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Automated atomic force spectroscopy (AAFS) experiments generate high amounts of force-distance (F-D) curves in a short time frame. Typically, less than 5% of these data contain useful information about the sample under investigation and only this smaller part needs to be further analysed. Currently the selection of suitable curves is done by hand, which is a time consuming task, lacking objective criteria and slowing down experimental progress.
To create an objective and convenient data selection platform, we developed a new user interface. This interface allows a visual and intuitive description of the steps necessary to analyze the F-D data. Predefined, extensible filters can be connected to tailor the analysis specifically to the aims of the investigation. In addition, this tool can be extended with customized filters reflecting the specified way of analysis in the selection process. To demonstrate the power of this new approach, we successfully implemented and compared five commonly used cluster methods.
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