JOSS submission analytics
2025-06-25 17:32:40.222689
Introduction
In this report, we extract information about published JOSS papers
and generate
graphics as well as a summary table that can be downloaded and used for
further analyses.
Load required R packages
suppressPackageStartupMessages({
library(tibble)
library(rcrossref)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(gh)
library(purrr)
library(jsonlite)
library(DT)
library(plotly)
library(citecorp)
library(readr)
library(rworldmap)
library(gt)
library(stringr)
library(openalexR)
})## Keep track of the source of each column
source_track <- c()
## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
dcap <- lubridate::today()
} else {
dcap <- ""
}## Get list of countries and populations (2022) from the rworldmap/gt packages
data("countrySynonyms")
country_names <- countrySynonyms |>
select(-ID) |>
pivot_longer(names_to = "tmp", values_to = "name", -ISO3) |>
filter(name != "") |>
select(-tmp)
## Country population data from the World Bank (https://data.worldbank.org/indicator/SP.POP.TOTL),
## distributed via the gt R package
country_populations <- countrypops |>
filter(year == 2022)## Read archived version of summary data frame, to use for filling in
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)
## Similarly for citation analysis, to avoid having to pull down the
## same information multiple times
citations_archive <- readr::read_delim(
url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
col_types = cols(.default = "c"), col_names = TRUE,
delim = "\t")Collect information about papers
Pull down paper info from Crossref and citation information from OpenAlex
We get the information about published JOSS papers from Crossref,
using the rcrossref R package. The openalexR R
package is used to extract citation counts from OpenAlex.
## First check how many records there are in Crossref
issn <- "2475-9066"
joss_details <- rcrossref::cr_journals(issn, works = FALSE) %>%
pluck("data")
(total_dois <- joss_details$total_dois)## [1] 3049## Pull down all records from Crossref
papers <- rcrossref::cr_journals(issn, works = TRUE, cursor = "*",
cursor_max = joss_details$total_dois * 2) %>%
pluck("data")
## Only keep articles
papers <- papers %>%
dplyr::filter(type == "journal-article")
dim(papers)## [1] 3049 28dim(papers %>% distinct())## [1] 3049 28## Check that all papers were pulled down and stop otherwise
if (!(nrow(papers %>% distinct()) >= total_dois)) {
stop("Not all papers were pulled down from Crossref!")
}
## A few papers don't have alternative.ids - generate them from the DOI
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- papers$doi[noaltid]
## Get citation info from Crossref and merge with paper details
# cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
# papers <- papers %>% dplyr::left_join(
# cit %>% dplyr::rename(citation_count = count),
# by = c("alternative.id" = "doi")
# )
## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")
dim(papers)## [1] 3048 28dim(papers %>% distinct())## [1] 3048 28papers$alternative.id[duplicated(papers$alternative.id)]## character(0)source_track <- c(source_track,
structure(rep("crossref", ncol(papers)),
names = colnames(papers)))## Get info from openalexR and merge with paper details
## Helper function to extract countries from affiliations. Note that this
## information is not available for all papers.
.get_countries <- function(df, wh = "first") {
if ((length(df) == 1 && is.na(df)) || is.null(df$affiliations)) {
""
} else {
if (wh == "first") {
## Only first affiliation for each author
tmp <- unnest(df, cols = c(affiliations), names_sep = "_") |>
dplyr::filter(!duplicated(id) & !is.na(affiliations_country_code)) |>
pull(affiliations_country_code)
} else {
## All affiliations
tmp <- unnest(df, cols = c(affiliations), names_sep = "_") |>
dplyr::filter(!is.na(affiliations_country_code)) |>
pull(affiliations_country_code)
}
if (length(tmp) > 0) {
tmp |>
unique() |>
paste(collapse = ";")
} else {
""
}
}
}
oa <- oa_fetch(entity = "works",
primary_location.source.id = "s4210214273") |>
mutate(affil_countries_all = vapply(authorships, .get_countries, "", wh = "all"),
affil_countries_first = vapply(authorships, .get_countries, "", wh = "first"))## Warning in oa_request(oa_query(filter = filter_i, multiple_id = multiple_id, :
