The Data Collection and Labeling market is experiencing robust growth, projected to reach $3108 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.5% from 2025 to 2033. This surge is driven by the escalating demand for high-quality data to fuel the advancements in artificial intelligence (AI), machine learning (ML), and deep learning applications across diverse sectors. The increasing adoption of AI and ML across industries like IT, BFSI (Banking, Financial Services, and Insurance), healthcare, and automotive is a major catalyst. Furthermore, the growing complexity of AI models necessitates larger and more diverse datasets, further fueling market expansion. The market is segmented by application (IT, Government, Automotive, BFSI, Healthcare, Retail & E-commerce, Others) and by data type (Text, Image/Video, Audio), each segment contributing to the overall market growth, with image/video data likely holding the largest share due to the increasing popularity of computer vision applications. Competitive pressures among market players like Reality AI, Scale AI, and Labelbox are driving innovation in data collection and annotation techniques, leading to improved efficiency and accuracy. The market's expansion, however, faces certain restraints. High costs associated with data collection and labeling, especially for complex datasets, can pose a challenge for smaller companies. Ensuring data privacy and security is another critical concern, especially with the rising regulations around data protection. Despite these challenges, the long-term prospects for the data collection and labeling market remain exceptionally positive. The continued development and adoption of AI across numerous sectors will drive sustained demand for high-quality, labeled data, leading to significant market growth in the coming years. Geographic expansion, particularly in emerging markets in Asia-Pacific and other regions, presents significant opportunities for market players. Strategic partnerships and technological advancements in automated data labeling tools will further contribute to the market's future trajectory.

We offer comprehensive data collection services that cater to a wide range of industries and applications. Whether you require image, audio, or text data, we have the expertise and resources to collect and deliver high-quality data that meets your specific requirements. Our data collection methods include manual collection, web scraping, and other automated techniques that ensure accuracy and completeness of data.

Our team of experienced data collectors and quality assurance professionals ensure that the data is collected and processed according to the highest standards of quality. We also take great care to ensure that the data we collect is relevant and applicable to your use case. This means that you can rely on us to provide you with clean and useful data that can be used to train machine learning models, improve business processes, or conduct research.

We are committed to delivering data in the format that you require. Whether you need raw data or a processed dataset, we can deliver the data in your preferred format, including CSV, JSON, or XML. We understand that every project is unique, and we work closely with our clients to ensure that we deliver the data that meets their specific needs. So if you need reliable data collection services for your next project, look no further than us.

This dataset consists of imagery, imagery footprints, associated ice seal detections and homography files associated with the KAMERA Test Flights conducted in 2019. This dataset was subset to include relevant data for detection algorithm development. This dataset is limited to data collected during flights 4, 5, 6 and 7 from our 2019 surveys.

The Data Collection and Labeling market is experiencing robust growth, driven by the increasing demand for high-quality training data to fuel the advancements in artificial intelligence (AI) and machine learning (ML) technologies. The market's expansion is fueled by the burgeoning adoption of AI across diverse sectors, including healthcare, automotive, finance, and retail. Companies are increasingly recognizing the critical role of accurate and well-labeled data in developing effective AI models. This has led to a surge in outsourcing data collection and labeling tasks to specialized companies, contributing to the market's expansion. The market is segmented by data type (image, text, audio, video), labeling technique (supervised, unsupervised, semi-supervised), and industry vertical. We project a steady CAGR of 20% for the period 2025-2033, reflecting continued strong demand across various applications. Key trends include the increasing use of automation and AI-powered tools to streamline the data labeling process, resulting in higher efficiency and lower costs. The growing demand for synthetic data generation is also emerging as a significant trend, alleviating concerns about data privacy and scarcity. However, challenges remain, including data bias, ensuring data quality, and the high cost associated with manual labeling for complex datasets. These restraints are being addressed through technological innovations and improvements in data management practices. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like Scale AI, Appen, and others are leading the market, offering comprehensive solutions that span data collection, annotation, and model validation. The presence of numerous companies suggests a fragmented yet dynamic market, with ongoing competition driving innovation and service enhancements. The geographical distribution of the market is expected to be broad, with North America and Europe currently holding significant market share, followed by Asia-Pacific showing robust growth potential. Future growth will depend on technological advancements, increasing investment in AI, and the emergence of new applications that rely on high-quality data.

2024.

Data Collection Software Market Outlook

The global data collection software market size is anticipated to significantly expand from USD 1.8 billion in 2023 to USD 4.2 billion by 2032, exhibiting a CAGR of 10.1% during the forecast period. This remarkable growth is fueled by the increasing demand for data-driven decision-making solutions across various industries. As organizations continue to recognize the strategic value of harnessing vast amounts of data, the need for sophisticated data collection tools becomes more pressing. The growing integration of artificial intelligence and machine learning within software solutions is also a critical factor propelling the market forward, enabling more accurate and real-time data insights.

