Icon645 is a large-scale dataset of icon images that cover a wide range of objects:

645,687 colored icons 377 different icon classes

These collected icon classes are frequently mentioned in the IconQA questions. In this work, we use the icon data to pre-train backbone networks on the icon classification task in order to extract semantic representations from abstract diagrams in IconQA. On top of pre-training encoders, the large-scale icon data could also contribute to open research on abstract aesthetics and symbolic visual understanding.

Dataset Card for svg-icons

  Dataset Description

This dataset contains SVG code examples for training and evaluating SVG models for image vectorization.

  Dataset Structure







  Features

The dataset contains the following fields:

Field Name Description

Filename Unique ID for each SVG

Svg SVG code

  Usage

from datasets import load_dataset

dataset = load_dataset("starvector/svg-icons")… See the full description on the dataset page: https://huggingface.co/datasets/starvector/svg-icons.

The Mobile Icon | Mobile Screenshot Dataset is a meticulously curated collection of 9,000+ high-quality mobile screenshots, categorized across 13 diverse application types. This dataset is designed to support AI/ML researchers, UI/UX analysts, and developers in advancing mobile interface understanding, image classification, and content recognition.

Each image has been manually reviewed and verified by computer vision professionals at DataCluster Labs, ensuring high-quality and reliable data for research and development purposes.

Categories Included

• Technical Applications
• Wallpapers
• News & Magazines
• Business & Finance
• Agriculture
• Entertainment and many more.

Potential Applications:

• AI & ML model training (image classification, UI/UX analysis, OCR).
• Mobile app usability and accessibility research.
• Content recognition and recommendation systems.

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

OmniSVG: A Unified Scalable Vector Graphics Generation Model

![Project Page]

  Dataset Card for MMSVG-Icon





  Dataset Description

This dataset contains SVG icon examples for training and evaluating SVG models for text-to-SVG and image-to-SVG task.

  Dataset Structure





  Features

The dataset contains the following fields:

Field Name Description

id Unique ID for each SVG

svg SVG code

description Description of the SVG

  Citation… See the full description on the dataset page: https://huggingface.co/datasets/OmniSVG/MMSVG-Icon.

The HWRT database of handwritten symbols contains on-line data of handwritten symbols such as all alphanumeric characters, arrows, greek characters and mathematical symbols like the integral symbol.

The database can be downloaded in form of bzip2-compressed tar files. Each tar file contains:

symbols.csv: A CSV file with the rows symbol_id, latex, training_samples, test_samples. The symbol id is an integer, the row latex contains the latex code of the symbol, the rows training_samples and test_samples contain integers with the number of labeled data.

train-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.

test-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.

All CSV files use ";" as delimiter and "'" as quotechar. The data is given in YAML format as a list of lists of dictinaries. Each dictionary has the keys "x", "y" and "time". (x,y) are coordinates and time is the UNIX time.

About 90% of the data was made available by Daniel Kirsch via github.com/kirel/detexify-data. Thank you very much, Daniel!

Zip file containing all data and analysis files for Experiment 2 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors

These datasets consist of preprocessed data for the training, validation, and testing of ICON-LEM over Germany and ICON-NWP over Holuhraun, comprising input-output pairs of cloud effective radius (m) and its corresponding autoconversion rates (kg m-3 s-1). The raw model output data used for deriving these datasets are available on request from tape archives at the DKRZ.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ICN Forecasts: ICON's Stock Poised for Steady Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ICON's (ICLR) Ascent: On the Rise to Pharmaceutical Greatness?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset is associated with the paper entitled "DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems"

Dataset Card for SVGRepo Icons

  Dataset Summary

This dataset contains a large collection of Scalable Vector Graphics (SVG) icons sourced from SVGRepo.com. The icons cover a wide range of categories and styles, suitable for user interfaces, web development, presentations, and potentially for training vector graphics or icon classification models. Each icon is provided under a specific open-source or permissive license, clearly indicated in its metadata. The SVG files in… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/svgrepo.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:ICON ICONIC LABS PLC

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

There was no predefined dataset of party symbols to be usedas a benchmark. We curated a dataset from various nationaland regional websites owned by the ECI. The dataset consists of symbols (image files) of 49 National and State registered parties approved by the ECI. For each image of theoriginal party symbol, 18 different distortions and transformations were created as variations to the training data. Each image is of the dimension 180 x 180. The final labeled dataset consists of 931 images of party symbols with their corresponding party names as the labels.

