Data Input Software Market Outlook

The global data input software market size is projected to grow from USD 5.2 billion in 2023 to USD 10.8 billion by 2032, driven by a CAGR of 8.7%. The primary growth factors include the increasing digitization of business processes, the rising need for automated data entry solutions, and the growing adoption of advanced technologies such as AI and machine learning.

One significant growth factor for the data input software market is the rapid digitization across various industries. Organizations are increasingly moving away from manual data entry methods to automated solutions to enhance efficiency, reduce human error, and save costs. This shift is particularly evident in sectors like healthcare, finance, and retail, where accurate and timely data input is crucial. The adoption of digital transformation initiatives is compelling businesses to invest in advanced data input software, which in turn, is driving market growth.

Another crucial factor contributing to the market's expansion is the growing integration of artificial intelligence (AI) and machine learning (ML) technologies. These technologies are revolutionizing data input processes by enabling software solutions to learn from data patterns, predict inputs, and automate repetitive tasks. This not only increases the speed and accuracy of data entry but also significantly reduces the workload on human employees, allowing them to focus on more strategic tasks. The continuous advancements and integration of AI and ML in data input software are expected to further propel market growth.

Additionally, the increasing need for compliance and regulation adherence in various industries is driving the demand for robust data input solutions. Industries such as finance and healthcare are heavily regulated, and any inaccuracies in data entry can result in significant penalties. Data input software helps organizations maintain compliance by ensuring data accuracy and providing audit trails. As regulatory pressures continue to mount, the adoption of reliable data input software is becoming a necessity, thereby contributing to the market's growth.

From a regional perspective, North America and Europe currently hold significant market shares due to the high adoption rates of advanced technologies and the presence of numerous key players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the burgeoning IT and telecommunications sector, increasing digitalization, and government initiatives supporting technological advancements. As businesses in this region continue to modernize their operations, the demand for data input software is anticipated to surge.

Component Analysis

The data input software market is segmented into software and services components. The software segment encompasses various types of data input software solutions designed to automate and streamline data entry processes. These solutions range from basic data entry tools to advanced platforms integrated with AI and ML capabilities. The rising adoption of these software solutions is primarily driven by their ability to enhance data accuracy, reduce manual effort, and improve overall operational efficiency. Organizations are increasingly seeking comprehensive software solutions that offer seamless integration with existing systems and provide real-time data processing capabilities.

In contrast, the services segment includes professional services such as implementation, integration, training, and support. As organizations deploy data input software, they often require assistance in customizing the solutions to fit their specific needs, integrating them with existing systems, and training their employees to effectively use the software. The demand for these services is growing as businesses recognize the importance of proper implementation and continuous support to maximize the benefits of their data input software investments. Furthermore, the increasing complexity of data input processes and the need for specialized knowledge are driving the demand for professional services in this market.

The software component is anticipated to hold the largest market share during the forecast period, owing to the continuous advancements in technology and the growing emphasis on automation. However, the services segment is also expected to witness substantial growth, driven by the increasing demand for customization, integration, and ongoing support. Organizations understand that investing in high-quali

This dataset features over 1,000,000 high-quality images of furniture sourced globally from photographers, designers, and decor enthusiasts. Engineered for AI and machine learning applications, it offers richly annotated, diverse, and scalable imagery across residential, commercial, and studio environments.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and structured annotations detailing furniture type (e.g., chair, sofa, table, bed), material (e.g., wood, metal, fabric), style (e.g., modern, vintage, minimalist), and use context (e.g., staged, in-use, catalog). Supports classification, segmentation, and recommendation model training.

1. Unique Sourcing Capabilities: images are sourced through a proprietary gamified photography platform, with frequent competitions on furniture and interior themes. Custom datasets can be provided within 72 hours targeting specific categories, styles, room settings, or demographics.

2. Global Diversity: contributions from over 100 countries capture a broad spectrum of furniture styles and household aesthetics—from Scandinavian minimalism to traditional Asian craftsmanship. Images cover a variety of settings including homes, offices, cafes, showrooms, and outdoor patios.

3. High-Quality Imagery: ranges from catalog-quality studio images to natural lifestyle photos in real-world settings. Resolutions span from standard to ultra-HD, ensuring detail-rich inputs for both aesthetic and functional AI models.

4. Popularity Scores: each image is assigned a popularity score based on its performance in GuruShots competitions. These scores can inform visual trend analysis, user preference modeling, and e-commerce optimization.

5. AI-Ready Design: the dataset is optimized for use in applications such as furniture recognition, style transfer, AR staging, virtual interior design, and visual search. Fully compatible with machine learning pipelines and retail visualization tools.

6. Licensing & Compliance: all images comply with intellectual property and content use regulations, with flexible licensing options for commercial, academic, and retail tech applications.

Use Cases: 1. Training AI for furniture classification, tagging, and style recognition in retail and AR platforms. 2. Powering virtual try-before-you-buy tools and room planners. 3. Enhancing search, recommendation, and personalization for e-commerce. 4. Supporting design analytics, home automation, and smart furniture recognition.

This dataset provides a robust, high-resolution foundation for AI applications in furniture tech, interior design, and retail innovation. Custom collections and filters are available. Contact us to learn more!

