This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v3.0", "robot_type": "unknown", "total_episodes": 1804, "total_frames": 338188, "total_tasks": 24, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:1804" }, "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4"… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/fmb.

Irc_test

## Overview

Irc_test is a dataset for object detection tasks - it contains Bb annotations for 90 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).

The Alzheimer's Disease Multiclass Dataset contains approximately 44,000 MRI images categorized into four distinct classes based on the severity of Alzheimer's disease. This dataset is intended for use in machine learning model training and testing. All images are skull-stripped and clean of non-brain tissue.

Dataset Structure The dataset is organized into the following four directories, each representing a different class of disease severity: NonDemented: Contains 12,800 MRI images of subjects with no signs of dementia. VeryMildDemented: Contains 11,200 MRI images of subjects with very mild symptoms of dementia. MildDemented: Contains 10,000 MRI images of subjects with mild dementia. ModerateDemented: Contains 10,000 MRI images of subjects with moderate dementia.

Image Details Total Number of Images: 44,000 Image Format: MRI scans as .JPG files Image Usage: Suitable for training and testing machine learning models focused on classifying Alzheimer's disease stages.

Disease Severity Classification The dataset follows a severity ranking system for Alzheimer's disease: NonDemented: No dementia. Very Mild Demented: Early signs of dementia, very mild symptoms. Mild Demented: Clear signs of dementia, but still mild. Moderate Demented: More pronounced symptoms of dementia, moderate severity.

This dataset is an augmented and upsampled version of the dataset below: https://www.kaggle.com/datasets/uraninjo/augmented-alzheimer-mri-dataset-v2

This dataset was upsampled as the original dataset had a large class imbalance.

The Canadian Institute for Cybersecurity has published several datasets for network intrusion detection. Four of them: CIC-IDS2017, CIC-DoS2017, CSE-CIC-IDS2018 and CIC-DDoS2019 are collated here into one collection, cleaned up and with harmonized labeling.

The intent behind this collection is simple: to have a larger, more varied set of NIDS samples for more powerful analyses by researchers. Too often, researchers still rely on the individual datasets even though the full set is compatible out-of-the-box. The parts have been created for the same purpose and they have been processed with the same feature extraction tool chain.

This collection also takes into account 2 articles in which flawed features were discovered. Those features have been removed from the dataset. See the cleanup notebook for more information.

If you make use of this combined version, please credit the original authors. The relevant publications are cited here on Kaggle alongside the individual datasets and they are also readily available at the CIC's official dataset distribution page

Hydrometeorological time series and catchment attributes from the CABra dataset. The manuscript of "CABra: a novel large-sample dataset for Brazilian catchments" is under review in Hydrology and Earth System Sciences (HESS) journal.

Here we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land-use and land-cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model and the coordinates of the streamflow stations provided by the Brazilian Water Agency (ANA), where only the stations with 30 years (1980-2010) of data and less than 10% of missing records were included. Catchment areas range from 9 to 4,800,000 km² and the mean daily streamflow varies from 0.02 to 9 mm day^-1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage areas for each catchment, aside from more than 100 attributes within the attribute classes.

Data can also be accessed at: thecabradataset.shinyapps.io/CABra

* This version includes water demand in CABra catchments for 2020 and 2040 (projection).

Insulator Full

## Overview

Insulator Full is a dataset for object detection tasks - it contains Insulator annotations for 394 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).

Human_push

## Overview

Human_push is a dataset for object detection tasks - it contains Agent Object annotations for 608 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).

Credits - Bappy, Md Tusher Ahmad; Hasan Rabbi, Kazi Mehedi ; Ahmed, Md Jonayed; Zeesan, Md Mahin; Rahman, Md. Wahidur (2023), “SeasVeg: An Image Dataset of Bangladeshi Seasonal Vegetables”, Mendeley Data, V1, doi: 10.17632/s6gyxb2cg9.1

This dataset named "SeasVeg" includes Ten different types of seasonal vegetables from various regions in Pabna and Dhaka, Bangladesh. The ten different categories are Carica Papaya, Momordica dioica, Abelmoschus esculentus, Lablab purpureus, Trichosanthes cucumerina, Trichosanthes dioica, Solanum Iycopersicum, Brassica oleracea, Momordica charantia, Raphanus sativus. The dataset contains a total of 1,500 original images, along with an additional 3000 augmented images. Each original image is under natural lighting conditions with an appropriate background.

so it is important to develop accurate and efficient models.

