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Christopher Schmied

dblp:177/8514 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0003-2058-1124ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 50% High-performance computing · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
parallel computing
0.112016
An automated workflow for parallel processing of large multiview SPIM recordings · Bioinform. 2016
High-performance computing
parallel workflow
0.112016
An automated workflow for parallel processing of large multiview SPIM recordings · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

snakemake workflow · 0.2
YearPublicationVenuePosition
2016 An automated workflow for parallel processing of large multiview SPIM recordings
abstract
UNLABELLED: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. AVAILABILITY AND IMPLEMENTATION: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Christopher Schmied, Peter Steinbach 0001, Tobias Pietzsch, Stephan Preibisch, Pavel Tomancak
Bioinform.1