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Benchmark dataset for the Voronoi diagram of 3D spherical balls
In this paper, we present a dataset to be used for the construction of the Voronoi diagram of 3D spherical balls (VD-B3). The dataset consists of sphere arrangements including general, anomaly, and extreme cases. The dataset also includes protein models downloaded from RCSB Protein Data Bank (PDB)....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679445/ https://www.ncbi.nlm.nih.gov/pubmed/36426006 http://dx.doi.org/10.1016/j.dib.2022.108605 |
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author | Song, Chanyoung Lee, Mokwon Choi, Seunghwan Kim, Deok-Soo |
author_facet | Song, Chanyoung Lee, Mokwon Choi, Seunghwan Kim, Deok-Soo |
author_sort | Song, Chanyoung |
collection | PubMed |
description | In this paper, we present a dataset to be used for the construction of the Voronoi diagram of 3D spherical balls (VD-B3). The dataset consists of sphere arrangements including general, anomaly, and extreme cases. The dataset also includes protein models downloaded from RCSB Protein Data Bank (PDB). The dataset can be used as a standard benchmark dataset to verify and validate the correctness, efficiency, and robustness of the construction algorithm. The dataset is simple and easy to understand. The details of the experiment and analysis based on this dataset are presented in the original research article: “Robust Construction of the Voronoi Diagram of Spherical Balls in the Three-Dimensional Space” which introduces the topology-oriented incremental algorithm for the construction that is thoroughly validated and compared with two implementations of the well known edge-tracing algorithm. |
format | Online Article Text |
id | pubmed-9679445 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96794452022-11-23 Benchmark dataset for the Voronoi diagram of 3D spherical balls Song, Chanyoung Lee, Mokwon Choi, Seunghwan Kim, Deok-Soo Data Brief Data Article In this paper, we present a dataset to be used for the construction of the Voronoi diagram of 3D spherical balls (VD-B3). The dataset consists of sphere arrangements including general, anomaly, and extreme cases. The dataset also includes protein models downloaded from RCSB Protein Data Bank (PDB). The dataset can be used as a standard benchmark dataset to verify and validate the correctness, efficiency, and robustness of the construction algorithm. The dataset is simple and easy to understand. The details of the experiment and analysis based on this dataset are presented in the original research article: “Robust Construction of the Voronoi Diagram of Spherical Balls in the Three-Dimensional Space” which introduces the topology-oriented incremental algorithm for the construction that is thoroughly validated and compared with two implementations of the well known edge-tracing algorithm. Elsevier 2022-09-14 /pmc/articles/PMC9679445/ /pubmed/36426006 http://dx.doi.org/10.1016/j.dib.2022.108605 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Song, Chanyoung Lee, Mokwon Choi, Seunghwan Kim, Deok-Soo Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title | Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title_full | Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title_fullStr | Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title_full_unstemmed | Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title_short | Benchmark dataset for the Voronoi diagram of 3D spherical balls |
title_sort | benchmark dataset for the voronoi diagram of 3d spherical balls |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679445/ https://www.ncbi.nlm.nih.gov/pubmed/36426006 http://dx.doi.org/10.1016/j.dib.2022.108605 |
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