Omkar2118/datasets huggingface dataset:
SGMs 4 PET

Data for Score-Based Generative Models for PET Image Reconstruction. All simuations based on BrainWeb dataset. The image simulation either taken from Georg Schramm's BrainWeb simulation in 2D, or in 3D it was simulated using BrainWeb package. The 2D measurements were simulated using pyParallelProj and 3D measurements using SIRF (with STIR backend).

Introducing paper: Score-Based Generative Models for PET Image Reconstruction
Austin Budget Survey Data FY2021 and FY2022

Data collected from two budget surveys (FY2021 in 2020 and FY2022 in 2021) in collaboration with the City of Austin budget department. Data contains preferences for each respondent and the day of their participation.

In the middle of the FY2021 survey, the murder on George Floyd by a police officer in Minneapolis and the subsequent protests resulted in nationwide upset and the number of daily responses increased about a hundredfold. The responses shifted significantly.

For FY2021, this data set contains 37,006 responses. For FY2022, this data set contains 1,525 responses.

Introducing paper: Opinion Change or Differential Turnout: Changing Opinions on the Austin Police Department in a Budget Feedback Process
GUISS dataset
Full name: Meshes, textures, Blend files, stereo datasets, depth maps, depth estimations)

We provide all the expected data inputs to GUISS such as meshes, texture images, and blend files. Generated datasets used in our experiments along with the stereo depth estimations can be downloaded. We have defined seven dataset types: scene_reconstructions, texture_variation, gaea_texture_variation, generative_texture, terrain_variation, rocks, and generative_texture_snow. Each dataset type contains renderings with varying values of different parameters such as lighting angle, texture imgs, albedo, etc. Position each dataset type folder under data/dataset/.

Details and links at: https://github.com/nasa-jpl/guiss

Introducing paper: Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy
Participatory Budgeting Preferences Data Set

The data set includes information about 120+ elections (configuration settings and descriptive statistics), projects and 125k+ anonymized voters and their budget preferences. Preferences were sollicited with different elicitation methods (K-approval, knapsack, K-ranking and K-token). For some elections, voters provided also preferences under a secondary elicitation method, resulting in vote pairs from the same voter on the same budgeting question but with a different elicitation method.

This data set was collected as anonymized votes from the Stanford Participatory Budgeting Platform.

Introducing paper: Rank, Pack, or Approve: Voting Methods in Participatory Budgeting
ICEWS-WIKI

HIghly Heterogeneous KGs for entity alignment research.

Introducing paper: Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
CBVS

A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenario

Introducing paper: CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios