How can we design better software for the life sciences?
Transparency in Information Analysis
One of the main reasons for developing life science software is to automate some part of an analytical process through the use of algorithms, easing the burden on scientists. Often, most of the logic of an algorithm tends to be abstracted away from the users - it’s a ‘black box’ that no one can see into to make sense of.
Information Hierarchy
A major challenge in the life sciences is the scale and richness of biological data. When using software tools for accessing or manipulating large biological datasets, it is easy to become overwhelmed, miss what you are looking for, and miss opportunities for discovery.
Flexible Workflows
Scientific workflows can switch rapidly from the ordinary to the novel as researchers respond to signals in their data. Software interfaces need to support streamlined completion of routine tasks as well as facilitating detours for more in-depth data exploration.
Encouraging Exploration of Results
Scientific information inherently lends itself to visualisation. Modern web browsers allow for visual representations that are multidimensional, dynamic, and interactive. However, because data in the life sciences is multidimensional and vast in size, it often makes it challenging to capture all significant information in a single visualisation format.
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