This dataset features over 600,000 high-quality images of household objects sourced from photographers worldwide. Designed to support AI and machine learning applications, it offers an extensively annotated and highly diverse collection of everyday indoor items across cultural and functional contexts.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is annotated with object labels, room context, material types, and functional categories—ideal for training models in object detection, classification, and scene understanding. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: images are gathered through a proprietary gamified platform featuring competitions focused on home environments and still life. This ensures a rich flow of authentic, high-quality submissions. Custom datasets can be created on-demand within 72 hours, targeting specific object categories, use-cases (e.g., kitchenware, electronics, decor), or room types.

2. Global Diversity: contributions from over 100 countries showcase household items from a wide range of cultures, economic settings, and design aesthetics. The dataset includes everything from modern appliances and utensils to traditional tools and furnishings, captured in kitchens, bedrooms, bathrooms, living rooms, and utility spaces.

3. High-Quality Imagery: includes images from standard to ultra-high-definition, covering both staged product-like photos and natural usage contexts. This variety supports robust training for real-world applications in cluttered or dynamic environments.

4. Popularity Scores: each image has a popularity score based on its performance in GuruShots competitions. These scores provide valuable input for training models focused on product appeal, consumer trend detection, or aesthetic evaluation.

5. AI-Ready Design: optimized for use in smart home applications, inventory systems, assistive technologies, and robotics. Fully compatible with major machine learning frameworks and annotation workflows.

6. Licensing & Compliance: all data is compliant with global privacy and content use regulations, with transparent licensing for both commercial and academic applications.

Use Cases: 1. Training AI for home inventory and recognition in smart devices and AR tools. 2. Powering assistive technologies for accessibility and elder care. 3. Enhancing e-commerce recommendation and visual search systems. 4. Supporting robotics for home navigation, object grasping, and task automation.

This dataset provides a comprehensive, high-quality resource for training AI across smart living, retail, and assistive domains. Custom requests are welcome. Contact us to learn more!

The AI in Oil and Gas market is experiencing robust growth, projected to reach $3.14 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.61% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the industry's increasing need for enhanced operational efficiency and reduced costs is fueling the adoption of AI-powered solutions for predictive maintenance, optimizing production processes, and improving safety protocols. Secondly, the abundance of data generated by oil and gas operations provides rich fodder for AI algorithms to analyze and extract valuable insights, leading to better decision-making and resource allocation. Finally, advancements in AI technologies, such as machine learning and deep learning, are continuously improving the accuracy and capabilities of AI applications within the sector, further boosting market adoption. The upstream segment, encompassing exploration and production, is expected to dominate the market due to the significant potential for AI to optimize drilling operations, reservoir management, and improve recovery rates. However, the downstream segment, focused on refining and distribution, is also witnessing substantial growth as AI is leveraged to improve supply chain optimization and refine product quality. Leading players like IBM, Microsoft, and NVIDIA are actively contributing to this growth by developing and deploying cutting-edge AI solutions tailored to the specific needs of the oil and gas industry. The market segmentation by operation (upstream, midstream, downstream) and type (platform, services) offers diverse opportunities for growth. While the upstream segment currently leads, the midstream and downstream segments are expected to witness accelerated adoption in the coming years. The services segment, encompassing AI-driven consulting and implementation, is poised for strong growth, driven by the increasing need for specialized expertise in deploying and managing AI solutions within oil and gas companies. Geographical distribution shows a strong presence in North America and Europe, driven by early adoption and technological advancements. However, Asia, particularly regions with significant oil and gas reserves, is predicted to experience substantial growth, fueled by increasing investments in digital transformation and rising demand for efficiency improvements. The ongoing focus on sustainability and environmental regulations will also shape the market, with AI playing a crucial role in optimizing resource utilization and reducing environmental impact. Recent developments include: March 2024: ADNOC, the Abu Dhabi National Oil Company, announced plans to harness artificial intelligence (AI) for oil production in the Belbazem offshore block. It aims to boost operational efficiency, bolster safety measures, and simultaneously slash emissions and costs. Teaming up with AIQ, ADNOC will leverage AIQ's WellInsight tool to scrutinize reservoir data and streamline operations, underscoring the burgeoning demand for AI solutions in the oil and gas industry., January 2024: Schlumberger (SLB) forged a strategic alliance with Geminus AI, a prominent player in physics-informed AI technology for the oil and gas industry. This collaboration grants SLB exclusive rights to deploy the industry's maiden physics-informed AI model builder. This innovative tool merges physics-based methodologies with operational data, crafting highly precise AI models that can be swiftly scaled at a reduced cost compared to conventional methods. Geminus' platform, distinguished by its physics-informed AI computing, embeds real-world constraints into its digital models. Notably, this platform operates efficiently with minimal data and can be seamlessly updated with new inputs. Such capabilities empower data scientists and engineers to make real-time, data-driven decisions, setting a solid foundation for future market expansion.. Key drivers for this market are: Increasing Focus to Easily Process Big Data, Rising Trend to Reduce Production Cost. Potential restraints include: Increasing Focus to Easily Process Big Data, Rising Trend to Reduce Production Cost. Notable trends are: The Upstream Operations Segment is Expected to Witness Significant Growth.

This is the test data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 480 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Problem Statement

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Traditional education systems often fail to address the diverse learning needs of students. A leading EdTech company faced challenges in providing tailored educational experiences, leading to decreased student engagement and inconsistent learning outcomes. The company sought an innovative solution to create adaptive learning platforms that cater to individual learning styles and pace.

Challenge

Creating a personalized education platform involved overcoming the following challenges:

Analyzing diverse datasets, including student performance, engagement metrics, and learning preferences.

