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.
5
datasets available to search
ShareScore release 0.7.1
Dataset results
5 results for “Asteroid tracking”
The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks
<p>This material constitutes the source data and codes used for the the computations and plots of the paper 'The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks' by Bigot, Lombardo et al. This article has been published in Nature Communications on 30 July, 2024.</p> <p>The folder "Source Data" contains an Excel document that provides the raw data used to make the figures and supplementary figures. </p> <p>The folder "Codes" contains the Matlab codes used for the computations of the results, including comments on the figures produced by each code. It also contains a .mat file that consitutes the topographic data of Didymos from Barnouin et al. (2024), used in the code 'TopographyDidymos.m'.</p> <p>The folder "Images" contains the three DART images (DRACO) and the Moon image from LROC used in this article.</p> <p> </p>
Data from: Deep learning-assisted near-Earth asteroid tracking in astronomical images
<p>This repository is the data release of our paper <em>Deep learning-assisted near-Earth asteroid tracking in astronomical images</em>. There are two categories in this repository:</p> <ul> <li>Simulated training dataset for training the star segmentation network. </li> </ul> <p>The dataset consists of two folders: image (grayscale images) and mask (binary images). The size of each image is 256*256.</p> <ul> <li>Example data for testing asteroid tracking algorithm.<br><br></li> </ul> <p>If you find this work useful, please cite our paper:</p> <div> <div>@article{du2024ASR,</div> <div>title = {Deep learning-assisted near-Earth asteroid tracking in astronomical images},</div> <div>journal = {Advances in Space Research},</div> <div>volume = {73},</div> <div>number = {10},</div> <div>pages = {5349-5362},</div> <div>year = {2024},</div> <div>issn = {0273-1177},</div> <div>doi = {https://doi.org/10.1016/j.asr.2024.02.048},</div> <div>url = {https://www.sciencedirect.com/science/article/pii/S0273117724001911},</div> <div>author = {Zhenhong Du and Hai Jiang and Xu Yang and Hao-Wen Cheng and Jing Liu},</div> <div>keywords = {Near-Earth asteroid, Deep learning, Convolutional neural network, Faint object extraction, Moving object linking},</div> <div>}</div> </div>
NEAR EARTH ASTEROID TRACKING
The Near-Earth Asteroid Tracking (NEAT) project began as a collaborative effort with the United States Air Force (USAF) in December 1995. It concentrated on the discovery and observations of near-Earth asteroids and comets, collectively called near-Earth objects (NEOs). NEAT ended its observations in April 2007. Throughout its history, NEAT utilized three 1m class telescopes - two on the Hawaiian island of Maui and the 1.2m Oschin Schmidt telescope at Palomar Observatory near San Diego, CA. Three unique cameras were developed and used throughout the program. These data are intended to be usable for photometric analysis of the various objects within the NEAT data. Most nights included calibration data, and the lists of photometric standard calibration fields.
NEAR EARTH ASTEROID TRACKING V1.0
The Near-Earth Asteroid Tracking (NEAT) project began as a collaborative effort with the United States Air Force (USAF) in December 1995. It concentrated on the discovery and observations of near-Earth asteroids and comets, collectively called near-Earth objects (NEOs). NEAT ended its observations in April 2007. Throughout its history, NEAT utilized three 1m class telescopes - two on the Hawaiian island of Maui and the 1.2m Oschin Schmidt telescope at Palomar Observatory near San Diego, CA. Three unique cameras were developed and used throughout the program. These data are intended to be usable for photometric analysis of the various objects within the NEAT data. Most nights included calibration data, and the lists of photometric standard calibration fields.
NEAR EARTH ASTEROID TRACKING V1.0
The Near-Earth Asteroid Tracking (NEAT) project began as a collaborative effort with the United States Air Force (USAF) in December 1995. It concentrated on the discovery and observations of near-Earth asteroids and comets, collectively called near-Earth objects (NEOs). NEAT ended its observations in April 2007. Throughout its history, NEAT utilized three 1m class telescopes - two on the Hawaiian island of Maui and the 1.2m Oschin Schmidt telescope at Palomar Observatory near San Diego, CA. Three unique cameras were developed and used throughout the program. These data are intended to be usable for photometric analysis of the various objects within the NEAT data. Most nights included calibration data, and the lists of photometric standard calibration fields.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.