## The following work(s) have truncated lists of authors: W3005984879.
## Query each work separately by its identifier to get full list of authors.
## For example:
## lapply(c("W3005984879"), \(x) oa_fetch(identifier = x))
## Details at https://docs.openalex.org/api-entities/authors/limitations.dim(oa)## [1] 3052 45length(unique(oa$doi))## [1] 3052papers <- papers %>% dplyr::left_join(
oa %>% dplyr::mutate(alternative.id = sub("https://doi.org/", "", doi)) %>%
dplyr::select(alternative.id, cited_by_count, id,
affil_countries_all, affil_countries_first) %>%
dplyr::rename(citation_count = cited_by_count,
openalex_id = id),
by = "alternative.id"
)
dim(papers)## [1] 3048 32dim(papers %>% distinct())## [1] 3048 32source_track <- c(source_track,
structure(rep("OpenAlex", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Pull down info from JOSS API
For each published paper, we use the JOSS API to get information about pre-review and review issue numbers, corresponding software repository etc.
joss_api <- list()
p <- 1
a0 <- NULL
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0 && !identical(a, a0)) {
joss_api <- c(joss_api, a)
p <- p + 1
a0 <- a
a <- tryCatch({
jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)},
error = function(e) return(numeric(0))
)
}
joss_api <- do.call(dplyr::bind_rows, lapply(joss_api, function(w) {
data.frame(api_title = w$title,
api_state = w$state,
author_affiliations = paste(unique(unlist(lapply(w$authors, "[[", "affiliation"))), collapse = ";"),
editor = paste(w$editor, collapse = ","),
reviewers = paste(w$reviewers, collapse = ","),
nbr_reviewers = length(w$reviewers),
repo_url = w$software_repository,
review_issue_id = sub("https://github.com/openjournals/joss-reviews/issues/",
"", w$paper_review),
doi = w$doi,
prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
w$meta_review_issue_id, NA_integer_),
languages = gsub(", ", ",", w$languages),
archive_doi = w$software_archive)
}))
dim(joss_api)## [1] 3050 12dim(joss_api %>% distinct())## [1] 3050 12## Check that all papers were pulled down and stop otherwise
if (!(nrow(joss_api %>% distinct()) >= total_dois)) {
stop("Not all papers were pulled down from the JOSS API!")
}
joss_api$repo_url[duplicated(joss_api$repo_url)]## [1] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [2] "https://github.com/nomad-coe/greenX"
## [3] "https://github.com/idaholab/moose"
## [4] "https://gitlab.com/libreumg/dataquier.git"
## [5] "https://github.com/idaholab/moose"
## [6] "https://github.com/dynamicslab/pysindy"
## [7] "https://github.com/landlab/landlab"
## [8] "https://github.com/landlab/landlab"
## [9] "https://github.com/symmy596/SurfinPy"
## [10] "https://github.com/bcgov/ssdtools"
## [11] "https://github.com/landlab/landlab"
## [12] "https://github.com/pvlib/pvlib-python"
## [13] "https://github.com/mlpack/mlpack"
## [14] "https://github.com/julia-wrobel/registr"
## [15] "https://github.com/barbagroup/pygbe"papers <- papers %>% dplyr::left_join(joss_api, by = c("alternative.id" = "doi"))
dim(papers)## [1] 3048 43dim(papers %>% distinct())## [1] 3048 43papers$repo_url[duplicated(papers$repo_url)]## [1] "https://github.com/mlpack/mlpack"
## [2] "https://github.com/nomad-coe/greenX"
## [3] "https://github.com/bcgov/ssdtools"
## [4] "https://github.com/barbagroup/pygbe"
## [5] "https://github.com/dynamicslab/pysindy"
## [6] "https://github.com/landlab/landlab"
## [7] "https://github.com/idaholab/moose"
## [8] "https://github.com/idaholab/moose"
## [9] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [10] "https://github.com/landlab/landlab"
## [11] "https://github.com/symmy596/SurfinPy"
## [12] "https://github.com/julia-wrobel/registr"
## [13] "https://github.com/pvlib/pvlib-python"
## [14] "https://gitlab.com/libreumg/dataquier.git"
## [15] "https://github.com/landlab/landlab"source_track <- c(source_track,
structure(rep("JOSS_API", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Combine with info from GitHub issues
From each pre-review and review issue, we extract information about review times and assigned labels.
## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues",
.limit = 15000, state = "all")## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
data.frame(title = i$title,
number = i$number,
state = i$state,
opened = i$created_at,
closed = ifelse(!is.null(i$closed_at),
i$closed_at, NA_character_),
ncomments = i$comments,
labels = paste(setdiff(
vapply(i$labels, getElement,
name = "name", character(1L)),
c("review", "pre-review", "query-scope", "paused")),
collapse = ","))
}))
## Split into REVIEW, PRE-REVIEW, and other issues (the latter category
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) &
!grepl("\\[REVIEW\\]", title))
dim(issother)## [1] 165 7head(issother)## title number
## 1 Rescind Paper Submission 8458
## 2 automesh paper submission 8314
## 3 from pydynpd import regression causes error. no solution so far. 8165
## 4 This repository can’t be reached 8161
## 5 Update reviewer_checklist.md 8090
## 6 README and Vignette 7970
## state opened closed ncomments labels
## 1 closed 2025-06-24T15:48:51Z 2025-06-24T15:48:54Z 1
## 2 closed 2025-05-29T22:39:18Z 2025-05-29T22:39:20Z 1
## 3 closed 2025-05-06T05:52:04Z 2025-05-06T05:52:07Z 1
## 4 closed 2025-05-04T16:41:18Z 2025-05-04T16:41:21Z 1
## 5 closed 2025-04-22T22:17:11Z 2025-04-23T07:55:15Z 1
## 6 closed 2025-04-01T14:31:53Z 2025-04-01T14:31:56Z 1## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
dplyr::mutate(alternative.id = paste0("10.21105/joss.",
nbrzeros,
number)) %>%
dplyr::select(-nbrzeros) %>%
dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))## For pre-review and review issues, respectively, get the number of
## issues closed each month, and the number of those that have the
## 'rejected' label
review_rejected <- iss %>%
dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "review")
prereview_rejected <- iss %>%
dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "pre-review")
all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)## For PRE-REVIEW issues, add information about the corresponding REVIEW
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!grepl("withdrawn", labels)) %>%
dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>%
dplyr::filter(!duplicated(title)) %>%
dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
dplyr::rename_all(~ paste0("prerev_", .))
papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>%
dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
dplyr::mutate(prerev_opened = as.Date(prerev_opened),
prerev_closed = as.Date(prerev_closed),
review_opened = as.Date(review_opened),
review_closed = as.Date(review_closed)) %>%
dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
days_in_rev = review_closed - review_opened,
to_review = !is.na(review_opened))
dim(papers)## [1] 3048 59dim(papers %>% distinct())## [1] 3048 59source_track <- c(source_track,
structure(rep("joss-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Add information from software repositories
## Reorder so that software repositories that were interrogated longest
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
software_urls[duplicated(software_urls)]## [1] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [2] "https://gitlab.com/libreumg/dataquier.git"
## [3] "https://github.com/mlpack/mlpack"
## [4] "https://github.com/nomad-coe/greenX"
## [5] "https://github.com/bcgov/ssdtools"
## [6] "https://github.com/barbagroup/pygbe"
## [7] "https://github.com/pvlib/pvlib-python"
## [8] "https://github.com/dynamicslab/pysindy"
## [9] "https://github.com/landlab/landlab"
## [10] "https://github.com/idaholab/moose"
## [11] "https://github.com/idaholab/moose"
## [12] "https://github.com/landlab/landlab"
## [13] "https://github.com/symmy596/SurfinPy"
## [14] "https://github.com/julia-wrobel/registr"
## [15] "https://github.com/landlab/landlab"is_github <- grepl("github", software_urls)
length(is_github)## [1] 3048sum(is_github)## [1] 2886software_urls[!is_github]## [1] "https://gitlab.com/grogra/groimp-plugins/Pointcloud"
## [2] "https://gitlab.com/morikawa-lab-osakau/vibir-parallel-compute"
## [3] "https://bitbucket.org/orionmhdteam/orion2_release1/src/master/"
## [4] "https://gitlab.com/ENKI-portal/ThermoCodegen"
## [5] "https://gitlab.kuleuven.be/ITSCreaLab/public-toolboxes/dyntapy"
## [6] "https://gitlab.dune-project.org/copasi/dune-copasi"
## [7] "https://gite.lirmm.fr/doccy/RedOak"
## [8] "https://codebase.helmholtz.cloud/mussel/netlogo-northsea-species.git"
## [9] "https://gitlab.com/bonsamurais/bonsai/util/ipcc"
## [10] "https://gitlab.com/cosmograil/starred"
## [11] "https://gitlab.com/ffaucher/hawen"
## [12] "https://gitlab.com/emd-dev/emd"
## [13] "https://gitlab.com/sails-dev/sails"
## [14] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [15] "https://gitlab.com/mantik-ai/mantik"
## [16] "https://gitlab.kitware.com/LBM/lattice-boltzmann-solver"
## [17] "https://gitlab.com/dsbowen/conditional-inference"
## [18] "https://gitlab.com/soleil-data-treatment/soleil-software-projects/remote-desktop"
## [19] "https://bitbucket.org/ocellarisproject/ocellaris"
## [20] "https://git.iws.uni-stuttgart.de/tools/frackit"
## [21] "https://bitbucket.org/berkeleylab/esdr-pygdh/"
## [22] "https://gitlab.com/moorepants/skijumpdesign"
## [23] "https://gitlab.com/cmbm-ethz/pourbaix-diagrams"
## [24] "https://gitlab.com/drti/basic-tools"
## [25] "https://bitbucket.org/cloopsy/android/"
## [26] "https://gitlab.com/pythia-uq/pythia"
## [27] "https://gitlab.com/fduchate/predihood"
## [28] "https://gitlab.com/myqueue/myqueue"
## [29] "https://gitlab.dune-project.org/dorie/dorie"
## [30] "https://jugit.fz-juelich.de/compflu/swalbe.jl/"
## [31] "https://gitlab.com/dmt-development/dmt-core"
## [32] "https://gitlab.com/wpettersson/kep_solver"
## [33] "https://gitlab.com/dlr-ve/esy/remix/framework"
## [34] "https://gitlab.com/gdetor/genetic_alg"
## [35] "https://gitlab.com/utopia-project/dantro"
## [36] "https://gitlab.com/InspectorCell/inspectorcell"
## [37] "https://plmlab.math.cnrs.fr/lmrs/statistique/smmR"
## [38] "https://framagit.org/GustaveCoste/off-product-environmental-impact/"
## [39] "https://gitlab.com/dlr-dw/ontocode"
