Introduction to Reproducible Science¶
The so-called reproducibility crisis (see 1 , 2 , 3) is something you have probably heard about (and maybe one of the reasons you have come to FOSS). Headlines in the media (such as Most scientists can't replicate studies by their peers) definitely give pause to researchers and ordinary citizens who hope that the science used to recommend a course of medical treatment or design self-driving cars is sound.
Before we go further, it’s actually important to ask what is reproducibility?
How do you define reproducible science?
In Reproducibility vs. Replicability, Hans Plesser gives the following useful definitions:
- Repeatability (Same team, same experimental setup): The measurement can be obtained with stated precision by the same team using the same measurement procedure, the same measuring system, under the same operating conditions, in the same location on multiple trials. For computational experiments, this means that a researcher can reliably repeat her own computation.
- Replicability (Different team, same experimental setup): The measurement can be obtained with stated precision by a different team using the same measurement procedure, the same measuring system, under the same operating conditions, in the same or a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using the author’s own artifacts.
- Reproducibility (Different team, different experimental setup): The measurement can be obtained with stated precision by a different team, a different measuring system, in a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using artifacts which they develop completely independently.
The paper goes on to further simplify:
- Methods reproducibility: provide sufficient detail about procedures and data so that the same procedures could be exactly repeated.
- Results reproducibility: obtain the same results from an independent study with procedures as closely matched to the original study as possible.
- Inferential reproducibility: draw the same conclusions from either an independent replication of a study or a reanalysis of the original study.
How do these definitions apply to your research/teaching?
Work with your fellow learners to develop a shortlist of ways reproducibility relates to your work. Try to identify challenges and even successes you’d like to share.
Often, when we say “reproducibility” we mean all or at least several of the concepts the proceeding discussion encompasses. Really, reproducibility can be thought of as set values such as some laboratories express in a code of conduct, (see for example Ross-Ibarra Lab code of conduct or Bahlai Lab Policies). Reproducibility comes from our obligations and desires to work ethically, honestly, and with confidence that the data and knowledge we produce is done has integrity. Reproducibility is also a “spectrum of practices”, not a single step. (See figure below from Peng 2011).
Assuming you have taken in the potentially anxiety inducing information above, the most important thing to know is that there is a lot of help to make reproducibility a foundation of all of your research.
In the following tutorial, we will introduce some of the software introduced at FOSS in the context of creating a reproducible experiment. The goal will be to give an example of how connecting various software pieces and practices can generate a well-documented research project. Though the example will be drawn from a biological example, the ideas an approaches apply to the sciences in general.
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