Scientific Software Tools
There is a vast array of scientific software written by researchers and developers around the world. This software is often written in a variety of languages, including C, C++, Fortran, perl, R and Python. There are a variety of ways these tools can be installed and used and this can be frustrating and time-consuming. Here we have instructions for the use of some common tools and some general advice on how to install and use scientific software.
Should I use the software?
When evaluating scientific software pay attention to the quality of the software before you use it. Some things to consider are:
- When was it last updated
- How many people are using it
- Are there any known issues
- Is there a community of users who can help you
- Is there a manual or other documentation
- Is there a license
If the software is not well maintained, has no documentation, or has no community of users, you may want to consider using a different tool.
Software is often distributed via GitHub and if so you can look at the issues and dates on the files, paper references and get a feel for it. It may be software from a major lab or company and so you can be more confident in its use.
How to install software
There are a variety of ways to install scientific software, including:
- Using conda or mamba (recommended)
- Using python virtual environments where the software can be "pip installed"
- Using "module load"
- Using containers (singularity) either built by the ICR to facilitate use, or by the provider.
When evaluating software: work out how best to install it, and if you need guidance ask for help from scientific computing.
There are an assortment of tools that have been asked about frequently that we have explicit instructions for, and these are listed below.