This directory contains the source code corresponding to the ICML 2018 paper Tree Edit Distance Learning via Adaptive Symbol Embeddings. In particular, it contains the MATLAB (R) script demo.m which performs a metric learning experiment on the 'strings' data set described in the paper. All dependencies to run the script are also included in this distribution.

Dataset Card for SVGFind Icons

  Dataset Summary

This dataset contains a large collection of Scalable Vector Graphics (SVG) icons sourced from SVGFind.com. The icons cover a wide range of categories and styles, suitable for user interfaces, web development, presentations, and potentially for training vector graphics or icon classification models. Each icon is provided under either a Creative Commons license or is in the Public Domain, as clearly indicated in its metadata.… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/svgfind.

The global speed and agility training equipment market is experiencing robust growth, driven by the increasing popularity of fitness and sports training across various demographics. The market's expansion is fueled by several key factors, including rising health consciousness, the growing prevalence of sports participation at both amateur and professional levels, and the increasing adoption of advanced training methodologies focused on improving speed, agility, and overall athletic performance. The market is segmented by application (online sales and offline sales) and equipment type (hydraulic equipment and functional trainers), with online sales showing significant growth potential due to increased e-commerce penetration and readily available online fitness resources. Functional trainers, offering versatility and customization, represent a considerable segment within the market. While the precise market size for 2025 is unavailable, a logical estimation based on industry reports and observed growth trends in related fitness sectors suggests a market value in the range of $1.5 billion to $2 billion. This estimation considers factors like the current market value of related fitness equipment and the projected CAGR. Geographic distribution reveals strong growth in North America and Europe, driven by established fitness cultures and higher disposable incomes. Asia-Pacific is expected to experience significant expansion in the coming years, fueled by increasing participation in sports and fitness activities, and rising awareness of athletic performance enhancement. However, challenges such as high initial investment costs for certain equipment and the need for proper training guidance may act as market restraints. The competitive landscape is marked by established players such as Cybex International, ICON Health and Fitness, and Technogym, alongside emerging brands. The market is characterized by continuous innovation in equipment design and technology, with a focus on data-driven training and personalized fitness solutions. Future growth will likely be driven by factors such as the integration of smart technology, the development of virtual and augmented reality training programs, and the continued expansion of the fitness industry globally. The development and adoption of more accessible and affordable training tools will also play a role in broadening market penetration. The forecast period (2025-2033) promises strong growth, with projections for a compound annual growth rate (CAGR) reflecting the market's dynamism and potential. Specific CAGR projections require more detailed data analysis beyond the provided information.

Dataset Card for svg-icons

  Dataset Description

This dataset contains SVG code examples for training and evaluating SVG models for image vectorization.

  Dataset Structure







  Features

The dataset contains the following fields:

Field Name Description

Filename Unique ID for each SVG

Svg SVG code

  Usage

from datasets import load_dataset

dataset = load_dataset("starvector/svg-icons")… See the full description on the dataset page: https://huggingface.co/datasets/mrfakename/starvector-svg-icons.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ICN ICON ENERGY LIMITED

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Data analysis files associated with the paper "Learning number notations – Comparison of a sign-value and place-value system".Includes the original JASP files, HTML exports of the results presented in each JASP file, the csv file to run all analyses and a ReadMe document which describes and explains all varaibles used in the csv file.Article abstractAlthough numbers are universal, there are great differences between languages and cultures in terms of how they are represented. Numerical notation can influence number processing. Two well-known types of notational systems are sign-value, such as the Roman numeral system, and place-value systems, such as the Indo-Arabic numeral system. What is involved in learning each system? Here we report a study that investigated adults’ abilities to implicitly learn an artificially created sign-value or place-value system. We asked if they could perform symbolic comparison and ordering tasks using the novel symbol system. We found adults could learn the ordinal meaning of symbols within either system and were able to extend the system to symbols not encountered during training. There was a relative advantage of the sign-value system over the place-value system for expressions encountered during the training, but also for expressions that had not previously been encountered. These results shed light on how easily the structure of place-value and sign-value systems can be learned.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ICLR ICON plc Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data