Explore the booming data collection and labeling market, driven by AI advancements. Discover key growth drivers, market trends, and forecasts for 2025-2033, essential for AI development across IT, automotive, and healthcare.

This dataset contains metadata (title, abstract, date of publication, field, etc) for around 1 million academic articles. Each record contains additional information on the country of study and whether the article makes use of data. Machine learning tools were used to classify the country of study and data use.

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the year 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that are included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample.

The empirical approach employed in this project utilizes text mining with Natural Language Processing (NLP). The goal of NLP is to extract structured information from raw, unstructured text. In this project, NLP is used to extract the country of study and whether the paper makes use of data. We will discuss each of these in turn.

To determine the country or countries of study in each academic article, two approaches are employed based on information found in the title, abstract, or topic fields. The first approach uses regular expression searches based on the presence of ISO3166 country names. A defined set of country names is compiled, and the presence of these names is checked in the relevant fields. This approach is transparent, widely used in social science research, and easily extended to other languages. However, there is a potential for exclusion errors if a country’s name is spelled non-standardly.

The second approach is based on Named Entity Recognition (NER), which uses machine learning to identify objects from text, utilizing the spaCy Python library. The Named Entity Recognition algorithm splits text into named entities, and NER is used in this project to identify countries of study in the academic articles. SpaCy supports multiple languages and has been trained on multiple spellings of countries, overcoming some of the limitations of the regular expression approach. If a country is identified by either the regular expression search or NER, it is linked to the article. Note that one article can be linked to more than one country.

The second task is to classify whether the paper uses data. A supervised machine learning approach is employed, where 3500 publications were first randomly selected and manually labeled by human raters using the Mechanical Turk service (Paszke et al. 2019).[1] To make sure the human raters had a similar and appropriate definition of data in mind, they were given the following instructions before seeing their first paper:

Each of these documents is an academic article. The goal of this study is to measure whether a specific academic article is using data and from which country the data came.

There are two classification tasks in this exercise:

1. identifying whether an academic article is using data from any country

2. Identifying from which country that data came.

For task 1, we are looking specifically at the use of data. Data is any information that has been collected, observed, generated or created to produce research findings. As an example, a study that reports findings or analysis using a survey data, uses data. Some clues to indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.

After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.[2]

For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper.

We expect between 10 and 35 percent of all articles to use data.

The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labelled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. 900 articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

The model was able to predict whether an article made use of data with 87% accuracy evaluated on the set of articles held out of the model training. The correlation between the number of articles written about each country using data estimated under the two approaches is given in the figure below. The number of articles represents an aggregate total of

Structured data vectors utilized in machine learning algorithms.

AI in Semi-supervised Learning Market Outlook

According to our latest research, the AI in Semi-supervised Learning market size reached USD 1.82 billion in 2024 globally, driven by rapid advancements in artificial intelligence and machine learning applications across diverse industries. The market is expected to expand at a robust CAGR of 28.1% from 2025 to 2033, reaching a projected value of USD 17.17 billion by 2033. This exponential growth is primarily fueled by the increasing need for efficient data labeling, the proliferation of unstructured data, and the growing adoption of AI-driven solutions in both large enterprises and small and medium businesses. As per the latest research, the surging demand for automation, accuracy, and cost-efficiency in data processing is significantly accelerating the adoption of semi-supervised learning models worldwide.

One of the most significant growth factors for the AI in Semi-supervised Learning market is the explosive increase in data generation across industries such as healthcare, finance, retail, and automotive. Organizations are continually collecting vast amounts of structured and unstructured data, but the process of labeling this data for supervised learning remains time-consuming and expensive. Semi-supervised learning offers a compelling solution by leveraging small amounts of labeled data alongside large volumes of unlabeled data, thus reducing the dependency on extensive manual annotation. This approach not only accelerates the deployment of AI models but also enhances their accuracy and scalability, making it highly attractive for enterprises seeking to maximize the value of their data assets while minimizing operational costs.

