Data Annotation Tools Market Outlook

According to our latest research, the global Data Annotation Tools market size reached USD 2.1 billion in 2024. The market is set to expand at a robust CAGR of 26.7% from 2025 to 2033, projecting a remarkable value of USD 18.1 billion by 2033. The primary growth driver for this market is the escalating adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which necessitates high-quality labeled data for model training and validation.

One of the most significant growth factors propelling the data annotation tools market is the exponential rise in AI-powered applications across sectors such as healthcare, automotive, retail, and BFSI. As organizations increasingly integrate AI and ML into their core operations, the demand for accurately annotated data has surged. Data annotation tools play a crucial role in transforming raw, unstructured data into structured, labeled datasets that can be efficiently used to train sophisticated algorithms. The proliferation of deep learning and natural language processing technologies further amplifies the need for comprehensive data labeling solutions. This trend is particularly evident in industries like healthcare, where annotated medical images are vital for diagnostic algorithms, and in automotive, where labeled sensor data supports the evolution of autonomous vehicles.

Another prominent driver is the shift toward automation and digital transformation, which has accelerated the deployment of data annotation tools. Enterprises are increasingly adopting automated and semi-automated annotation platforms to enhance productivity, reduce manual errors, and streamline the data preparation process. The emergence of cloud-based annotation solutions has also contributed to market growth by enabling remote collaboration, scalability, and integration with advanced AI development pipelines. Furthermore, the growing complexity and variety of data types, including text, audio, image, and video, necessitate versatile annotation tools capable of handling multimodal datasets, thus broadening the market's scope and applications.

The market is also benefiting from a surge in government and private investments aimed at fostering AI innovation and digital infrastructure. Several governments across North America, Europe, and Asia Pacific have launched initiatives and funding programs to support AI research and development, including the creation of high-quality, annotated datasets. These efforts are complemented by strategic partnerships between technology vendors, research institutions, and enterprises, which are collectively advancing the capabilities of data annotation tools. As regulatory standards for data privacy and security become more stringent, there is an increasing emphasis on secure, compliant annotation solutions, further driving innovation and market demand.

From a regional perspective, North America currently dominates the data annotation tools market, driven by the presence of major technology companies, well-established AI research ecosystems, and significant investments in digital transformation. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid industrialization, expanding IT infrastructure, and a burgeoning startup ecosystem focused on AI and data science. Europe also holds a substantial market share, supported by robust regulatory frameworks and active participation in AI research. Latin America and the Middle East & Africa are gradually catching up, with increasing adoption in sectors such as retail, automotive, and government. The global landscape is characterized by dynamic regional trends, with each market contributing uniquely to the overall growth trajectory.

Component Analysis

The data annotation tools market is segmented by component into software and services, each playing a pivotal role in the market's overall ecosystem. Software solutions form the backbone of the market, providing the technical infrastructure for auto

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

Data cleaning : annotated buccinids in background removed

Please, use version 4 of this dataset where deeptools package has been updated.

AI-Powered Medical Imaging Annotation Market Outlook

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

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

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

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

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

Component Analysis

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

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

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

The AI training services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across diverse industries. The market's expansion is fueled by several key factors. Firstly, the rising demand for high-quality, labeled data to train sophisticated AI models is pushing organizations to leverage specialized training services. Secondly, the complexity of developing and deploying AI solutions is leading businesses to outsource training tasks to experts, reducing internal resource burdens and accelerating time-to-market. Thirdly, advancements in cloud computing and the accessibility of powerful AI tools are making AI training services more affordable and accessible to a wider range of businesses, from startups to large enterprises. While the market faces some challenges, such as the need for skilled data scientists and the potential for data bias, the overall trajectory remains strongly positive. We project a substantial market expansion over the next decade, driven by continuous technological innovation and the growing adoption of AI across various sectors like healthcare, finance, and manufacturing. The competitive landscape is dynamic, with established technology giants like Google, Microsoft, and AWS competing with specialized AI training service providers like Clarifai, DataRobot, and OpenAI. The market is witnessing increased consolidation, with mergers and acquisitions becoming increasingly common as larger players aim to expand their market share and service offerings. Future growth will be shaped by factors like the emergence of new AI training techniques (e.g., federated learning), the development of more efficient and scalable training platforms, and the increasing focus on ethical considerations in AI development. Regional variations in market growth are expected, with North America and Europe likely to maintain strong leadership due to high technological maturity and early adoption of AI. However, Asia-Pacific is poised for significant growth in the coming years, fueled by increasing investments in AI and a burgeoning digital economy.

Social scientists working in rule-bound and evidence-based traditions need to show how they know what they know. The less visible the process that produced a conclusion, the less one can see of the conclusion. A sufficiently diminished view of that process undermines the claim.

