Anne Hobert 3/6/2020
In this document we describe the analysis of our sample of publications
from German research institutions. We work with the dataset pubs_cat
generated in data_gathering.Rmd.
The goal is to answer the three research questions
- Has the OA fraction of the publication output of German universities and research institutions increased constantly over time?
- Can we observe differences between the research sectors of the German science system? Are there obvious explanations for this (like different missions or subject profiles?
- Which OA type is the most prevalent OA approach and can we identify different patterns of adoption to OA?
First, we look at how the overall OA share developed over time. The following figure displays the number of publications associated with one of the German research institutions we considered and highlights they part that is freely accessible online according to Unpaywall over the considered time period from 2010 until 2018. The total number of articles over the whole period is 1113479 with an overall OA share of 49 %.
Open access to journal articles from German research institutions according to Unpaywall. Blue area represents journal articles with at least one freely available full-text, grey area represents toll-access articles.
As can be seen, the total number of articles, as well as the part that is OA increases constantly over time. The number of articles that are not openly available, is quite stable with a slow increase from 51899 in 2010 to 55023 in 2013, and decreasing again continuously from that point onwards to 50190 publications in 2018. Since the number of OA articles increases continuously from 31593 publications in 2010 to 59362 in 2018, the relative proportion of OA articles rises significantly from 37.84 % in 2010 to 54.19 % in 2018.
In order to investigate what role the different sectors play in OA
publishing in Germany and how they contribute to the OA
development/overall OA shares, we distplay the development over time of
the number of OA articles for each sector in the following figure. Note
that scales for the y-axes are not the same, since the total
publication output varies significantly among sectors.
Open access to journal articles per sector according to Unpaywall. Blue
area represents journal articles with at least one freely available
full-text, grey area represents toll-access articles. Sectors are
ordered by publication output with the highest output top left and
lowest at the bottom. Note that scales for the y-axes are not the
same, since the total publication output varies significantly among
sectors.
In order to investigate the variability of OA publishing within the sectors, we now go one level deeper and examine OA shares of individual institutions, grouped by the sector they belong to. We only include institututions with a publication output of at least 100 publications in the observed time period of 9 years. Of the 473 institutions in total, 347 fulfill this condition. This means, that in the following institution specific analyses, 126 insitutions, or 6351 articles are not considered. Of the remaining institutions, we first calculate the individual OA shares.
The following figure displays scatterplots where the OA share of an institution over the whole time period is shown with respect to its publication output.
Open Access shares of research institutions in Germany with respect to their total publication output grouped by the sector they belong to. Only institutions with at least 100 publications are shown. Blue points correspond to single insitutions, gray lines are obained by linear regression within the sector, gray areas are pointwise symmetric 95% t-distribution confidence bands. Scales of the x-axes vary across subplots in order to adapt to the different publication volumes. Dashed lines show the median value per sector for the OA share (red) and the total number of publications (orange).
The most striking observations from this figure are the high OA shares of most of the Max-Planck and Helmholtz institutes and the very low OA fractions of almost all of the state and federal institutes as well as the ones from the Fraunhofer Society. Universities and Leibniz-Society have many institutes with OA shares close to one half. We can further see very well that the universities have by far the largest publication volumes, followed by the Helmholtz-Society. The linear trend of higher publication volume implying higher OA shares is most distinctive for the university sector (narrowest confidence bands).
The following box plot quantifies the observations regarding the variability of OA shares within sectors made before.
OA shares of German research institutions per sector. The color of the boxes groups sectors into universities with a typically high total journal publication output, research-oriented institutes with a medium journal publication output and practise oriented institutions with a comparatively low journal publication output. Gray points display the OA shares for individual institutions. Notches indicate approximate 95 % confidence intervals for the median values. Non-overlapping notches imply a strong indication that median values are significantly different.
Significance: HGF against all non-research oriented, MPS against all non-research oriented, FhS against all others Significance groups: research against all others, practise against all others, mixed not well separated from universities (maybe sector_cat classification should be inst_cat, i.e. on the level of institutions - would have to be manually at least for some sectors)
As mentioned in the previous chapters, there are several ways of providing open access to scientific journal articles. In this section, we want to investigate the prevalence of the most widespread OA routes: Green OA and Gold OA. We further distinguish these two main categories as described in the methodology section (see Table 1) according to whether the journal is fully OA (Gold OA), and into deposition on disciplinary, institutional, or OpenDOAR-listed repositories (Green OA). Note that, as mentioned before, the OA categories are not exclusive, that is, an article might be counted for several categories and numbers not necessarily sum up to the total number of articles published. As a preliminary step, we therefore illustrate the most common combinations of OA categories in our dataset.
Keeping in mind that our categories are non-exclusive, as just shown, we now visualise the number of articles per category on the national level, that is, without differentiation by sector. As a first step, we investigate the two main OA routes via a journal or via a repository.
Development of the number of articles per OA host type and their overlap. Highlighted in blue are the number of articles per OA host type with articles made available only via a journal on the left, articles available only in repositories on the right and the overlap, that is, articles openly accessible via both a journal and a repository, in the middle. Grey Area shows the remaining OA articles.
Development of the percentage of journal articles per OA category (as per schema in Table 1) over time. Categories are non-exclusive, that is some articles may be counted for more than one category. Colors correspond to the OA category. On the left, access provided via a journal is displayed, on the right via repositories. Grey area shows the total percentage of OA via the corresponding route (journal or repository).
Observations:
- drop in other oa journal -> Delayed OA
- slight drop in other_repo -> more sources registered, published more in registered sources
- apart from this: all OA categories increase, not oa decreases
- most prevalent category: subject-specific repos, registered with OpenDOAR
Again, we go one step further and look at sector specific OA proportions.
OA shares per category and sector. Coloring and size of the points displays the percentage in the respective category. Grey numbers display the percentage value explicitly. The bottom row shows the overall OA share of the sectors, the rightmost column the percentage of articles in the corresponding category regardless of the sector (on the national level). Ordering of the sectors is according to total publication output for the entire sector (highest: universities, lowest: Fraunhofer Society).
Generate tables for export:
- Upset plot of overlapping evidence categories to show influence of semantic scholar, webscraping.
In order to demonstrate the prevalence of evidence categories in Unpaywall, we load the original, non-categorized Unpaywall data:
We now determine the evidence combinations for all matched DOIs and then calculate the frequency of each combination found.
We now prepare the data for plotting with the UpSetR package and visualise the overlapping evidence categories.