exported on May 29, 2025, at 4:59 PM GMT and is now publicly available on Kaggle under the CC BY 4.0 license.

Overview

• Purpose: The dataset supports the development of computer vision models for detecting various types of vehicles in traffic scenarios.
• Classes: The dataset includes annotations for 7 vehicle types:
  • Bicycle
  • Bus
  • Car
  • Motorbike
  • Rickshaw
  • Truck
  • Van
• Number of Images: The dataset contains 9,440 images, split into training, validation, and test sets:
  • Training: Images located in ../train/images
  • Validation: Images located in ../valid/images
  • Test: Images located in ../test/images
• Annotation Format: Images are annotated in YOLOv11 format, suitable for training state-of-the-art object detection models.
• Pre-processing: Each image has been resized to 640x640 pixels (stretched). No additional image augmentation techniques were applied.

Source and Creation

This dataset was created and exported via Roboflow, an end-to-end computer vision platform that facilitates collaboration, image collection, annotation, dataset creation, model training, and deployment. The dataset is part of the ai-traffic-system project (version 1) under the workspace object-detection-sn8ac. For more details, visit: https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system/dataset/1.

Usage

This dataset is ideal for researchers, data scientists, and developers working on vehicle detection and traffic monitoring systems. It can be used to: - Train and evaluate deep learning models for object detection, particularly using the YOLOv11 framework. - Develop AI-powered traffic management systems, autonomous driving applications, or urban mobility solutions. - Explore computer vision techniques for real-world traffic scenarios.

For advanced training notebooks compatible with this dataset, check out: https://github.com/roboflow/notebooks. To explore additional datasets and pre-trained models, visit: https://universe.roboflow.com.

License

The dataset is licensed under CC BY 4.0, allowing for flexible use, sharing, and adaptation, provided appropriate credit is given to the original source.

This dataset is a valuable resource for building robust vehicle detection models and advancing computer vision applications in traffic systems.

We drew a stratified sample of 20 tweets, that were pre-annotated in a study by Davidson et al. (2017) for Hate Speech / Offensive Language / Neither. The stratification was done with respect to majority-voted class and level of disagreement.

We then recruited 1000 Prolific workers to annotate each of the 20 tweets. Annotators were randomly selected into one of six experimental conditions, as shown in the following figures. In these conditions, they were asked to assign the labels Hate Speech / Offensive Language / Neither.

In addition, we collected a variety of demographic variables (e.g. age and gender) and some para data (e.g. duration of the whole task, duration per screen).

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Methods Creating a training and validation image set

We downloaded 40,761 research-grade observations of A. petiolata from iNaturalist, ranging from 1995 to 2020. Observations on the iNaturalist platform are considered “research-grade if the observation is verifiable (includes image), includes the date and location observed, is growing wild (i.e. not cultivated), and at least two-thirds of community users agree on the species identification. From this dataset, we used a subset of images for model training. The total number of observations in the iNaturalist dataset are heavily skewed towards more recent years. Less than 5% of the images we downloaded (n=1,790) were uploaded between 1995-2016, while over 50% of the images were uploaded in 2020. To mitigate temporal bias, we used all available images between the years 1995 and 2016 and we randomly selected images uploaded between 2017-2020. We restricted the number of randomly-selected images in 2020 by capping the number of 2020 images to approximately the number of 2019 observations in the training set. The annotated observation records are available in the supplement (supplementary data sheet 1). The majority of the unprocessed records (those which hold a CC-BY-NC license) are also available on GBIF.org (2021).

One of us (R. Reeb) annotated the phenology of training and validation set images using two different classification schemes: two-stage (non-flowering, flowering) and four-stage (vegetative, budding, flowering, fruiting). For the two-stage scheme, we classified 12,277 images and designated images as ‘flowering’ if there was one or more open flowers on the plant. All other images were classified as non-flowering. For the four-stage scheme, we classified 12,758 images. We classified images as ‘vegetative’ if no reproductive parts were present, ‘budding’ if one or more unopened flower buds were present, ‘flowering’ if at least one opened flower was present, and ‘fruiting’ if at least one fully-formed fruit was present (with no remaining flower petals attached at the base). Phenology categories were discrete; if there was more than one type of reproductive organ on the plant, the image was labeled based on the latest phenophase (e.g. if both flowers and fruits were present, the image was classified as fruiting).

For both classification schemes, we only included images in the model training and validation dataset if the image contained one or more plants with clearly visible reproductive parts were clear and we could exclude the possibility of a later phenophase. We removed 1.6% of images from the two-stage dataset that did not meet this requirement, leaving us with a total of 12,077 images, and 4.0% of the images from the four-stage leaving us with a total of 12,237 images. We then split the two-stage and four-stage datasets into a model training dataset (80% of each dataset) and a validation dataset (20% of each dataset).

