UNSW-NB15 is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,332 records from the different types, attack and normal. Source: Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs Image Source: https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/ Paper: UNSW-NB15: a comprehensive data set for network intrusion detection systems

UCI Machine Learning Repository is a collection of over 550 datasets.

Includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. The dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size. Source: Creative Flow+ Dataset

Titanic Dataset Description ## Overview The data is divided into two groups: - Training set (train.csv): Used to build machine learning models. It includes the outcome (also called the "ground truth") for each passenger, allowing models to predict survival based on “features” like gender and class. Feature engineering can also be applied to create new features. - Test set (test.csv): Used to evaluate model performance on unseen data. The ground truth is not provided; the task is to predict survival for each passenger in the test set using the trained model. Additionally, gender_submission.csv is provided as an example submission file, containing predictions based on the assumption that all and only female passengers survive. --- ## Data Dictionary | Variable | Definition | Key | |------------|------------------------------------------|-------------------------------------------------| | survival | Survival | 0 = No, 1 = Yes | | pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd | | sex | Sex | | | age | Age in years | | | sibsp | # of siblings/spouses aboard the Titanic | | | parch | # of parents/children aboard the Titanic | | | ticket | Ticket number | | | fare | Passenger fare | | | cabin | Cabin number | | | embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton | --- ## Variable Notes - pclass: Proxy for socio-economic status (SES): - 1st = Upper - 2nd = Middle - 3rd = Lower - age: - Fractional if less than 1 year. - Estimated ages are represented in the form xx.5. - sibsp: Defines family relations as: - Sibling: Brother, sister, stepbrother, stepsister. - Spouse: Husband, wife (excluding mistresses and fiancés). - parch: Defines family relations as: - Parent: Mother, father. - Child: Daughter, son, stepdaughter, stepson. Some children traveled only with a nanny, so parch = 0 for them.

Dataset of >200 synthetic cardboard texture images that were rendered with DoubeGum's cardboard shader in Blender. Used to generate Parcel3D, the dataset for our paper on single image 3D reconstructions of potentially damaged parcels.

Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine. The dataset is generated with the data generation engine. The engine simulates a user agent and an assistant agent in a dialogue, where the user agent has a persona and the assistant agent has access to a set of tools. Detailed information about the data generation process can be found in the paper. Each entry in the dataset contains: - seed: Gold tool for the generated dialogue. - user_persona: Persona of the simulated user agent. - messages: List of messages in the dialogue, where each message is a dictionary containing: - distractor_tools: List of distractor tools that are not the gold tool but are relevant to the dialogue. These tools are used by user agent to generate utterances that are hard to disambiguate from the gold tool. - retrieved_tools: List of tools retrieved by the assistant agent and used by the assistant agent in order to ask clarifying questions to the user agent.

MediBeng Dataset The MediBeng dataset contains synthetic code-switched dialogues in Bengali and English for training models in speech recognition (ASR), text-to-speech (TTS), and machine translation in clinical settings. The dataset is available under the CC-BY-4.0 license. - Access the dataset on Hugging Face. - For detailed instructions on dataset creation and storage, visit the GitHub Repository ### Dataset Details: - Number of Audio Files: 4800 - Total Duration: 7.11 hours - Number of Speakers: 2 (1 Male, 1 Female) - Utterance Pitch Mean: 335 - 673 Hz - Utterance Pitch Standard Deviation: 210 - 493 Hz - Sampling Rate: 16000 Hz - Data Split: Train and Test - Duration Range: 3.71s - 6.98s - Languages: Code-mixed Bengali-English - Gender Distribution: 1 Male, 1 Female - Total File Size: 324 MB - Speech Type: Medical-related - Data Type: Synthetic - Languages: Bengali, English - Tasks: ASR, TTS, Machine Translation - Context: Clinical (Healthcare) - License: CC-BY-4.0 ### MediBeng Dataset Columns - audio: Synthetic Bengali-English clinical conversations. - text: Code-switched Bengali-English conversations. - translation: English translation. - speaker_name: Speaker's gender (e.g., Male, Female). - utterance_pitch_mean: Mean pitch of the audio in Hertz (Hz). - utterance_pitch_std: Pitch variation (standard deviation in Hertz). ### Dataset Creation: 1. Audio Collection: Conversations in Bengali-English for healthcare. 2. Transcription: Code-switched sentences. 3. Translation: Code-switched sentences English translation. 4. Feature Engineering: Calculating pitch features. 5. Storage: Available in Parquet format on Hugging Face. ### Citation: bibtex @misc{promila_ghosh_2025, author = {Promila Ghosh}, title = {MediBeng (Revision b05b594)}, year = 2025, url = {https://huggingface.co/datasets/pr0mila-gh0sh/MediBeng}, doi = {10.57967/hf/5187}, publisher = {Hugging Face} }

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2, 370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209, 000 satellite images. Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction.

A public open dataset of synthetic chest X-ray images of COVID-19. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images. Images are generated using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training from source datasets RSNA Kaggle Dataset and COVID-19 Image Data Collection. Implementation of the algorithm is available here.

About Dataset The File contains 3D point cloud data of a Fabricate plant with 10 sequences. Each sequence contains 0-19 days data at every growth stage of the specific sequence. The Importance of Synthetic Plant Datasets Synthetic Plant Datasets: Synthetic plant FIle are carefully curated collections of computer- Beginning images that mimic the diverse appearance and growth stages of real plants. Training and Evaluation: By fabricated plant file, researchers can train and evaluate machine learning models in a controlled environment, free from the limitations of real-world data collection. This controlled setting enables more efficient code development and ensures consistent performance across various environmental conditions. Applications in Agricultural Technology Plant Phenotyping: Synthetic plant file enable researchers to analyze plant traits and characteristics on a large scale, facilitating plant phenotyping studies aimed at understanding genetic traits, environmental influences, and crop performance. Crop Monitoring: With the rise of precision agriculture, fabricated plant file play a crucial role in developing remote sensing techniques for monitoring crop health, detecting pest infestations, and optimizing irrigation strategies. Advancements in Computer Vision and Machine Learning Object Detection: It serve as benchmarking tools for training and evaluating object detection algorithms tailored to identifying plants, fruits, and diseases in agricultural settings. Future Directions and Challenges Dataset Diversity: As the demand for more diverse and realistic grows, researchers face the challenge of generating data that accurately reflects the variability observed in real-world agricultural environments. Researchers continue to explore techniques for bridging the gap between synthetic and real data to enhance model robustness and applicability. Conclusion Synthetic plant datasets represent a cornerstone in the development of cutting-edge technologies for agricultural monitoring, plant phenotyping, and disease diagnosis. By harnessing the power of synthetic data generation and machine learning, researchers can unlock new insights into plant biology and revolutionize the future of agriculture. This dataset is sourced from Kaggle.

TF1-EN-3M: Three Million Synthetic Moral Fables for Open Language Models TF1-EN-3M is a large-scale synthetic dataset of 3,000,000 English-language moral fables, generated by instruction-tuned language models with no more than 8 billion parameters. The stories are aimed at child-friendly educational and moral reasoning applications and follow a consistent six-part narrative scaffold: character → trait → setting → conflict → resolution → moral. ## Dataset Characteristics - Size: 3 million stories (~1B tokens) - Format: JSON Lines, with detailed metadata including prompt elements, model configuration, generation time, token counts, and costs - Generation Models: Evaluated across 10 open-weight LLMs; final dataset generated using LLaMA-3.1-8B-Instruct for optimal quality and cost balance - Story Structure: Each story ends with an explicit moral and follows a template-driven structure - Target Audience: Designed primarily for children aged 4–7 (age group B), with simple vocabulary and accessible narratives ## Motivation Natural language processing lacks large, structured corpora of fables that combine creative storytelling with explicit moral lessons. Existing human-authored datasets like Aesop's Fables are limited in scale and diversity. TF1-EN-3M bridges this gap by: - Demonstrating that mid-sized open models can reliably generate coherent, instructive stories - Enabling research into value alignment, narrative intelligence, and low-resource model fine-tuning - Offering a reproducible, cost-efficient alternative to proprietary LLM pipelines ## Summary of Content Each entry in the dataset includes: - A structured prompt with narrative elements - The generated fable text - Metadata (model name, inference time, token usage, cost, etc.) - Quality assessments (via LLM-based scoring for grammar, creativity, moral clarity, and structure adherence) ## Use Cases TF1-EN-3M is suitable for a wide range of tasks and applications: - Training small or medium-sized LLMs for story generation or moral reasoning - Benchmarking models on tasks like moral inference, story-to-moral mapping, or story quality evaluation - Educational AI tools, such as interactive storytelling tutors or automated moral education platforms - Creative NLP research, including literary analysis and narrative generation - Multilingual extension by swapping out prompt elements for other languages ## Citation If you use TF1-EN-3M, please cite: bibtex @misc{nadas2025tf1en3m, title={TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models}, author={Mihai Nădaș and Laura Dioșan and Andreea Tomescu and Andrei Pișcoran}, year={2025}, eprint={2504.20605}, archivePrefix={arXiv}, primaryClass={cs.CL} } ## Dataset Access - Hugging Face: klusai/ds-tf1-en-3m - Generation & Evaluation Code: TinyFabulist GitHub