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Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
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From Quick Script to Reproducible Workflow: Small Research Software Engineering Habits, Big Impact

Authors: Wittke, Samantha; Pöhner, Ina;

From Quick Script to Reproducible Workflow: Small Research Software Engineering Habits, Big Impact

Abstract

BioExcel webinar 17.02.2026AbstractReproducible analysis pipelines sound great in theory, but what does that look like in the life of a busy researcher doing biomolecular system analysis with a “quick and dirty” script on their laptop computer? In this webinar, we walk you through how a bit of structure and version control can make everyday work easier to repeat, share, and build on. Using a simple molecular dynamics (MD) analysis as a case study, we will show how to go from a one-off script to an organized reproducible collaborative project: tracking changes with git, updating the README as the workflow evolves, and handling common situations like “I found a bug, now what?” through issues and pull requests. Along the way, the session will introduce the CodeRefinery project (https://coderefinery.org/) and give a taste of other topics you can learn about in CodeRefinery workshops, such as recording software environments (for example with conda) and automated testing. If you run command-line tools, write small scripts in any language (for example, simple bash scripts or Python code, perhaps using BioBBs), or maintain workflows and pipelines, for example, for MD analysis, attend and learn how lightweight research software engineering habits can directly support reproducible workflows, results and publications.

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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