Following on from the last post about publication lag times at cell biology journals, I went ahead and crunched the numbers for all journals in PubMed for one year (2013). Before we dive into the numbers, a couple of points about this kind of information.
- Some journals “reset the clock” on the received date with manuscripts that are resubmitted. This makes comparisons difficult.
- The length of publication lag is not necessarily a reflection of the way the journal operates. As this comment points out, manuscripts are out of the journals hands (with the reviewers) for a substantial fraction of the time.
- The dataset is incomplete because the deposition of this information is not mandatory. About 1/3 of papers have the date information deposited (see below).
- Publication lag times go hand-in-hand with peer review. Moving to preprints and post-publication review would eradicate these delays.
Thanks for all the feedback on my last post, particularly those that highlighted the points above.
To see how all this was done, check out the Methods bit below, where you can download the full summary. I ended up with a list of publication lag times for 428500 papers published in 2013 (see left). To make a bit more sense of this, I split them by journal and then found the publication lag time stats for each. This had to be done per journal since PLoS ONE alone makes up 45560 of the records.
To try and visualise what these publication lag times look like for all journals, I made a histogram of the Median lag times for all journals using a 10 d bin width. It takes on average ~100 d to go from Received to Accepted and a further ~120 d to go from Accepted to Published. The whole process on average takes 239 days.
To get a feel for the variability in these numbers I plotted out the ranked Median times for each journal and overlaid Q25 and Q75 (dots). The IQR for some of the slower journals was >150 d. So the papers that they publish can have very different fates.
Is the publication lag time longer at higher tier journals? To look at this, I used the Rec-Acc time and the 2013 Journal Impact Factor which, although widely derided and flawed, does correlate loosely with journal prestige. I have fewer journals in this dataset, because the lookup of JIFs didn’t find every journal in my starting set, either because the journal doesn’t have one or there were minor differences in the PubMed name and the Thomson-Reuters name. The median of the median Rec-Acc times for each bin is shown. So on average, journals with a JIF <1 will take 1 month longer to accept your paper than journal with an IF ranging from 1-10. After this it rises again, to ~2 months longer at journals with an IF over 10. Why? Perhaps at the lower end, the trouble is finding reviewers; whereas at the higher end, multiple rounds of review might become a problem.
The executive summary is below. These are the times (in days) for delays at all journals in PubMed for 2013.
- Median time from ovulation to birth of a human being is 268 days.
- Mark Beaumont cycled around the world (29,446 km) in 194 days.
- Ellen MacArthur circumnavigated the globe single-handed in 72 days.
On the whole it seems that publishing in Cell Biology is quite slow compared to the whole of PubMed. Why this is the case is a tricky question. Is it because cell biologists submit papers too early and they need more revision? Are they more dogged in sending back rejected manuscripts? Is it because as a community we review too harshly and/or ask too much of the authors? Do Editors allow too many rounds of revision or not give clear guidance to expedite the time from Received-to-Accepted? It’s probably a combination of all of these factors and we’re all to blame.
Finally, this amusing tweet to show the transparency of EMBO J publication timelines raises the question: would these authors have been better off just sending the paper somewhere else?
— The EMBO Journal (@embojournal) March 10, 2015
Methods: I searched PubMed using
journal article[pt] AND ("2013/01/01"[PDAT] : "2013/12/31"[PDAT]) this gave a huge xml file (~16 GB) which nokogiri balked at. So I divided the query up into subranges of those dates (1.4 GB) and ran the script on all xml files. This gave 1425643 records. I removed records that did not have a received date or those with greater than 12 in the month field (leaving 428513 records). 13 of these records did not have a journal name. This gave 428500 records from 3301 journals. Again, I filtered out negative values (papers accepted before they were received) and a couple of outliers (e.g. 6000 days!). With a bit of code it was quite straightforward to extract simple statistics for each of the journals. You can download the data here to look up the information for a journal of your choice (wordpress only allows xls, not txt/csv). The fields show the journal name and the number of valid articles. Then for Acc-Pub, Rec-Acc and Rec-Pub, the number, Median, lower quartile, upper quartile times in days are given. I set a limit of 5 or more articles for calculation of the stats. Blank entries are where there was no valid data. Note that there are some differences with the table in my last post. This is because for that analysis I used a bigger date range and then filtered the year based on the published field. Here my search started out by specifying PDAT, which is slightly different.
The data are OK, but the publication date needs to be taken with a pinch of salt. For many records it was missing a month or day, so the date used for some records is approximate. In retrospect using the Entrez date or one of the other required fields would have probably be better. I liked the idea of the publication date as this is when the paper finally appears in print which still represents a significant delay at some journals. The Recieved-to-Accepted dates are valid though.