One major growth factor for the data collection software market is the rising importance of real-time analytics. In an era where time-sensitive decisions can define business success, the capability to gather and analyze data in real-time is invaluable. This trend is particularly evident in sectors like healthcare, where prompt data collection can impact patient care, and in retail, where immediate insights into consumer behavior can enhance customer experience and drive sales. Additionally, the proliferation of the Internet of Things (IoT) has further accelerated the demand for data collection software, as connected devices produce a continuous stream of data that organizations must manage efficiently.

The digital transformation sweeping across industries is another crucial driver of market growth. As businesses endeavor to modernize their operations and customer interactions, there is a heightened demand for robust data collection solutions that can seamlessly integrate with existing systems and infrastructure. Companies are increasingly investing in cloud-based data collection software to improve scalability, flexibility, and accessibility. This shift towards cloud solutions is not only enabling organizations to reduce IT costs but also to enhance collaboration by making data more readily available across different departments and geographies.

The intensified focus on regulatory compliance and data protection is also shaping the data collection software market. With the introduction of stringent data privacy regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt data collection practices that ensure compliance and protect customer information. This necessitates the use of sophisticated software capable of managing data responsibly and transparently, thereby fueling market growth. Moreover, the increasing awareness among businesses about the potential financial and reputational risks associated with data breaches is prompting the adoption of secure data collection solutions.

Component Analysis

The data collection software market can be segmented into software and services, each playing a pivotal role in the ecosystem. The software component remains the bedrock of this market, providing the essential tools and platforms that enable organizations to collect, store, and analyze data effectively. The software solutions offered vary in complexity and functionality, catering to different organizational needs ranging from basic data entry applications to advanced analytics platforms that incorporate AI and machine learning capabilities. The demand for such sophisticated solutions is on the rise as organizations seek to harness data not just for operational purposes but for strategic insights as well.

The services segment encompasses various offerings that support the deployment and optimization of data collection software. These services include consulting, implementation, training, and maintenance, all crucial for ensuring that the software operates efficiently and meets the evolving needs of the user. As the market evolves, there is an increasing emphasis on offering customized services that address specific industry requirements, thereby enhancing the overall value proposition for clients. The services segment is expected to grow steadily as businesses continue to seek external expertise to complement their internal capabilities, particularly in areas such as data analytics and cybersecurity.

Integration services have become particularly important as organizations strive to create seamless workflows that incorporate new data collection solutions with existing IT infrastructure. This need for integration is driven by the growing complexity of enterprise IT environments, where disparate systems and applications must wo

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

As the AI training dataset market continues to evolve, the role of Perception Dataset Management Platforms is becoming increasingly crucial. These platforms are designed to handle the complexities of managing large-scale datasets, ensuring that data is not only collected and stored efficiently but also annotated and curated to meet the specific needs of AI models. By providing tools for data organization, quality control, and collaboration, these platforms enable organizations to streamline their data management processes and enhance the overall quality of their AI training datasets. This is particularly important as the demand for diverse and high-quality datasets grows, driven by the expanding scope of AI applications across various industries.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological

Imagery and Footage Data Collection | Annotation & Labelling services for Artificial Intelligence, Machine Learning and Computer Vision projects at any scale.

The global data collection and labeling market is experiencing robust growth, driven by the escalating demand for high-quality training data to fuel the advancements in artificial intelligence (AI) and machine learning (ML). This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an impressive $70 billion by 2033. This significant expansion is fueled by several key factors. The increasing adoption of AI across diverse sectors, including IT, automotive, BFSI (Banking, Financial Services, and Insurance), healthcare, and retail and e-commerce, is a primary driver. Furthermore, the growing complexity of AI models necessitates larger and more diverse datasets, thereby increasing the demand for professional data labeling services. The emergence of innovative data annotation tools and techniques further contributes to market growth. However, challenges remain, including the high cost of data collection and labeling, data privacy concerns, and the need for skilled professionals capable of handling diverse data types. The market segmentation highlights the significant contributions from various sectors. The IT sector leads in adoption, followed closely by the automotive and BFSI sectors. Healthcare and retail/e-commerce are also exhibiting rapid growth due to the increasing reliance on AI-powered solutions for improved diagnostics, personalized medicine, and enhanced customer experiences. Geographically, North America currently holds a substantial market share, followed by Europe and Asia Pacific. However, the Asia Pacific region is poised for the fastest growth due to its large and rapidly developing digital economy and increasing government initiatives promoting AI adoption. Key players like Reality AI, Scale AI, and Labelbox are shaping the market landscape through continuous innovation and strategic acquisitions. The market's future trajectory will be significantly influenced by advancements in automation technologies, improvements in data annotation methodologies, and the growing awareness of the importance of high-quality data for successful AI deployments.

This database was firstly created for the scientific article entitled: "Reviewing Machine Learning of corrosion prediction: a data-oriented perspective"

L.B. Coelho 1 , D. Zhang 2 , Y.V. Ingelgem 1 , D. Steckelmacher 3 , A. Nowé 3 , H.A. Terryn 1

1 Department of Materials and Chemistry, Research Group Electrochemical and Surface Engineering, Vrije Universiteit Brussel, Brussels, Belgium 2 A Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China 3 VUB Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium

Different metrics are possible to evaluate the prediction accuracy of regression models. However, only papers providing relative metrics (MAPE, R²) were included in this database. We tried as much as possible to include descriptors of all major ML procedure steps, including data collection (“Data acquisition”), data cleaning feature engineering (“Feature reduction”), model validation (“Train-Test split”*), etc.

*the total dataset is typically split into training sets and testing (unknown data) sets for performance evaluation of the model. Nonetheless, sometimes only the training or the testing performances were reported (“?” marks were added in the respective evaluation metric field(s)). The “Average R²” was sometimes considered for studies employing “CV” (cross-validation) on the dataset. For a detailed description of the ML basic procedures, the reader could refer to the References topic in the Review article.

This dataset contains key characteristics about the data described in the Data Descriptor A cone-beam X-ray computed tomography data collection designed for machine learning. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON formatVersioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

As per Cognitive Market Research's latest published report, the Global Machine Learning market size was USD 24,345.76 million in 2021 and it is forecasted to reach USD 206,235.41 million by 2028. Machine Learning Industry's Compound Annual Growth Rate will be 42.64% from 2023 to 2030. Market Dynamics of Machine Learning Market

Key Drivers for Machine Learning Market

Explosion of Big Data Across Industries: The substantial increase in both structured and unstructured data generated by sensors, social media, transactions, and IoT devices is driving the demand for machine learning-based data analysis.

Widespread Adoption of AI in Business Processes: Machine learning is facilitating automation, predictive analytics, and optimization in various sectors such as healthcare, finance, manufacturing, and retail, thereby enhancing efficiency and outcomes.

Increased Availability of Open-Source Frameworks and Cloud Platforms: Resources like TensorFlow, PyTorch, and scalable cloud infrastructure are simplifying the process for developers and enterprises to create and implement machine learning models.

Growing Investments in AI-Driven Innovation: Governments, venture capitalists, and major technology companies are making substantial investments in machine learning research and startups, which is accelerating progress and market entry.

Key Restraints for Machine Learning Market

Shortage of Skilled Talent in ML and AI: The need for data scientists, machine learning engineers, and domain specialists significantly surpasses the available supply, hindering scalability and implementation in numerous organizations.

High Computational and Operational Costs: The training of intricate machine learning models necessitates considerable computing power, energy, and infrastructure, resulting in high costs for startups and smaller enterprises.

Data Privacy and Regulatory Compliance Challenges: Issues related to user privacy, data breaches, and adherence to regulations such as GDPR and HIPAA present obstacles in the collection and utilization of data for machine learning.

Lack of Model Transparency and Explainability: The opaque nature of certain machine learning models undermines trust, particularly in sensitive areas like finance and healthcare, where the need for explainable AI is paramount.

Key Trends for Machine Learning Market

Growth of AutoML and No-Code ML Platforms: Automated machine learning tools are making AI development more accessible, enabling individuals without extensive coding or mathematical expertise to construct models.

Integration of ML with Edge Computing: Executing machine learning models locally on edge devices (such as cameras and smartphones) is enhancing real-time performance and minimizing latency in applications.

Ethical AI and Responsible Machine Learning Practices: Increasing emphasis on fairness, bias reduction, and accountability is shaping ethical frameworks and governance in ML adoption.

Industry-Specific ML Applications on the Rise: Custom ML solutions are rapidly emerging in sectors like agriculture (crop prediction), logistics (route optimization), and education (personalized learning).

COVID-19 Impact:

Similar to other industries, the covid-19 situation has affected the machine learning industry. Despite the dire conditions and uncertain collapse, some industries have continued to grow during the pandemic. During covid 19, the machine learning market remains stable with positive growth and opportunities. The global machine learning market faces minimal impact compared to some other industries.The growth of the global machine learning market has stagnated owing to automation developments and technological advancements. Pre-owned machines and smartphones widely used for remote work are leading to positive growth of the market. Several industries have transplanted the market progress using new technologies of machine learning systems. June 2020, DeCaprio et al. Published COVID-19 pandemic risk research is still in its early stages. In the report, DeCaprio et al. mentions that it has used machine learning to build an initial vulnerability index for the coronavirus. The lab further noted that as more data and results from ongoing research become available, it will be able to see more practical applications of machine learning in predicting infection risk. What is&nbs...

The Data Collection System (DCS) market is experiencing robust growth, driven by the increasing need for efficient data management across diverse industries. The market's expansion is fueled by several key factors, including the proliferation of connected devices generating massive datasets, the rising adoption of cloud-based solutions for data storage and analysis, and the growing demand for real-time data insights for improved decision-making. Businesses across sectors, from manufacturing and healthcare to finance and retail, are increasingly relying on DCS to optimize operations, enhance customer experiences, and gain a competitive edge. The market is segmented by various deployment models (cloud, on-premise, hybrid), data types (structured, unstructured), and industry verticals. While the specific market size and CAGR aren't provided, a reasonable estimation based on similar technology markets suggests a 2025 market size of approximately $15 billion, growing at a CAGR of 12% from 2025 to 2033. This growth is expected to be propelled by advancements in Artificial Intelligence (AI) and Machine Learning (ML) integration within DCS, enabling more sophisticated data analysis and predictive capabilities. However, challenges such as data security concerns, the complexity of integrating various data sources, and the need for skilled professionals to manage and interpret the collected data may somewhat restrain market growth. Competition within the DCS market is intense, with established players like Zapier, Formstack, and AnswerRocket alongside emerging specialized providers vying for market share. The success of individual companies hinges on their ability to offer robust, scalable solutions that address the unique needs of their target industries. Future growth will likely be driven by the development of more user-friendly interfaces, improved data integration capabilities, and enhanced data visualization tools. Furthermore, the increasing focus on data privacy and compliance regulations will necessitate the development of secure and compliant DCS solutions. This market offers significant opportunities for both established and emerging companies, but success requires a strategic focus on innovation, security, and customer needs.

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

This upload contains samples 17 - 24 from the data collection described in

Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019)

Abstract:
"Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."

The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.

Please refer to the paper for all further technical details.

The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42

The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github

For more information or guidance in using these dataset, please get in touch with

• henri.dersarkissian [at] gmail.com
• Felix.Lucka [at] cwi.nl

1. Overview With extensive experience in speech recognition, Nexdata has resource pool covering more than 50 countries and regions. Our linguist team works closely with clients to assist them with dictionary and text corpus construction, speech quality inspection, linguistics consulting and etc.

2. Our Capacity -Global Resources: Global resources covering hundreds of languages worldwide

-Compliance: All the Machine Learning (ML) Data are collected with proper authorization -Quality: Multiple rounds of quality inspections ensures high quality data output

-Secure Implementation: NDA is signed to gurantee secure implementation and Machine Learning (ML) Data is destroyed upon delivery.

1. About Nexdata Nexdata is equipped with professional Machine Learning (ML) Data collection devices, tools and environments, as well as experienced project managers in data collection and quality control, so that we can meet the data collection requirements in various scenarios and types. Please visit us at https://www.nexdata.ai/service/speech-recognition?source=Datarade

The Data Annotation and Collection Services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse sectors. The market, estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $45 billion by 2033. This significant expansion is fueled by several key factors. The surge in autonomous driving initiatives necessitates high-quality data annotation for training self-driving systems, while the burgeoning smart healthcare sector relies heavily on annotated medical images and data for accurate diagnoses and treatment planning. Similarly, the growth of smart security systems and financial risk control applications demands precise data annotation for improved accuracy and efficiency. Image annotation currently dominates the market, followed by text annotation, reflecting the widespread use of computer vision and natural language processing. However, video and voice annotation segments are showing rapid growth, driven by advancements in AI-powered video analytics and voice recognition technologies. Competition is intense, with both established technology giants like Alibaba Cloud and Baidu, and specialized data annotation companies like Appen and Scale Labs vying for market share. Geographic distribution shows a strong concentration in North America and Europe initially, but Asia-Pacific is expected to emerge as a major growth region in the coming years, driven primarily by China and India's expanding technology sectors. The market, however, faces certain challenges. The high cost of data annotation, particularly for complex tasks such as video annotation, can pose a barrier to entry for smaller companies. Ensuring data quality and accuracy remains a significant concern, requiring robust quality control mechanisms. Furthermore, ethical considerations surrounding data privacy and bias in algorithms require careful attention. To overcome these challenges, companies are investing in automation tools and techniques like synthetic data generation, alongside developing more sophisticated quality control measures. The future of the Data Annotation and Collection Services market will likely be shaped by advancements in AI and ML technologies, the increasing availability of diverse data sets, and the growing awareness of ethical considerations surrounding data usage.

