Course introduction

Last updated on 2024-07-04 | Edit this page

Estimated time: 30 minutes

Overview

Questions

  • What is open, reproducible and FAIR research?
  • Why are these practices important?

Objectives

After completing this episode, participants should be able to:

  • Understand the concept of open and reproducible research
  • Understand why these principles are of value in the research community

Jargon busting

Before we start with the course, below we cover the terminology and explain terms, phrases, and concepts associated with software development in reproducible research that we will use in this course.

  • Reproducibility - the ability to be reproduced or copied; the extent to which consistent results are obtained when an experiment is repeated (definition from Google’s English dictionary is provided by Oxford Languages)
  • Computational reproducibility - obtaining consistent results using the same input data, computational methods (code), and conditions of analysis; work that can be independently recreated from the same data and the same code (definition by the Turing Way’s “Guide to Reproducible Research”)
  • Reproducible research - the idea that scientific results should be documented in such a way that their deduction is fully transparent (definition from Wikipedia)
  • Open research - research that is openly accessible by others; concerned with making research more transparent, more collaborative, more wide-reaching, and more efficient (definition from Wikipedia)
  • FAIR - an acronym that stands for Findable, Accessible, Interoperable, and Reusable
  • Sustainable software development - software development practice that takes into account longevity and maintainability of code (e.g. beyond the lifetime of the project), environmental impact, societal responsibility and ethics in our software practices.

Computational reproducibility

In this course, we use the term “reproducibility” as a synonym for “computational reproducibility”.

What does open and reproducible research mean to you?

Think about the questions below. Your instructors may ask you to share your answers in a shared notes document and/or discuss them with other participants.

  • What do you understand by the words “open” and “reproducible” in the context of research?
  • How many people or groups can you list that might benefit from your work being open and reproducible?
  • How many times did you wish that someone else’s work you came across was more open or accessible to you? Can you provide some examples?

What is reproducible research?

The Turing Way’s “Guide to Reproducible Research” provides an excellent overview of definitions of “reproducibility” and “replicability” found in literature, and their different aspects and levels.

In this course, we adopt the Turing Way’s definitions:

  • Reproducible research: a result is reproducible when the same analysis steps performed on the same data consistently produce the same answer.
    • For example, two different people drop a pen 10 times each and every time it falls to the floor. Or, we run the same code multiple times on different machines and each time it produces the same result.
  • Replicable research: a result is replicable when the same analysis performed on different data produces qualitatively similar answers.
    • For example, instead of a pen, we drop a pencil, and it also falls to the floor. Or, we collect two different datasets as part of two different studies and run the same code over these datasets with the same result each time.
  • Robust research: a result is robust when the same data is subjected to different analysis workflows to answer the same research question and a qualitatively similar or identical answer is produced.
    • For example, I lend you my pen and you drop it out the window, and it still falls to the floor. Or we run the same analysis implemented in both Python and R over the same data and it produces the same result.
  • Generalisable research: combining replicable and robust findings allow us to form generalisable results that are broadly applicable to different types of data or contexts.
    • For example, everything we drop - falls, therefore gravity exists.
Four cartoon images depicting two researchers at two machines which take in data and output the same landscape image in 4 different ways. These visually describe the four scenarios listed above.
The Turing Way project illustration of aspects of reproducible research by Scriberia, used under a CC-BY 4.0 licence, DOI: 10.5281/zenodo.3332807

In this course we mainly address the aspect of reproducibility - i.e. enabling others to run our code to obtain the same results.

We can also further differentiate between:

  • Computational reproducibility: when detailed information is provided about code, software, hardware and implementation details.
  • Empirical reproducibility: when detailed information is provided about non-computational empirical scientific experiments and observations. In practice, this is enabled by making the data and details of how it was collected freely available.
  • Statistical reproducibility: when detailed information is provided, for example, about the choice of statistical tests, model parameters, and threshold values. This mostly relates to pre-registration of study design to prevent p-value hacking and other manipulations.

In this course, we are concerned with computational reproducibility, i.e. when the application of computer science and software engineering is used to aid solving research problems.

Why do reproducible research?

Scientific transparency and rigor are key factors in research. Scientific methodology and results need to be published openly and replicated and confirmed by several independent parties. However, research papers often lack the full details required for independent reproduction or replication. Many attempts at reproducing or replicating the results of scientific studies have failed in a variety of disciplines ranging from psychology (The Open Science Collaboration (2015)) to cancer sciences (Errington et al (2021)). These are called the reproducibility and replicability crises - ongoing methodological crises in which the results of many scientific studies are difficult or impossible to repeat.

Reproducible research is a practice that ensures that researchers can repeat the same analysis multiple times with the same results. It offers many benefits to those who practice it:

  • Reproducible research helps researchers remember how and why they performed specific tasks and analyses; this enables easier explanation of work to collaborators and reviewers.
  • Reproducible research enables researchers to quickly modify analyses and figures - this is often required at all stages of research and automating this process saves loads of time.
  • Reproducible research enables reusability of previously conducted tasks so that new projects that require the same or similar tasks become much easier and efficient by reusing or reconfiguring previous work.
  • Reproducible research supports researchers’ career development by facilitating the reuse and citation of all research outputs - including both code and data.
  • Reproducible research is a strong indicator of rigor, trustworthiness, and transparency in scientific research. This can increase the quality and speed of peer review, because reviewers can directly access the analytical process described in a manuscript. It increases the probability that errors are caught early on - by collaborators or during the peer-review process, helping alleviate the reproducibility crisis.

However, reproducible research often requires that researchers implement new practices and learn new tools. This course aims to teach some of these practices and tools pertaining to the use of software to conduct reproducible research.

Software in research and research software

Software is fundamental to modern research - some of it would even be impossible without software. From short, thrown-together temporary scripts written to help with day-to-day research tasks, through an abundance of complex data analysis spreadsheets, to the hundreds of software engineers and millions of lines of code behind international efforts such as the Large Hadron Collider, there are few areas of research where software does not have a fundamental role.

However, it is important to note that not all software that is used in research is “research software”. We define “research software” as software or code that is used to generate, process or analyse results of a research for publication. For example, software used to guide a telescope is not considered “research software”. On the other hand, formulas or macros in spreadsheets used to analyse data are considered “research code” as they are a form of computer programming that allow one to create, calculate, and change data sets in a number of different ways.

Quote: Research Software includes source code files, algorithms, scripts, computational workflows and executables that were created during the research process or for a research purpose. Software components (e.g., operating systems, libraries, dependencies, packages, scripts, etc.) that are used for research but were not created during or with a clear research intent should be considered software in research and not Research Software.
Definition of “research software” from the FAIR4RS working group, image by the Netherlands eScience Center licensed under CC-BY 4.0

In the software survey conducted by the Software Sustainability Institute in 2014, 92% of researchers indicated they used some kind of software to aid or conduct their research. This was not limited to researchers from computational science (aka scientific computing), the “hard” sciences or to those involved in “traditional” uses of computing infrastructure such as running simulations or developing computational methods. The use of research software is ubiquitous and fairly even across all disciplines.

Research software is increasingly being developed - researchers do not just use “off the shelf” software and the majority of researchers develop their own. In order to be able to produce quality software that outputs correct and verifiable results and that can be reused over time - researchers require training. This course teaches good practises and reproducible working methods that are agnostic of a programming language (although we will use Python code in our examples) and aims to provide researchers with the tools and knowledge to feel confident when writing good quality and sustainable software to support their research. Typically, we think of such software as being FAIR.

In the rest of the course, we will explore what exactly we mean by “FAIR research software”, why it is important and what practices can help us along our “FAIRification” journey.

Further reading

We recommend the following resources for some additional reading on the topic of this episode:

Acknowledgements and references

The content of this course borrows from or references various work.