From the website:
Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Extensions for bioinformatics and text mining. Packed with features for data analytics.
I had to look at the merge data widget.
Which is said to: Merges two data sets based on the values of selected attributes.
According to the documentation:
Merge Data widget is used to horizontally merge two data sets based on the values of selected attributes. On input, two data sets are required, A and B. The widget allows for selection of an attribute from each domain which will be used to perform the merging. When selected, the widget produces two outputs, A+B and B+A. The first output (A+B) corresponds to instances from input data A which are appended attributes from B, and the second output (B+A) to instances from B which are appended attributes from A.
The merging is done by the values of the selected (merging) attributes. For example, instances from from A+B are constructed in the following way. First, the value of the merging attribute from A is taken and instances from B are searched with matching values of the merging attributes. If more than a single instance from B is found, the first one is taken and horizontally merged with the instance from A. If no instance from B match the criterium, the unknown values are assigned to the appended attributes. Similarly, B+A is constructed.
Which illustrates the problem that topic maps solves rather neatly:
- How does a subsequent researcher reliably duplicate such a merger?
- How does a subsequent researcher reliably merge that data with other data?
- How do other researchers reliably merge that data with their own data?
Answer is: They can’t. Not enough information.
Question: How would you change the outcome for those three questions? In detail. (5-7 pages, citations)