Another critical driver propelling the growth of the AI in Semi-supervised Learning market is the increasing sophistication of AI algorithms and the integration of advanced technologies such as deep learning, natural language processing, and computer vision. These advancements have enabled semi-supervised learning models to achieve remarkable performance in complex tasks like image and speech recognition, medical diagnostics, and fraud detection. The ability to process and interpret vast datasets with minimal supervision is particularly valuable in sectors where labeled data is scarce or expensive to obtain. Furthermore, the ongoing investments in research and development by leading technology companies and academic institutions are fostering innovation, resulting in more robust and scalable semi-supervised learning frameworks that can be seamlessly integrated into enterprise workflows.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments are also contributing significantly to the expansion of the AI in Semi-supervised Learning market. Cloud-based deployment offers unparalleled scalability, flexibility, and cost-efficiency, allowing organizations of all sizes to access cutting-edge AI tools and infrastructure without the need for substantial upfront investments. This democratization of AI technology is empowering small and medium enterprises to leverage semi-supervised learning for competitive advantage, driving widespread adoption across regions and industries. Additionally, the emergence of AI-as-a-Service (AIaaS) platforms is further simplifying the integration and management of semi-supervised learning models, enabling businesses to accelerate their digital transformation initiatives and unlock new growth opportunities.

From a regional perspective, North America currently dominates the AI in Semi-supervised Learning market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI vendors, robust technological infrastructure, and high investments in AI research and development are key factors driving market growth in these regions. Asia Pacific is expected to witness the fastest CAGR during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing government initiatives to promote AI adoption. Meanwhile, Latin America and the Middle East & Africa are also showing promising growth potential, supported by rising awareness of AI benefits and growing investments in digital transformation projects across various sectors.

Component Analysis

The component segment of the AI in Semi-supervised Learning market is divided into software, hardware, and services, each playing a pivotal role in the adoption and implementation of semi-s

Telecom Data Labeling Market Outlook

According to our latest research, the global Telecom Data Labeling market size reached USD 1.42 billion in 2024, driven by the exponential growth in data generation, increasing adoption of AI and machine learning in telecom operations, and the rising complexity of communication networks. The market is forecasted to expand at a robust CAGR of 22.8% from 2025 to 2033, reaching an estimated USD 10.09 billion by 2033. This strong momentum is underpinned by the escalating demand for high-quality labeled datasets to power advanced analytics and automation in the telecom sector.

The growth trajectory of the Telecom Data Labeling market is fundamentally propelled by the surging data volumes generated by telecom networks worldwide. With the proliferation of 5G, IoT devices, and cloud-based services, telecom operators are inundated with massive streams of structured and unstructured data. Efficient data labeling is essential to transform raw data into actionable insights, fueling AI-driven solutions for network optimization, predictive maintenance, and fraud detection. Additionally, the mounting pressure on telecom companies to enhance customer experience and operational efficiency is prompting significant investments in data labeling infrastructure and services, further accelerating market expansion.

Another critical growth factor is the rapid evolution of artificial intelligence and machine learning applications within the telecommunications industry. AI-powered tools depend on vast quantities of accurately labeled data to deliver reliable predictions and automation. As telecom companies strive to automate network management, detect anomalies, and personalize user experiences, the demand for high-quality labeled datasets has surged. The emergence of advanced labeling techniques, including semi-automated and automated labeling methods, is enabling telecom enterprises to keep pace with the growing data complexity and volume, thus fostering faster and more scalable AI deployments.

Furthermore, regulatory compliance and data privacy concerns are shaping the landscape of the Telecom Data Labeling market. As governments worldwide tighten data protection regulations, telecom operators are compelled to ensure that data used for AI and analytics is accurately labeled and anonymized. This necessity is driving the adoption of robust data labeling solutions that not only facilitate compliance but also enhance data quality and integrity. The integration of secure, privacy-centric labeling platforms is becoming a competitive differentiator, especially in regions with stringent data governance frameworks. This trend is expected to persist, reinforcing the marketÂ’s upward trajectory.

AI-Powered Product Labeling is revolutionizing the telecom industry by providing more efficient and accurate data annotation processes. This technology leverages artificial intelligence to automate the labeling of large datasets, reducing the time and costs associated with manual labeling. By utilizing AI algorithms, telecom operators can ensure that their data is consistently labeled with high precision, which is crucial for training machine learning models. This advancement not only enhances the quality of labeled data but also accelerates the deployment of AI-driven solutions across various applications, such as network optimization and customer experience management. As AI-Powered Product Labeling continues to evolve, it is expected to play a pivotal role in the telecom sector's digital transformation journey, enabling operators to harness the full potential of their data assets.

From a regional perspective, Asia Pacific is emerging as a powerhouse in the Telecom Data Labeling market, fueled by rapid digitalization, expanding telecom infrastructure, and the early adoption of 5G technologies. North America remains a significant contributor, owing to its mature telecom ecosystem and high investments in AI research and development. Europe is also witnessing steady growth, driven by regulatory mandates and increasing focus on data-driven network management. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with investments in digital transformation and telecom modernization initiatives providing new growth avenues. These regional dynamics collectively underscore the global nature

Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system"

All timestamps are given in ISO 8601 format.

The following files are included:

Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv

Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.

timestamp: Date and time of the detection.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).

waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_dances.csv

Automatic detections of dance behavior during our recording period in 2019.

dancer_id: Unique ID of the individual bee.

dance_id: Unique ID of the dance.

ts_from, ts_to: Date and time of the beginning and end of the dance.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

median_x, median_y: Median position of the individual during the dance.

feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

Berlin2019_followers.csv

Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.

dance_id: Unique ID of the dance being attended or followed.

follower_id: Unique ID of the individual attending or following the dance.

ts_from, ts_to: Date and time of the beginning and end of the interaction.

label: “attendance” or “follower”

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

Berlin2019_dances_with_manually_verified_times.csv

A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).

dance_id: Unique ID of the dance.

dancer_id: Unique ID of the dancing individual.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.

Berlin2019_dance_classifier_labels.csv

Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.

timestamp: Timestamp of the individual frame the behavior was observed in.

frame_id: Unique ID of the video frame the behavior was observed in.

bee_id: Unique ID of the individual bee.

label: One of “nothing”, “waggle”, “follower”

Berlin2019_dance_classifier_unlabeled.csv

Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.

Berlin2021_waggle_phase_classifier_labels.csv

Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.

detection_id: Unique ID of the waggle phase.

label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.

orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).

metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.

Berlin2021_waggle_phase_classifier_ground_truth.zip

The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.

Berlin2019_tracks.zip

Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training. We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.

The individual files contain the following columns:

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

timestamp: Date and time of the detection.

frame_id: Unique ID of the video frame of the recording from which the detection was extracted.

track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.

bee_id: Unique ID of the individual bee.

bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.

x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.

orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_feeder_experiment_log.csv

Experiment log for our feeder experiments in 2019.

date: Date given in the format year-month-day.

feeder_cam_id: Numeric ID of the feeder.

coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.

time_opened, time_closed: Date and time when the feeder was set up or closed again. sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.

Software used to acquire and analyze the data:

bb_pipeline: Tag localization and decoding pipeline

bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline

bb_binary: Raw detection data storage format

bb_irflash: IR flash system schematics and arduino code

bb_imgacquisition: Recording and network storage

bb_behavior: Database interaction and data (pre)processing, feature extraction

bb_tracking: Tracking of bee detections over time

bb_wdd2: Automatic detection and decoding of honey bee waggle dances

bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector

bb_dance_networks: Detection of dancing and following behavior from trajectories

This dataset contains the structured data used in the systematic review titled "Machine Learning and Generative AI in Learning Analytics for Higher Education: A Systematic Review of Models, Trends, and Challenges". The dataset includes metadata extracted from 101 studies published between 2018 and 2025, covering variables such as year, country, educational context, AI models, application types, techniques, and methodological categories. It was used for descriptive, thematic, and cluster-based analyses reported in the article. The dataset is shared to support transparency, reproducibility, and further research in the field of Learning Analytics and Artificial Intelligence.

The CIFAR-100 dataset is a widely used dataset for training and evaluating machine learning models, particularly in the realm of image classification and computer vision. Here are some key aspects of the CIFAR-100 dataset:

1. Image Content:

   • The dataset consists of 60,000 color images.
   • Each image is 32x32 pixels in size.
   • The images are categorized into 100 classes, which are further grouped into 20 superclasses.

2. Class Structure:

   • Each class contains 600 images.
   • The 100 classes are grouped into 20 superclasses, where each superclass encompasses 5 classes.
   • Examples of superclasses include "aquatic mammals," "vehicles 1," and "fruit and vegetables," among others.
   • Each image has a "fine" label indicating its class and a "coarse" label indicating its superclass.

