Omkar2118/datasets huggingface dataset:
CVE
Full name: Common Vulnerabilities and Exposures

CVE stands for Common Vulnerabilities and Exposures. CVE is a glossary that classifies vulnerabilities. The glossary analyzes vulnerabilities and then uses the Common Vulnerability Scoring System (CVSS) to evaluate the threat level of a vulnerability. A CVE score is often used for prioritizing the security of vulnerabilities.
standard atomic contexts
Full name: standard contexts for the lattices of atomic lattices

The dataset contains standard contexts of the lattices of all atomic lattices in the Concept Explorer format.

Introducing paper: On the Cryptomorphism between Davis' Subset Lattices, Atomic Lattices, and Closure Systems under T1 Separation Axiom
High-Resolution Stereo Scans of 100 Sorghum Panicles

This dataset contains stereo images, depth data, camera position/orientation, and camera information for 100 sorghum panicles (the seed-bearing head of the sorghum stalk), as well as semantic segmentation labels for a subset of the data. The 100 sampled sorghum stalks are drawn from 10 different species, in groups of 10. For image capture an illumination-invariant flash camera developed at CMU was swept around the panicle using a UR5 robotic arm, and approximately 150 image pairs were captured for each panicle, giving a full 3D view of each stalk.

Introducing paper: 3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection
MSU FR VQA Database
Full name: MSU Full-Reference Video Quality Assessment Database

The dataset was created for video quality assessment problem. It was formed with 36 clips from Vimeo, which were selected from 18,000+ open-source clips with high bitrate (license CCBY or CC0).

The clips include videos recorded by both professionals and amateurs. Almost half of the videos contain scene changes and high dynamism. Moreover, the synthetic to natural lightning ratio is approximately 1 to 3.

Content type: nature, sport, humans close up, gameplays, music videos, water stream or steam, CGI
Effects and distortions: shaking, slow-motion, grain/noisy, too dark/bright regions, macro shooting, captions (text), extraneous objects on the camera lens or just close to it
Resolution: 1920x1080 as the most popular modern video resolution (more in the future)
Format: yuv420p
FPS: 24, 25, 30, 39, 50, 60
Videos duration: mainly 10 seconds
Such content diversity helps simulate near-realistic conditions. The choice of videos collected for the benchmark dataset employed clustering in terms of space-time complexity to obtain a representative distribution.

For compression we used 40 codecs of 10 compression standards (H.264, AV1, H.265, VVC, etc.). Each video was compressed with 3 target bitrates: 1,000 Kbps, 2,000 Kbps, and 4,000 Kbps, and different real-life encoding modes: constant quality (CRF) and variable bitrate (VBR). The choice of bitrate range simplifies the subjective comparison procedure since the video quality is more difficult to distinguish visually at higher bitrates.

The subjective assessment involved pairwise comparisons using crowdsourcing service Subjectify.us. To increase the relevance of the results, each pair of videos received at least 10 responses from participants. In total, 766362 valid answers were collected from more than 10800 unique participants.

Introducing paper: Video compression dataset and benchmark of learning-based video-quality metrics