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Sometimes you need to refer readers to a reference for more details about a subject. I wonder what is the best way to accomplish this. Or if there is a specific way to do it for scientific papers that target journals in the field of computer science and engineering.

I use something similar to this "For more details about flying monkeys see [1,2,3].", but I feel there's something wrong about it (other than the flying monkeys).

  • "More details" as in "less terse treatment, in case the reader can't follow yours" or as in "more related material that might satisfy the reader's curiosity"? – darij grinberg Nov 2 '18 at 17:19
  • Also, I generally advise against chaining references like [1, 2, 3]. It makes it harder to see where a given paper is being cited: e.g., searching for "[2" in the PDF will not see its appearance in "[1, 2, 3]". – darij grinberg Nov 2 '18 at 17:21
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I would try to work the references into your text and include why you're referring the reader to them. I might write something like:

Smith et al. [1] provides additional background on flying monkeys and their use in underwater basket making.

Or

Several recent articles review flying monkey [1, 2, 3].

Or

Recent tutorials and overviews of flying moneys exist for readers unfamiliar with the their background and use [1, 2, 3].

As a style note, I would not simply refer to the number inline (e.g. see [1, 2, 3])).

For more inspiration, I would skim and read recent articles in your target journals. I find reading to be a one method for improving my own writing style.

Here's an example from my own publication:

For readers who are unfamiliar with IPMs, recent overviews, introductions, and tutorials of integral projection models exist (e.g., Ellner and Rees, 2006, Ramula et al., 2009, Ellner et al., 2010, Merow et al., 2014).

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What I like to is to use [1,2] if I cite something and if I want the reader to check a article for more details I would write the name of the first author et al. [5].

So as example: For a more detailed analysis on the algorithm behind deep learning, I would recommend Smith et al. [x] detailed analysis on this topic...

Something like this.

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