Icon

## Overview

Icon is a dataset for object detection tasks - it contains Exel annotations for 573 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

<<< This dataset is not released yet. Release date: 1st September, 2025. >>>

The Cartographic Sign Detection Dataset (CaSiDD) comprises 796 manually annotated historical map samples, corresponding to 18,750 cartographic signs, like icons and symbols. Moreover, the signs are categorized into 24 distinct classes, like tree, mill, hill, religious edifice, or grave. The original images are part of the Semap dataset [1].

The dataset is published in the context of R. Petitpierre's PhD thesis: Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration [2]. Details on annotation, and statistics on annotated cartographic signs are provided in the manuscript.

Organization of the data

To come soon.

Descriptive statistics

Number of distinct classes: 24 + hapaxes
Number of image samples: 796
Number of annotations: 18,750
Study period: 1492–1948.

Use and Citation

For any mention of this dataset, please cite :

@misc{casidd_petitpierre_2025,
author = {Petitpierre, R{\'{e}}mi and Jiang, Jiaming},
title = {{Cartographic Sign Detection Dataset (CaSiDD)}},
year = {2025},
publisher = {EPFL},
url = {https://doi.org/10.5281/zenodo.16278381}}

@phdthesis{studying_maps_petitpierre_2025,
author = {Petitpierre, R{\'{e}}mi},
title = {{Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration}},
year = {2025},
school = {EPFL}}

Corresponding author

Rémi PETITPIERRE - remi.petitpierre@epfl.ch - ORCID - Github - Scholar - ResearchGate

Work ethics

85% of the data were annotated by RP. The remainder was annotated by JJ, a master's student from EPFL, Switzerland.

License

This project is licensed under the CC BY 4.0 License.

Liability

We do not assume any liability for the use of this dataset.

References

1. Petitpierre R, Gomez Donoso D, Krisel B (2025) Semantic Segmentation Map Dataset (Semap). EPFL. https://doi.org/10.5281/zenodo.16164782
2. Petitpierre R (2025) Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration. PhD thesis. École Polytechnique Fédérale de Lausanne.

AWS Icon Detector

## Overview

AWS Icon Detector is a dataset for object detection tasks - it contains AWS Icons annotations for 210 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

GitHub Icons

## Overview

GitHub Icons is a dataset for object detection tasks - it contains Icons annotations for 339 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

[The SeizeIT1 dataset will no longer be shared upon request due to expiry of the ethical approval. You can get access to SeizeIT2 if you sign up to the challenge at https://biomedepi.github.io/seizure_detection_challenge/] This dataset is obtained during an ICON project (2017-2018) in collaboration with KU Leuven (ESAT-STADIUS), UZ Leuven, UCB, Byteflies and Pilipili. The goal of this project was to design a system using Behind the ear (bhE) EEG electrodes for monitoring the patient in a home environment. This way, a nice balance can be found between sufficient accuracy of seizure detection algorithms (because EEG is used) and wearability (bhe EEG is relatively subtle, similar to a hear-aid device). The dataset acquired in the hospital during presurgical evaluation. During such presurgical evaluation, neurologists try to see if a specific part of the brain is causing the seizures, and if so, if that part of the brain can be removed during surgery. During the presurgical evaluation, patients are monitored using the vEEG for multiple days (typically a week). Patients are however restricted to move within their room because of the wiring and video analysis. In this dataset, following data is available per patient: • Full 10-20 scalp EEG data of the patient during the presurgical evaluation. • Behind-the-ear data (2 sensors positioned behind each ear) • Single-lead ECG data (typically lead II) Seizures are annotated by the clinicians based on the gold standard vEEG system. These seizure annotations are also available in the dataset. In total 82 patients were recorded between 23/01/2017 and 26/10/2018. From those patients, 54 were recorded with the bhe channels. Forty-two of those patients had seizures during their presurgical evaluation, while for twelve patients no seizure has been recorded. The number of seizures per patient ranged from 1 to 22, with a median of 3 seizures per patient. The duration of the seizures, the time difference of seizure EEG onset and end, varied between 11 and 695 seconds with a median of 50 seconds. 89% of the seizures were Focal Impaired Awareness seizures. 91% of the seizures originated from the (fronto-) temporal lobe. In the folder ’Data’ the raw data in the form of .edf, are provided with annotations for all the patients. The annotations are provided in .tsv (tab separated values) files. For every seizure the first column represents the starting point (in seconds) of the seizure, the second one the end point of the seizure, the third one the type of the seizure, while in the last column extra information are provided. The extra information includes the origin of the seizure, the hemisphere and if the seizure can be noted from the behind the ear channels (bhe:1 in that case). In the header section of every file information concerning the dataset and the annotations used are included. For every subject and for every session (even if no seizure is present) two different sets of annotations are provided. The ”a1”set of annotations is the annotations as provided by the doctors. The ”a2” set of annotations are the annotations used in [2] for training of the algorithm. The annotations provided from the doctors were not always perfectly aligned with the typical rhythmic ictal pattern, hence in ”a2” a refinement of the start of each annotation was performed visually by an engineer. Furthermore, in the annotations of the doctor the end point of some seizures was missing (”none”) in the ”a2” subset of annotations each seizure was considered with a stable length of 10 seconds.