Training a two-stage and four-stage CNN

We adapted techniques from studies applying machine learning to herbarium specimens for use with community science images (Lorieul et al. 2019; Pearson et al. 2020). We used transfer learning to speed up training of the model and reduce the size requirements for our labeled dataset. This approach uses a model that has been pre-trained using a large dataset and so is already competent at basic tasks such as detecting lines and shapes in images. We trained a neural network (ResNet-18) using the Pytorch machine learning library (Psake et al. 2019) within Python. We chose the ResNet-18 neural network because it had fewer convolutional layers and thus was less computationally intensive than pre-trained neural networks with more layers. In early testing we reached desired accuracy with the two-stage model using ResNet-18. ResNet-18 was pre-trained using the ImageNet dataset, which has 1,281,167 images for training (Deng et al. 2009). We utilized default parameters for batch size (4), learning rate (0.001), optimizer (stochastic gradient descent), and loss function (cross entropy loss). Because this led to satisfactory performance, we did not further investigate hyperparameters.

Because the ImageNet dataset has 1,000 classes while our data was labeled with either 2 or 4 classes, we replaced the final fully-connected layer of the ResNet-18 architecture with fully-connected layers containing an output size of 2 for the 2-class problem and 4 for the 4-class problem. We resized and cropped the images to fit ResNet’s input size of 224x224 pixels and normalized the distribution of the RGB values in each image to a mean of zero and a standard deviation of one, to simplify model calculations. During training, the CNN makes predictions on the labeled data from the training set and calculates a loss parameter that quantifies the model’s inaccuracy. The slope of the loss in relation to model parameters is found and then the model parameters are updated to minimize the loss value. After this training step, model performance is estimated by making predictions on the validation dataset. The model is not updated during this process, so that the validation data remains ‘unseen’ by the model (Rawat and Wang 2017; Tetko et al. 1995). This cycle is repeated until the desired level of accuracy is reached. We trained our model for 25 of these cycles, or epochs. We stopped training at 25 epochs to prevent overfitting, where the model becomes trained too specifically for the training images and begins to lose accuracy on images in the validation dataset (Tetko et al. 1995).

We evaluated model accuracy and created confusion matrices using the model’s predictions on the labeled validation data. This allowed us to evaluate the model’s accuracy and which specific categories are the most difficult for the model to distinguish. For using the model to make phenology predictions on the full, 40,761 image dataset, we created a custom dataloader function in Pytorch using the Custom Dataset function, which would allow for loading images listed in a csv and passing them through the model associated with unique image IDs.

Hardware information

Model training was conducted using a personal laptop (Ryzen 5 3500U cpu and 8 GB of memory) and a desktop computer (Ryzen 5 3600 cpu, NVIDIA RTX 3070 GPU and 16 GB of memory).

Comparing CNN accuracy to human annotation accuracy

We compared the accuracy of the trained CNN to the accuracy of seven inexperienced human scorers annotating a random subsample of 250 images from the full, 40,761 image dataset. An expert annotator (R. Reeb, who has over a year’s experience in annotating A. petiolata phenology) first classified the subsample images using the four-stage phenology classification scheme (vegetative, budding, flowering, fruiting). Nine images could not be classified for phenology and were removed. Next, seven non-expert annotators classified the 241 subsample images using an identical protocol. This group represented a variety of different levels of familiarity with A. petiolata phenology, ranging from no research experience to extensive research experience (two or more years working with this species). However, no one in the group had substantial experience classifying community science images and all were naïve to the four-stage phenology scoring protocol. The trained CNN was also used to classify the subsample images. We compared human annotation accuracy in each phenophase to the accuracy of the CNN using students

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

AI Dataset Search Platform Market Outlook

According to our latest research, the global AI Dataset Search Platform market size is valued at USD 1.18 billion in 2024, with a robust year-over-year expansion driven by the escalating demand for high-quality datasets to fuel artificial intelligence and machine learning initiatives across industries. The market is expected to grow at a CAGR of 22.6% from 2025 to 2033, reaching an estimated USD 9.62 billion by 2033. This exponential growth is primarily attributed to the increasing recognition of data as a strategic asset, the proliferation of AI applications across sectors, and the need for efficient, scalable, and secure platforms to discover, curate, and manage diverse datasets.

One of the primary growth factors propelling the AI Dataset Search Platform market is the exponential surge in AI adoption across both public and private sectors. Businesses and institutions are increasingly leveraging AI to gain competitive advantages, enhance operational efficiencies, and deliver personalized experiences. However, the effectiveness of AI models is fundamentally reliant on the quality and diversity of training datasets. As organizations strive to accelerate their AI initiatives, the need for platforms that can efficiently search, aggregate, and validate datasets from disparate sources has become paramount. This has led to a significant uptick in investments in AI dataset search platforms, as they enable faster data discovery, reduce development cycles, and ensure compliance with data governance standards.

