Omkar2118/datasets huggingface dataset:
Wallhack1.8k

The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).

To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:

LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system
LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system
NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system
NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system

These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.

Introducing paper: Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition
WiFiCam

WiFiCam dataset for through-wall imaging based on WiFi channel state information. The corresponding source code repository is located at: https://github.com/StrohmayerJ/wificam

Dataset Structure:

/wificam

├── j3

       └── 320  <-- 320x240 resolution subset

              └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3

              └── csiComplex.npy <-- complex CSI sequence (cache)

              └── 92108.png <-- 320x240 RGB image

              └── 92112.png

              └── ...

       └── 640 <-- 640x480 resolution subset

              └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3

              └── csiComplex.npy <-- complex CSI sequence (cache)

              └── 154.png <-- 640x480 RGB image

              └── 155.png

              └── ...

├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images

├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images

Introducing paper: Through-Wall Imaging based on WiFi Channel State Information
ADORE
Full name: A benchmark dataset for machine learning in ecotoxicology

ADORE is a benchmark dataset for machine learning for ecotixicology, covering acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.

The dataset contains acute toxicity data (lethal concentration 50; LC50 or effective concentration 50; EC50) on 2,408 chemicals in 203 different species of algae, crustaceans, and fish. This encompasses a total of 33K data points, 26K of which are on fish (140 species).

The task is to predict the ecotoxicological outcome based on historic ecotoxicity data.

ADORE was originally published in Nature ScientificData:
Schür, Christoph, Lilian Gasser, Fernando Perez-Cruz, Kristin Schirmer, and Marco Baity-Jesi. 2023. “A Benchmark Dataset for Machine Learning in Ecotoxicology.” Scientific Data 10 (1): 718. https://doi.org/10.1038/s41597-023-02612-2.