Icons

## Overview

Icons is a dataset for object detection tasks - it contains Icons annotations for 5,296 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects, such as key signatures or time signatures. There are 23352 notes in the dataset, of which 21356 have a full notehead, 1648 have an empty notehead, and 348 are grace notes. For each annotated object in an image, we provide both the bounding box, and a pixel mask that defines exactly which pixels within the bounding box belong to the given object. Composite constructions, such as notes, are captured through explicitly annotated relationships of the notation primitives (noteheads, stems, beams...). This way, the annotation provides an explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature.

MUSCIMA++ has annotations for 140 images from the CVC-MUSCIMA dataset [2], used for handwritten music notation writer identification and staff removal. CVC-MUSCIMA consists of 1000 binary images: 20 pages of music were each re-written by 50 musicians, binarized, and staves were removed. We had 7 different annotators marking musical symbols: each annotator marked one of each 20 CVC-MUSCIMA pages, with the writers selected so that the 140 images cover 2-3 images from each of the 50 CVC-MUSCIMA writers. This setup ensures maximal variability of handwriting, given the limitations in annotation resources.

The MUSCIMA++ dataset is intended for musical symbol detection and classification, and for music notation reconstruction. A thorough description of its design is published on arXiv [2]: https://arxiv.org/abs/1703.04824 The full definition of the ground truth is given in the form of annotator instructions.

Analysis of ‘Board Games’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/board-games on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains data collected on board games from the BoardGameGeek (BGG) website in February 2021. BGG is the largest online collection of board game data which consists of data on more than 100,000 total games (ranked and unranked).

The voluntary online community contributes to the site with reviews, ratings, images, videos, session reports and live discussion forums on the expanding database of board games.

This data set contains all ranked games (~20,000) as of the date of collection from the BGG database. Unranked games are ignored as they have not been rated by enough BGG users (a game should receive at least 30 votes to be eligible for ranking).

How to use this dataset

• Predict board game rating based on its mechanics and features.
• Explore the landscape of board games

Highlighted Notebooks

• Board Game Analysis by Karnika Kapoor
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

Dilini Samarasinghe, July 5, 2021, "BoardGameGeek Dataset on Board Games", IEEE Dataport, doi: https://dx.doi.org/10.21227/9g61-bs59.

License

CC BY 4.0

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Icon by Freepik on FlatIcon.

