monolingual.tgz contains a vast collection of Ancient Chinese sentences. trans.tgz combines vairous types of parallel corpora, with Ancient Chinese sentences aligned with Modern Chinese sentences. finetune.tgz selects certain classical books as benchmarks, with Ancient Chinese sentences aligned with Modern Chinese sentences. The data in trans.tgz and finetune.tgz does not overlap. More information can be found here RUCAIBox/Erya (github.com).

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.

According to the Wikipedia, an ultramarathon, also called ultra distance or ultra running, is any footrace longer than the traditional marathon length of 42.195 kilometres (26 mi 385 yd). Various distances are raced competitively, from the shortest common ultramarathon of 31 miles (50 km) to over 200 miles (320 km). 50k and 100k are both World Athletics record distances, but some 100 miles (160 km) races are among the oldest and most prestigious events, especially in North America.}

The data in this file is a large collection of ultra-marathon race records registered between 1798 and 2022 (a period of well over two centuries) being therefore a formidable long term sample. All data was obtained from public websites.

Despite the original data being of public domain, the race records, which originally contained the athlete´s names, have been anonymized to comply with data protection laws and to preserve the athlete´s privacy. However, a column Athlete ID has been created with a numerical ID representing each unique runner (so if Antonio Fernández participated in 5 races over different years, then the corresponding race records now hold his unique Athlete ID instead of his name). This way I have preserved valuable information.

The dataset contains 7,461,226 ultra-marathon race records from 1,641,168 unique athletes.

The following columns (with data types) are included:

• Year of event (int64)
• Event dates (object)
• Event name (object)
• Event distance/length (object)
• Event number of finishers (int64)
• Athlete performance (object)
• Athlete club (object)
• Athlete country (object)
• Athlete year of birth (float64)
• Athlete gender (object)
• Athlete age category (object)
• Athlete average speed (object)
• Athlete ID (int64)

The Event name column include country location information that can be derived to a new column, and similarly seasonal information can be found in the Event dates column beyond the Year of event (these can be extracted with a bit of processing).

The Event distance/length column describes the type of race, covering the most popular UM race distances and lengths, and some other specific modalities (multi-day, etc.):

• Distances: 50km, 100km, 50mi, 100mi
• Lengths: 6h, 12h, 24h, 48h, 72h, 6d, 10d

Additionally, there is information of age, gender and speed (in km/h) in other columns.

Dataset Card for "Darija-Stories-Dataset"

Darija (Moroccan Arabic) Stories Dataset is a large-scale collection of stories written in Moroccan Arabic dialect (Darija).

  Dataset Description

Darija (Moroccan Arabic) Stories Dataset contains a diverse range of stories that provide insights into Moroccan culture, traditions, and everyday life. The dataset consists of textual content from various chapters, including narratives, dialogues, and descriptions. Each story… See the full description on the dataset page: https://huggingface.co/datasets/alielfilali01/Darija-Stories-Dataset.

Dataset

This dataset was created by JAYAPRAKASHPONDY

Released under CC0: Public Domain

Contents

Push In Out

## Overview

Push In Out is a dataset for object detection tasks - it contains Push In Out annotations for 2,814 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 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

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area.

Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency.

For additional project information, see the INGENIOUS project site linked in the submission.

Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

This dataset is based on the CityFlow benchmark. Due to licensing restrictions, users are advised to download the dataset from the official source. This upload is intended only for experimental and educational purposes.

Ailiance — Embedded Systems & Linux Q&A

🇫🇷 Ailiance — curated by Ailiance for production deployment ; co-published with the upstream electron-rare/mascarade-embedded-dataset. 🇪🇺 Compatible EU AI Act (Template AI Office, July 2025).

Q&A bilingue (FR/EN) sur les systèmes embarqués génériques : RTOS (FreeRTOS, Zephyr), Linux embarqué (Yocto, Buildroot), device tree, bootloaders (U-Boot, BL31), drivers noyau, et toolchains croisées.

  Statistics

Métrique… See the full description on the dataset page: https://huggingface.co/datasets/Ailiance-fr/mascarade-embedded-dataset.

This depository contains extracted data used for the proof-of-concept classifier training in the study Non-Emergency Notification Timing in Autonomous Vehicles. It is part of the Non-Emergency Notification Timing in Autonomous Vehicles artifact collection: 10.1184/R1/c.7894613. We created clips of behavioral and vehicular data streams (see 10.1184/R1/29410371, "Processed Data", for the data origin) with various time windows (ranging from 5s to 20s) and calculated the mean, standard deviation, and "change" (difference between means in the first and second half of the time window) of individual data streams. This results in 700 data points in total. Note that three participants (D101, D109, and D110) always provided positive labels during their sessions. For more details about the content of this dataset, please refer to README.txt.

The ARG Database is a huge collection of labeled and unlabeled graphs realized by the MIVIA Group. The aim of this collection is to provide the graph research community with a standard test ground for the benchmarking of graph matching algorithms.

Local Air Quality Management Areas (AQMA) in York. For further information on AQMAs please visit City of York Council's website. For more air quality related data please see the following datasets: • Air Quality Monitoring Stations Locations in York • York Air Quality Monitoring Stations Data • Diffusion Tubes (NO2) Locations in York • York Diffusion Tubes (NO2) Data *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Merge 3.3

## Overview

Merge 3.3 is a dataset for object detection tasks - it contains Trash Objects 207U OxvF 1C4 Vg0q annotations for 1,529 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 Building TimeSeries (BTS) dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique Brick classes. Moreover, the metadata is standardized using the Brick schema.To get started, download the data and run the DIEF_inspect_raw.ipynb file.For more info, including data cards: https://github.com/cruiseresearchgroup/DIEF_BTS

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.

This dataset contains the digitized treatments in Plazi based on the original journal article Elizabeth Moore, Terrence Gosliner (2014): Additions to the Genus Phyllodesmium, with a Phylogenetic Analysis and its Implications to the Evolution of Symbiosis. The Veliger 51 (4): 237-251, DOI: 10.5281/zenodo.20947

A subset of the full dataset demonstrating data structure, geospatial precision, and column headers.

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 is data of cortisol concentrations in aceh cattle before and after transportation and data of testosterone concentrations in bulls and cows measured using a commercial enzyme-linked immunosorbent assay kits (DRG Instrument GmbH, Germany). In this data there are additional data of cortisol (11 samples before and after transportation) and testosterone (10 bulls and 10 cows)