Guide to Medical Data Collection Key techniques, ethics, and tech advancements reshaping healthcare data management for improved care.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

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.

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.

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.

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 Speech 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

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

Discover the booming Data Collection Software market! Our analysis reveals a $15 billion market in 2025, projected to reach $45 billion by 2033, driven by cloud adoption, AI, and regulatory compliance. Explore key trends, leading companies (Logikcull, AmoCRM, Tableau, etc.), and regional growth forecasts.

MLFMF MLFMF (Machine Learning for Mathematical Formalization) is a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. With more than 250,000 entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format. In addition to benchmarking the recommendation systems, the data sets can also be used for benchmarking node classification and link prediction algorithms. The four data sets Each data set is derived from a library of formalized mathematics written in proof assistants Agda or Lean. The collection includes

the largest Lean 4 library Mathlib, the three largest Agda libraries:

the standard library the library of univalent mathematics Agda-unimath, and the TypeTopology library. Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the syntax trees give complete and easily parsed information about each entry. The Lean library data set was obtained by converting .olean files into s-expressions (see the lean2sexp tool). The Agda data sets were obtained with an s-expression extension of the official Agda repository (use either master-sexp or release-2.6.3-sexp branch). For more details, see our arXiv copy of the paper. Directory structure First, the mlfmf.zip archive needs to be unzipped. It contains a separate directory for every library (for example, the standard library of Agda can be found in the stdlib directory) and some auxiliary files. Every library directory contains

the network file from which the heterogeneous network can be loaded, a zip of the entries directory that contains (many) files with abstract syntax trees. Each of those files describes a single entry of the library. In addition to the auxiliary files which are used for loading the data (and described below), the zipped sources of lean2sexp and Agda s-expression extension are present. Loading the data In addition to the data files, there is also a simple python script main.py for loading the data. To run it, you will have to install the packages listed in the file requirements.txt: tqdm and networkx. The easiest way to do so is calling pip install -r requirements.txt. When running main.py for the first time, the script will unzip the entry files into the directory named entries. After that, the script loads the syntax trees of the entries (see the Entry class) and the network (as networkx.MultiDiGraph object). Note. The entry files have extension .dag (directed acyclic graph), since Lean uses node sharing, which breaks the tree structure (a shared node has more than one parent node). More information For more information about the data collection process, detailed data (and data format) description, and baseline experiments that were already performed with these data, see our arXiv copy of the paper. For the code that was used to perform the experiments and data format description, visit our github repository https://github.com/ul-fmf/mlfmf-data. Funding Since not all the funders are available in the Zenodo's database, we list them here:

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0024. The authors also acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and No. P1-0294.

Nexdata is equipped with professional recording equipment and has resources pool of 70+ countries and regions, and provide various types of speech recognition data collection services for Machine Learning (ML) Data.

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.

The Data Collection and Labeling market is poised for explosive growth, fundamentally driven by the escalating demand for high-quality data to train artificial intelligence (AI) and machine learning (ML) models. As industries from automotive and healthcare to retail and finance increasingly adopt AI, the need for accurately annotated datasets has become a critical bottleneck and a significant market opportunity. This market encompasses the collection of raw data and the subsequent process of adding informative labels or tags, making it understandable for machine learning algorithms. The global expansion is marked by intense innovation in automation and a burgeoning ecosystem of service providers. Regional dynamics show Asia-Pacific leading in market size, while North America remains a hub for technological advancement. The market's trajectory is directly tied to the advancement of AI, with challenges around data privacy, cost, and quality shaping its future.

Key strategic insights from our comprehensive analysis reveal:

The market is in a hyper-growth phase, with a global CAGR of over 27%, indicating a massive, industry-wide shift towards data-centric AI development. This presents a significant opportunity for first-movers and innovators to establish market dominance.
Asia-Pacific is the dominant region, acting as both a major service provider and a rapidly growing consumer of data labeling services. Its leadership is fueled by a combination of a large tech workforce, government initiatives in AI, and burgeoning technology sectors in countries like China and India.
The increasing complexity of AI models, especially in fields like autonomous driving and medical diagnostics, is driving a demand for higher-quality, more nuanced, and specialized data labeling, shifting the focus from quantity to quality and expertise.