What an author needs to do to fulfill this transparency obligation differs depending on the nature of the work, the data that were used, and the analyses that were undertaken. For a scholar arriving at a conclusion using a statistical software package to analyze a quantitative dataset, making the claim transparent would include providing the dataset and software commands.

Research transparency is a much newer proposition for qualitative social science, especially where “granular” data are generated from individual sources, and the data are analyzed individually or in small groups.
Because the data are not used holistically as a dataset, however, new ways have to be developed to associate the claims with the granular data and their analysis.

The Qualitative Data Repository has been working on annotation for transparent inference (ATI) for some time, and has made considerable progress, particularly in specifying what information needs to be surfaced for readers to be able to understand and evaluate published claims. With these requirements in mind, this paper will develop a list of functional specifications and a set of criteria for choosing an annotation standard to use as the basis for ATI.

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

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

AI Data Management Market Drivers

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

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

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

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

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

Access to Data ChIP-chip has become a popular technique to identify genome-wide in vivo protein-DNA interactions. With genome tiling microarrays commercially available from Affymetrix, Nimblegen and Agilent, more and more academic laboratories are adopting this technology to detect cis-regulatory elements in mammalian genomes. Despite the importance of ChIP-chip, there is a shortage of web servers developed for integrating the necessary downstream analysis functions with the capability of processing genome-scale ChIP-regions. So far all the big ChIP-chip papers in mammalian systems are published as a direct result of powerful bioinformatics support (e.g. Rick Young with David Gifford, Mike Snyder with Mark Gerstein, Kevin Struhl with Tom Gingeras, and Myles Brown with X. Shirley Liu), which is something not available for smaller labs. Cis-regulatory Element Annotation System (CEAS) integrates many useful tools to simplify ChIP-chip analysis for biologists. It can handle hundreds or thousands of regions from high throughput ChIP-chip experiments. Given genome-scale ChIP-regions in UCSC genome browser .bed file format, our CEAS server retrieves information from different sources to help with downstream analysis. Specifically, it provides the following information: 1. Fully repeat-masked genome DNA sequence for the ChIP-regions for qPCR validation and transcription factor motif finding. Current UCSC genome browser does not remove segmental duplication and simple repeats in its DNA retrieval function, which could create complications for qPCR primer design and sequence motif finding. 2. GC content and evolutionary conservation of each ChIP-region and their average. CEAS uses PhastCons conservation scores from UCSC Genome Bioinformatics, which is based on multiz alignment of human, chimp, mouse, rat, dog, chicken, fugu, and zebrafish genomic DNA. CEAS generates thumbnail conservation plot for each ChIP-region and the average conservation plot for all the ChIP-regions, which can be directly used in ChIP-chip biologists' manuscript. 3. ChIP-region nearby gene mapping. CEAS examines both upstream and downstream sequences on both strands to map the nearest RefSeq and miRNA gene up to 300KB away. In each direction, CEAS reports the distance between a ChIP-region and its nearest gene. When a ChIP-region is within a gene, CEAS reports whether the ChIP-region is mapped to 5'UTR, 3'UTR, coding exon, or intron. CEAS also provides a summary statistics for the location of all the ChIP-regions based on this gene mapping. 4. Transcription factor motif finding on the fully repeat-masked ChIP sequences. CEAS finds enriched TRANSFAC and JASPAR motifs in the ChIP-regions that are the putative binding motifs for the transcription factor of interest (against which ChIP-chip is conduced) and its cooperative binding partners. CEAS provides sequence logo, motif enrichment fold change and p-value for each enriched motif, and combine redundant enriched motifs. CEAS pre-computes all the motif occurrence information to store in the database, whereas current TRANSFAC motif-matching programs could not handle thousands of input sequences. In summary, CEAS retrieves useful information (e.g. sequence retrieval) for the validation of ChIP-chip experiments, assembles important knowledge (e.g. conservation plot, nearby gene mapping, and motif logos) to be included in biologists' publication, and generates useful hypothesis (e.g. transcription factor cooperative partner) for further study.

This dataset has been created and curated to support visualization and exploratory analysis of functional annotations generated by the FAVOR (Functional Annotation of Variants Online Resource) platform. It aggregates key metrics and annotation scores that are instrumental in interpreting genomic variants, enabling researchers and data scientists to gain insights into the functional impacts of these variants through interactive visual tools.

Description:

Sample of 1,200 comments posted under articles of the Austrian Newspaper Der Standard collected between January 2019 and November 2021. This dataset is published in context with the paper "PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments" and serves the purpose of detecting populist statements in user comments under news articles in the German language. Details about the sampling and annotation process can be found in the paper as well as the accompanying GitHub repository (https://github.com/ahmadouw/COV-Populism-Standard)

Abstract: Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore, our work provides a rich resource for future work on the classification of populist user comments in the German language.