Designing adaptive content delivery that adjusts to each student’s progress in real-time.

Maintaining a balance between personalized learning and curriculum standards.

Solution Provided

An adaptive learning system was developed using machine learning algorithms and AI-driven educational software. The solution was designed to:

Analyze student data to identify strengths, weaknesses, and preferred learning styles.

Provide personalized learning paths, including targeted content, quizzes, and feedback.

Continuously adapt content delivery based on real-time performance and engagement metrics.

Development Steps

Data Collection

Aggregated student data, including assessment scores, engagement patterns, and interaction histories, from existing learning management systems.

Preprocessing

Cleaned and structured data to identify trends and learning gaps, ensuring accurate input for machine learning models.

Model Training

Built recommendation algorithms to suggest tailored learning materials based on student progress. Developed predictive models to identify students at risk of falling behind and provide timely interventions.

Validation

Tested the system with diverse student groups to ensure its adaptability and effectiveness in various educational contexts.

Deployment

Integrated the adaptive learning platform with the company’s existing educational software, ensuring seamless operation across devices.

Monitoring & Improvement

Established a feedback loop to refine algorithms and enhance personalization based on new data and evolving student needs.

Results

Enhanced Student Engagement

The platform increased student participation by providing interactive and tailored learning experiences.

Improved Learning Outcomes

Personalized learning paths helped students grasp concepts more effectively, resulting in better performance across assessments.

Tailored Educational Experiences

The adaptive system offered individualized support, catering to students with diverse needs and learning styles.

Proactive Support

Predictive insights enabled educators to identify struggling students early and provide necessary interventions.

Scalable Solution

The platform scaled efficiently to accommodate thousands of students, ensuring consistent quality and personalization.

This dataset includes compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Personalized Fragrance AI Market Outlook

According to our latest research, the global Personalized Fragrance AI market size reached USD 1.47 billion in 2024. The market is experiencing robust expansion, supported by a remarkable CAGR of 17.3% from 2025 to 2033. By the end of the forecast period in 2033, the Personalized Fragrance AI market is projected to attain a value of USD 6.02 billion. This impressive growth is primarily driven by increasing consumer demand for bespoke scent experiences, rapid advancements in artificial intelligence technologies, and the ongoing digital transformation across the fragrance and personal care sectors.

The primary growth factor propelling the Personalized Fragrance AI market is the shift in consumer preferences toward individualized products and experiences. Today’s consumers are more informed, digitally engaged, and seek products that resonate with their unique identities. This trend has compelled fragrance brands and retailers to leverage AI-driven platforms that analyze user preferences, lifestyle data, and even biometric inputs to create custom scents. The proliferation of smart devices and wearable technology has further enabled real-time data collection, allowing AI algorithms to continuously refine scent recommendations. As a result, brands are able to deliver hyper-personalized fragrance solutions that foster deeper customer loyalty and enhance brand differentiation in a highly competitive marketplace.

Another significant driver is the integration of advanced AI technologies such as machine learning, natural language processing, and computer vision within the fragrance sector. These technologies are not only enhancing the accuracy and sophistication of scent recommendation systems but also enabling the creation of entirely new olfactory experiences. Machine learning models are trained on vast datasets of consumer feedback, scent formulations, and market trends, allowing them to predict and generate novel scent profiles that align with evolving consumer tastes. Meanwhile, natural language processing enables intuitive conversational interfaces, making it easier for users to communicate their preferences and receive personalized recommendations. The convergence of these AI capabilities is revolutionizing the way fragrances are designed, marketed, and consumed.

The growing adoption of e-commerce and digital retail channels is also fueling the expansion of the Personalized Fragrance AI market. Online platforms equipped with AI-powered scent discovery tools are making it easier for consumers to explore and purchase custom fragrances from the comfort of their homes. This digital transformation has been accelerated by the pandemic, which shifted consumer behaviors toward online shopping and contactless experiences. Retailers and fragrance brands are increasingly investing in AI-driven solutions to enhance their online offerings, streamline the customization process, and provide interactive virtual consultations. As digital engagement becomes a cornerstone of the fragrance industry, AI-enabled personalization is expected to become standard practice, further boosting market growth.

Regionally, North America and Europe are at the forefront of the Personalized Fragrance AI market, driven by high consumer awareness, advanced technological infrastructure, and a strong presence of leading fragrance brands. However, the Asia Pacific region is emerging as a significant growth engine, supported by a burgeoning middle class, increasing disposable incomes, and rapid digitalization. Countries such as China, Japan, and South Korea are witnessing a surge in demand for premium and personalized beauty products, creating lucrative opportunities for AI-driven fragrance solutions. The market’s regional dynamics are further influenced by cultural preferences, regulatory frameworks, and the pace of technological adoption, necessitating tailored strategies for market entry and expansion.

Product Type Analysis

The Product Type segment of the Personalized Fragrance AI market encompasses customizable perfumes, scent recommendation systems, AI-driven scent creation platforms, and other innovative offerings. Customizable perfumes represent a significant portion of the market, as consumers increasingly seek fragrances that reflect their unique personalities and emotional states. Brands have responded by introducing AI-powered platforms that allow users to select and blend ingredients based on the

This dataset features over 1,000,000 high-quality images of footwear sourced from photographers, fashion creators, and enthusiasts worldwide. Designed to meet the demands of AI and machine learning applications, it provides a richly annotated, diverse, and scalable dataset covering a wide range of shoe types, styles, and use contexts.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and detailed annotations for shoe category (e.g., sneakers, boots, heels, sandals), brand visibility, usage context (e.g., worn, shelf, outdoor), and visual orientation (top view, side view, close-up). Ideal for training in classification, detection, segmentation, and style matching.