3. Data Split:

   • The dataset is divided into training and testing sets.
   • There are 50,000 training images and 10,000 testing images.
   • Each class has 500 training images and 100 testing images.

4. File Format:

   • The dataset is available in binary version, Python version, and Matlab version.
   • In the Python version, the dataset files are in a "pickled" format which can be loaded using Python's pickle module.
   • In the binary version, each image's data is preceded by its labels and followed by the pixel values of the image.

5. Dataset Usage:

   • CIFAR-100 is commonly used for training and evaluating models for image classification, object recognition, and other computer vision tasks.
   • It's also used for benchmarking different machine learning algorithms and comparing their performance on a standard dataset.

6. Dataset Origin:

   • The CIFAR-100 dataset was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
   • It's a subset of the 80 million tiny images dataset and is curated to provide a well-structured, labeled dataset for machine learning research.

7. Downloading and Citing:

   • If you intend to use the CIFAR-100 dataset for your work, it's customary to cite the dataset's creators and the associated technical report.

The CIFAR-100 dataset provides a robust, well-organized set of images for machine learning and computer vision applications, making it a valuable resource for researchers and practitioners in the field.

Relation extraction (RE) is concerned with developing methods and models that automatically detect and retrieve relational information from unstructured data. It is crucial to information extraction (IE) applications that aim to leverage the vast amount of knowledge contained in unstructured natural language text, for example, in web pages, online news, and social media; and simultaneously require the powerful and clean semantics of structured databases instead of searching, querying, and analyzing unstructured text directly. In practical applications, however, relation extraction is often characterized by limited availability of labeled data, due to the cost of annotation or scarcity of domain-specific resources. In such scenarios it is difficult to create models that perform well on the task. It therefore is desired to develop methods that learn more efficiently from limited labeled data and also exhibit better overall relation extraction performance, especially in domains with complex relational structure. In this thesis, I propose to use transfer learning to address this problem, i.e., to reuse knowledge from related tasks to improve models, in particular, their performance and efficiency to learn from limited labeled data. I show how sequential transfer learning, specifically unsupervised language model pre-training, can improve performance and sample efficiency in supervised and distantly supervised relation extraction. In the light of improved modeling abilities, I observe that better understanding neural network-based relation extraction methods is crucial to gain insights that further improve their performance. I therefore present an approach to uncover the linguistic features of the input that neural RE models encode and use for relation prediction. I further complement this with a semi-automated analysis approach focused on model errors, datasets, and annotations. It effectively highlights controversial examples in the data for manual evaluation and allows to specify error hypotheses that can be verified automatically. Together, the researched approaches allow us to build better performing, more sample efficient relation extraction models, and advance our understanding despite their complexity. Further, it facilitates more comprehensive analyses of model errors and datasets in the future.

DFT Training Data for fitting Moment Tensor Potentials for the system Mg/Al/Ca. See https://github.com/eisenforschung/mgalca-mtp-data for further notes and usage examples.

Context, Sources, and Inspirations Behind the Dataset When developing a hybrid model that combines human-like reasoning with neural network precision, the choice of dataset is crucial. The datasets used in training such a model were selected and curated based on specific goals and requirements, drawing inspiration from a variety of contexts. Below is a breakdown of the datasets, their origins, sources, and the inspirations behind selecting them:

1. Context of the Dataset Selection Objective: To create a model capable of generalizing across diverse tasks, including classification, regression, language understanding, and visual recognition. The model is designed to tackle challenges involving unseen data, complex reasoning, and multi-modal inputs. Approach: A combination of publicly available benchmark datasets and proprietary datasets from specific domains was used. The data sources aimed to provide comprehensive coverage of real-world scenarios and diverse input types to enhance the model's robustness.
2. Data Sources Public Benchmark Datasets ImageNet and COCO (Common Objects in Context):

Inspiration: Widely recognized for image classification and object detection tasks. They provide a large and varied set of labeled images, covering thousands of object categories. Source: Open datasets maintained by research communities. Usage: Used for training and testing the vision component of the hybrid model, focusing on object recognition and scene understanding. MultiWOZ (Multi-Domain Wizard-of-Oz):

Inspiration: A comprehensive dialogue dataset covering multiple domains (e.g., restaurant booking, hotel reservations). Source: Created by dialogue researchers, it provides annotated conversations mimicking real-world human interactions. Usage: Leveraged for training the language understanding and dialogue generation capabilities of the model. ConceptNet:

