Skip to contents

Transition towards Open Access

Source: vignettes/articles/institution.Rmd
institution.Rmd

Following the template in OpenAlex’s oa-percentage tutorial, this vignette uses openalexR to answer:

How many of recent journal articles from the University of Pennsylvania are open access? And how many aren’t?

We first need to find the openalex.id for University of Pennsylvania. We can do this by fetching for the institutions entity and put “University of Pennsylvania” in display_name or display_name.search:

oa_fetch(
  entity = "inst", # same as "institutions"
  display_name.search = "\"University of Pennsylvania\""
) %>%
  select(display_name, ror) %>% 
  knitr::kable()
display_name ror
University of Pennsylvania https://ror.org/00b30xv10
California University of Pennsylvania https://ror.org/01spssf70
Hospital of the University of Pennsylvania https://ror.org/02917wp91
University of Pennsylvania Health System https://ror.org/04h81rw26
Indiana University of Pennsylvania https://ror.org/0511cmw96
University of Pennsylvania Press https://ror.org/03xwa9562
Cheyney University of Pennsylvania https://ror.org/02nckwn80

We will use the first ror, 00b30xv10, as one of the filters for our query.

Alternatively, we could go to the autocomplete endpoint at https://explore.openalex.org/ to search for “University of Pennsylvania” and find the ror there!

All other filters are straightforward and explained in detail in the original jupyter notebook tutorial. The only difference here is that, instead of grouping by is_oa, we’re interested in the “trend” over the years, so we’re going to group by publication_year, and perform the query twice, one for is_oa = "true" and one for is_oa = "false" .

open_access <- oa_fetch(
  entity = "works",
  institutions.ror = "00b30xv10",
  type = "article",
  from_publication_date = "2012-08-24",
  is_paratext = "false",
  is_oa = "true",
  group_by = "publication_year"
)

closed_access <- oa_fetch(
  entity = "works",
  institutions.ror = "00b30xv10",
  type = "article",
  from_publication_date = "2012-08-24",
  is_paratext = "false",
  is_oa = "false",
  group_by = "publication_year"
)

uf_df <- closed_access %>%
  select(- key_display_name) %>%
  full_join(open_access, by = "key", suffix = c("_ca", "_oa")) 

uf_df
#>     key count_ca key_display_name count_oa
#> 1  2018     4659             2018     5906
#> 2  2014     4407             2014     4780
#> 3  2013     4403             2013     4481
#> 4  2015     4355             2015     5006
#> 5  2017     4238             2017     5464
#> 6  2020     4104             2020     8564
#> 7  2019     4043             2019     6912
#> 8  2016     4039             2016     5299
#> 9  2022     3943             2022     8121
#> 10 2021     3937             2021     8716
#> 11 2024     3443             2024     8599
#> 12 2025     3315             2025     8624
#> 13 2023     3234             2023     8814
#> 14 2026     2552             2026     5031
#> 15 2012     1444             2012     1189

Finally, we compare the number of open vs. closed access articles over the years:

uf_df %>%
  filter(key <= 2021) %>% # we do not yet have complete data for 2022 and after
  pivot_longer(cols = starts_with("count")) %>%
  mutate(
    year = as.integer(key),
    is_oa = recode(
      name,
      "count_ca" = "Closed Access",
      "count_oa" = "Open Access"
    ),
    label = if_else(key < 2021, NA_character_, is_oa)
  ) %>% 
  select(year, value, is_oa, label) %>%
  ggplot(aes(x = year, y = value, group = is_oa, color = is_oa)) +
  geom_line(size = 1) +
  labs(
    title = "University of Pennsylvania's progress towards Open Access",
    x = NULL, y = "Number of journal articles") +
  scale_color_brewer(palette = "Dark2", direction = -1) +
  scale_x_continuous(breaks = seq(2010, 2024, 2)) +
  geom_text(aes(label = label), nudge_x = 0.1, hjust = 0) +
  coord_cartesian(xlim = c(NA, 2022.5)) +
  guides(color = "none")
plot of chunk oa-trend

plot of chunk oa-trend