Author(s): Barbara Mirel
The author reviews 15 scientists workflow needs and notes that broadly the existing software tools do not do as much as might be hoped (note, this article was 2008). Specifically this refers to tools that explore and analyse data, rather than parsing.
Tools have advanced to the point of being able to support users fairly successfully in finding and reading off data (e.g. to classify and find multidimensional relationships of interest) but not in being able to interactively explore these complex relationships in context to infer causal explanations and build convincing biological stories amid uncertainty.
- existing tools allow strict categorisation but little novel creative analysis.
- the tool that was analysed (MiMI) no longer exists :(
- comments on the testing included a regular desire to know how we know a given statement is true (i.e. what is the provenance of the data I see?)
- The general structure of the paper looks good for a BlueGenes usability paper.
- it provides some nice heuristics that might be good general recommendations for science / bio papers.
- explain provenance of data
- ensure data can be manipulated exploratively easily.
- different views of data are important for different task types:
For example, users benefit most from side-by-side views – such as the network and tabular views in MiMI-Cytoscape – when their tasks involve detecting patterns of interest and making transitions to new modes of reasoning. But they need single views rich in relevant information and conceptual associations when their goal is to understand causal relationships and diagnose problems . Conceiving and then designing these rich views are vital but challenging.