library(hoaddata)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionDatasets
This package provides the following data tables.
oam_hybrid_jns
Hybrid Journals listed in the Open Access Monitor. Data were gathered from https://doi.org/10.26165/JUELICH-DATA/VTQXLM and enriched with ISSN variants.
oam_hybrid_jns
#> # A tibble: 14,603 × 3
#> vertrag issn_l issn
#> <chr> <chr> <chr>
#> 1 De Gruyter (SUB Göttingen) 0306-0322 0306-0322
#> 2 De Gruyter (SUB Göttingen) 0306-0322 1865-8717
#> 3 De Gruyter (SUB Göttingen) 0003-5696 0003-5696
#> 4 De Gruyter (SUB Göttingen) 0003-5696 1613-0421
#> 5 De Gruyter (SUB Göttingen) 1438-2091 1438-2091
#> 6 De Gruyter (SUB Göttingen) 1438-2091 1868-9426
#> 7 De Gruyter (SUB Göttingen) 0232-8461 0232-8461
#> 8 De Gruyter (SUB Göttingen) 0232-8461 2196-6761
#> 9 De Gruyter (SUB Göttingen) 0003-6390 0003-6390
#> 10 De Gruyter (SUB Göttingen) 0003-6390 2156-7093
#> # ℹ 14,593 more rowsNumber of investigated journals: 7512
By agreement
oam_hybrid_jns %>%
group_by(vertrag) %>%
summarise(journals = n_distinct(issn_l)) %>%
arrange(desc(journals))
#> # A tibble: 20 × 2
#> vertrag journals
#> <chr> <int>
#> 1 Springer Hybrid (DEAL) 2116
#> 2 Elsevier (DEAL) 1859
#> 3 Wiley Hybrid (DEAL) 1412
#> 4 Sage (BSB) 980
#> 5 CUP (BSB) 332
#> 6 TaylorFrancis (ZBW) 270
#> 7 De Gruyter (SUB Göttingen) 105
#> 8 Karger (BSB) 71
#> 9 De Gruyter (ZBW) 70
#> 10 ACM (hebis) 60
#> 11 IOP (TIB) 56
#> 12 RSC (TIB) 38
#> 13 Nature (MPDL) 35
#> 14 Hogrefe (SUB Göttingen) 32
#> 15 AIP (TIB) 30
#> 16 BMJ (BSB) 28
#> 17 SPIE (TIB) 11
#> 18 Thieme (ZB MED) 7
#> 19 Portland Press (TIB) 5
#> 20 ECS (TIB) 2
jn_ind
Aggregated data about the prevalence of Creative Commons license variants by year and hybrid journal as obtained from Crossref.
jn_ind
#> # A tibble: 146,133 × 6
#> issn_l cr_year cc cc_total jn_all prop
#> <chr> <fct> <fct> <int> <int> <dbl>
#> 1 0001-0782 2017 NA 0 281 0
#> 2 0001-0782 2018 NA 0 302 0
#> 3 0001-0782 2019 NA 0 280 0
#> 4 0001-0782 2020 NA 0 293 0
#> 5 0001-0782 2021 NA 0 318 0
#> 6 0001-0782 2022 NA 0 295 0
#> 7 0001-0782 2023 NA 0 281 0
#> 8 0001-0782 2024 NA 0 195 0
#> 9 0001-1541 2017 CC BY 4 432 0.00926
#> 10 0001-1541 2017 CC BY-NC 4 432 0.00926
#> # ℹ 146,123 more rowsNumber of active journals: 13113
Number of open access articles with Creative Commons license: 1403948
Creative Commons Breakdown:
jn_aff
First author country affiliations per journal, year and Creative Commons license
jn_aff
#> # A tibble: 2,257,976 × 6
#> issn_l cr_year country_code cc articles_under_cc_va…¹ articles_total
#> <chr> <int> <chr> <chr> <int> <int>
#> 1 1083-4362 2024 NA NA 6 8
#> 2 0013-7952 2024 NA NA 48 53
#> 3 0262-2750 2024 NA NA 7 7
#> 4 2576-3164 2024 NA NA 15 15
#> 5 2567-4765 2024 NA NA 215 215
#> 6 1046-1310 2024 NA NA 185 246
#> 7 0271-5309 2024 NA CC BY-N… 1 5
#> 8 0197-4556 2024 NA NA 14 16
#> 9 0160-2446 2024 NA NA 10 10
#> 10 0343-2521 2024 NA CC BY 6 46
#> # ℹ 2,257,966 more rows
#> # ℹ abbreviated name: ¹articles_under_cc_variant
cc_articles
Article-level affiliation data from first authors as obtained from OpenAlex.
cc_articles
#> # A tibble: 1,851,712 × 6
#> doi issn_l cr_year cc country_code ror
#> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 10.1002/1438-390x.1017 1438-3896 2019 CC BY AT https://r…
#> 2 10.1002/1438-390x.1019 1438-3896 2019 CC BY-NC-ND US https://r…
#> 3 10.1002/1438-390x.12015 1438-3896 2019 CC BY-NC-ND ZA https://r…
#> 4 10.1002/1438-390x.12016 1438-3896 2019 CC BY-NC JP https://r…
#> 5 10.1002/1438-390x.12018 1438-3896 2019 CC BY-NC-ND JP https://r…
#> 6 10.1002/1438-390x.12018 1438-3896 2019 CC BY-NC-ND JP https://r…
#> 7 10.1002/1438-390x.12019 1438-3896 2019 CC BY JP https://r…
#> 8 10.1002/1438-390x.12019 1438-3896 2019 CC BY JP https://r…
#> 9 10.1002/1438-390x.12021 1438-3896 2019 CC BY US https://r…
#> 10 10.1002/1438-390x.12022 1438-3896 2019 CC BY JP https://r…
#> # ℹ 1,851,702 more rows
cc_articles %>%
group_by(cc) %>%
summarise(articles = n_distinct(doi))
#> # A tibble: 6 × 2
#> cc articles
#> <chr> <int>
#> 1 CC BY 899567
#> 2 CC BY-NC 140917
#> 3 CC BY-NC-ND 360185
#> 4 CC BY-NC-SA 2547
#> 5 CC BY-ND 208
#> 6 CC BY-SA 524
cr_md
Crossref metadata coverage.
cr_md
#> # A tibble: 100,582 × 9
#> cr_year issn_l article_total tdm_total orcid_total funder_total
#> <int> <chr> <int> <int> <int> <int>
#> 1 2017 0001-1541 8 8 8 7
#> 2 2018 0001-1541 17 17 14 17
#> 3 2019 0001-1541 23 23 23 14
#> 4 2019 0001-1541 11 11 11 7
#> 5 2020 0001-1541 30 30 29 22
#> 6 2020 0001-1541 10 10 10 7
#> 7 2021 0001-1541 47 47 47 26
#> 8 2021 0001-1541 16 16 16 7
#> 9 2022 0001-1541 60 60 60 38
#> 10 2022 0001-1541 12 12 12 8
#> # ℹ 100,572 more rows
#> # ℹ 3 more variables: abstract_total <int>, ref_total <int>, cat <chr>