Structure

• Each row contains an anonymized user comment and the binary labels given by each of the three annotators (per comment) for every motive
• anti1, anti2, anti3 indicate whether or not anti-elitism was found in the given comment
• cent1, cent2, cent3 indicate whether or not people-centrism was found in the given comment
• sov1, sov2, sov3 indicate whether or not people-sovereignty was found in the given comment
• none1, none2, none 3 indicate whether or not none of the motives was found in the given comment
• Populism is the final label that is assigned by majority vote, if any of the motives is present in the given comment

Further Details

• The data set is available for researchers upon request

observatories provide continuous access to both coastal and deep-sea ecosystems, particularly from underwater imaging that is a non-destructive method for examining biodiversity on unprecedented time and space scales.the success of imagery data for scientific purposes leads to new challenges linked to the processing of the exponential amount of data collected, which can be time-consuming and tedious.annotated images databases are generated by scientists, students, technical staff in laboratories, as well as by citizens through online platforms. they can be used to train machines -through ai models- for automatic processing of images collected by cameras at observatories underwater sites, identifying and analysing fauna and habitats for ecosystem monitoring purposes.in this case, we prepared the citizen science annotations from deep sea spy as a training dataset for yolov8. indeed, deep sea spy is a participative science platform launched in 2017, that provides access to images from emso-azores and ocean networks canada observatories for annotation purposes.we also used an expert annotated dataset for model validation.the archive includes: an images directory containing 3979 images from both observatories the raw dataset containing 253323 annotations with 15 labeled classes from deep sea spy : alvinocaridid shrimp, brittle star, buccinoid snail, bythograeid crab, cataetyx fish, chimera fish, mussel bed, polynoid worm, polynoid worms, pycnogonid (sea spider), spider crab, tubicolous worm bed, zoarcid fish, microbial mat, other fish the cleaned dataset containing 14967 annotations with the buccinidae and bythograeidae classes the expert dataset used for training validation of the buccinid class yolov8 trained models on buccinidae and bythograeidae (.pt files)more information about data format, data cleaning and model training is available in the readme file.the full pipeline is freely available on github.com/ai4os-hub/deep-species-detection

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

Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (n = 6,183), we show that ChatGPT outperforms crowd-workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average, while ChatGPT's intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003---about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.

The Deep Learning System Software market is experiencing robust growth, driven by the increasing adoption of AI across various industries. The market's expansion is fueled by the need for efficient and scalable solutions to handle the massive datasets required for training sophisticated deep learning models. Key factors contributing to this growth include the proliferation of cloud computing services offering readily accessible deep learning platforms, the development of more powerful and energy-efficient hardware (GPUs and specialized AI chips), and the rising demand for automated decision-making systems in sectors like healthcare, finance, and manufacturing. The market is segmented by software type (e.g., frameworks, libraries, tools), deployment model (cloud, on-premise), and industry vertical. Leading players like Microsoft, Nvidia, Google (Alphabet), and Intel are actively investing in R&D and strategic acquisitions to strengthen their market positions. Competition is intense, with companies focusing on providing specialized solutions tailored to specific industry needs and improving the ease of use and accessibility of their software. While challenges remain, such as the need for skilled data scientists and the ethical considerations surrounding AI deployment, the overall market outlook remains positive, projecting significant expansion over the forecast period. Despite the positive outlook, several restraints could potentially hinder market growth. These include the high cost of implementation, the complexity of deep learning systems requiring specialized expertise, and concerns regarding data security and privacy. The need for continuous updates and maintenance to keep pace with technological advancements also presents a challenge. However, ongoing research and development in areas such as automated machine learning (AutoML) and edge AI are expected to mitigate some of these challenges. The market is likely to witness increased consolidation as larger players acquire smaller companies with specialized technologies. Furthermore, the growing importance of data annotation and model explainability will create new market opportunities for specialized service providers. The future of the Deep Learning System Software market is characterized by innovation, competition, and the ongoing need to address ethical and practical concerns. We expect the market to demonstrate a steady and considerable increase in value throughout the forecast period.

Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically.

Dataset content

OpenChart-SE, version 1 corpus (txt files and and dataset.csv)

The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.

Codebook.xlsx

The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.

suppl_data_1_openchart-se_form.pdf

OpenChart-SE mock emergency care EHR form.

suppl_data_3_openchart-se_dataexploration.ipynb

This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.