1. Unique Sourcing Capabilities: images are sourced via a proprietary gamified photography platform, with fashion- and product-focused competitions generating high-quality, stylistically rich content. Custom datasets can be delivered within 72 hours for specific categories, demographics (e.g., men's, women's, kids'), or settings (studio, streetwear, retail).

2. Global Diversity: contributors from over 100 countries provide visual access to a wide variety of footwear traditions, fashion trends, climates, and consumer segments. This ensures inclusivity across seasons, cultures, and economic tiers—from designer pieces to everyday wear.

3. High-Quality Imagery: images range from standard to ultra-HD, captured in diverse lighting and backgrounds. Both professional studio shots and in-use lifestyle photography are included, supporting robust AI training in realistic and commercial scenarios.

4. Popularity Scores: each image includes a popularity score based on performance in GuruShots competitions, offering valuable input for models analyzing visual appeal, trend prediction, or consumer preference.

5. AI-Ready Design: formatted for seamless use in machine learning workflows, including fashion recognition, virtual try-on systems, inventory management, and visual search. Integrates easily with retail and recommendation platforms.

6. Licensing & Compliance: fully compliant with commercial use and intellectual property standards. Licensing is transparent and flexible for fashion tech, retail AI, and academic use cases.

Use Cases: 1. Training AI for footwear classification, tagging, and visual search in e-commerce. 2. Powering virtual try-on applications and personalized recommendation engines. 3. Supporting trend analysis and fashion forecasting tools. 4. Enhancing inventory intelligence, style comparison, and social commerce platforms.

This dataset offers a powerful, high-resolution foundation for AI innovation across the footwear, fashion, and retail technology sectors. Custom filtering, formats, and metadata enrichment available. Contact us to learn more!

Problem Statement

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A social media platform faced challenges in moderating an increasing volume of user-generated content. Inappropriate or harmful content, including hate speech, explicit images, and misinformation, negatively impacted user safety and platform credibility. The company needed an automated solution to detect and filter such content in real time, reducing reliance on manual moderation and improving user experience.

Challenge

Implementing an automated content moderation system involved addressing several challenges:

Analyzing vast amounts of text, images, and videos uploaded daily to identify harmful content.

Ensuring high accuracy in detecting contextually inappropriate content without removing legitimate posts.

Balancing automation with manual review for edge cases and ambiguous content.

Solution Provided

An AI-powered content moderation system was developed using Natural Language Processing (NLP) and computer vision technologies. The solution was designed to:

Automatically analyze text, images, and videos to detect inappropriate or harmful content.

Classify flagged content into categories such as hate speech, explicit imagery, and misinformation for targeted actions.

Provide tools for moderators to review and manage flagged content efficiently.

Development Steps

Data Collection

Collected datasets of labeled harmful content, including text, images, and videos, from publicly available sources and internal archives.

Preprocessing

Cleaned and normalized text data, while annotating images and videos for training computer vision models to recognize harmful visual elements.

Model Development

Trained NLP models to identify harmful language, hate speech, and misinformation. Built computer vision models to detect explicit imagery and other inappropriate visual content.

Validation

Tested models on live data streams to evaluate accuracy, false-positive rates, and performance under varying content types and languages.

Deployment

Integrated the system with the platform’s content management tools, enabling real-time flagging and moderation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models using moderator input and evolving content patterns.

Results

Maintained Platform Safety

The automated system effectively flagged and filtered harmful content, ensuring a safer environment for users.

Reduced Manual Moderation Efforts

Automation significantly decreased the volume of content requiring manual review, freeing moderators to focus on complex cases.

Improved User Experience

Proactive content filtering enhanced user trust and satisfaction by minimizing exposure to inappropriate material.

Scalable Moderation Solution

The system scaled seamlessly to handle growing volumes of user-generated content across multiple languages and regions.

Real-Time Content Analysis

The system’s ability to analyze content in real time reduced delays in moderation, ensuring timely actions against harmful posts.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

This repository contains processed data and result files for the paper Secondary control activation analysed and predicted with explainable AI . The code for producing the processed data and the results is available at github.

Data

The data folder contains the feature and target data used to train the ML model. The data for Germany comprises the following folders and files:

• raw_input_data.h5 : The aggregated external features without additional engineered features.
• inputs_: The input features for the different model types used in the paper including the engineered features. Depending on the model type, the input files also contain the IGCC features.
• outputs.h5 : The activated aFRR volumes in Germany.
• version_2021-08-20: Folder containing the training and test sets used for the results.
• documentation_of_data_download: Information files concerning the ENTSO-E raw data and its aggregation.

In addition to the German time series, the data folder contains the raw input data for the remaining IGCC states. Note that the results contain more model types as actually discussed in the paper.

Data sources

The data for input features (raw_input_data.h5 and input_) is derived from ENTSO-E Transparency Platform data [1]. The target data (outputs.h5) is based on publicly available data from the German Transmission System Operators (TSOs) [2].

Results

The result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The model type, the loss function to train the model and the data set for prediction/interpretation were varied.

• cv_results_ : Performance results for each combination in the hyper-parameter grid search.
• cv_best_params_ : Hyper-parameters used in the final (optimized) model.
• shap_values_ : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.
• y_pred_ : Predictions of daily profile predictor and Machine Learning models.

