Supporting data for "Unifying package managers, workflow engines, and containers with BioNix for computational reproducibility"

Dataset type: Software, Workflow
Data released on August 21, 2020

Bedő J; Di Stefano LS; Papenfuss AT (2020): Supporting data for "Unifying package managers, workflow engines, and containers with BioNix for computational reproducibility" GigaScience Database. http://dx.doi.org/10.5524/100782

DOI10.5524/100782

A challenge for computational biologists is to make our analyses reproducible, that is, easy to rerun, combine, and share, with the assurance that equivalent runs will generate identical results. The current best practice aims at this using a combination of package managers, workflow engines, and containers.
We present BioNix, a lightweight library built on the Nix deployment system. BioNix manages software dependencies, computational environments, and workflow stages together using a single abstraction: pure functions. This lets us specify workflows in a way that is more reproducible and modular than current best practices.
BioNix is implemented in the Nix expression language and is released on GitHub under the 3-clause BSD license: https://github.com/PapenfussLab/bionix.

Additional details

Read the peer-reviewed publication(s):

(PubMed: 33205815)

Github links:

https://github.com/PapenfussLab/bionix





File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivezip1.42 MB2020-10-08
readme.txtTEXT1.87 KB2020-08-21
Displaying 1-2 of 2 File(s).
Funding body Awardee Award ID Comments
Australian National Health and Medical Research Council A T Papenfuss 1054618
Australian National Health and Medical Research Council A T Papenfuss 116955
Date Action
August 20, 2020 Dataset publish
October 8, 2020 bionix-master.zip: file attribute updated
October 8, 2020 File bionix-master.zip updated following updates to GitHub repo during manuscript final revisions after this dataset was originally published.
October 14, 2020 Manuscript Link added : 10.1093/gigascience/giaa121
October 7, 2022 Manuscript Link updated : 10.1093/gigascience/giaa121