## [40] "https://gitlab.com/dlr-ve/esy/amiris/amiris"
## [41] "https://bitbucket.org/glotzer/rowan"
## [42] "https://code.usgs.gov/umesc/quant-ecology/fishstan/"
## [43] "https://gitlab.com/fame-framework/fame-io"
## [44] "https://gitlab.com/thartwig/asloth"
## [45] "https://gitlab.com/fame-framework/fame-core"
## [46] "https://gitlab.com/dlr-ve/autumn/"
## [47] "https://gitlab.com/habermann_lab/phasik"
## [48] "https://gitlab.com/ags-data-format-wg/ags-python-library"
## [49] "https://gitlab.com/datafold-dev/datafold/"
## [50] "https://gitlab.com/tesch1/cppduals"
## [51] "https://gitlab.com/celliern/scikit-fdiff/"
## [52] "https://gitlab.com/materials-modeling/calorine"
## [53] "https://gitlab.com/tue-umphy/software/parmesan"
## [54] "https://gitlab.com/open-darts/open-darts"
## [55] "https://gitlab.mpikg.mpg.de/curcuraci/bmiptools"
## [56] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [57] "https://savannah.nongnu.org/projects/complot/"
## [58] "http://mutabit.com/repos.fossil/grafoscopio/"
## [59] "https://gitlab.com/cerfacs/batman"
## [60] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [61] "https://gitlab.inria.fr/miet/miet"
## [62] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"
## [63] "https://gitlab.com/akantu/akantu"
## [64] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"
## [65] "https://gitlab.com/marinvaders/marinvaders"
## [66] "https://bitbucket.org/bmskinner/nuclear_morphology"
## [67] "https://git.rwth-aachen.de/ants/sensorlab/imea"
## [68] "https://bitbucket.org/sciencecapsule/sciencecapsule"
## [69] "https://gitlab.ruhr-uni-bochum.de/ee/cd2es"
## [70] "https://gitlab.ifremer.fr/resourcecode/resourcecode"
## [71] "https://gitlab.com/lheea/CN-AeroModels"
## [72] "https://gitlab.com/bioeconomy/forobs/biotrade/"
## [73] "https://www.idpoisson.fr/fullswof/"
## [74] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [75] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"
## [76] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [77] "https://gitlab.com/gims-developers/gims"
## [78] "https://gitlab.com/ampere2/metalwalls"
## [79] "https://gitlab.eudat.eu/coccon-kit/proffastpylot"
## [80] "https://forgemia.inra.fr/migale/easy16s"
## [81] "https://git.mpib-berlin.mpg.de/castellum/castellum"
## [82] "https://gitlab.awi.de/sicopolis/sicopolis"
## [83] "https://gitlab.com/programgreg/tagginglatencyestimator"
## [84] "https://c4science.ch/source/tamaas/"
## [85] "https://git.ligo.org/asimov/asimov"
## [86] "https://gitlab.pasteur.fr/vlegrand/ROCK"
## [87] "https://gitlab.com/jason-rumengan/pyarma"
## [88] "https://gitlab.inria.fr/bcoye/game-engine-scheduling-simulation"
## [89] "https://gitlab.com/petsc/petsc"
## [90] "https://bitbucket.org/berkeleylab/hardware-control/src/main/"
## [91] "https://gitlab.com/culturalcartography/text2map"
## [92] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [93] "https://gitlab.com/sissopp_developers/sissopp"
## [94] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"
## [95] "https://bitbucket.org/clhaley/Multitaper.jl"
## [96] "https://gitlab.com/project-dare/dare-platform"
## [97] "https://bitbucket.org/sbarbot/motorcycle/src/master/"
## [98] "https://forgemia.inra.fr/pherosensor/pherosensor-toolbox"
## [99] "https://gitlab.com/remram44/taguette"
## [100] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [101] "https://gitlab.com/sigcorr/sigcorr"
## [102] "https://git.geomar.de/digital-earth/dasf/dasf-messaging-python"
## [103] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [104] "https://gitlab.inria.fr/mosaic/bvpy"
## [105] "https://gitlab.com/costrouc/pysrim"
## [106] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [107] "https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy"
## [108] "https://gitlab.com/permafrostnet/teaspoon"
