All data files were downloaded on 2 June 2016.
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Katharina Rieck, Doris Haslinger, Sasa Meischke-Ilic, Ünzüle Kirindi-Hentschel, & Falk Reckling. (2016). Austrian Science Fund (FWF) Publication Cost Data 2015. Figshare. http://doi.org/10.6084/m9.figshare.3180166
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Falk Reckling, & Katharina Rieck. (2015). Austrian Science Fund (FWF) Publication Cost Data 2014. Figshare. http://doi.org/10.6084/m9.figshare.1378610
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Stuart Lawson. (2015). APC data for 25 UK higher education institutions - 2014. Figshare. http://doi.org/10.6084/m9.figshare.1305596.v5
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Stuart Lawson. (2016). APC data for 27 UK higher education institutions in 2015. Figshare. http://doi.org/10.6084/m9.figshare.1507481.v4
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Robert Kiley. (2016). Wellcome Tust/COAF spend on open access publishing (article processing charges) - 2014-15. Figshare. http://doi.org/10.6084/m9.figshare.3118936.v1
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Robert Kiley. (2015). Wellcome Trust open access (OA) spend and compliance monitoring: 2013-14. Figshare. http://doi.org/10.6084/m9.figshare.1321361.v5
Before loading cost data into the R console, we removed windows encoding from the Jisc data files. We also removed currency symbols from the Wellcome Trust spreadsheets and converted them to csv files with LibreOffice.
Pre-processed files were stored in this folder.
To summarize Wellcome Trust's and Jisc's spending, we converted the prices from GBP to Euro in accordance with the average Euro foreign exchange reference rates provided by the European Central Bank. Our comparison between the open data initiatives focussed on the last two years 2014 and 2015. Because Wellcome Trust's spending was reported for the periods 2013 - 2014 and 2014 - 2015, we referred to the average exchange rates of the full two-year period as we could not determine the actual invoicing dates from the data.
Note, we used the accompanying data report to determine FWF publication fee spending
jisc_14 <- read.csv("jisc_2014.csv", header = TRUE, sep =",")
jisc_14$APC <- as.numeric(as.character(jisc_14$APC.paid....including.VAT.if.charged))## Warning: NAs durch Umwandlung erzeugt
# convert to average euro exchange rate
jisc_14$APC <- jisc_14$APC * 1.2411
apc_sum <- aggregate(jisc_14$APC, list(jisc_14$Type.of.publication), sum, na.rm = TRUE)
apc_n <- as.data.frame(table(jisc_14$Type.of.publication))
tt <- data.frame(apc_n, apc_sum[,c("x")])
colnames(tt) <- c("Type", "Freq", "Fees paid in Euro")
tt$`Mean Fee` <- tt$`Fees paid in Euro` / tt$Freq
tt## Type Freq Fees paid in Euro
## 1 2812 4861771.996
## 2 Book 1 7822.939
## 3 Book chapter 10 21746.368
## 4 Conference Paper/Proceeding/Abstract 21 16630.430
## 5 journal Article/Review (Full OA journal) 2 4042.660
## 6 Journal Article/Review (Full OA journal) 1159 1893819.672
## 7 Journal Article/Review (Hybrid journal) 2938 5409623.345
## Mean Fee
## 1 1728.9374
## 2 7822.9388
## 3 2174.6368
## 4 791.9252
## 5 2021.3299
## 6 1634.0118
## 7 1841.2605
sum(tt$`Fees paid in Euro`)## [1] 12215457
write.csv(tt, "aggregated_jisc_2014.csv")jisc_15 <- read.csv("jisc_2015.csv", header = TRUE, sep =",")
jisc_15$APC <- as.numeric(as.character(jisc_15$APC.paid....including.VAT.if.charged))## Warning: NAs durch Umwandlung erzeugt
# convert to average euro exchange rate
jisc_15$APC <- jisc_15$APC * 1.3785
apc_sum <- aggregate(jisc_15$APC, list(jisc_15$Type.of.publication), sum, na.rm = TRUE)
apc_n <- as.data.frame(table(jisc_15$Type.of.publication))
tt <- data.frame(apc_n, apc_sum[,c("x")])
colnames(tt) <- c("Type", "Freq", "Fees paid in Euro")
tt$`Mean Fee` <- tt$`Fees paid in Euro` / tt$Freq
tt## Type Freq Fees paid in Euro
## 1 178 345215.70
## 2 Book 1 15714.90
## 3 Conference paper 4 4257.47
## 4 Journal Article/Review 62 156555.51
## 5 Journal Article/Review (Full OA journal) 1168 2211958.18
## 6 Journal Article/Review (Hybrid journal) 2944 6977753.23
## Mean Fee
## 1 1939.414
## 2 15714.900
## 3 1064.367
## 4 2525.089
## 5 1893.800
## 6 2370.161
sum(tt$`Fees paid in Euro`)## [1] 9711455
write.csv(tt, "aggregated_jisc_2015.csv")wellcome_13_14 <- read.csv("wellcome_13_14.csv", header = T, sep =",")
wellcome_13_14$APC <- wellcome_13_14$Cost......inc.VAT.when.charged.
