Sorry for another quitting post. I really need some suggestions. I am at the end of my third year on a Computer Sicence PhD. I had a quite nice started, past my upgrade without problems, had my first paper accepted in a small conference on the 1.5 years. But due to funding problems, my supervisor and I decided to withdraw the paper. After that, nothing went right. I need to write a software to process data. I stacked at a technical problem for like several months and seriously depressed. Then I took a leave of absence for 8 months. I finally manage to solve the coding problem after returning study and starting to gather data. After I got the data, I found many other problems in my software. It looks like this route only can get some bad results.
I had no issues with my supervisor. He is a nice person. The only problem of him is he persuades me to do the route I don't like. But this is ok.
Now the situation is really bad for me. At the end of the third year, some raw data, a crap software, no results and no publication. Based on all this, I may be able to put together one and a half chapters that's all I've got now. I have one writing up year left. I am very doubt about whether I can finish it or not? Or I simply walk away from the PhD?.
I recommend you have a meeting with your supervisor and discuss options. Before that, you should take stock of what you have in terms of data and output, and determine whether you could actually shape it into a PhD thesis. You may be surprised at what you have, especially given the time you have been working on it. If you can find enough to put into 3 data chapters, then you have a PhD.
Don't worry about publications at this stage, you can always publish after you have your PhD. Conference papers are are a 'nice to have', but not essential, especially if you aren't on top of your thesis.
It would be a waste of 3 years if you quit, and you would always have regrets.
Is it possible that the problems you've encountered with your software and data are worth reporting? Of course, it always feels better to get the results we're hoping for, but when things go wrong, it's often the case that something worthwhile can be reported from that too, such as the reasons why the results weren't as expected, and what should be done in future studies.
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