Country information about corresponding authors play a crucial role in open access funding (Schimmer, Geschuhn, and Vogler 2015). The Web of Science contains data about this author role including affiliations, making it a valuable source to determine publisher’s market shares per country in terms of articles published.
In this work, a dataset is compiled providing information about productivity of corresponding authors between 2014 - 2018. More specificially, the following data points from the Web of of Science will be investigated:
- Web of Science collections SCI, SSCI and WOS.AHCI
- Article types Original Articles and Reviews
- Country of affiliations
- ISSN
- Publication year
This dataset will be extended with:
- Publisher information from Crossref
- Journal open access status provided by the ISSN GOLD OA list
In this document, every step needed to create the dataset is described. This document is written in RMarkdown. It can be re-compiled calling
require(rmarkdown)
require(tidyverse)
require(RJDBC)
require(rJava)
require(writexl)
rmarkdown::render("003_kb_fetch_ca.Rmd")Obtain publication data from the Web of Science database hosted by the German Competence Centre for Bibliometrics
# deal with rJava memory allocation
# https://stackoverflow.com/questions/34624002/r-error-java-lang-outofmemoryerror-java-heap-space
options(java.parameters = "-Xmx1024m")
require(tidyverse)
require(RJDBC)
require(rJava)
.jinit()
jdbcDriver <-
JDBC(driverClass = "oracle.jdbc.OracleDriver", classPath = "../inst/jdbc_driver/ojdbc8.jar")
jdbcConnection <-
dbConnect(
jdbcDriver,
"jdbc:oracle:thin:@//biblio-p-db01:1521/bibliodb01.fiz.karlsruhe",
Sys.getenv("kb_user"),
Sys.getenv("kb_pwd")
) select
wos_b_2019.d_items_authors_institutions.inst_countrycode,
d.pubyear,
d.issn,
count(distinct(d.ut_eid)) as pubs
from
wos_b_2019.d_items_authors_institutions
inner join
(
select
pk_items,
fk_authors,
ut_eid,
wos_b_2019.items.pubyear,
issn
from
wos_b_2019.items
inner join
wos_b_2019.databasecollection
on wos_b_2019.databasecollection.fk_items = wos_b_2019.items.pk_items
inner join
wos_b_2019.items_authors_institutions
on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items
inner join
wos_b_2019.issues
on wos_b_2019.issues.pk_issues = wos_b_2019.items.fk_issues
where
wos_b_2019.databasecollection.edition_value in (
'WOS.SCI', 'WOS.SSCI', 'WOS.AHCI'
)
and wos_b_2019.items.doctype in (
'Article', 'Review'
)
and wos_b_2019.items.pubyear in (
2014, 2015, 2016, 2017, 2018
)
and wos_b_2019.items_authors_institutions.type = 'RP'
) d
on (
wos_b_2019.d_items_authors_institutions.fk_items = d.pk_items
and wos_b_2019.d_items_authors_institutions.fk_authors = d.fk_authors
)
where
wos_b_2019.d_items_authors_institutions.inst_countrycode is not null
group by
wos_b_2019.d_items_authors_institutions.inst_countrycode,
d.pubyear,
d.issn# dump
write_csv(rp_country, "../data/rp_data_14_18.csv")Publisher names used in the Web of Science are ambigue, and sometimes
journals can change the publisher. To obtain most frequent publisher and
journals names, Crossref was queried for every distinct journal by ISSN
as described in fetch_data.R.
As a result, a matching table between Crossref and the Web of Science journals was created. In the following, this table is joined with the WoS data.
cr_journals <- readr::read_csv("../data/cr_wos.csv") %>%
# merge springer nature brands
mutate(publisher = ifelse(grepl("Springer", publisher, fixed = FALSE), "Springer Nature", publisher))
rp_country <- readr::read_csv("../data/rp_data_14_18.csv")
issn_l <- readr::read_tsv("../data/20190818.ISSN-to-ISSN-L.txt") %>%
# manual fix Journal - American Water Works
add_row(ISSN = "2164-4535", `ISSN-L` = "0003-150X")
rp_df <- rp_country %>%
left_join(issn_l, by = "ISSN") %>%
left_join(cr_journals, by = c(ISSN = "issn")) %>%
distinct()Crossref indexed 90.5184969 % of Web of Science journals.
The following table shows the number of journals per publisher.
rp_df %>%
distinct(ISSN, publisher) %>%
count(publisher, sort = TRUE) %>%
mutate(prop = n /sum(n))## # A tibble: 1,186 x 3
## publisher n prop
## <chr> <int> <dbl>
## 1 Elsevier BV 1862 0.136
## 2 Springer Nature 1813 0.132
## 3 Informa UK Limited 1393 0.101
## 4 <NA> 1302 0.0948
## 5 Wiley 1292 0.0941
## 6 SAGE Publications 661 0.0481
## 7 Oxford University Press (OUP) 328 0.0239
## 8 Cambridge University Press (CUP) 301 0.0219
## 9 Ovid Technologies (Wolters Kluwer Health) 213 0.0155
## 10 Walter de Gruyter GmbH 201 0.0146
## # … with 1,176 more rows
Bruns et al. provide a matching table for fully open access journals, joining multiple sources into one dataset.
