If you double-click in the data browser onto a list item's preview image or filename the spectroscopy data viewer will open for one-dimensional spectroscopy files:

The window is separated into several regions:

1. (A) shows all measurement properties that could be extracted from the file header.
2. (B) shows the comment entered during measurement by the user.
3. (C) if this tab is selected (default) the plot of the spectrum will be shown. The properties of the plot can be changed by the buttons in the lower left an right corners.
4. (D) if this tab is selected the measurement data is shown as a data table, which can be copied directly, or searched for entries.
5. (E) if this tab is selected the extended comment editor is shown. Here you can add additional comments, that are stored together with the file.
6. (F) if (C) is selected, (F) is a simple data extraction tool. If you double click on the spectrum it will extract the closest data point available. You can simply change the format of the output, to paste-and-copy the output to third-party software, such as Excel.

Use you mouse-wheel to zoom in and out of the plot. A right-click on will open a context menu, where you can re-adjust the zoom-settings, or copy the plot to the clipboard, or save it to a file.

The plotted data be changed in the sidebar, which is opened by hovering over the "data columns"-button in the top right corner in the toolbar on top of the plot:

The style of the plot can be changed in the side-bar, which is opened by hovering over the "style"-button in the top left corner in the toolbar on top of the plot:

Both side-bars are shown below:

In the "style"-sidebar you can change the stacking of different plots, if multiple spectra are plotted. The plotted data can be adapted in its color, symbol and line-thickness for each dataset.

In the "data"-sidebar you can select single or multiple columns to be plotted in the y-axis, versus a single x-axis column. The column-list can be filtered by clicking on the filter-icon next to the listbox, which may be useful for large datasets.