# convert to average euro exchange rate
wellcome_13_14$APC <- wellcome_13_14$APC * 1.2094
apc_n <- data.frame(table(wellcome_13_14$Journal.Type))
apc_sum <- aggregate(wellcome_13_14$APC, list(wellcome_13_14$Journal.Type), sum, na.rm = TRUE)
apc_mean <- aggregate(wellcome_13_14$APC, list(wellcome_13_14$Journal.Type), mean, na.rm = TRUE)
tt <- data.frame(apc_n, apc_sum[,c("x")], apc_mean[,c("x")])
colnames(tt) <- c("Type", "Freq", "Fees paid in Euro", "Mean Fee paid in Euro")
tt## Type Freq Fees paid in Euro Mean Fee paid in Euro
## 1 55 117261.3 2132.023
## 2 hybrid 1894 4648878.5 2454.529
## 3 oa 607 911301.9 1501.321
sum(tt$`Fees paid in Euro`)## [1] 5677442
write.csv(tt, "aggregated_wellcome_13_14.csv")wellcome_14_15 <- read.csv("wellcome_14_15.csv", header = T, sep =",")
wellcome_14_15$APC <- wellcome_14_15$Cost......inc.VAT.when.charged.
# convert to average euro exchange rate
wellcome_14_15$APC <- wellcome_14_15$APC * 1.3099
apc_n <- data.frame(table(wellcome_14_15$Journal.Type))
apc_sum <- aggregate(wellcome_14_15$APC, list(wellcome_14_15$Journal.Type), sum, na.rm = TRUE)
apc_mean <- aggregate(wellcome_14_15$APC, list(wellcome_14_15$Journal.Type), mean, na.rm = TRUE)
tt <- data.frame(apc_n, apc_sum[,c("x")], apc_mean[,c("x")])
colnames(tt) <- c("Type", "Freq", "Fees paid in Euro", "Mean Fee paid in Euro")
tt## Type Freq Fees paid in Euro Mean Fee paid in Euro
## 1 102 266422.4 2611.985
## 2 hybrid 2065 5690177.6 2755.534
## 3 oa 775 1418097.2 1829.803
sum(tt$`Fees paid in Euro`)## [1] 7374697
write.csv(tt, "aggregated_wellcome_14_14.csv")my_apc <- jsonlite::stream_in(file("../cr-apc-all.json"), verbose = FALSE)
# data cleaning
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, union
## remove Bielefelds Zero value
my_apc <- dplyr::filter(my_apc, euro > 0)
# remove intech book chapters
my_apc <- dplyr::filter(my_apc, !is.na(journal_full_title))
# remove Austrian FWF data
my_apc <- dplyr::filter(my_apc, !institution == "FWF - Austrian Science Fund")
# data from 2014 and 2015
my_apc <- dplyr::filter(my_apc, period == "2015" | period == "2014")
aggregate(my_apc$euro, by = list(my_apc$is_hybrid, my_apc$period), sum)## Group.1 Group.2 x
## 1 FALSE 2014 2353664.81
## 2 TRUE 2014 26545.88
## 3 FALSE 2015 2820444.76
## 4 TRUE 2015 23412.44
aggregate(my_apc$euro, by = list(my_apc$is_hybrid, my_apc$period), mean)## Group.1 Group.2 x
## 1 FALSE 2014 1284.752
## 2 TRUE 2014 1769.725
## 3 FALSE 2015 1416.597
## 4 TRUE 2015 2926.555
aggregate(my_apc$euro, by = list(my_apc$is_hybrid, my_apc$period), length)## Group.1 Group.2 x
## 1 FALSE 2014 1832
## 2 TRUE 2014 15
## 3 FALSE 2015 1991
## 4 TRUE 2015 8