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Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581025/ https://www.ncbi.nlm.nih.gov/pubmed/36303730 http://dx.doi.org/10.3389/fbinf.2021.757291 |
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author | Mitra-Behura, Shilpita Fiolka, Reto Paul Daetwyler, Stephan |
author_facet | Mitra-Behura, Shilpita Fiolka, Reto Paul Daetwyler, Stephan |
author_sort | Mitra-Behura, Shilpita |
collection | PubMed |
description | Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containers as a useful tool to run and share image analysis workflows among many users, even years later after establishing them. Unfortunately, containers are rarely used so far in the image analysis field. To address this lack of use, we provide a detailed step-by-step protocol to package a state-of-the-art segmentation algorithm into a container on a local Windows machine to run the container on a high-performance cluster computer. |
format | Online Article Text |
id | pubmed-9581025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95810252022-10-26 Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows Mitra-Behura, Shilpita Fiolka, Reto Paul Daetwyler, Stephan Front Bioinform Bioinformatics Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containers as a useful tool to run and share image analysis workflows among many users, even years later after establishing them. Unfortunately, containers are rarely used so far in the image analysis field. To address this lack of use, we provide a detailed step-by-step protocol to package a state-of-the-art segmentation algorithm into a container on a local Windows machine to run the container on a high-performance cluster computer. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC9581025/ /pubmed/36303730 http://dx.doi.org/10.3389/fbinf.2021.757291 Text en Copyright © 2022 Mitra-Behura, Fiolka and Daetwyler. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Mitra-Behura, Shilpita Fiolka, Reto Paul Daetwyler, Stephan Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title | Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title_full | Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title_fullStr | Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title_full_unstemmed | Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title_short | Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows |
title_sort | singularity containers improve reproducibility and ease of use in computational image analysis workflows |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581025/ https://www.ncbi.nlm.nih.gov/pubmed/36303730 http://dx.doi.org/10.3389/fbinf.2021.757291 |
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