## [109] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [110] "https://gitlab.com/free-astro/siril"
## [111] "https://bitbucket.org/dghoshal/frieda"
## [112] "https://gitlab.com/moerman1/fhi-cc4s"
## [113] "https://gitlab.com/pyFBS/pyFBS"
## [114] "https://gitlab.com/jtagusari/hrisk-noisemodelling"
## [115] "https://bitbucket.org/manuela_s/hcp/"
## [116] "https://gitlab.com/libreumg/dataquier.git"
## [117] "https://gitlab.com/ProjectRHEA/flowsolverrhea"
## [118] "https://gitlab.com/fibreglass/pivc"
## [119] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"
## [120] "https://gitlab.com/dlr-ve/esy/vencopy/vencopy"
## [121] "https://gitlab.com/jesseds/apav"
## [122] "https://gitlab.inria.fr/melissa/melissa"
## [123] "https://gitlab.com/cosapp/cosapp"
## [124] "https://gitlab.com/qc-devs/aqcnes"
## [125] "https://gitlab.com/vibes-developers/vibes"
## [126] "https://gitlab.uliege.be/smart_grids/public/gboml"
## [127] "https://bitbucket.org/basicsums/basicsums"
## [128] "https://framagit.org/GustaveCoste/eldam"
## [129] "https://gitlab.com/cracklet/cracklet.git"
## [130] "https://gitlab.com/eidheim/Simple-Web-Server"
## [131] "https://gricad-gitlab.univ-grenoble-alpes.fr/deformvis/insarviz"
## [132] "https://git.ufz.de/despot/pysewer/"
## [133] "https://gitlab.com/materials-modeling/wulffpack"
## [134] "https://gitlab.com/energyincities/besos/"
## [135] "https://bitbucket.org/mituq/muq2.git"
## [136] "https://gitlab.com/robizzard/libcdict"
## [137] "https://bitbucket.org/robmoss/particle-filter-for-python/"
## [138] "https://gitlab.inria.fr/bramas/tbfmm"
## [139] "https://bitbucket.org/meg/cbcbeat"
## [140] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
## [141] "https://gitlab.com/utopia-project/utopia"
## [142] "https://bitbucket.org/hammurabicode/hamx"
## [143] "https://gitlab.com/davidwoodburn/itrm"
## [144] "https://gitlab.com/picos-api/picos"
## [145] "https://gitlab.com/tum-ciip/elsa"
## [146] "https://gitlab.com/binary_c/binary_c-python/"
## [147] "https://bitbucket.org/cdegroot/wediff"
## [148] "https://gitlab.com/QComms/cqptoolkit"
## [149] "https://gitlab.com/toposens/public/ros-packages"
## [150] "https://gitlab.inria.fr/azais/treex"
## [151] "https://gitlab.com/pvst/asi"
## [152] "https://gitlab.com/davidtourigny/dynamic-fba"
## [153] "https://bitbucket.org/likask/mofem-cephas"
## [154] "https://gitlab.com/chaver/choco-mining"
## [155] "https://gitlab.com/cosmograil/PyCS3"
## [156] "https://gitlab.com/MartinBeseda/sa-oo-vqe-qiskit.git"
## [157] "https://bitbucket.org/cmutel/brightway2"
## [158] "https://gitlab.com/davidwoodburn/r3f"
## [159] "https://gitlab.com/geekysquirrel/bigx"
## [160] "https://gitlab.com/dglaeser/fieldcompare"
## [161] "https://gitlab.com/dlr-ve/esy/sfctools/framework/"
## [162] "https://gitlab.com/libreumg/dataquier.git"df <- do.call(dplyr::bind_rows, lapply(unique(software_urls[is_github]), function(u) {
u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
if (grepl("/tree/", u0)) {
u0 <- strsplit(u0, "/tree/")[[1]][1]
}
if (grepl("/blob/", u0)) {
u0 <- strsplit(u0, "/blob/")[[1]][1]
}
info <- try({
gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
})
languages <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"),
.limit = 500)
})
topics <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"),
.accept = "application/vnd.github.mercy-preview+json", .limit = 500)
})
contribs <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"),
.limit = 500)
})
if (!is(info, "try-error") && length(info) > 1) {
if (!is(contribs, "try-error")) {
if (length(contribs) == 0) {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
} else {
repo_nbr_contribs <- length(contribs)
repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
if (is.na(repo_nbr_contribs_2ormore)) {
print(contribs)
}
}
} else {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
}
if (!is(languages, "try-error")) {
