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I am doing a research of the processing times of papers published in journals in my field. I have noticed that the metrics that the journals advertise (e.g. the Elsevier journal insights) do not correspond to my experience, nor to the recently published papers, so I wanted to make my own survey. (My guess is that they take into account papers which are immediately rejected by the editor without being sent to a review, so the average looks quite favourable. I am more interested in the average time of the papers which are actually accepted.)

I plan to cover all recently (last 12 months) published papers in 10-20 journals of different publishers (e.g. Elsevier, T&F, Wiley), which will result in hundreds of papers. Basically, I will take the date when the paper was submitted, accepted, and published online, and calculate the average per journal.

Is there a way to automatically extract this information?

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Have you checked this data is actually made available for your preferred journals? IME not all make their accepted/submitted/first-online dates very easily accessible, though it has improved a bit recently.

If it's there, your best bet is probably to screenscrape the HTML. Some journals provide nice clean XML to play with, but this is usually new online-only titles rather than legacy ones from traditional publishers.

Elsevier use a simple HTML tag (class="articleDates") which contains the core dates -

Received 23 March 2015, Revised 15 May 2015, Accepted 18 May 2015, Available online 9 June 2015

Taylor & Francis have similar information to Elsevier: the element you'd need is again "articleDates", but it unfortunately has a lot of linebreaks in it for no good reason!

Finally, Wiley don't seem to expose submitted/accepted dates (at least not for all journals); "publicationHistoryDetails" just gives first-online, which isn't much help.

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  • An excellent example of what you can do if the data is available, looking at the PLoS journals: metarabbit.wordpress.com/2013/06/05/… – Andrew Jun 14 '15 at 19:57
  • Thanks for the suggestion, I didn't notice that it'd be fairly easy to get the info from the HTML. And yes, not all journals (especially Wiley's) provide such data, but luckily they are in a small minority. – paginated Jun 14 '15 at 20:38
  • @paginated there's a good chance they'll expose this data soon (the UK HEFCE open-access mandate relies on it, and it would be a lot easier if they reported it, so there's some pressure...) - but for now, I think the html's your best bet. Let us know how you get on! I'd be quite interested to see the results. – Andrew Jun 14 '15 at 20:43
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This is an interesting question (+1). And I like @Andrew's answer (+1). However, I would like to suggest an approach, somewhat alternative to Web scraping. I mean using meta-repositories and their APIs. For example, you can consider using CrossRef, which offers, along with other services, CrossRef Metadata Services. There is a free of charge subset of this offering, which can be used via what is referred to as End-User Lookup Affiliate (other metadata services seem to be paid).

With that repositories/APIs approach in mind, if you use R programming environment, there is an interesting initiative rOpenSci, which is comprised of an open science-focused set of projects, developing R packages for interacting with various repositories, including meta-repositories. In particular, rmetadata package seems like the project that is the most relevant on the topic (note that it is not a mature project yet). A more mature, but still relevant project is rcrossref package. Hopefully, some other rOpenSci packages also might be of your present and future interests.

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  • Great, I was not aware of any of these. Thanks. – paginated Jun 14 '15 at 20:38
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    Anything that doesn't involve screenscraping would certainly be an improvement from a practical perspective. However... it's all a bit moot if the other data repositories don't actually contain the relevant metadata! From a quick check of the Elsevier example it doesn't look like they do... – Andrew Jun 14 '15 at 20:41
  • @paginated: My pleasure. – Aleksandr Blekh Jun 14 '15 at 20:46
  • @Andrew: I wouldn't make conclusions, based on sample with N=1. Plus, considering current trends, we can expect at least some progress in representativeness and data quality of repositories and meta-repositories in the near future. So, trying this approach can give researchers a sense of comfort (or lack of) for automating bibliographic information discovery and analysis and help build their skills foundation for, hopefully, not-so-distant time, when this approach will become mainstream. – Aleksandr Blekh Jun 14 '15 at 20:51

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