Omkar2118/datasets huggingface dataset:
PaviaATN

The PaviaATN data consists of 62 4-channel fluorescence microscopy images of size 2720 × 2720. The four channels, in order, label Nuclei (first two), Actin and Tubulin. It was imaged in the Synthetic Physiology Laboratory(https://www.syntheticphysiologylab.com/) of the University of Pavia, and introduced in μSplit: image decomposition for fluorescence microscopy(https://arxiv.org/abs/2211.12872), published at ICCV 2023.

Detailed Description
The PaviaATN dataset comprises static lambda-stacks from a human keratinocytes cell line (HaCaT) expressing GFP-tubulin, RFP-LifeAct, and a customized version of the cell cycle indicator FastFUCCI that uses various combinations of a yellow fluorescent protein (YPF, mTurquoise2) and a far-red fluorescent protein (iRFP, miRFP670) to indicate multiple phases of the cell cycle. 

When a cell is in the G1 phase, increasing intensities of YFP fluorescence are detected in the nucleus. As a cell moves from G1 to S phase (G1/S), both YFP and iRFP fluorescence are detected in the nucleus of the cell. 
Finally, the sole iRFP fluorescence is detected in the nucleus during the S-G2-M phase. At the onset of the G1 phase, the nucleus shows no visible fluorescence intensity. In [μSplit: image decomposition for fluorescence microscopy](https://arxiv.org/abs/2211.12872), this dataset was used to resolve overlapping structures.

All Images were acquired in a Nikon Ti2 microscope (100x silicon oil objective) equipped with an Okolab environmental control chamber and a Crest V3 spinning disk confocal in widefield mode.

Introducing paper: μSplit: efficient image decomposition for microscopy data
Laser Data

This dataset contains two types of audio recordings. The first set of audio recordings consists of MEMS microphone response to acoustic activities (e.g., 19 participants reading provided text in front of the Google Home Smart Assistant). The second set of audio recordings consists of MEMS microphone response to photo-acoustic activities (laser modulated--with audio recordings of 19 participants, firing at the MEMS microphone of Google Home Smart Assistant). A total of 19 students (10 male and 9 female) were enrolled for data collection. All participants were asked to read the following 5 sentences in the microphone, Hey Google, Open the garage door, Hey Google, Close the garage door, Hey Google, Turn the light on, Hey Google, Turn the light off, Hey Google, What is the weather today?. Each audio sample was injected into the microphone through a laser, and the response of the microphone was recorded. This method produced a total data set of 95 acoustic- and 95 laser-induced audio recordings.

Introducing paper: Protecting Voice-Controlled Devices against LASER Injection Attacks
CommercialAdsDataset
Full name: CommercialAdsDataset

A large commercial Ads Dataset includes 480K labeled query-ad pairwise data with structured information of image, title, seller, description, and so on.

Introducing paper: AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search
SSCBench

SSCBench establishes a large-scale SSC benchmark in street views that facilitates the training of robust and generalizable SSC models. Overall, SSCBench consists of three subsets, including 38,562 frames for training, 15,798 frames for validation, and 12,553 frames for testing respectively, amounting totally to 66,913 frames.

Introducing paper: SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving
MSU Video Saliency Prediction

The dataset presents open high-resolution test clips set with different types of content: movie fragments, sport streams, live caption clips.  Used clips of 1920×1080 resolution and with duration from 13 to 38 seconds. And Performed reliable data collection from 50 observers (19–24 y. o.) using 500 Hz SMI iViewXTM Hi-Speed 1250 eye-tracker. Also used cross-fade which ensures the independence of the received fixations between different clips. The final ground-truth saliency map was estimated as a Gaussian mixture with centers at the fixation points. A standard deviation for the Gaussians equal to 120 was chosen (this value matches 8 angular degrees, which is known to be the sector of sharp vision).
JoinGym

All possible intermediate result cardinalities for 3300 queries on IMDb.

Introducing paper: JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
EMBED
Full name: Emory Breast Imaging Dataset

EMBED contains 364,000 screening and diagnostic mammographic exams for 110,000 patients from four hospitals over an 8-year period. The EMBED AWS Open Data release represents 20% of the dataset divided into two equal cohorts at the patient level. This release of the dataset includes 2D and C-view images. Digital breast tomosynthesis, ultrasound, and MRI exams will be added at a later date.
RainDS
Full name: RainDS

We managede to collect a real-world rain dataset, named RainDS, includinnumerousus image pairs in various lighting conditions and different scenes. Each pair contains four images: a rain streak image, a raindrop image, and an image including both types of rain, as well as their rain-free counterparts.

Introducing paper: Removing Raindrops and Rain Streaks in One Go
GPlay:SAppKG
Full name: SAppKG

The dataset is from google play store applications containing apps from different Google Play Store categories

Introducing paper: SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework
Noise-SF

Based on RADDLE and SNIPS , we construct Noise-SF, which includes two different perturbation settings. For single perturbations setting, we include five types of noisy utterances (character-level: \textbf{Typos}, word-level: \textbf{Speech}, and sentence-level: \textbf{Simplification}, \textbf{Verbose}, and \textbf{Paraphrase}) from RADDLE. For mixed perturbations setting, we utilize TextFlint to introduce character-level perturbation (\textbf{EntTypos}), word-level perturbation (\textbf{Subword}), and sentence-level perturbation (\textbf{AppendIrr}) and combine them to get a mixed perturbations dataset.