Another key driver for the market is the growing complexity and volume of data generated from emerging technologies such as IoT, edge computing, and connected devices. The sheer scale and heterogeneity of data sources necessitate advanced search platforms equipped with intelligent indexing, semantic search, and metadata management capabilities. These platforms not only facilitate the identification of relevant datasets but also support data annotation, labeling, and preprocessing, which are critical for building robust AI models. Furthermore, the integration of AI-powered search algorithms within these platforms enhances the accuracy and relevance of search results, thereby improving the overall efficiency of data scientists and AI practitioners.

Additionally, regulatory pressures and the increasing emphasis on ethical AI have underscored the importance of transparent and auditable data sourcing. Organizations are compelled to demonstrate the provenance and integrity of the datasets used in their AI models to mitigate risks related to bias, privacy, and compliance. AI dataset search platforms address these challenges by providing traceability, version control, and access management features, ensuring that only authorized and compliant datasets are utilized. This not only reduces legal and reputational risks but also fosters trust among stakeholders, further accelerating market adoption.

From a regional perspective, North America dominates the AI Dataset Search Platform market in 2024, accounting for over 38% of the global revenue. This leadership is driven by the presence of major technology providers, a mature AI ecosystem, and substantial investments in research and development. Europe follows closely, benefiting from stringent data privacy regulations and strong government support for AI innovation. The Asia Pacific region is experiencing the fastest growth, propelled by rapid digital transformation, expanding AI research communities, and increasing government initiatives to foster AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven solutions.

Component Analysis

The AI Dataset Search Platform market by component is segmented into platforms and services, each playing a pivotal role in the ecosystem. The platform segment encompasses the core software infrastructure that enables users to search, index, curate, and manage datasets. This segmen

Scientific Data Management System Market Outlook

According to our latest research, the global Scientific Data Management System (SDMS) market size in 2024 stands at USD 2.84 billion, with a robust CAGR of 12.6% expected through the forecast period. By 2033, the SDMS market is projected to reach approximately USD 8.23 billion, driven by the growing need for efficient data handling, regulatory compliance, and the explosion of scientific research data worldwide. The market’s upward trajectory is fueled by the integration of advanced analytics, artificial intelligence, and cloud-based solutions, which collectively enhance the capabilities and scalability of scientific data management systems.

One of the primary growth factors for the Scientific Data Management System market is the exponential increase in scientific research activities across multiple disciplines. The proliferation of high-throughput technologies in genomics, proteomics, and drug discovery has resulted in the generation of massive volumes of complex data that require robust management solutions. SDMS platforms are becoming indispensable tools for research organizations, enabling them to organize, store, retrieve, and analyze data efficiently. The demand for reproducibility and transparency in scientific research further underscores the need for comprehensive data management systems, as institutions strive to maintain data integrity and facilitate collaboration across geographically dispersed teams.

Another significant driver is the regulatory landscape governing scientific research, particularly in sectors such as pharmaceuticals, biotechnology, and healthcare. Compliance with stringent regulations such as FDA 21 CFR Part 11, GLP, and GDPR mandates the adoption of secure, auditable, and traceable data management systems. SDMS solutions are designed to address these compliance requirements by offering features like electronic signatures, audit trails, and secure access controls. As regulatory scrutiny intensifies, organizations are prioritizing investments in SDMS platforms to mitigate risks, ensure data security, and streamline their workflows, thereby accelerating their research and development timelines.

Technological advancements are also playing a pivotal role in shaping the SDMS market landscape. The integration of artificial intelligence and machine learning algorithms into SDMS platforms is transforming the way scientific data is processed, interpreted, and utilized. These technologies enable automated data annotation, predictive analytics, and real-time insights, which significantly enhance research productivity and decision-making. Additionally, the growing adoption of cloud-based SDMS solutions is facilitating seamless data sharing and collaboration among global research teams, while reducing infrastructure costs and improving scalability. The convergence of these technological innovations is expected to sustain market growth and open new avenues for scientific discovery.

From a regional perspective, North America continues to dominate the Scientific Data Management System market, accounting for the largest share due to its well-established research infrastructure, significant investments in life sciences, and a strong regulatory framework. However, the Asia Pacific region is emerging as a high-growth market, propelled by increasing research activities, expanding pharmaceutical and biotechnology sectors, and rising government initiatives to promote scientific innovation. Europe also holds a considerable market share, supported by collaborative research projects and robust funding for scientific advancements. The Middle East & Africa and Latin America markets are gradually gaining traction as local research ecosystems mature and digital transformation initiatives accelerate.

Component Analysis

The component segment of the Scientific Data Management System market is broadly categorized into software, hardware, and services, each playing a distinct role in the overall ecosystem. SDMS software forms the backbone of the market, encompassing platforms that facilitate data capture, storage, retrieval, and analysis. These software solutions are increasingly being designed with user-friendly interfaces, customizable workflows, and advanced analytical capabilities to cater to the evolving needs of research organizations. The demand for software is further amplified by the growing complexity and volume