Omkar2118/datasets huggingface dataset:
ACSPublicCoverage

ACSPublicCoverage: predict whether an individual is covered by public health insurance, after filtering the ACS PUMS data sample to only include individuals under the age of 65, and those with an income of less than $30,000. This filtering focuses the prediction problem on low-income individuals who are not eligible for Medicare.

Introducing paper: Retiring Adult: New Datasets for Fair Machine Learning
OCB
Full name: Open Circuit Benchmark

OCB contains two graph datasets, Ckt-Bench-101 and Ckt-Bench-301, for representation learning over analog circuits. Ckt-Bench-101 and Ckt-Bench-301 contain graphs (DAGs) that represent analog circuits and provide their corresponding graph-level properties: DC gain (Gain), bandwidth (BW), phase margin (PM),Figure of Merit (FoM), which characterize the circuit performance.

* Motivation: Facilitate research in representation learning and automation of analog circuits.
* Tasks: graph-level prediction/regression; analog circuit search (ACS).
* Node features: discrete feature that characterize device type (e.g. capacitor); and continues feature of the device (e.g. capacitance).
* First open source benchmark for graph learning in analog circuits.

Introducing paper: CktGNN: Circuit Graph Neural Network for Electronic Design Automation
The HYPSO-1 Sea-Land-Cloud-Labeled Dataset

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. Our dataset supports applications like super-resolution, anomaly detection, image fusion, classification, and unmixing. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://ntnu-smallsat-lab.github.io/hypso1_sea_land_clouds_dataset/ .

Introducing paper: An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite