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Real-Time Data Processing using Python in DigitalMicrograph

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Processing and visualizing data after a transmission electron microscopy (TEM) experiment is an essential step toward generating scientific conclusions. While detailed processing and visualization may take place long after data… Click to show full abstract

Processing and visualizing data after a transmission electron microscopy (TEM) experiment is an essential step toward generating scientific conclusions. While detailed processing and visualization may take place long after data collection, it can be beneficial to perform initial processing and visualization rapidly during a session on the microscope. This initial analysis enables users to determine whether good data is being collected, the experimental conditions need to be modified, or additional data collection is needed. Enabling the user to do this assessment while the experiment is still in progress can save hours or days of preparation for follow-up experiments if deficiencies with the data are not discovered until the TEM session is over. This time savings is especially true for in-situ experiments where a researcher may only have one chance to capture irreversible dynamics in any given TEM sample. With in situ experiments, it is critical to analyze what is occurring with the sample in real-time since the next experimental step at the microscope is often decided by interpreting the dynamic changes happening in the sample.

Keywords: time; microscopy; real time; time data; data processing; processing using

Journal Title: Microscopy and Microanalysis
Year Published: 2019

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