if (length(languages) == 0) {
repolang <- ""
} else {
repolang <- paste(paste(names(unlist(languages)),
unlist(languages), sep = ":"), collapse = ",")
}
} else {
repolang <- ""
}
if (!is(topics, "try-error")) {
if (length(topics$names) == 0) {
repotopics <- ""
} else {
repotopics <- paste(unlist(topics$names), collapse = ",")
}
} else {
repotopics <- ""
}
data.frame(repo_url = u,
repo_created = info$created_at,
repo_updated = info$updated_at,
repo_pushed = info$pushed_at,
repo_nbr_stars = info$stargazers_count,
repo_language = ifelse(!is.null(info$language),
info$language, NA_character_),
repo_languages_bytes = repolang,
repo_topics = repotopics,
repo_license = ifelse(!is.null(info$license),
info$license$key, NA_character_),
repo_nbr_contribs = repo_nbr_contribs,
repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
)
} else {
NULL
}
})) %>%
dplyr::mutate(repo_created = as.Date(repo_created),
repo_updated = as.Date(repo_updated),
repo_pushed = as.Date(repo_pushed)) %>%
dplyr::distinct() %>%
dplyr::mutate(repo_info_obtained = lubridate::today())if (length(unique(df$repo_url)) != length(df$repo_url)) {
print(length(unique(df$repo_url)))
print(length(df$repo_url))
print(df$repo_url[duplicated(df$repo_url)])
}
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)## [1] 1442 12## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$repo_url)) %>%
dplyr::arrange(desc(repo_info_obtained)) %>%
dplyr::filter(!duplicated(repo_url))
head(dfarchive)## # A tibble: 6 × 12
## repo_url repo_created repo_updated repo_pushed repo_nbr_stars repo_language
## <chr> <date> <date> <date> <int> <chr>
## 1 https://gi… 2024-08-02 2025-06-16 2025-06-09 10 Python
## 2 https://gi… 2023-08-19 2025-06-07 2025-06-07 2 HTML
## 3 https://gi… 2022-11-18 2025-06-02 2024-10-11 37 Julia
## 4 https://gi… 2022-12-21 2025-06-14 2025-06-10 58 Python
## 5 https://gi… 2016-08-02 2025-06-02 2025-01-09 96 C++
## 6 https://gi… 2021-04-03 2025-02-20 2025-06-18 14 R
## # ℹ 6 more variables: repo_languages_bytes <chr>, repo_topics <chr>,
## # repo_license <chr>, repo_nbr_contribs <int>,
## # repo_nbr_contribs_2ormore <int>, repo_info_obtained <date>dim(dfarchive)## [1] 1590 12df <- dplyr::bind_rows(df, dfarchive)
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)## [1] 3032 12papers <- papers %>% dplyr::left_join(df, by = "repo_url")
dim(papers)## [1] 3048 70source_track <- c(source_track,
structure(rep("sw-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Clean up a bit
## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
dplyr::mutate(published.date = as.Date(published.print)) %>%
dplyr::mutate(
halfyear = paste0(year(published.date),
ifelse(month(published.date) <= 6, "H1", "H2"))
) %>% dplyr::mutate(
halfyear = factor(halfyear,
levels = paste0(rep(sort(unique(year(published.date))),
each = 2), c("H1", "H2")))
) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
dim(papers)## [1] 3048 70dupidx <- which(papers$alternative.id %in% papers$alternative.id[duplicated(papers)])
papers[dupidx, ] %>% arrange(alternative.id) %>% head(n = 10)## # A tibble: 0 × 70
## # ℹ 70 variables: alternative.id <chr>, container.title <chr>, created <chr>,
## # deposited <chr>, published.print <chr>, doi <chr>, indexed <chr>,
## # issn <chr>, issue <chr>, issued <chr>, member <chr>, page <chr>,
## # prefix <chr>, publisher <chr>, score <chr>, source <chr>,
## # reference.count <chr>, references.count <chr>,
## # is.referenced.by.count <chr>, title <chr>, type <chr>, url <chr>,
## # volume <chr>, short.container.title <chr>, author <list>, …papers <- papers %>% dplyr::distinct()
dim(papers)## [1] 3048 70source_track <- c(source_track,
structure(rep("cleanup", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Tabulate number of missing values
In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.
