Skip to main content
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

3

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

3 results for “automotive engineering”

Learn how ShareScore rates datasets ↗
zenodo36/100

DARTER: Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers

<p>The DARTER dataset contains a sample (for now) of driving data (camera, LiDAR, IMU, steering wheel angle, etc.) without labels gathered at AstaZero&#39;s test track using the Chalmers ReVeRe lab&#39;s SnowFox test vehicle.&nbsp;DARTER stands for Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers.&nbsp;This research was possible thanks to the funds of a SAFER pre-study grant.</p> <p>&nbsp;</p> <p><strong>About DARTER:</strong></p> <p>Verification and validation (V&amp;V) of Intelligent Transport Solutions and their components in real traffic is difficult (costs, passers-by, etc.) and running tests under all possible conditions (weather, traffic, etc.) is impossible. For these reasons, controlled proving grounds and simulations are being used to safely increase the coverage of V&amp;V.&nbsp;</p> <p>The DARTER project, a SAFER pre-study, addresses two issues connected to these alternatives. On the one hand, the limited access to real proving grounds and their corresponding high-fidelity simulations of academic researchers, key in evaluating the benefits and social and environmental harms that technological advances can pose. On the other hand, the fidelity gap between the virtual and the real world, that prevents the usage of simulations at vehicle integration test level and needs to be understood and measured.&nbsp;</p> <p>SAFER pre-studies&nbsp;https://www.saferresearch.com/content/safer-pre-studies&nbsp;</p>

opencc-by-4.0Sep 2023View details →
zenodo24/100

Learning the Characteristics of Engineering Optimization Problems with Applications in Automotive Crash

<p>Oftentimes the characteristics of real-world engineering optimization problems are not well understood. In this paper, we introduce an approach for characterizing highly nonlinear and Finite Element (FE) simulation-based engineering optimization problems, focusing on ten representative problem instances from automotive crashworthiness optimization. By computing characteristic Exploratory Landscape Analysis (ELA) features, we show that these ten crashworthiness problem instances exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used Black-Box Optimization Benchmarking (BBOB) problem set. Using clustering approaches, we demonstrate that these ten problem instances are clearly distinct from the BBOB test functions. Further analysis of the crashworthiness problem instances reveal that, as far as ELA concerns, they are most similar to a class of artificially generated functions. We identify such artificially generated functions and propose to use them as scalable and fast-to-evaluate representatives of the real-world problems. Such artificially generated functions could be used for the automated design of an optimization algorithm for specific real-world problem classes.</p>

opencc-by-4.0Apr 2022View details →
zenodo12/100

A Delphi Study on Integrating Technological Advancements into Automotive Engineering Curriculum

<p>Delphi first round raw data</p>

restrictedcc-by-4.0Mar 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record