Omkar2118/datasets huggingface dataset:
ArEEG_Words

ArEEG_Words dataset is a novel EEG dataset recorded from 22 participants with mean age of 22 years (5 female, 17 male) using a 14-channel Emotiv Epoc X device. The participants were asked to be free from any effects on their nervous system, such as coffee, alcohol, cigarettes, and so 8 hours before recording. They were asked to stay calm in a clam room during imagining one of the 16 Arabic Words for 10 seconds. The words include 16 commonly used words such as up, down, left, and right. A total of 352 EEG recordings were collected, then each recording was divided into multiple 250ms signals, resulting in a total of 15,360 EEG signals.

Introducing paper: ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words
ArEEG_Chars

ArEEG_Chars, the first EEG dataset for Arabic characters, consists of high-quality recordings for 31 unique characters from 30 participants (21 males and 9 females) using the Epoc X 14-channel device. Each participant was asked to focus on all Arabic letters for 10-second segments each.  
Each Folder is named after the letter and it contains 30 CSV file of wave records.

Introducing paper: ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters
Artificial fluorescent bacteria dataset
Full name: Artificial fluorescent bacteria dataset

These images consist of a series of bacteria of the type Bacillus Subtilis that are suspended and
captured by a digital microscope. The fluorescent bacteria dataset can be created as desired, defining the number of bacteria per image and the total number of images. It comes with 3280x2464 resolution images and centroid locations of each bacteria, useful for enumeration or density map estimation.

Introducing paper: Vision Transformers for Weakly-Supervised Microorganism Enumeration
Blue Cells enumeration dataset
Full name: Learning to Count Objects in Images blue cells dataset

Dataset created in the paper "Learning to Count Objects in Images" by Victor Lempitsky and Andrew Zisserman exists as a benchmark to have a dataset useful for cell enumeration. The dataset comes with 200 images of 256x256 resolution of artificial blue fluorescent cells, and the groundtruth consists of the spatial coordinates of each cells centroid. Useful for regression counting or density map estimation.

Introducing paper: Learning To Count Objects in Images