Inspiration: Designed to provide commonsense knowledge, helping models reason beyond factual information by understanding relationships and contexts. Source: An open-source project that aggregates data from various crowdsourced resources like Wikipedia, WordNet, and Open Mind Common Sense. Usage: Integrated into the reasoning module to improve multi-hop and commonsense reasoning. UCI Machine Learning Repository:

Inspiration: A well-known repository containing diverse datasets for various machine learning tasks, such as loan approval and medical diagnosis. Source: Academic research and publicly available datasets contributed by the research community. Usage: Used for structured data tasks, particularly in financial and healthcare analytics. B. Proprietary and Domain-Specific Datasets Healthcare Records Dataset:

Inspiration: The increasing demand for predictive analytics in healthcare motivated the use of patient records to predict health outcomes. Source: Anonymized data collected from healthcare providers, including patient demographics, medical history, and diagnostic information. Usage: Trained and tested the model's ability to handle regression tasks, such as predicting patient recovery rates and health risks. Financial Transactions and Loan Application Data:

Inspiration: To address risk analytics in financial services, loan application datasets containing applicant profiles, credit scores, and financial history were used. Source: Collaboration with financial institutions provided access to anonymized loan application data. Usage: Focused on classification tasks for loan approval predictions and credit scoring. C. Synthesized Data and Augmented Datasets Synthetic Dialogue Scenarios: Inspiration: To test the model's performance on hypothetical scenarios and rare cases not covered in standard datasets. Source: Generated using rule-based models and simulations to create additional training samples, especially for edge cases in dialogue tasks. Usage: Improved model robustness by exposing it to challenging and less common dialogue interactions. 3. Inspirations Behind the Dataset Choice Diverse Task Requirements: The hybrid model was designed to handle multiple types of tasks (classification, regression, reasoning), necessitating diverse datasets covering different input formats (images, text, structured data). Real-World Relevance: The selected datasets were inspired by real-world use cases in healthcare, finance, and customer service, reflecting common scenarios where such a hybrid model could be applied. Challenging Scenarios: To test the model's reasoning capabilities, datasets like ConceptNet and synthetic scenarios were included, inspired by the need to handle complex logical reasoning and inferencing tasks. Inclusivity and Fairness: Public datasets were chosen to ensure coverage across various demographic groups, reducing bias and improving fairness in predictions. 4. Pre-Processing and Data Preparation Standardization and Normalization: Structured data were ...

LigPCDS: Labeled Dataset of X-ray Protein Ligand 3D Images in Point Clouds and Validated Deep Learning Models

The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled images of ligands in 3D point clouds, named LigPCDS.

Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from Lig-PCDB. The data from organic molecules (ligands) was obtained from the world Protein Data Bank with resolutions ranging from 1.5 to 2.2 Å. The ligands' images were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These annotations were applied pointwise to the ligands' images using an atomic sphere model. The databases and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines.

The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% and 77.4% in terms of Intersection over Union (mIoU) metric and between 62.4% and 87.0% in F1-score (mF1).

The code used to create and validated the Lig-PCDB is available at the following repository: https://github.com/danielatrivella/np3_ligand

This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from Lig-PCDB.

License

LigPCDS by Cristina Freitas Bazzano, Luiz G. Alves,Guilherme P. Telles, Daniela B. B. Trivella is marked with CC0 1.0 Universal .

Dataset Description: Human Faces and Objects Dataset (HFO-5000) The Human Faces and Objects Dataset (HFO-5000) is a curated collection of 5,000 images, categorized into three distinct classes: male faces (1,500), female faces (1,500), and objects (2,000). This dataset is designed for machine learning and computer vision applications, including image classification, face detection, and object recognition. The dataset provides high-quality, labeled images with a structured CSV file for seamless integration into deep learning pipelines.

Column Description: The dataset is accompanied by a CSV file that contains essential metadata for each image. The CSV file includes the following columns: file_name: The name of the image file (e.g., image_001.jpg). label: The category of the image, with three possible values: "male" (for male face images) "female" (for female face images) "object" (for images of various objects) file_path: The full or relative path to the image file within the dataset directory.

