Time flies and I can’t contain my excitement that I will be participating in the Discoverability of African Scholarship Online. Practical strategies and collaborative approaches workshop in Nairboi, Kenya, organised by the Open UCT Initiative. For me there’s three very important A’s in scholarly communications: Africa, Access and Altmetrics.

I have been doing some digging, refining and visualising this week, and today I shared two first rough drafts of a couple of alluvial charts I made visualising a dataset of the 25 highest scoring peer-reviewed articles with the term “Africa” in the title (within the timeframe of one year). To collect the articles data I used the Altmetric Explorer. The data corresponds to a report I exported on the 19th of February 2014.

The Altmetric score is a quantative measure of the quality and quantity of attention that a scholarly article has received. It takes into account three main factors:

  1. Volume. The score for an article rises as more people mention it. The Explorer only counts one mention from each person per source, so if someone tweet about the same paper more than once Altmetric will ignore everything but the first.
  2. Sources. Each category of mention contributes a different base amount to the final score. For instance, a newspaper article contributes more than a blog post which contributes more than a tweet.  Altmetric looks at how often the author of each mention talks about scholarly articles, whether or not there’s any bias towards a particular journal or publisher and at who their audience is.
  3. Authors.  For example, a scholar sharing a link with other scholars counts for far more than a journal account pushing the same link out automatically.

The focus of my study, however, is not necessarily the Altmetric score itself. One of my goals is to try to discover patterns or correlations between journal title, country of affiliation of Principal Investigator, access type of the article and the attention the article in question gets online. Logically the dataset I obtained and refined and its visualisations are not representative of all scholarly outputs with “Africa” in the title out there, but only of the data Altmetric is able to track in the first place.

The original dataset contained 2826 articles. I refined this set using Open Refine, to ensure there were no duplicates, text encoding errors, irrelevant entries (for example articles not about Africa but by authors whose first name is Africa, or academic news items that are not peer-reviewed). I then manually edited a CSV file of the top 25 peer-reviewed articles, and then created another one so I had only the categories I wanted to visualise and added other columns like PI country and Access Type.

I used Raw to make the diagrams. Alluvial diagrams can be helfpul to visualise flows and reveal correlations between categories; visually linking to the number of elements sharing the same categories. I wanted to see if this kind of diagram could provide a quick and clear insight on any possible correlations between access type and a higher number of online mentions. I manually looked at all the 25 articles, to check access type and country of affiliation of the Principal Investigators.

Though painstakingly time-consuming, I made some interesting discoveries in doing this by hand (for example many articles about Africa are co-authored by PIs based outside Africa with collaborators from African institutions, with an overwhelming South African majority). Another insight in the data but not visualised in these two charts is the dominance of articles with a focus on South Africa only.

I will share the original dataset file later on, as I still want to make sure the file is presentable enough to share publicly. In the meanwhile I have deposited both diagrams as figures to Figshare, and posted them here for your perusal. I will keep working on these diagrams, as they need to be edited to add different colours, etc., and to write-up a proper qualitative narrative of what we make of the data.

 Priego, Ernesto (2014): Alluvial Diagram- 25 Highest Scoring Academic Articles with 'Africa" in the Title, including Access Type. figshare. http://dx.doi.org/10.6084/m9.figshare.942285
Priego, Ernesto (2014): Alluvial Diagram- 25 Highest Scoring Academic Articles with ‘Africa” in the Title, including Access Type. figshare.
http://dx.doi.org/10.6084/m9.figshare.942285
 Priego, Ernesto (2014): Alluvial Diagram- 25 Highest Scoring Academic Articles with 'Africa" in the Title, including Access Type. figshare. http://dx.doi.org/10.6084/m9.figshare.942285
Priego, Ernesto (2014): Alluvial Diagram- Country of Affiliation of Principal Investigator/Author of the 25 Highest Scoring Academic Articles with ‘Africa” in the Title, including Journal and Access Type. figshare.
http://dx.doi.org/10.6084/m9.figshare.942286

A quick insight from both diagrams is that open access articles are having, according to Altmetric, more mentions online, on blogs, media and online social networks. African Principal Investigators, however, are the minority in this top 25 set, with only South African researchers representing the whole continent.

There is only one article which is not within the STEM disciplinary boundaries proper– on mobile phone coverage and its relationship to political violence, published in the American Political Science Review. This might also be a reflection on the sources Altmetric tracks (where social sciences, arts and humanities are a minority).

It is also noticeable there are two different articles in the Journal of Infectious Diseases on antiretroviral therapy, with very similar titles, one open access and the other paywalled. The former has a slightly higher Altmetric score. I could not find out if the authors of the paywalled article were the same, as the paywall did not link to author information.

It also appears that at least for articles with the term “Africa” in the title, from the journals that Altmetric tracks, UK authors are divided in their adoption of open access.

24 February 2014. Correction: I had accidentally added the same caption to both diagrams; I have corrected this so the second diagram has the correct caption and doi.

A follow-up was published here.