The worst thing I am worried about is losing my legitimate data and then maybe being accused of falsifying it.
how does the university differentiate the data fabrication and honest error? (I have lost all the data used for my deep learning training, and I cannot use the raw data to defend myself). Did the reproducibility problems of deep learning contribute to academic misconduct?
The dataset collected and made by myself has been lost. However, I can repeat the experiment based on my code (I have backed up all my code but lost the private dataset, the method in the thesis is feasible). The experimental results will be different from my thesis results if I repeat the experiment (because I have to collect the dataset again from websites, and the image dataset will change). In addition, the reproducibility flaw of deep learning will impede the reproduction of the previous results (there are many random parameters in GPU or other settings).
I did my thesis by myself without any data fabrication thoughts. But the lost dataset and the reproducibility flaw of deep learning made me worried about my thesis if someone accused me of data fabrication. Readers can get similar results and conclusions by repeating my experiment (they can use their own dataset to get something). But readers cannot get the same results in my thesis again. Can I defend myself by the similar results and replacing the previous experimental results in the thesis?