Uniqueness and Key Features: 1) Balanced Distribution: The dataset maintains an even distribution of human faces (male and female) to minimize bias in classification tasks. 2) Diverse Object Selection: The object category consists of a wide variety of items, ensuring robustness in distinguishing between human and non-human entities. 3) High-Quality Images: The dataset consists of clear and well-defined images, suitable for both training and testing AI models. 4) Structured Annotations: The CSV file simplifies dataset management and integration into machine learning workflows. 5) Potential Use Cases: This dataset can be used for tasks such as gender classification, facial recognition benchmarking, human-object differentiation, and transfer learning applications.

Conclusion: The HFO-5000 dataset provides a well-structured, diverse, and high-quality set of labeled images that can be used for various computer vision tasks. Its balanced distribution of human faces and objects ensures fairness in training AI models, making it a valuable resource for researchers and developers. By offering structured metadata and a wide range of images, this dataset facilitates advancements in deep learning applications related to facial recognition and object classification.

Snapshot img { margin: 10px !important; } Generative AI In Data Labeling Solution And Services Market Size 2025-2029

The generative ai in data labeling solution and services market size is forecast to increase by USD 31.7 billion, at a CAGR of 24.2% between 2024 and 2029.

The global generative AI in data labeling solution and services market is shaped by the escalating demand for high-quality, large-scale datasets. Traditional manual data labeling methods create a significant bottleneck in the ai development lifecycle, which is addressed by the proliferation of synthetic data generation for robust model training. This strategic shift allows organizations to create limitless volumes of perfectly labeled data on demand, covering a comprehensive spectrum of scenarios. This capability is particularly transformative for generative ai in automotive applications and in the development of data labeling and annotation tools, enabling more resilient and accurate systems.However, a paramount challenge confronting the market is ensuring accuracy, quality control, and mitigation of inherent model bias. Generative models can produce plausible but incorrect labels, a phenomenon known as hallucination, which can introduce systemic errors into training datasets. This makes ai in data quality a critical concern, necessitating robust human-in-the-loop verification processes to maintain the integrity of generative ai in healthcare data. The market's long-term viability depends on developing sophisticated frameworks for bias detection and creating reliable generative artificial intelligence (AI) that can be trusted for foundational tasks.

What will be the Size of the Generative AI In Data Labeling Solution And Services Market during the forecast period?

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The global generative AI in data labeling solution and services market is witnessing a transformation driven by advancements in generative adversarial networks and diffusion models. These techniques are central to synthetic data generation, augmenting AI model training data and redefining the machine learning pipeline. This evolution supports a move toward more sophisticated data-centric AI workflows, which integrate automated data labeling with human-in-the-loop annotation for enhanced accuracy. The scope of application is broadening from simple text-based data annotation to complex image-based data annotation and audio-based data annotation, creating a demand for robust multimodal data labeling capabilities. This shift across the AI development lifecycle is significant, with projections indicating a 35% rise in the use of AI-assisted labeling for specialized computer vision systems.Building upon this foundation, the focus intensifies on annotation quality control and AI-powered quality assurance within modern data annotation platforms. Methods like zero-shot learning and few-shot learning are becoming more viable, reducing dependency on massive datasets. The process of foundation model fine-tuning is increasingly guided by reinforcement learning from human feedback, ensuring outputs align with specific operational needs. Key considerations such as model bias mitigation and data privacy compliance are being addressed through AI-assisted labeling and semi-supervised learning. This impacts diverse sectors, from medical imaging analysis and predictive maintenance models to securing network traffic patterns against cybersecurity threat signatures and improving autonomous vehicle sensors for robotics training simulation and smart city solutions.

How is this Generative AI In Data Labeling Solution And Services Market segmented?

The generative ai in data labeling solution and services market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. End-userIT dataHealthcareRetailFinancial servicesOthersTypeSemi-supervisedAutomaticManualProductImage or video basedText basedAudio basedGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaSouth KoreaJapanAustraliaIndonesiaEuropeGermanyUKFranceItalyThe NetherlandsSpainSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

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The it data segment is estimated to witness significant growth during the forecast period.