Disclaimer

The data might be subject to copyright or related rights. Please consult the primary data owner.

This is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 384 named entity recognition AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Handwriting Input Market Outlook

The global handwriting input market size was valued at approximately USD 3.2 billion in 2023 and is expected to reach around USD 5.8 billion by 2032, growing at a CAGR of 6.7% during the forecast period. The market is witnessing significant expansion due to the growing need for digital transformation across various sectors, which emphasizes the importance of efficient data entry methods. The increasing adoption of digital devices, along with advancements in machine learning and AI technologies, are major driving forces enhancing the usability and accuracy of handwriting input systems. As organizations strive for more seamless integration of digital and traditional data entry methods, the handwriting input market is poised for substantial growth in the coming years.

One of the primary growth factors in the handwriting input market is the widespread adoption of digital learning tools and educational technologies. As educational institutions worldwide embrace digital transformation, there is an increasing demand for tools that can facilitate remote and hybrid learning environments. Handwriting input technologies offer students and educators the ability to interact with digital content more naturally, supporting note-taking, problem-solving, and creative expression. The integration of handwriting input in educational platforms allows for a more personalized learning experience, catering to diverse learning styles and enhancing student engagement. As a result, the education sector's continued focus on digital tools is expected to drive significant growth in the handwriting input market.

In the healthcare sector, the adoption of handwriting input technologies is driven by the need for efficient and accurate documentation. With the increasing digitization of healthcare records, medical professionals require reliable tools that can capture handwritten notes and prescriptions seamlessly. Handwriting input systems equipped with advanced character recognition technologies enable healthcare providers to maintain accurate patient records, reduce errors, and improve overall efficiency. Moreover, the integration of these technologies into telehealth platforms has become increasingly important, especially post-pandemic, as they provide healthcare practitioners with tools to enhance patient interactions and streamline documentation processes. This growing necessity for improved healthcare documentation solutions is a key factor contributing to the market's expansion.

The banking, financial services, and insurance (BFSI) sector is another significant contributor to the growth of the handwriting input market. As financial institutions continue to digitize their operations, the need for secure and efficient data entry solutions becomes paramount. Handwriting input technologies provide an effective means of capturing customer information and signatures digitally, reducing the reliance on paper-based processes. This transition not only enhances operational efficiency but also improves customer experience by allowing for more seamless interactions. As digital banking and fintech solutions expand, the BFSI sector's investment in innovative handwriting input solutions is likely to grow, further propelling market growth.

Regionally, the Asia Pacific region is anticipated to witness substantial growth in the handwriting input market. The region's rapid technological advancements, increased investment in digital infrastructure, and a large base of tech-savvy consumers contribute to this growth. Countries like China, Japan, and India are at the forefront of adopting digital technologies, which includes handwriting input solutions across various sectors such as education, finance, and healthcare. Additionally, government initiatives promoting digital literacy and the adoption of smart devices in schools and offices further drive the demand for handwriting input technologies. As a result, the Asia Pacific region is expected to continue its dominant trajectory in the handwriting input market over the forecast period.

Component Analysis

The handwriting input market can be segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. Software solutions are integral to the handwriting input market, as they provide the necessary algorithms and interfaces needed to interpret and process handwritten data. These solutions often incorporate advanced technologies such as machine learning and artificial intelligence to enhance the accuracy and efficiency of handwriting recognition. The software market is driven by continuous advancements in

The text analytics market is experiencing robust growth, projected to reach $10.49 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 39.90% from 2019 to 2033. This expansion is fueled by several key drivers. The increasing volume of unstructured data generated across various industries, including healthcare, finance, and customer service, necessitates sophisticated tools for extracting actionable insights. Furthermore, advancements in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) are empowering text analytics solutions with enhanced capabilities, such as sentiment analysis, topic modeling, and entity recognition. The rising adoption of cloud-based solutions also contributes to market growth, offering scalability, cost-effectiveness, and ease of access. Major industry players like IBM, Microsoft, and SAP are actively investing in research and development, driving innovation and expanding the market's capabilities. Competitive pressures are fostering a continuous improvement in the accuracy and efficiency of text analytics tools, making them increasingly attractive to businesses of all sizes. The growing demand for real-time insights and improved customer experience further propels market expansion. While the market enjoys significant growth momentum, certain challenges persist. Data security and privacy concerns remain paramount, necessitating robust security measures within text analytics platforms. The complexity of implementing and integrating these solutions into existing IT infrastructures can also pose a barrier to adoption, particularly for smaller businesses lacking dedicated data science teams. Furthermore, the accuracy and reliability of text analytics outputs can be affected by the quality and consistency of the input data. Overcoming these challenges through improved data governance, user-friendly interfaces, and robust customer support will be crucial for continued market expansion. Despite these restraints, the overall market outlook remains positive, driven by the continuous evolution of technology and the growing reliance on data-driven decision-making across diverse sectors. Recent developments include: January 2023- Microsoft announced a new multibillion-dollar investment in ChatGPT maker Open AI. ChatGPT, automatically generates text based on written prompts in a more creative and advanced than the chatbots. Through this investment, the company will accelerate breakthroughs in AI, and both companies will commercialize advanced technologies., November 2022 - Tntra and Invenio have partnered to develop a platform that offers comprehensive data analysis on a firm. Throughout the process, Tntra offered complete engineering support and cooperation to Invenio. Tantra offers feeds, knowledge graphs, intelligent text extraction, and analytics, which enables Invenio to give information on seven parts of the business, such as false news identification, subject categorization, dynamic data extraction, article summaries, sentiment analysis, and keyword extraction.. Key drivers for this market are: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Potential restraints include: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Notable trends are: Retail and E-commerce to Hold a Significant Share in Text Analytics Market.

