Publishing culture in computer science is quite different from natural sciences. Here are my impressions as an author and a reviewer in both, and as an editor in computer science (data mining and machine learning). In CS I’m mostly familiar with publishing related to data analysis, in NS I’m familiar with forestry, atmospheric sciences, palaeoecology, and medicine domains. Counterexamples can always be founds, but here are general trends that I have observed.

  1. In CS the main publishing is in conference proceedings, in NS - in journals.

    In CS conferences publish full peer reviewed articles. In NS conferences are for presenting early work, only extended abstracts are published sometimes with a light reviewing. In both cases conferences are for getting publicity, in NS more than in CS conferences are also for getting an early feedback.

  2. Journal decisions are much faster in NS.

    In NS journal decision typically comes within 1-2 months. In CS (data analysis) 3 months is considered fast. It is not unusual to take 6 months, sometimes even a year for the first decision.

    NS papers typically follow very standard structure (intro, materials and methods, results, discussion). I think this is easier to review: “is the methodology ok?”, “are the conclusions ok?”.

    The main reason for long reviewing processes, I think, is reviewer overload in CS (data analysis). In NS reviewers still take pride in reviewing. In CS it is mostly considered as a burden. It is not unusual for an editor to invite 20-30 candidates for reviewing a paper before three reviewers agree.

  3. NS papers have many more co-authors than CS.

    I think this is because the cost of producing a paper is generally higher in NS, and the time for producing a paper is longer, it often involves fieldwork or lab work. Sometimes in NS the whole lab may become co-authors. In many NS publications people who share data become co-authors, even if this data has already been published. I have heard stories that museum curators become co-authors for allowing to access collections.

  4. The order of authors is more important in NS.

    In NS the order of names is a big deal. In CS, not often so. Sometimes in CS authors even go alphabetically. The last author position is important in NS. That is typically the head of the project or the had of the lab. This is perhaps because here are so many authors in NS often contributing indirectly, so it is important to mark who gets what credit.

  5. In NS responsibilities of the co-authors are much more divided.

    In CS (data analysis) most often everybody works on everything altogether. In NS one author supplies this data, another author supplies that data, another author does a statistical test, another author writes the discussion and so on.

  6. In NS authors take more pride in their text. In CS authors take more pride in their visuals.

    In NS what is claimed in the text is more important, figures and visuals come secondary. In CS (data analysis) visuals, algorithms, formulas and figures are more import, text comes secondary.

  7. In NS papers have stronger and more stand-alone conclusions.

    In CS (data analysis) conclusions often summarise what has been done, focus on the process of analysis, and list the contributions. In NS conclusions focus on what has been found. In NS conclusions can be used as claims for further research, they can be stand-alone take-home messages. In CS conclusions can rarely be used stand-alone, one has to get back to the actual paper.