# The Full-scale Assembly Simulation Testbed (FAST) Dataset

arXiv: https://arxiv.org/abs/2403.08969 · HTML: https://mlsanity.com/paper/2403.08969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [A privacy-preserving approach to streaming eye-tracking data](https://mlsanity.com/paper/2102.01770/) (2021)
- [Who Is Alyx? A new Behavioral Biometric Dataset for User Identification in XR](https://mlsanity.com/paper/2308.03788/) (2023)
- [A Large-Scale Study of Personal Identifiability of Virtual Reality Motion Over Time](https://mlsanity.com/paper/2303.01430/) (2023)

## Built on by (most cited first, 1 of 1)

- [Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning](https://mlsanity.com/paper/2509.08539/) (2025)

## Abstract (arXiv)

In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108 participants (50 females, 56 males, 2 non-binary) learning how to assemble two distinct full-scale structures in VR. In addition to explaining how the dataset was collected and describing the data included, we discuss how the dataset may be used by future researchers.