The Enterprise Governance, Risk And Compliance (eGRC) Market size was valued at USD 54.61 billion in 2023 and is projected to reach USD 134.98 billion by 2032, exhibiting a CAGR of 13.8 % during the forecasts period. The Enterprise Governance, Risk, and Compliance (eGRC) market is centered on the approach that targets integrated solutions that would allow the organization address various aspects of governance, risk as well as compliance. Such systems offer rationalizations for Avon for risks’ evaluation and management, adherence to legal requirements, and enhancement of the company’s governance. It applies to many areas of concern, including the finance area, health care, and manufacturing area to meet needs like risk evaluation, policy making, and audit. They are the expanding use of artificial intelligence and machine learning in eGRC as well as the use of a single platform to integrate eGRC with other enterprise systems, the use of real-time monitoring and reporting for increased and dynamic decision making and control. Recent developments include: In November 2023, IBM announced that Watsonx.governance is expected to be widely accessible by December 2023. This platform is designed to aid organizations in dispelling misconceptions surrounding AI models, the data input into the system, and the resulting outputs. While the business world is witnessing a surge in applications leveraging generative AI powered by Large Language Models (LLM), it also grapples with associated risks and complexities. These challenges encompass issues ranging from the need for clearer sourcing of training data from the internet to the generation of outputs that need more explain ability. Watsonx.governance equips organizations with tools to manage risks effectively, foster transparency, and prepare for forthcoming regulations focused on AI. , In November 2023, Brillio forged a collaboration with Microsoft to collaboratively develop cutting-edge horizontal and industrial solutions utilizing the Microsoft Azure OpenAI Service. These intelligent solutions, accessible through the Microsoft Azure Marketplace, will merge Brillio's extensive industry and digital proficiency with Microsoft's AI and analytics platforms. This synergy is poised to empower businesses across diverse sectors, including healthcare, banking, retail, financial services, life sciences, consumer packaged goods, and insurance. The aim is to catalyze the transformation of business models, expedite innovation, and capitalize on new growth prospects. , In November 2023, MetricStream introduced a cloud GRC solution that leverages Amazon Web Services (AWS) AWS Audit Manager in conjunction with MetricStream CyberGRC. Customers will be able to manage risks, compliance standards, frameworks centrally, and controls with MetricStream's new cloud GRC solution, which also offers automated evidence collecting and assessments for both on-premises and AWS settings. , In September 2023, Oracle Access Governance has been updated to assist IT teams in assigning, tracking, and managing user access to apps and other digital resources more effectively. By limiting access to restricted assets like source code, patents, databases, apps, and infrastructure resources like cloud servers and services, only authorized users can use, view, or interact with them. This cloud-native service helps lower risk by offering a comprehensive insight into how users interact with tech resources. .

Digital Twin Electrical Panel Market Outlook

According to our latest research, the global Digital Twin Electrical Panel market size reached USD 1.21 billion in 2024, driven by the accelerating adoption of Industry 4.0 and the increasing need for predictive maintenance across sectors. The market is forecasted to expand at a robust CAGR of 15.8% from 2025 to 2033, reaching a projected value of USD 4.41 billion by 2033. This remarkable growth is primarily fueled by digital transformation initiatives, the integration of IoT and AI technologies, and rising investments in smart infrastructure and energy management systems worldwide.

The growth trajectory of the Digital Twin Electrical Panel market is underpinned by several key factors. The increasing complexity of electrical distribution networks and the growing demand for real-time monitoring and optimization are compelling industries to adopt digital twin solutions for their electrical panels. These digital replicas enable predictive maintenance, reduce downtime, and enhance operational efficiency by providing actionable insights derived from continuous data analysis. As industries strive to optimize asset performance and minimize energy consumption, the deployment of digital twins becomes indispensable, especially in sectors such as manufacturing, utilities, and data centers. Furthermore, stringent regulatory requirements for energy efficiency and safety are prompting organizations to leverage digital twin technology to ensure compliance and streamline auditing processes.

Another significant driver is the rapid advancement in data analytics, artificial intelligence, and machine learning technologies. The integration of these technologies with digital twin platforms has revolutionized the way electrical panels are managed, allowing for advanced simulations, anomaly detection, and automated decision-making. The proliferation of IoT-enabled sensors and cloud-based platforms further enhances the scalability and accessibility of digital twin solutions, making them viable for organizations of all sizes. Additionally, the rise of smart buildings, grid modernization projects, and the adoption of renewable energy sources are propelling the demand for intelligent electrical panel management, thereby expanding the market for digital twin solutions.

The market is also benefitting from increased investments in digital infrastructure and the growing focus on sustainability. As organizations prioritize energy conservation and carbon footprint reduction, digital twin electrical panels offer a tangible solution by facilitating real-time energy monitoring, fault detection, and optimization of electrical loads. The convergence of IT and OT (operational technology) is further blurring the lines between traditional electrical engineering and advanced digital solutions, opening new avenues for innovation and collaboration. Strategic partnerships among technology providers, utility companies, and industrial players are accelerating the adoption of digital twin technologies, while government incentives and funding for digital transformation initiatives are providing an additional boost to market growth.