Data Collection and Labeling Market Outlook 2032

The global data collection and labeling market size was USD 27.1 Billion in 2023 and is likely to reach USD 133.3 Billion by 2032, expanding at a CAGR of 22.4 % during 2024–2032. The market growth is attributed to the increasing demand for high-quality labeled datasets to train artificial intelligence and machine learning algorithms across various industries.



Growing adoption of AI in e-commerce is projected to drive the market in the assessment year. E-commerce platforms rely on high-quality images to showcase products effectively and improve the online shopping experience for customers. Accurately labeled images enable better product categorization and search optimization, driving higher conversion rates and customer engagement.



Rising adoption of AI in the financial sector is a significant factor boosting the need for data collection and labeling services for tasks such as fraud detection, risk assessment, and algorithmic trading. Financial institutions leverage labeled datasets to train AI models to analyze vast amounts of transactional data, identify patterns, and detect anomalies indicative of fraudulent activity.

Impact of Artificial Intelligence (AI) in Data Collection and Labeling Market

The use of artificial intelligence is revolutionizing the way labeled datasets are created and utilized. With the advancements in AI technologies, such as computer vision and natural language processing, the demand for accurately labeled datasets has surged across various industries.



AI algorithms are increasingly being leveraged to automate and streamline the data labeling process, reducing the manual effort required and improving efficiency. For instance,

• 
  

  In April 2022, Encord, a startup, introduced its beta version of CordVision, an AI-assisted labeling application that inten

Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

The following data and source files are included.

review-protocol.md: The protocol employed in this tertiary study

data/

dl-search/

input/

acm_comput_surveys_overviews.bib: Surveys of ACM Computing Surveys journal

acm_comput_surveys_overviews_titles.txt: Titles of surveys

acm_comput_ml_surveys.bib: Machine learning (ML)-related surveys of ACM Computing Surveys journal

acm_comput_ml_surveys_titles.txt: Titles of ML-related surveys

dl_search_queries.txt: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus

ml_keywords.txt: ML-related keywords extracted from ML-related survey titles and used in the search queries

se_keywords.txt: Software Engineering (SE)-related keywords derived from the 15 SWEBOK Knowledge Areas (KAs—except for Computing Foundations, Mathematical Foundations, and Engineering Foundations) and used in the search queries

secondary_studies_keywords.txt: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham et al. (2010), and the survey titles, and used in the search queries

output/

acm/

acm{1–9}.bib: Search results from ACM Digital Library

ieee.csv: Search results from IEEE Xplore

scopus_analyze_year.csv: Yearly distribution of ML and SE documents extracted from Scopus's Analyze search results page

scopus.csv: Search results from Scopus

study-selection/

backward_snowballing.csv: Additional secondary studies found through the backward snowballing process

backward_snowballing_references.csv: References of quality-accepted secondary studies

cohen_kappa_agreement.csv: Inter-rater reliability of reviewers in study selection

dl_search_results.csv: Aggregated search results of all three digital libraries

forward_snowballing_reviewer_{1,2}.csv: Divided forward snowballing citations of quality-accepted studies assessed by reviewer 1 and 2, correspondingly, based on IC/EC

study_selection_reviewer_{1,2}.csv: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC

quality-assessment/

dare_assessment.csv: Quality assessment (QA) of selected secondary studies based on the Database of Abstracts of Reviews of Effects (DARE) criteria by York University, Centre for Reviews and Dissemination

quality_accepted_studies.csv: Details of quality-accepted studies

studies_for_review.bib: Bibliography details and QA scores of selected secondary studies

data-extraction/

further_research.csv: Recommendations for further research of quality-accepted studies

further_research_general.csv: The complete list of associated studies for each general recommendation

knowledge_areas.csv: Classification of quality-accepted studies using the SWEBOK KAs and subareas

ml_techniques.csv: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies

primary_studies.csv: Details of reviewed primary studies by the quality-accepted secondary

research_methods.csv: Citations of the research methods employed by the quality-accepted studies

research_types_methods.csv: Research types and methods employed by the quality-accepted studies

src/

data-analysis.ipynb: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers, QA scores, and SWEBOK KAs) and creation of all figures included in the study

scopus-year-analysis.ipynb: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus

study-selection-preprocessing.ipynb: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process

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