Global Market Overview & Dynamics of Data Collection And Labeling Market Analysis The global Data Collection and Labeling market is on a trajectory of unprecedented expansion, projected to grow from $1,418.38 million in 2021 to $25,367.2 million by 2033, at a compound annual growth rate (CAGR) of 27.167%. This surge is a direct consequence of the AI revolution, where the performance of machine learning models is fundamentally dependent on the quality and volume of the training data. The market is evolving from manual, labor-intensive processes to more sophisticated, AI-assisted, and automated platforms to meet the scale and complexity required by modern applications. This shift is creating opportunities across the entire value chain, from data sourcing and annotation to quality assurance and platform development.

Global Data Collection And Labeling Market Drivers

Proliferation of AI and Machine Learning: The increasing integration of AI/ML technologies across various sectors such as automotive (autonomous vehicles), healthcare (medical imaging analysis), retail (e-commerce personalization), and finance (fraud detection) is the primary driver demanding vast quantities of labeled data.
Demand for High-Quality Training Data: The accuracy and reliability of AI models are directly correlated with the quality of the data they are trained on. This necessitates precise and contextually rich data labeling, pushing organizations to invest in professional data collection and labeling services.
Growth of Big Data and IoT: The explosion of data generated from IoT devices, social media, and other digital platforms has created a massive pool of unstructured data (images, text, videos) that requires labeling to be utilized for machine learning applications.

Global Data Collection And Labeling Market Trends

Rise of Automation and AI-assisted Labeling: To enhance efficiency and reduce costs, companies are increasingly adopting automated and semi-automated labeling tools that use AI to pre-label data, leaving human annotators to perform verification and correction tasks.
Synthetic Data Generation: The trend of generating artificial, algorithmically-created data is gaining traction. This helps overcome challenges related to data scarcity, privacy concerns, and the need to train models on rare edge cases not present in real-world datasets.
Emergence of Data-as-a-Service (DaaS) Platforms: There is a growing trend towards platforms offering pre-labeled, off-the-shelf datasets for common use cases, allowing companies to accelerate their AI development without undertaking the entire data...

Data Collection and Labeling Market size was valued at USD 18.18 Billion in 2024 and is projected to reach USD 93.37 Billion by 2032 growing at a CAGR of 25.03% from 2026 to 2032.

Key Market Drivers: • Increasing Reliance on Artificial Intelligence and Machine Learning: As AI and machine learning become more prevalent in numerous industries, the necessity for reliable data gathering and categorization grows. By 2025, the AI business is estimated to be worth $126 billion, emphasizing the significance of high-quality datasets for effective modeling. • Increasing Emphasis on Data Privacy and Compliance: With stronger requirements such as GDPR and CCPA, enterprises must prioritize data collection methods that assure privacy and compliance. The global data privacy industry is expected to grow to USD $6.7 Bbillion by 2023, highlighting the need for responsible data handling methods in labeling processes. • Emergence Of Advanced Data Annotation Tools: The emergence of enhanced data annotation tools is being driven by technological improvements, which are improving efficiency and lowering costs. Global Data Annotation tools market is expected to grow significantly, facilitating faster and more accurate labeling of data, essential for meeting the increasing demands of AI applications.

According to a survey of the deployment of artificial intelligence (AI) and machine learning (ML) models in the cloud and edge computing environment, it is found that the collection of data, as well as training and inferencing of AI/ML models take place both at the edge and in the cloud. The computing/networking devices and proximate datacenters at the edge, as well as distant datacenters and public cloud, are places where most data collection, training and inferencing take place.

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.

The MAAD dataset represents a comprehensive collection of Arabic news articles that may be employed across a diverse array of Arabic Natural Language Processing (NLP) applications, including but not limited to classification, text generation, summarization, and various other tasks. The dataset was diligently assembled through the application of specifically designed Python scripts that targeted six prominent news platforms: Al Jazeera, BBC Arabic, Youm7, Russia Today, and Al Ummah, in conjunction with regional and local media outlets, ultimately resulting in a total of 602,792 articles. This dataset exhibits a total word count of 29,371,439, with the number of unique words totaling 296,518; the average word length has been determined to be 6.36 characters, while the mean article length is calculated at 736.09 characters. This extensive dataset is categorized into ten distinct classifications: Political, Economic, Cultural, Arts, Sports, Health, Technology, Community, Incidents, and Local. The data fields are categorized into five distinct types: Title, Article, Summary, Category, and Published_ Date. The MAAD dataset is structured into six files, each named after the corresponding news outlets from which the data was sourced; within each directory, text files are provided, containing the number of categories represented in a single file, formatted in txt to accommodate all news articles. This dataset serves as an expansive standard resource designed for utilization within the context of our research endeavors.

