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I'm a PhD candidate in Computer Science and I'm working on my first paper that should be published in a peer-reviewed journal (until now, I published only papers in conferences). My project is a FPGA memory module, which uses a specific approach (the main idea) to obtain better performance in terms of access philosophy and capacity.

Now, the mentioned idea is naturally the bulk of the paper, with the math, proofs, algorithms, etc. behind it. But, since the module was synthesized and tested, I feel obliged to address its obtained "measures".

The process generated quite a lot data which become even more after the simulations were done. So, I'm not sure what charts/tables are appropriate to analyze and publish in a paper? (e.g. input->output delay for a range of inputs; parameters like max clock, number of logic elements; perhaps even (parts) of the schema; etc.)

As this seems a bit specific, I would like to broaden the scope of the question. Namely, the general issue here is how to filter data which does not directly contribute to the understanding of the main idea, but is the result of a finished project and can be used to reinforce the researched concept with practical measurements and simulations. This obtained data is large, so I'm asking for guidelines what could be considered concise enough to put into a paper.

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What is FPGA? Is it an academic term? If not, why does this question belong here? – scaaahu May 6 '14 at 11:37
You need to generalize your question so that non computer science users could understand what you are asking. – scaaahu May 6 '14 at 13:04
@user3209815 - Please keep the tone of dicourse professional and courteous. LMGTFY links are inappropriate here. – eykanal May 6 '14 at 13:30
This question appears to be off-topic because it is about computer or computational science. There are SE sites devoted to each of those, and the question will probably get a better answer there. – David Ketcheson May 6 '14 at 14:55
@DavidKetcheson yes, perhaps the question is not worded well, but it is about writing an academic paper that involves a lot of data. Please look at the forest, not the trees. – mkennedy May 6 '14 at 17:22
up vote 6 down vote accepted

Read a couple of dozen papers that are similar in nature (that is, in terms of the method and how the research question is addresssed) to yours and are in your target journal. Look at how they've supplied the sort of detailed information you're talking about.

As a rule-of-thumb, put it all into supplementary information. And then follow the advice of your edit and peer-reviewers, if they advise moving things. If there are particular numbers that contribute to your main narrative, put those numbers in the body of the paper, too.

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Use the Internet

Put the raw dataset in an easily accessible format (and it's description) on a public online repository, preferably in one actively maintained by your institution - most of them seem to have such resources.

If it "does not directly contribute to the understanding of the main idea", then simply refer to the relevant URL in your paper - the data is still useful in verification, replication, and further analysis by others.

Do make sure that the repository and URL is one that would stick around for decades, not only a couple of years.

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