The 2019 Journal Citation report was released a few months ago — it includes the new journal impact factor calculations for 2018. To recap from my previous posts discussing this issue, a journal impact factor is calculated by dividing the total number of citations in a given year (to articles published in the previous 2 years) by the total number of articles published in the previous 2 years. It is a not a metric for individual papers, and applying it to individual papers is a misapplication. A metric to see the full range of citations garnered by papers published in the previous two years is the journal citation distribution — the number of papers published in the past 2 years that have a given number of citations in the following year. Citation distributions can be calculated using the same set of data as a Journal Impact Factor (1 year of citations for previous 2 years of papers). I became aware of citation distributions after reading a preprint by Larivière et al. (2016).
Below I calculate the citation distributions for AGU’s JGR-ES with data for the 2017 and 2018 journal impact factor calculation (so for the 2018 calculation — 2018 citations to 2017 and 2016 publications ; for the 2017 calculation, 2017 citations to 2016 and 2015 publications). All of the data was downloaded from Scopus.
Just to clarify further — this plot reports the number of papers (on the y axis) with a given number of citations (on the x axis) — 256 papers in the left plot. 243 papers in the right plot.
Distributions are skewed, as is expected. The 2017 distribution has a spike in papers with 3 citations, which disappears in the 2018 distribution. The center of mass moves slightly rightward in 2018 — more papers had more citations. So what is in the tail (far to the right)? in 2018, it’s a bunch of cryosphere papers. I’m not going to name names…
To be honest, I’m not sure what I learned from these graphs and data tables — There are no strong signals like SfM in the 2014 and 2015 data.
I’ve done this for several years now, here is a list of the previous posts:
Geomorphology Journals (ESurf, Geomorphology, ESPL, and JGR-ES) for IF: 2014, 2015 and 2016.
Preprints are non-peer-reviewed scholarly documents that precede publication in a peer-reviewed journal. Several disciplines like Physics, Astronomy and computer Science have been using preprints through arXiv for decades. Other disciplines are catching on, notably the biological sciences (See bioRxiv), and a variety of other discipline specific preprint services (e.g., here). There are many great articles and blog posts discussing preprints recently— common questions, critiques, misconceptions, concerns, etc. — here are three especially useful introductions:
To me, there are too many interesting facets about preprints to discuss in a single post. Here, I focus on some ways in which preprints compliment existing, more traditional ways of publishing — so we need to start by looking at scholarly communication and scholarly publishing, specifically journals.
I want to focus on discussing preprints in relation to task 1 (Registering) and 5 (Archiving). These are tasks that a scientific journal currently does by giving submission dates and assigning a persistent identifier to a journal article (i.e., the digital object identifier; DOI). In my opinion, these are tasks that we do not necessarily need a scientific journal to do. Instead, a preprint can accomplish these tasks — establishing precedent for an idea, and providing a means of citing the idea via a DOI.
If we rely on journals to do these tasks, the process can be attenuated. Peer-review can take months (or even years) before an article is published and visible to a community of peers. This is not a complaint against peer-review or the peer-review process — I am arguing here that several steps can occur before peer-review. My opinion is that bundling the registration of an idea (Task 1) and the archiving of idea (Task 5) with the peer-review process is suboptimal for one key reason:
No one can read, cite, or respond to an idea when the paper is hidden in review — only the editor, AE, reviewers, and coauthors can read, engage with, explore, and think about the work. These ideas may be presented at conferences, but in the written record, they do not exist (e.g., many journals have policies discouraging citations to conference abstracts). Ideas that are preprinted have persistent identifiers (DOIs) and can (and should!) be cited and discussed by others — preprints exist.
As an early career scientist, this is especially important. Scholarly work in review with no preprint remains invisible to the broader community. Early career scientists often mention ‘in prep’ or in review articles on CVs — I’d argue that this is far less meaningful than linking to a preprint version (where people could actually read and cite you work). Again, preprints exist.
