Data for "PerfectlyAverage: a classical open-source software method to determine the optimal averaging parameters in laser scanning fluorescence microscopy"

This dataset contains Python code, a Windows executable file, and both .tif and .lif microscope images. The image data was produced on a microscope for the purpose of testing the Python code. The Python code, in .py format, helps to determine the optimal number of averages for laser scanning microscopy. The Windows executable file is a standalone version of the Python code so that users without coding experience can make the same assessment of their data.

New data added to this dataset on 28/04/2025 in response to reviewer feedback thus, file PerfectlyAverage_for_Pure_28April25.zip:

Figure 2 – noisy boats: Image of boats from FIJI/ImageJ (boats.tif), serves the input to Python file ‘Generate a noisy timelapse for Figure 2.py’. After running the code, two outputs are provided, namely the noisy instances of the input image, and the geometric series 2^n of the data.

Figure 3 – Safranin paper: Two files, one input (Safranin paper crop bright 1-256), one output (Safranin paper crop bright 1-256 averaged) from PerfectlyAverage.py, the geometric series of the averaged data.

Figure 4 – 3T3: Two files, one input (3T3 ActinGreen 24April25 1-256), one output (3T3 ActinGreen 24April25 1-256 averaged) from PerfectlyAverage.py, the geometric series of the averaged data.

Supplementary Info 1 – dim ROI: As for Figure 3, except a dim region of interest.

Supplementary Info 2 - image diameter: Input image (2048_crop), and iteratively cropped version down to 8_crop, using code Iteratively cropped data for Supplementary Info 2. Py. The resultant plot shown in the paper draft is iterative crop.tif.