Dataset Information

Rang: Reconstructing reproducible R computational environments.

ABSTRACT: A complete declarative description of the computational environment is usually missing when researchers share their materials. Without such description, software obsolescence and missing system components can jeopardize computational reproducibility in the future, even when data and computer code are available. The R package rang is a complete solution for generating the declarative description for other researchers to automatically reconstruct the computational environment at a specific time point. The reconstruction process, based on Docker, has been tested for R code as old as 2001. The declarative description generated by rang satisfies the definition of a reproducible research compendium and can be shared as such. In this contribution, we show how rang can be used to make otherwise unexecutable code, spanning fields such as computational social science and bioinformatics, executable again. We also provide instructions on how to use rang to construct reproducible and shareable research compendia of current research. The package is currently available from CRAN (https://cran.r-project.org/web/packages/rang/index.html) and GitHub (https://github.com/chainsawriot/rang).

SUBMITTER: Chan CH

PROVIDER: S-EPMC10249795 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

rang: Reconstructing reproducible R computational environments.

Chan Chung-Hong CH Schoch David D

PloS one 20230608 6

A complete declarative description of the computational environment is usually missing when researchers share their materials. Without such description, software obsolescence and missing system components can jeopardize computational reproducibility in the future, even when data and computer code are available. The R package rang is a complete solution for generating the declarative description for other researchers to automatically reconstruct the computational environment at a specific time po ...[more]

PMID: 37289797

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Project description:BACKGROUND:The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. RESULTS:In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. CONCLUSIONS:With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

Dataset Information

Rang: Reconstructing reproducible R computational environments.

Publications

rang: Reconstructing reproducible R computational environments.

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