Being unable to read and cite articles that are in review in a transparent way hampers our ability to do science. Hiding articles through the review process is a form of information asymmetry — and a bizarre, imperfect hiding. I know about lots of work that remains hidden — I read about it as a grant or paper reviewer, I hear about it in passing from colleagues, and conference presentations give a glimpse of what will be published in the next few years — but I cannot cite these ideas or these works unless there are preprints. Put another way — there is a subset of ideas that I know about, but can’t share with colleagues. This is strange.
This is where preprints come into the picture. Preprint services like EarthArXiv can 1) store papers (i.e., registering intellectual claims associated with author names and submission timestamps), and 2) assign DOIs and archive scholarly artifacts. Therefore preprint services accomplish Task 1 (registering) and Task 5 (archiving) in the Wouters et al (2019) taxonomy. Preprints leave the other tasks (curating, evaluating, and disseminating) for other services such as scientific journals.
My argument here is that we should unbundle the services that journals provide to increase the flow of information. Preprints can accomplish some of these tasks faster, cheaper, and better than traditional journals.
I missed the 2017 Fall AGU meeting, but I did follow along on twitter. However the coverage was spotty — some sessions were mentioned, some not at all. From this experience I kept wondering about the digital traces of the meeting on twitter. Lo and behold I saw this tweet from Dr. Christina K. Pikas (@cpikas) at the beginning of this year:
So let’s look at this awesome dataset that Dr. Pikas collected and published on figshare:. First, this data was collected using TAGS, and contains tweets from Nov. 4th, 2017 to Jan. 4th, 2018 that used the hashtag #AGU17. There are a total of 31,909 tweets in this dataset. In this post I am subsetting the data to look only at the meeting (with a 1 day buffer, so Sunday Dec. 10, 2017 to Saturday Dec. 17, 2017) — a total of 25,531 tweets during the 7 days:
Twitter activity decays through the week (fatigue? do most people just tweet their arrival? Daily attendance variations?)
There is a noticeable lunch break on M, W, Th, and F
Each day twitter activity starts suddenly, but has a slow(er) decay at the end of the day (late night activities?)
Retweets account for 44% of the 25,531 tweets during the meeting. Removing RTs yields an almost identical plot, but there is small peak that appears at the end of each day (pre-bedtime tweets?):
Lastly, the biggest #AGU17 twitter user is @theAGU (by far), which sent 1063 tweets during the week. Here is the timeseries with only @theAGU tweets:
I see the lunch break and not as many late nights for the organization.
Thanks @cpikas for collecting and publishing the data! It is available on figshare:
In previous posts I have looked at several aspects of Earth and Space Science citations in Wikipedia. As part of a project I am working on, I’m interested in expanding this work to look at some mechanics of citations in Wikipedia to articles in Geophysical Research Letters (e.g., when do they appear, who writes the edit, on what Wikipedia pages, etc.). In this post, I want to walk through my method for getting the data that I will analyze. All the code is available (note that I am not a good R programmer).
Data on Wikipedia mentions are aggregated by Altmetric. rOpenSci built a tool to get altmetric data (rAltmetric) using the Altmetric API. rAltmetric works by retrieving data for each paper using the paper’s DOIs — so I need the DOIs for any/all papers before I do anything. Fortunately, rOpenSci has a tool for this too — rcrossref — which queries the Crossref database for relevant DOIs given some condition.
Since my project is focused on Geophysical Research Letters, I only need the DOIs for papers published in GRL. Using the ISSN for GRL, I downloaded 36,960 DOIs associated with GRL and then the associated Altmetric data (using rAltmetric).
The data from rAltmetric returns the number of times a given article is cited in Wikipedia. But I want some extra detail:
The name of the Wikipedia article where the GRL citation appears
When the edit was made
and Who made the edit
This information is returned through the Altmetric commercial API — you can email Stacy Konkiel at Altmetric to get a commerical API key through Altmetric’s ‘researcher data access program’ (free access for those doing research). I got the data another way, via webscraping. To keep everything in R, I used rvest to scrape the Altmetric page (for each GRL article) to get Wikipedia information — the Wikipedia page that was edited, the author, and the edit date. Here is an example of an Altmetric.com page for a GRL article:
The Wikipedia page (‘Overwash’), the user (‘Ebgoldstein’ — hey that’s me!), and the edit date (’10 Feb 2017′) are all mentioned… this is the data that I scraped for.