More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the Publisher's Website. Data Generation The research project engages a story about perceptions of fairness in criminal justice decisions. The specific focus involves a debate between ProPublica, a news organization, and Northpointe, the owner of a popular risk tool called COMPAS. ProPublica wrote that COMPAS was racist against blacks, while Northpointe posted online a reply rejecting such a finding. These two documents were the obvious foci of the qualitative analysis because of the further media attention they attracted, the confusion their competing conclusions caused readers, and the power both companies wield in public circles. There were no barriers to retrieval as both documents have been publicly available on their corporate websites. This public access was one of the motivators for choosing them as it meant that they were also easily attainable by the general public, thus extending the documents’ reach and impact. Additional materials from ProPublica relating to the main debate were also freely downloadable from its website and a third party, open source platform. Access to secondary source materials comprising additional writings from Northpointe representatives that could assist in understanding Northpointe’s main document, though, was more limited. Because of a claim of trade secrets on its tool and the underlying algorithm, it was more difficult to reach Northpointe’s other reports. Nonetheless, largely because its clients are governmental bodies with transparency and accountability obligations, some of Northpointe-associated reports were retrievable from third parties who had obtained them, largely through Freedom of Information Act queries. Together, the primary and (retrievable) secondary sources allowed for a triangulation of themes, arguments, and conclusions. The quantitative component uses a dataset of over 7,000 individuals with information that was collected and compiled by ProPublica and made available to the public on github. ProPublica’s gathering the data directly from criminal justice officials via Freedom of Information Act requests rendered the dataset in the public domain, and thus no confidentiality issues are present. The dataset was loaded into SPSS v. 25 for data analysis. Data Analysis The qualitative enquiry used critical discourse analysis, which investigates ways in which parties in their communications attempt to create, legitimate, rationalize, and control mutual understandings of important issues. Each of the two main discourse documents was parsed on its own merit. Yet the project was also intertextual in studying how the discourses correspond with each other and to other relevant writings by the same authors. Several more specific types of discursive strategies were of interest in attracting further critical examination: Testing claims and rationalizations that appear to serve the speaker’s self-interest Examining conclusions and determining whether sufficient evidence supported them Revealing contradictions and/or inconsistencies within the same text and intertextually Assessing strategies underlying justifications and rationalizations used to promote a party’s assertions and arguments Noticing strategic deployment of lexical phrasings, syntax, and rhetoric Judging sincerity of voice and the objective consideration of alternative perspectives Of equal importance in a critical discourse analysis is consideration of what is not addressed, that is to uncover facts and/or topics missing from the communication. For this project, this included parsing issues that were either briefly mentioned and then neglected, asserted yet the significance left unstated, or not suggested at all. This task required understanding common practices in the algorithmic data science literature. The paper could have been completed with just the critical discourse analysis. However, because one of the salient findings from it highlighted that the discourses overlooked numerous definitions of algorithmic fairness, the call to fill this gap seemed obvious. Then, the availability of the same dataset used by the parties in conflict, made this opportunity more appealing. Calculating additional algorithmic equity equations would not thereby be troubled by irregularities because of diverse sample sets. New variables were created as relevant to calculate algorithmic fairness equations. In addition to using various SPSS Analyze functions (e.g., regression, crosstabs, means), online statistical calculators were useful to compute z-test comparisons of proportions and t-test comparisons of means. Logic of Annotation Annotations were employed to fulfil a variety of functions, including supplementing the main text with context, observations, counter-points, analysis, and source attributions. These fall under a few categories. Space considerations. Critical discourse analysis offers a rich method...

The 18 Mozart piano sonatas with some form, harmony, and texture annotations This dataset is an archive of the “Mozart Piano Sonatas” corpus (scores, measure maps, analyses, recordings, synchronizations, metadata). It provides both raw data and data for integration with the Dezrann music web platform: https://www.dezrann.net/explore/mozart-piano-sonatas. Wolfgang Amadeus Mozart (1756–1791) was a composer of 18th century Classical style period, recognized as one of the three principal figures of the First Viennese School, alongside Joseph Haydn and Ludwig van Beethoven. He expressed his versatile music ideas in a large palette of genres. His piano sonatas, published over a 15-year period, were composed for various purposes, including educational material and private commissions from aristocrats. The classical sonata (typically for a solo keyboard instrument) is composed of usually three movements, of which the first generally follows sonata form. Mozart’s sonatas are well known to have a remarkable structural and textural composition. The corpus consists of complete scores of all 18 sonatas with form, harmony, and cadence annotations (Hentschel et al., 2021). Sonatas 1 (K279), 2 (K280) and 5 (K283) also have texture annotations (Couturier et al., 2022). Some movements also have synchronized audio. The corpus uses measure maps (Gotham et al., 2023) to improve annotation interoperability. License: CC-BY-NC-SA-4.0 (scores), ODbL (annotations), CC0-1.0, CC-BY-NC-SA-3.0 (specific recordings) Maintainers: Louis Couturier louis.couturier@algomus.fr, Mathieu Giraud mathieu@algomus.fr References (Hentschel et al., 2021), (Couturier et al., 2022) https://dx.doi.org/10.57745/OHRWPC https://www.algomus.fr/data