In the IT data segment, generative AI is transforming the creation of training data for software development, cybersecurity, and network management. It addresses the need for realistic, non-sensitive data at scale by producing synthetic code, structured log files, and diverse threat signatures. This is crucial for training AI-powered developer tools and intrusion detection systems. With South America representing an 8.1% market opportunity, the demand for localized and specia

The Data Annotation Service Market size was valued at USD 1.89 Billion in 2024 and is projected to reach USD 10.07 Billion by 2032, growing at a CAGR of 23% from 2026 to 2032.Global Data Annotation Service Market DriversThe data annotation service market is experiencing robust growth, propelled by the ever-increasing demand for high-quality, labeled data to train sophisticated artificial intelligence (AI) and machine learning (ML) models. As AI continues to permeate various industries, the need for accurate and diverse datasets becomes paramount, making data annotation a critical component of successful AI development. This article explores the key drivers fueling the expansion of the data annotation service market.Rising Demand for Artificial Intelligence (AI) and Machine Learning (ML) Applications: One of the most influential drivers of the data annotation service market is the surging adoption of artificial intelligence (AI) and machine learning (ML) across industries. Data annotation plays a critical role in training AI algorithms to recognize, categorize, and interpret real-world data accurately. From autonomous vehicles to medical diagnostics, annotated datasets are essential for improving model accuracy and performance. As enterprises expand their AI initiatives, they increasingly rely on professional annotation services to handle large, complex, and diverse datasets. This trend is expected to accelerate as AI continues to penetrate industries such as healthcare, finance, automotive, and retail, driving steady market growth.Expansion of Autonomous Vehicle Development: The growing focus on autonomous vehicle technology is a major catalyst for the data annotation service industry. Self-driving cars require immense volumes of labeled image and video data to identify pedestrians, road signs, vehicles, and lane markings with precision.

This dataset contains grid files for subsurface maps created in GES interpretation software and exported as Zmap formated grid files. Depth values in SSTVD (subsea true vertical depth). The methods used for analysis and a detailed discussion of the results are presented in a paper by McCleery et al., (2018).

Cytochrome P450 3A4 (CYP3A4) is one of the major drug metabolizing enzymes in the human body and metabolizes ∼30–50% of clinically used drugs. Inhibition of CYP3A4 must always be considered in the development of new drugs. Time-dependent inhibition (TDI) is an important P450 inhibition type that could cause undesired drug–drug interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient way is of great importance to any new drug discovery effort. Here, we report the development of in silico classification models for prediction of potential CYP3A4 time-dependent inhibitors. On the basis of the CYP3A4 TDI data set that we manually collected from literature and databases, both conventional machine learning and deep learning models were constructed. The comparisons of different sampling strategies, molecular representations, and machine-learning algorithms showed the benefits of a balanced data set and the deep-learning model featured by GraphConv. The generalization ability of the best model was tested by screening an external data set, and the prediction results were validated by biological experiments. In addition, several structural alerts that are relevant to CYP3A4 time-dependent inhibitors were identified via information gain and frequency analysis. We anticipate that our effort would be useful for identification of potential CYP3A4 time-dependent inhibitors in drug discovery and design.

The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.

Usage Notes

The datasets are formatted in the HDF5 format developed by the HDF5 Group. We utilized and thus recommend the usage of Python bindings pyHDF to handle the datasets.

The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets:

raw/raw-0
label/label-0
landmark/landmark-0
landmark/landmark-1
landmark/landmark-2

Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets.

Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows:

{'coordsys': 'LPS',
 'id': 1,
 'ijk_position': array([181, 188, 100]),
 'label': 'CochleaTop',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}

{'coordsys': 'LPS',
 'id': 2,
 'ijk_position': array([222, 182, 145]),
 'label': 'OvalWindow',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}

{'coordsys': 'LPS',
 'id': 3,
 'ijk_position': array([223, 209, 147]),
 'label': 'RoundWindow',
 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]),
 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}

The dataset includes 3 files, which were used for the training/ testing the machine learning model. a. Static_Model_OneDOF_Data contains the dataset for the load, stiffness, and displacement values for the single degree of freedom (SDOF) static model. b. Dynamic_Model_Data_No_Damping contains the dataset for the (SDOF) dynamic model and includes the values of circular frequency of structure (ω = 1 for mass = 1 and k = 1), displacement of the column, and acceleration of the column for the free vibration case. c. Dynamic_Model_Data_Damping contains the dataset used for the dynamic model and includes the values of circular frequency of structure (ω = 1 for mass = 1 and k = 1), displacement of the column, and acceleration of the column with a 5% damping for the free vibration case.