However, in addition to poorly controlled computing environment variables, computational methods become increasingly complex pipelines of data handling and processing.
In contrast, computational methods should be inherently scientifically reproducible since computer chips perform computations in the same way, removing some variations that are difficult to control. Even small changes in input and method ultimately lead to an altered output.
Reproducibility in biochemistry lab experiments remains challenging to address, as it depends on the quality and purity of reagents, unstable environmental conditions, and the accuracy and skill with which the experiments are performed. Another analysis published by PLOS One in 2015 concluded that, in the US alone, about half of preclinical research was irreproducible, amounting to a total of about $28 billion being wasted per year 2! Over 70% of the over 1500 researchers surveyed were unable to reproduce another scientist’s experiments and over half were unable to reproduce their own experiments. In a survey published by Nature in 2016, 90% of scientists responded that there is a reproducibility crisis 1. Reproducibility in science is a systemic problem. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
#CS SOURCE CONTENT SERVER UNREACXHABLE SOFTWARE#
The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. High performance computing cluster integration allows these benchmarks to run continuously and automatically. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Nature Communications volume 12, Article number: 6947 ( 2021)Įach year vast international resources are wasted on irreproducible research. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks
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