Additionally I scraped the GRL article page to get the date that the GRL article first appeared online (not when it was formally typeset and published). Here is an exampLE of a GRL article landing page:
Notice that the article was first published on 15 Dec 2016. However, if you click the ‘Full Publication History’ link, you find out that the article first appeared online 24 Nov 2016 — so potential Wikipedia editors could add a citation prior to the formal ‘publication date’ of the GRL article.
So now that I have that data, what does it look like? Out of 36,960 GRL articles, 921 appear in Wikipedia, some are even cited multiple times. Below is a plot with the number of GRL articles (y-axis) that appear in Wikipedia, tallied by the number of times they are cited in Wikipedia — note the log y-axis.
GRL articles are spread over a range of Wikipedia pages, but some Wikipedia pages have many references to GRL articles (note the log scale of the y-axis):
553 Wikipedia Articles have a reference to only a single GRL article, while some articles contain many GRL references. Take for instance the ‘Cluster II (spacecraft)‘ page, with 25 GRL citations, or ‘El Niño‘ with 11 GRL references).
I’ll dive into data I collected over the next few weeks in a series of blog posts, but I want to leave you with some caveats about the code and the data so far. (Edited after the initial posting)Altmetric.com only shows the data for up to 5 Wikipedia mentions for a given journal articles unless you have paid (instituitonal) access. Several GRL articles were cited in >5 Wikipedia articles, so I manually added the missing data. Hopefully i will make a programmatic work-around sometime.After I wrote this post, I was informed that the commerical Altmetric API gives all of the Wikipedia data (edit, editor, date). To get a commerical API key through Altmetric’s ‘researcher data access program’ (free access for those doing research), email Stacy Konkiel at Altmetric (thanks Stacy!).
Furthermore, many of the edit times that you see here could be re-edits, therefore ‘overprinting’ the date and editor for the first appearance of the wikipedia citation. This will be the subject of a future post, though I haven’t yet found an easy way to get the original edit…
On May 15 1984, Russell and Reiff published a (jokey) flow chart of the AGU editorial and peer review process with several time delay terms and a ‘counting’ step for the multiple revisions. This set off 6 responses in EOS, similar to the episode in 2003-2004.
On Oct 23, 1984, Baum wrote in to discuss how peer review tended to filter out controversial new ideas. Baum recommended that authors be allowed to publish controversial new ideas even if reviewers protested, but reviewers should also be allowed to publish their criticisms. In addition Baum offered some mathematical changes to the Russell and Reiff flow chart.
On Dec. 25, 1984, Sonnerup (the editor of JGR-Space Physics) wrote to EOS in support the idea that peer review should permit new and unorthodox ideas. Additionally, Sonnerup provides additional details regarding the review process at JGR-Space Physics.
On Feb 19, 1985 Walker and van der Voo wrote in to EOS to discuss the editorial process at GRL. Choice quote (bold type highlighted by me): “Because of the importance attached to prompt publication in GRL we will generally use only one reviewer for each paper, communicating with this reviewer, when necessary, by telephone or telemail. More reviewers are used only when a paper seems likely to be particularly controversial or is otherwise difficult to deal with.”
Baker wrote in on April 25, 1985 to suggest that JGR collect the rejected papers and publish them. Baker stated, in jest, that there is likely a “large body of unpublished papers out there which have been rejected by Neanderthal referees. I say let’s do something about it! I suggest that all of these brilliant, creative, earthshaking papers be collected into a special JGR issue each year.”
Murdoch wrote in on March 10, 1987 to suggest that abstracts of rejected papers be published. If a scientist wanted to see the rejected paper, then the author could provide the paper AND the critical reviews.