--- Original source retains full ownership of the source dataset ---

App Icon

## Overview

App Icon is a dataset for object detection tasks - it contains Icon annotations for 770 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This archive contains A) the data and B) the analysis scripts corresponding to the manuscript “Controls on subtropical cloud reflectivity during a waterbelt scenario for the Cryogenian glaciations” by Christoph Braun [1], Aiko Voigt [2], Corinna Hoose [1], Annica M. L. Ekman [3], and Joaquim G. Pinto [1] [1] IMK-TRO, Karlsruhe Institute of Technology [2] IMG, University of Vienna [3] Department of Meteorology and Bolin Center for Climate Research, Stockholm University, Stockholm, Sweden Please contact Christoph Braun (christoph.braun@kit.edu) or Aiko Voigt (aiko.voigt@univie.ac.at) if you have questions regarding the content of this archive. A) Data The dataset contains postprocessed model output data produced with the general circulation models “Community Atmosphere Model v3.1” (CAM) from the National Center for Atmospheric Research, Boulder, Colorado, and “ICOsahedral Nonhydrostatic” model with the climate physics package (version AES 1.3.00), and the numerical weather prediction and large eddy model packages (version 2.6.1) from the Max-Planck Institute of Meteorology, Hamburg, Germany and the German Weather Service, Offenbach, Germany. The dataset contains - 2d data for all global simulations and 3d data for selected global simulations; zonal-mean and global-mean monthly-mean and climatological data (format: netcdf) - 2d data, cross sections and vertical profiles for ICON-NWP model hierarchy - initial and boundary conditions (format: netcdf) or the scripts to obtain initial and boundary conditions from conducted simulations - runscripts (located in the subdirectories corresponding to each model) The data is organized in 9 subdirectories. 4 subdirectories contain the simulation output data corresponding to the 4 models CAM, ICON-AES, ICON-NWP, and ICON-LEM. We describe how we obtained and modified the models in the files “CAM_description”, “ICON-AES_description”, and “ICON-NWP_description”. We also provide the runscripts for the conducted simulations. One subdirectory contains the grids required for the simulations as well as the files to create the limited area grids using the grid generator provided by the DWD ICON-TOOLS 2.3.3. 4 sudirectories contain the initial and boundary conditions as well as the remapping scripts to obtain the initial and boundary conditions for the ICON-NWP/-LEM model hierarchy. The latter is described in the file “Create_initial_and_boundary_conditions_ICON-NWP_and_ICON-LEM”. Postprocessing of the simulation output data was performed using the Climate Data Operator software provided by DKRZ. B) Analysis scripts The analysis scripts are located in the subdirectory “analysis_scripts”. The directory "analysis" contains intermediate results generated by the analysis scripts and the directory "figures" contains the figures generated by the analysis scripts. To run the analysis scripts we applied the Kernel „Python 3“ as provided by the German Climate Computing Center (DKRZ).

The Satellite Ships Segmentation Dataset is a specialized collection for remote sensing applications, derived from high-resolution satellite imagery with dimensions ranging from 14,722 x 20,949 to 38,133 x 14,604 pixels. This dataset is focused on semantic segmentation, featuring annotations for ships including Automatic Identification System (AIS) information and satellite icon notes, facilitating detailed maritime monitoring and analysis.

The ICONS Atlas provides online access to global crustal and lithospheric structure data for 242 intercontinental sedimentary basins.

The ICONS Atlas was created by Dr Christian Heine as part of a PhD project investigating the formation and evolution of intracontinental basins. The ICONS Atlas combines information about crustal structure, plate kinematics and mantle dynamics in one coherent HTML-based presentation. The aim is to provide quantitative constraints for next-generation basin modeling approaches which integrate deep-earth dynamics, plate tectonics and lithospheric modeling.

The full version of the ICONS Atlas uses a PostGIS geospatial database and a series of Python and XML based workflows to compute grids and tie plate tectonic data to mantle convection models. All software tools used within the ICONS Atlas are open-source and utilise non-proprietary file formats.

