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Research Data Management: Research Reproducibility

Data Management for researchers provides a framework for preserving, sharing, and archiving data.

What is research reproducibility (RR)?

RR refers to the ability of an experiment to be reproduced by the same or other researchers. It also refers to coding in algorithms, which must be error-free so that others can apply the same algorithm.

Why research reproducibility?

Just as funding agents are requiring data to be made available, so are they requiring that the research be reproducible. Journals too are concerned about the quality of research they publish.

On research reproducibility

Nature editorial: "Announcement: reducing our irreproducibility"

Nature editorial: "Journals unite for reproducibility"

Re Claerbout's Principle:  "Approaches and barriers to reproducible practices in biostatistics"

"An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generate the figures." - Claerbout

Statistical challenges in assessing and fostering the reproducibility of scientific results, National Academies Press, 2016.

Achieving research reproducibility

The Datahub - offers a place to publish and manage data.

GitHub - a place to review and manage code

VisTrails - for data exploration and visualization

Collage Authoring Environment - enables publishing executable papers

R Studio - for making interactive reports and visualizations


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Rani Anand