(The full motivation, rule set, and previous results for this model are collected here)
Today I am adding a new rule into my toy peer review model. Suppose some fraction of the population is set to ‘retaliate’ when called upon to be a reviewer. Specifically, these agents assign review scores based on their feelings (postive or negative) toward the author. This is an example of biases that might influence a reviewers decision (e.g., Lee et al., 2012).
So the new rule is:
If a ‘retaliator’ is assigned a review, and they feel positively or negatively toward the author, the review is postive or negative, respectively (overriding a random review).
(n.b.: A more gentle statement of this review could instead focus solely on ‘cliques’ — if a reviewer feels positively toward the author, the review is positive. if the review feels negatively, the review is random. )
The issue is now there are 4 types of peple in the model:
Those who sign reviews, and do not retaliate
Those who sign reviews, and retaliate
Those who do not sign reviews, and do not retaliate
Those who do not sign reviews, and retaliate
Again I will use the range of incoming and outgoing node weights to visualize model results. As a reminder:
is the maximum incoming weight minus the minimum incoming weight. This represents the range of feelings all other scientists have about scientist i.
is the maximum outgoing weight minus the minimum outgoing weight. This represents the range of feelings scientist i has about all other scientists in the discpline.
So here are the results with 30% of all scientists being ‘retaliators’.
Compared to the previous results, the same trends hold: Rin is larger for signed reviewers (blue), and Rout is roughly the same for signed vs unsigned. (ranges are different for the previous results because of a change in the number of model timesteps).
Unsigned retaliators (empty orange markers) are similar to Unsigned non-retaliators. If you never sign reviews, no author will end up knowing that you are a retaliator (the editor is a different story).
Signed retaliators (empty blue markers) have a large Rin — they are polarizing figures. Authors are either on the good side of these people (they are friends) or on the bad side (they are enemies).
All of the reviews for Earth Surface Dynamics are open, published, and citable. Today I do a bit of webscraping to determine the % of mix of signed and blind reviews for the 198 paper reviewed in EsurfD. Also, since reviews occur in sequence (i.e., R1 submits their review before R2), we can exame how R1’s decision to sign a review influences the decision of R2.
The code to do the webscraping is here. Note that R is not my best language, but I am using it because of all the cool packages written for R to interface with Crossref (rcrossref, for obtaining publication DOIs), and the easy webscraping (rvest).
The code works by:
Pulling (from Crossref) details for all ESurf Discussion publications using the ISSN number.
Going to every EsurfD page (following the DOI link)
Scraping the webpage for author, editor, and reviewer comment (see this helpful tutorial on using rvest).
Checking for descriptive words, for instance “Anonymous Comment #1”, to determine if Reviewer 1 and/or Reviewer 2 were anonymous.
Check to see if a Reviewer 3 exists (to exclude the data… I only want to deal with papers with 2 reviewers for this initial study).
I imagine some specific pathological cases in review comments may have slipped through this code, but a cursory check shows it captures relevant information. After the code runs, I am left with 135 papers with 2 reviewers, for a total of 270 reviews. In total, 41% reviews are signed — this matches previous reports such as 40% reported by Okal (2003) and the 40% reported by PeerJ
Reviewer 1 totals are 74 unsigned, 61 signed —55% unsigned, 45% signed
For the 74 papers where Review 1 is unsigned,
Reviewer 2 data is 59 unsigned, 15 signed — 80% unsigned, 20% signed
For the 61 papers where Review 1 is signed,
Reviewer 2 data is 27 unsigned, 34 signed — 44% unsigned, 54% signed.
There is one clear confounding factor here, which is how positive/negative reviews impact the likelyhood to sign a review (both for R1 and R2). I imagine referee suggestions to the editor (e.g., minor revisions, major revisions, reject) and/or text mining could provide some details. (I can think of a few other confounds beyond this one)…. Furthermore, I would assume that since many (all?) journals from Copernicus/EGU have open review, this analysis could be scaled…