This archive contains A) the data and B) the analysis scripts corresponding to the manuscript “Ice-free tropical waterbelt during Snowball Earth events questioned by uncertain clouds” by Christoph Braun [1], Johannes Hörner [2], Aiko Voigt [2], and Joaquim G. Pinto [1] [1] IMK-TRO, Karlsruhe Institute of Technology [2] IMG, University of Vienna Please contact Christoph Braun (christoph.braun@kit.edu) or Aiko Voigt (aiko.voigt@univie.ac.at) if you have questions regarding the content of this archive. A) Data The dataset contains postprocessed model output data produced with the general circulation models “Community Atmosphere Model v3.1” (CAM) from the National Center for Atmospheric Research, Boulder, Colorado, and “ICOsahedral Nonhydrostatic” model with the climate physics package (version AES 1.3.00) from the Max-Planck Institute of Meteorology, Hamburg, Germany. The dataset contains - 2d data for all simulations and 3d data for selected simulations; zonal-mean and global-mean monthly-mean and climatological data (format: netcdf) - initial and boundary conditions (format: netcdf) - runscripts (located in the subdirectories corresponding to each model) - analysis code The data is organized in 5 subdirectories, each corresponding to one of the following model configurations. - CAM - CAM pCOOKIE - CAM pCOOKIE subtropical columns - ICON - ICON WBF We describe how we obtained and modified CAM and ICON in the files “CAM_description” and “ICON_description”. We also provide the runscripts for the conducted simulations. We further describe the postprocessing steps performed with CDO in the file “Postprocessing_CDO”. B) Analysis scripts The analysis scripts are located in the subdirectory “analysis_scripts”. The directory "analysis" will contain intermediate results generated by the analysis scripts and the directory "figures" will contain the figures generated by the analysis scripts. To run the analysis scripts we applied the following libraries as provided by the German Climate Computing Center (DKRZ) using the Kernel „ESMValTool“. * xarray (0.17.0) * numpy (1.20.1) * matplotlib (3.3.4) * scipy (1.5.3) * cftime (1.2.1) * pandas (1.2.2) * statsmodels (0.12.2)

The Real-World Evidence (RWE) Solutions market is experiencing robust growth, projected to reach $828.46 million in 2025 and expand at a compound annual growth rate (CAGR) of 13% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of RWE in regulatory decision-making, fueled by the need for more efficient and cost-effective drug development, is a primary driver. Furthermore, the rising availability of large, diverse datasets from electronic health records (EHRs), claims databases, and wearable devices provides rich sources of real-world data for analysis. Pharmaceutical companies and healthcare providers are actively investing in RWE solutions to improve clinical trial design, enhance post-market surveillance, and optimize treatment strategies, further bolstering market growth. The market is segmented by type (e.g., software, services) and application (e.g., drug development, post-market surveillance), each exhibiting unique growth trajectories influenced by specific technological advancements and regulatory landscapes. Competitive strategies among leading companies, such as Clinigen Group Plc, ICON Plc, and IQVIA Inc., focus on strategic partnerships, technological innovation, and expansion into new geographical markets. These companies are engaged in developing advanced analytical tools and data integration platforms to cater to growing demands for comprehensive RWE solutions. The North American market currently holds a substantial share, driven by robust regulatory frameworks and advanced healthcare infrastructure. However, other regions, particularly Asia Pacific, are expected to witness significant growth in the coming years due to increasing healthcare expenditure and technological advancements. The restraints on market growth are primarily related to data privacy concerns, regulatory hurdles in accessing and utilizing real-world data, and the need for robust data standardization across different sources. However, proactive measures like developing better data security protocols, clarifying regulatory guidelines, and investing in data harmonization initiatives are mitigating these challenges. The future of the RWE Solutions market hinges on continuous technological innovation, particularly in areas like artificial intelligence (AI) and machine learning (ML), which can enhance data analysis and generate valuable insights from complex datasets. Further growth will depend on fostering collaboration among stakeholders, including regulatory bodies, healthcare providers, and technology companies, to create a more conducive environment for RWE adoption.

Icon Detection

## Overview

Icon Detection is a dataset for object detection tasks - it contains Icons annotations for 1,038 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Icon Finder

## Overview

Icon Finder is a dataset for object detection tasks - it contains Icons annotations for 290 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1 Icon

## Overview

1 Icon is a dataset for object detection tasks - it contains Study Room annotations for 361 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Icons Detector

## Overview

Icons Detector is a dataset for object detection tasks - it contains Captcha Icons annotations for 5,879 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

App Ui Icons

## Overview

App Ui Icons is a dataset for object detection tasks - it contains Objects annotations for 414 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Icon Detection With Picture Background

## Overview

Icon Detection With Picture Background is a dataset for object detection tasks - it contains Icon annotations for 313 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).