The safest solution would be to store copies of every object, ever created during the data analysis. All forks, wrong paths, everything. Along with detailed information which functions with what parameters were used to generate each result. Something like the ultimate TimeMachine or GitHub for R objects. With such detailed information, every analysis would be auditable and replicable. Right now the full tracking of all created objects is not possible without deep changes in the R interpreter. The archivist is the light-weight version of such solution.
The editors of the new track for reproducible research outline the parameters for future peer review, submission, and access, highlighting the magazine’s previous work in this field and some of the challenges still to come.
Summary: This manuscript introduces and describes Dugong, a Docker image based on Ubuntu 16.04, which automates installation of more than 3500 bioinformatics tools (along with their respective libraries and dependencies), in alternative computational environments. The software operates through a user-friendly XFCE4 graphic interface that allows software management and installation by users not fully familiarized with the Linux command line and provides the Jupyter Notebook to assist in the delivery and exchange of consistent and reproducible protocols and results across laboratories, assisting in the development of open science projects.
Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook.
Reproducibility has become one of biology’s most pressing issues. This impasse has been fueled by the combined reliance on increasingly complex data analysis methods and the exponential growth of biological datasets. When considering the installation, deployment and maintenance of bioinformatic pipelines, an even more challenging picture emerges due to the lack of community standards. The effect of limited standards on reproducibility is amplified by the very diverse range of computational platforms and configurations on which these applications are expected to be applied (workstations, clusters, HPC, clouds, etc.). With no established standard at any level, diversity cannot be taken for granted.
Explore how Singularity liberates non-privileged users and host resources (such as interconnects, resource managers, file systems, accelerators …) allowing users to take full control to set-up and run in their native environments. This talk explores Singularity how it combines software packaging models with minimalistic containers to create very lightweight application bundles which can be simply executed and contained completely within their environment or be used to interact directly with the host file systems at native speeds. A Singularity application bundle can be as simple as containing a single binary application or as complicated as containing an entire workflow and is as flexible as you will need.