From a regional perspective, North America and Europe are leading the adoption of digital twin electrical panels, owing to their mature industrial bases, high levels of technological innovation, and supportive regulatory environments. Asia Pacific, however, is emerging as the fastest-growing region, driven by rapid industrialization, urbanization, and significant investments in smart grid and infrastructure projects. The Middle East & Africa and Latin America are also witnessing increased adoption, albeit at a slower pace, as governments and private players recognize the benefits of digitalization for enhancing energy security and operational efficiency.

Component Analysis

The Digital Twin Electrical Panel market is segmented by component into Software, Hardware, and Services, each playing a critical role in the ecosystem. The software segment dominates the market, accounting for a substantial share due to the increasing need for sophisticated simulation, monitoring, and analytics platforms. These software solutions enable organizations to create accurate digital replicas of physical electrical panels, simulate various scenarios, and optimize performance based on real-time data inputs. The integration of AI and machine learning algorithms within these software platforms further e

This dataset contains a collection of Midjourney user prompts and their corresponding generated image URLs from 2022. It has been reformatted from a previous "Midjourney User Prompts & Generated Images" dataset, making it particularly well-suited for text search applications designed to display associated images. The dataset offers multiple versions to cater to different analytical needs: a main version includes re-runs that may result in similar image outputs, while a reduced version excludes re-runs, though it might contain duplicate text with differing arguments. A raw version is also available but is generally not recommended due to the inclusion of errors, chat, and server messages.

Columns

• timestamp: The precise date and time the message was recorded.
• _message: The original message content from the user, which may include commands, arguments, and other textual elements.
• thumb_url: A URL for a thumbnail image, which is another form of an image URL.
• img_url: The proxy URL for the generated image. This path requires a prefix of either https://cdn.discordapp.com/attachments/ or https://media.discordapp.net/attachments/ to form a complete, usable image URL.
• cmd: The extracted command portion from the _message field.
• job_id: A unique 36-character hexadecimal identifier for the specific Midjourney generation task.
• text: The cleaned text of the /imagine command, specifically excluding any arguments or input URLs.

Distribution

The dataset is provided in CSV format and is split into several files with varying row counts and characteristics: * midjourney_2022_250k_raw.csv: Contains approximately 251,390 rows, including raw messages with commands and potentially unwanted content. URLs in this file are in full length. * midjourney_2022_250k.csv: Contains approximately 248,069 rows. This version is suitable for text search and includes re-runs, which might lead to similar output images. URLs are shortened to conserve memory. * midjourney_2022_reduced.csv: Contains approximately 130,407 rows. This version excludes re-runs but may feature duplicate text entries associated with different arguments. URLs are also shortened.

The URLs in midjourney_2022_250k.csv and midjourney_2022_reduced.csv are partial and need the base URL (https://cdn.discordapp.com/attachments/ or https://media.discordapp.net/attachments/) re-attached to be fully functional.

Usage

This dataset is ideally suited for data science and analytics projects focused on: * Developing text search functionalities to retrieve images based on descriptive prompts. * Analysing trends in user prompts and their impact on generated imagery. * Training or evaluating AI models related to natural language processing (NLP), such as prompt engineering or text-to-image synthesis. * Exploring the relationship between textual prompts and visual outputs in generative AI systems. * Building recommender systems for creative content.

Coverage

The data spans the year 2022. Its geographic scope is global. While the primary dataset includes re-runs, which might show similar images, a dedicated reduced version is available that filters these out, potentially offering a cleaner set of unique prompt-image pairs. The raw version should be approached with caution as it contains uncleaned data.

License

The dataset is free to use. A specific license URL is not available in the provided materials.

Who Can Use It

This dataset is beneficial for a wide range of users, including: * Data scientists and analysts: For exploring and modelling generative AI data. * Machine learning engineers: For training and testing models in areas like computer vision, NLP, and recommender systems. * Researchers: Studying AI prompt engineering, image generation, and user behaviour in creative AI platforms. * Developers: Building applications that leverage text-to-image search or prompt analysis. * AI art enthusiasts: Curious about the prompts behind AI-generated images.

Dataset Name Suggestions

• Midjourney Prompts and Images 2022
• AI Image Generation Prompt Data
• Midjourney AI Art Prompts (2022)
• Text-to-Image Prompts Archive

Attributes

Original Data Source: Midjourney 2022 - 250k [CSV]

Dataset Category Suggestions

• AI & ML Data
• Computer Vision
• Natural Language Processing
• Generative AI
• Data Science
• Programming

Dataset SEO Keyword Suggestions

Midjourney, Prompts, AI, Images, Generative

AI-Generated Classroom Worksheet Market Outlook

According to our latest research, the global AI-Generated Classroom Worksheet market size reached USD 1.37 billion in 2024, reflecting a robust adoption rate across educational institutions worldwide. The market is projected to grow at a CAGR of 18.2% from 2025 to 2033, reaching an estimated USD 6.35 billion by the end of the forecast period. The primary growth factor driving this expansion is the increasing demand for personalized learning experiences and the need for scalable educational resources powered by artificial intelligence.