The booming Data Annotation & Collection Services market is projected to reach $75 Billion by 2033, fueled by AI adoption in autonomous driving, healthcare, and finance. Explore market trends, key players (Appen, Amazon, Google), and regional growth in this comprehensive analysis.

Data size: 10,000 Hours，including high quality data 1000h (Chinese and English)

Data format: The video format is commonly used formats such as MP4, and the annotation format is json

Data type: human video

Data Format: The image data format is commonly used formats such as. jpg, the video format is commonly used formats such as MP4, and the annotation format is json

\(\color{#9911ff}{\mathcal{CONTEXT}}\) This data collection was created for exercises in Machine Learning. Images are generated completely artificially using the math parametric functions with three random coefficients. One of them (an integer number) became the "label" for classification, the other two (real numbers) - the "targets" for regression analysis. Different random colors are planned to be a "noise" for predictions. Of course, the data is free for noncommercial and nongovernmental goals.

\(\color{#9911ff}{\mathcal{CONTENT}}\) The process of data building - Synthetic Data 3. All images, labels, and targets are numeric arrays with the same data types and shapes. They are collected here in .h5 files. In every file: - images (float32 => 288x288 pixels, 3 color channels); - labels (int32 => 7 classes); - targets (float32 => 2 coefficients).

\(\color{#9911ff}{\mathcal{ACKNOWLEDGMENTS}}\) Thanks for your attention.

\(\color{#9911ff}{\mathcal{INSPIRATION}}\) Discovering the capabilities of algorithms in the recognition of absolutely synthetic data.

Field Data Collection App Market Outlook

According to our latest research, the global field data collection app market size reached USD 1.98 billion in 2024, reflecting robust digital transformation across sectors. The market is growing at a compelling CAGR of 14.2% and is forecasted to attain USD 5.28 billion by 2033. This impressive growth trajectory is primarily driven by the increasing need for real-time data capture, enhanced operational efficiency, and the proliferation of mobile devices across industries. As per our 2025 insights, organizations are rapidly adopting field data collection apps to streamline workflows, integrate with cloud infrastructure, and support decision-making with accurate, timely information.

One of the primary growth factors propelling the field data collection app market is the rising emphasis on digital transformation and automation in both public and private sectors. Organizations are increasingly shifting from traditional paper-based methods to digital solutions that enable faster, more accurate, and cost-effective data collection in the field. The ability to capture, validate, and transmit data instantly from remote locations is significantly reducing manual errors and administrative overhead. Moreover, industries such as agriculture, utilities, and construction are leveraging these apps to monitor assets, track resources, and ensure compliance with regulatory standards. The integration of GPS, photo capture, and offline functionality further enhances the utility of these applications, making them indispensable tools in modern field operations.

Another significant driver is the evolution of cloud computing and the widespread availability of affordable mobile devices. Cloud-based deployment models are enabling organizations to centralize data management, facilitate real-time collaboration, and ensure seamless access to critical information regardless of geographical constraints. The scalability and flexibility offered by cloud infrastructure are particularly attractive to small and medium enterprises (SMEs), which can now leverage enterprise-grade solutions without incurring prohibitive costs. Additionally, advancements in mobile technology, including improved battery life, ruggedized devices, and enhanced connectivity, are fostering the adoption of field data collection apps across diverse environments, from remote agricultural fields to urban infrastructure projects.

Data-driven decision-making is also fueling the expansion of the field data collection app market. As organizations recognize the value of actionable insights derived from field data, there is a growing demand for advanced analytics, reporting, and integration capabilities within these applications. The ability to visualize trends, identify anomalies, and generate comprehensive reports in real time is empowering managers to make informed decisions, optimize resource allocation, and improve service delivery. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) into field data collection platforms is enhancing predictive capabilities, automating routine tasks, and enabling proactive maintenance and risk management.

From a regional perspective, North America continues to dominate the field data collection app market, accounting for the largest share due to early technology adoption, significant investments in digital infrastructure, and stringent regulatory requirements. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, expanding industrial sectors, and government initiatives promoting digital transformation. Europe is also witnessing substantial growth, particularly in sectors such as utilities, construction, and environmental monitoring. Latin America and the Middle East & Africa are gradually catching up, supported by increasing mobile penetration and the need to modernize legacy systems.

Component Analysis

The field data collection app market is segmented by component into software and services, each playing a critical role in the ecosystem. Software solutions form the backbone of the market, providing the core functionalities for data capture, validation, storage, and analysis. These applications are designed to be user-friendly, customizable, and compatible with a wide range of devices, ensuring seamless adoption across different industries. The evolution of software platforms has led to the integration of advanced features such as geotagging