1. Categories
  2. Publication year
  3. Authors
Application Catalogue
10 RShiny apps , 1 RMarkdown
Geographical scope


Open Science and the development of open-access tools is an increasingly popular way to make science accessible, reproducible, scalable, and replicable (Lowndes et al., 2017). The most popular open-access platform for analysis and reporting of data is R (R Development Core Team, 2016). R provides more than 5000 packages for data exploration, visualisation, modelling and statistical analysis.

In addition, the R Shiny and RMarkdown packages allow for the creation of web tools, allowing users to interact with science outputs and methods, without any requirement for software (only web access is required) or a need to understand complex code. Another advantage of R Shiny is that it allows scientists to directly create web tools without any of the disadvantages associated with using 3rd party programmers (e.g. high cost, difficulty communicating ideas or interpreting science). RMarkdown is used to document code used within RShiny web apps, supporting reproducibility, transparency and reuse of existing code.

Within Cefas (Lowestoft and Weymouth) and collaboration with University of East Anglia, multiple tools have been created and more are in development .

These tools are designed to be open access and shared to drive scientific innovation . To ensure Cefas tools/applications are developed and deployed in a way which satisfies Open Science criteria, a framework (Fig. 1) has been developed. This framework ensures that tools are based on solid foundations of:

  1. access to data using Cefas systems,
  2. high quality, open access peer review publications, and transparency of methods using code repositories such as gitHub.

Fig.1 - Open Science Framework workflow


The workflow set out in the framework is designed to produce four different science outputs during its implementation:

  1. Databases servers: Data are organised and stored in relational databases. This method of storage allows data to be accessed in R and in web applications.
  2. Scientific publications: Scientific outputs are published as reports or papers (where possible in open access journals). Typically these publications will also include the analysis scripts, either as supplementary material or via a link to a GitHub repository.
  3. Methods: The scripts created during investigations can be organised as functions within an R package. These packages support transparency and reproducibility. Published scripts could be tested, improved and re-used by the open science community.
  4. Web applications: Developed methods can be share with users via web applications. These apps do away with the need to install software on users computer (apps are hosted on a remote server) and data can be managed centrally .


If you have any question regarding the Open Science Framework or web applications, please contact us .