The surge in demand for AI-generated classroom worksheets is fundamentally fueled by the growing emphasis on individualized education. Schools and training institutions are rapidly adopting AI-powered tools to tailor content to the unique needs of each student, thereby enhancing engagement and learning outcomes. AI-driven platforms can analyze student performance data in real-time, allowing educators to generate worksheets that address specific knowledge gaps and learning styles. This level of personalization is difficult to achieve with traditional methods, making AI solutions increasingly attractive to both K-12 and higher education providers. Furthermore, the integration of adaptive learning technologies into curriculum planning is fostering a culture of innovation, encouraging institutions to invest in AI-generated content as a strategic asset.

Another significant growth driver is the ongoing digital transformation within the education sector. The COVID-19 pandemic accelerated the adoption of digital tools, and educational institutions continue to prioritize investments in technology to support hybrid and remote learning environments. AI-generated worksheets offer a scalable solution for educators to create high-quality, curriculum-aligned materials with minimal manual input. This efficiency not only reduces teachers' administrative workloads but also ensures that students receive up-to-date and relevant content. As a result, both developed and emerging economies are witnessing increased funding and policy support for the integration of AI in education, further propelling market growth.

Additionally, the growing collaboration between EdTech companies, educational publishers, and academic institutions is expanding the reach and capabilities of AI-generated worksheet platforms. These partnerships enable the development of sophisticated algorithms capable of generating diverse content across multiple subjects and languages. The rise of cloud-based deployment models has also democratized access to these technologies, allowing even resource-constrained schools to benefit from AI-driven educational tools. As the ecosystem matures, we expect to see further innovation in content generation, assessment, and analytics, driving sustained demand for AI-generated classroom worksheets globally.

From a regional perspective, North America remains the largest market, accounting for over 38% of the global revenue in 2024, thanks to its advanced digital infrastructure and high adoption rates of EdTech solutions. However, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 20.1% through 2033, driven by large student populations, increasing internet penetration, and proactive government initiatives supporting digital education. Europe is also witnessing steady growth, particularly in countries with strong educational systems and innovation-driven economies. Latin America and the Middle East & Africa are gradually catching up, supported by investments in educational modernization and the expansion of cloud-based services.

Component Analysis

The AI-Generated Classroom Worksheet market by component is primarily segmented into Software and Services. Software solutions dominate the market, accounting for a significant share of total revenue in 2024. These platforms leverage advanced AI algorithms to automate the creation of customized worksheets, quizzes, and assessments, catering to various learning objectives and student abilities. The software segment benefits from continuous advancements in natural language processing, machine learning, and data analytics, which collectively enhance the quality and relevance of generated content. As educational institutions seek to optimize resource allocation and

Market Overview:

The global AI workstation market was valued at $2.5 billion in 2018 and is expected to grow at a CAGR of 19% during the forecast period 2022-2030. The growth in this market can be attributed to the increasing demand for AI-enabled devices and services, the growing adoption of AI in various industries, and rising investments in R&D activities for developing new and innovative applications of AI. Based on type, the global AI workstation market can be segmented into portable AI workstations, benchtop workstations, and others. PortableAI workstations are expected to witness higher growth during the forecast period as they offer greater flexibility and mobility as compared to other types of workstations.

Product Definition:

An AI workstation is a computer system that can be used for artificial intelligence (AI) tasks. These systems typically include a central processing unit (CPU), memory, and storage, as well as graphics and input devices such as a keyboard and mouse. They are designed to allow users to carry out complex AI tasks on a single platform.

Portable AI Workstation:

A portable AI workstation is a computer system that can be easily moved from one place to another. It is designed for use in field or laboratory settings and typically has a high-resolution display, fast processing capabilities, and ample storage space. Portable AI workstations are often used for tasks such as data analysis, machine learning, and scientific research.

Bench AI Workstation:

Bench AI Workstation is a type of AI workstation that is used for data analysis and machine learning. It is a portable device that can be used to perform various tasks such as data entry, data analysis, and machine learning. Bench AI Workstation is also known as a desktop replacement for traditional computers.

Application Insights:

The market is segmented by application into an expert system, knowledge inference, and others. The other segment includes virtual assistants and robotics. The expert system application is expected to witness the highest CAGR over the forecast period owing to its increasing adoption across several industry verticals for various applications such as predictive maintenance and asset management. The other applications include image recognition and natural language processing which are majorly used in autonomous vehicles or robots for customer interaction purposes. Image recognition has witnessed tremendous adoption in smartphones where users can easily add filters or effects that enhance their photos with just a few clicks on a mobile device without having to take an actual photo of everything around them which was previously done manually by photographers.

Regional Analysis:

The North American regional market dominated the global demand in 2016, with a revenue share of over 35.0%. The growth is attributed to the presence of major players such as Google Inc., Microsoft Corporation, and IBM Corporation in this region. Moreover, increasing investments by these companies in developing advanced algorithms are expected to drive the demand further. For instance, Google Inc. announced its plan to invest more than $600 million between 2018 and 2020 in AI-related projects such as computer vision and machine learning for creating intelligent systems that can see as a human can see; understand language as humans do; and act accordingly under various circumstances. Such initiatives are anticipated to fuel market growth across North America during the forecast period from 2022 to 2030. However other regions such as Asia Pacific are projected to exhibit lucrative growth over this period owing to their increasing adoption of AI technology across several sectors including healthcare & life sciences; manufacturing & production management; transportation & logistics etc.

Growth Factors:

• Increasing demand for AI-enabled devices and services.
• The growing number of AI startups.
• Rising adoption of AI by enterprises.
• The proliferation of big data and the emergence of advanced analytics.
• Development of new applications for artificial intelligence.

Report Scope