Bruns, A., Lenke, C., Schmidt, C., & Taubert, N. C. (2019). ISSN-Matching of Gold OA Journals (ISSN-GOLD-OA) 3.0. Bielefeld University. doi:[10.4119/unibi/2934907](https://doi.org/10.4119/unibi/2934907)
The linking ISSN is used for matching.
u <- "https://pub.uni-bielefeld.de/download/2934907/2934908/ISSN_Gold-OA_3.0.csv"
bie_oa <- readr::read_csv(u) %>%
# remove missing entries with missing ISSN_L
filter(!is.na(ISSN_L))
# add info to rp_df
rp_df_oa <- rp_df %>%
mutate(oa_journal = `ISSN-L` %in% bie_oa$`ISSN_L`)The following table shows the number and proportion of open access journals
rp_df_oa %>%
distinct(`ISSN-L`, oa_journal) %>%
count(oa_journal) %>%
mutate(prop = n / sum(n))## # A tibble: 2 x 3
## oa_journal n prop
## <lgl> <int> <dbl>
## 1 FALSE 11580 0.854
## 2 TRUE 1981 0.146
CSV-based dump
rp_df_oa %>%
select(
issn_wos = ISSN,
country_code = INST_COUNTRYCODE,
publication_year = PUBYEAR,
articles = PUBS,
journal_title, publisher, oa_journal,
issn_l = `ISSN-L`) %>%
write_csv("../data/rp_jn_14_18.csv")Excel dump. To avoid loading issues because of too many rows in one sheet, split the dataset into several sheets per year.
require(writexl)
rp_df <- readr::read_csv("../data/rp_jn_14_18.csv")
tmp <- map(2014:2018, function(x) {
rp_df %>%
filter(publication_year == x)
}
)
# names
names(tmp) <- paste0("pubyear_", 2014:2018)
write_xlsx(tmp, "../data/rp_jn_14_18.xlsx")Data Schema:
| Variable | Description | Source |
|---|---|---|
issn_wos |
ISSN, a standardized journal id. | KB Web of Science: wos_b_2019.issues.issn |
country_code |
Country of affiliation corresponding author, represented as ISO 3 code | KB Web of Science: wos_b_2019.d_items_authors_institutions.inst_countrycode |
publication_year |
Year of publication, obtained from KB Web of Science | KB Web of Science: wos_b_2019.items.pubyear |
articles |
Number of original articles and reviews published. Whole counting where internationally co-located corresponding authorships were assigned to each contributing country equally. | KB Web of Science: Grouped counts over wos_b_2019.issues.issn, wos_b_2019.d_items_authors_institutions.inst_countrycode and wos_b_2019.items.pubyear |
journal_title |
Most frequently used journal title in terms of articles published between 2014 - 2018. If missing, the journal was not indexed in Crossref | Crossref |
publisher |
Most frequently used publisher name in terms of articles published between 2014 - 2018. If missing, the journal was not indexed in Crossref | Crossref |
oa_journal |
Is the journal publishing all articles open access without delay (full open access)? | Bielefeld GOLD OA List V3 |
issn_l |
Linking ISSN, a journal id that groups the different media of the same serial publication, e.g. ISSN for print with electronic issn. | CIEPS |
rp_jn <- readr::read_csv("../data/rp_jn_14_18.csv") %>%
group_by(country_code, publication_year, publisher, oa_journal) %>%
summarise(n_publications = sum(articles, na.rm = TRUE),
n_journals = n_distinct(issn_l))
# export to csv and excel
readr::write_csv(rp_jn, "../data/rp_publisher_14_18.csv")
writexl::write_xlsx(rp_jn, "../data/rp_publisher_14_18.xlsx")Data Schema:
| Variable | Description | Source |
|---|---|---|
country_code |
Country of affiliation corresponding author, represented as ISO 3 code | KB Web of Science: wos_b_2019.d_items_authors_institutions.inst_countrycode |
publication_year |
Year of publication, obtained from KB Web of Science | KB Web of Science: wos_b_2019.items.pubyear |
publisher |
Most frequently used publisher name in terms of articles published between 2014 - 2018. If missing, the journal was not indexed in Crossref | Crossref |
oa_journal |
Is the journal publishing all articles open access without delay (full open access)? | ISSN GOLD OA List V3 |
n_publications |
Number of original articles and reviews published | Aggregate data |
n_journals |
Number of distinct journals with corresponding publication output | Aggregate data |