Kansallisgalleria - The Finnish National Gallery art museum - Copyright Free Images. Upscaled with Real-ESRGAN.

This dataset contains the digitized treatments in Plazi based on the original journal article Konstantinov, Alexander S. (2005): New species of Middle Asian Longitarsus Latreille with discussion of their subgeneric placement (Coleoptera: Chrysomelidae). Zootaxa 1056: 19-42, DOI: 10.5281/zenodo.170096

Abstract

The subgenus Testergus Weise of Longitarsus Latreille is redefined based on the characters of male genitalia and four new species are described and illustrated: L. borisi sp. nov. (Tadzhikistan), L. danilevskyi sp. nov. (Kazakhstan), L. igori sp. nov. (Tadzhikistan), and L. nadiae sp. nov. (Kirgizstan). The lectotypes for L. fuscoaeneus Redtenbacher, L. tmetopterus Jacobson and L. nurataicus Palij are designated. A key to the known Testergus species from Greece, Caucasus and Middle Asia is provided.

Key words: Chrysomelidae, flea beetle, Longitarsus, Testergus, Truncatus

This dataset contains 12,680 ultrasound images of Polycystic Ovary Syndrome (PCOS) cases, sourced from publicly available datasets. It is intended to support research in the development of machine learning models for automated PCOS detection and the application of Explainable AI techniques. The dataset can be used for training, validation, and testing purposes in medical image analysis, offering diverse cases for exploring classification accuracy in PCOS diagnosis.

Agriculture-Plan-Diseases-QA-Pairs-Dataset

Released freely — support more experiments like it:

More: https://huggingface.co/YuvrajSingh9886

DEADF1SH/self-alignment-dataset-a3 dataset hosted on Hugging Face and contributed by the HF Datasets community

Soldier_Tank_detection

## Overview

Soldier_Tank_detection is a dataset for object detection tasks - it contains Tank annotations for 3,634 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).

The University of Washington Indoor Object Manipulation (UW IOM) dataset comprises videos (and corresponding skeletal tracking information) of twenty participants within the age group of 18-25 years, of which fifteen are males and the remaining five are females. The videos are recorded using a Kinect Sensor for Xbox One at an average rate of twelve frames per second. Each participant carries out the same set of tasks in terms of picking up six objects (three identical empty boxes and three identical rods) from three different vertical racks, placing them on a table, putting them back on the racks from where they are picked up, and then walking out of the scene carrying the box from the middle rack. The boxes are manipulated with both the hands while the rods are manipulated using only one hand. The above tasks are repeated in the same sequence three times such that the duration of every video is approximately three minutes. We categorize the actions into seventeen labels, where each label follows a four-tier hierarchy. The first tier indicates whether the box or the rod is manipulated, the second tier denotes human motion (walk, stand, and bend), the third tier captures the type of object manipulation if applicable (reach, pick-up, place, and hold), and the fourth tier represents the relative height of the surface where manipulation is taking place (low, medium, and high).

Specific up-to-date data on each of the playable characters in Genshin Impact. Selected data include vision, rarity, release date, character stats and various ascension materials. Other fields may also be added as requested.

Dataset Notes:

All characters belong to HoYoVerse. All character data was scraped from the Genshin Impact Fandom Wiki using the BeautifulSoup package in Python but may still have logical inconsistencies. Note that the .csv file is encoded in the 'UTF-8' format to ensure proper display of special characters.

Dataset updates:

Version 1 (13 Aug 2024): All playable characters available as of game version 4.8, at the end of Fontaine and right before the Natlan release.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

Dataset

This dataset was created by Muhammad Hassan

Contents

The data file requires special tools for extracting. These tools are available at our support GitHub repository: https://github.com/aiqm/ANI1x_datasets