DT::datatable(
data.frame(variable = colnames(papers),
nbr_missing = colSums(is.na(papers))) %>%
dplyr::mutate(source = source_track[variable]),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Number of published papers per month
monthly_pubs <- papers %>%
dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
dplyr::group_by(pubmonth) %>%
dplyr::summarize(npub = n())
ggplot(monthly_pubs,
aes(x = factor(pubmonth), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per month", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
DT::datatable(
monthly_pubs %>%
dplyr::rename("Number of papers" = "npub",
"Month of publications" = "pubmonth"),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Number of published papers per year
yearly_pubs <- papers %>%
dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
dplyr::group_by(pubyear) %>%
dplyr::summarize(npub = n())
ggplot(yearly_pubs,
aes(x = factor(pubyear), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per year", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
DT::datatable(
yearly_pubs %>%
dplyr::rename("Number of papers" = "npub",
"Year of publications" = "pubyear"),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Fraction rejected papers
The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.
ggplot(all_rejected,
aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) +
geom_bar(stat = "identity") +
theme_minimal() +
facet_wrap(~ itype, ncol = 1) +
labs(x = "Month of issue closing", y = "Fraction of issues rejected",
caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Citation distribution
Papers with 20 or more citations are grouped in the “>=20” category.
ggplot(papers %>%
dplyr::mutate(citation_count = replace(citation_count,
citation_count >= 20, ">=20")) %>%
dplyr::mutate(citation_count = factor(citation_count,
levels = c(0:20, ">=20"))) %>%
dplyr::group_by(citation_count) %>%
dplyr::tally(),
aes(x = citation_count, y = n)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(x = "OpenAlex citation count", y = "Number of publications", caption = dcap)
Most cited papers
The table below sorts the JOSS papers in decreasing order by the number of citations in OpenAlex.
DT::datatable(
papers %>%
dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>",
url,"</a>")) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::select(title, url, published.date, citation_count),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Citation count vs time since publication
plotly::ggplotly(
ggplot(papers, aes(x = published.date, y = citation_count, label = title)) +
geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() +
geom_smooth() +
labs(x = "Date of publication", y = "OpenAlex citation count", caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: label.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?Power law of citation count within each half year
Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.
ggplot(papers %>% dplyr::group_by(halfyear) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::mutate(idx = seq_along(citation_count)),
aes(x = idx, y = citation_count)) +
geom_point(alpha = 0.5) +
facet_wrap(~ halfyear, scales = "free") +
theme_bw() +
labs(x = "Index", y = "OpenAlex citation count", caption = dcap)## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
Pre-review/review time over time
In these plots we investigate whether the time a submission spends in the pre-review or review stage (or their sum) has changed over time. The blue curve corresponds to a rolling median for submissions over 120 days.
## Helper functions (modified from https://stackoverflow.com/questions/65147186/geom-smooth-with-median-instead-of-mean)
rolling_median <- function(formula, data, xwindow = 120, ...) {
## Get order of x-values and sort x/y
ordr <- order(data$x)
x <- data$x[ordr]
y <- data$y[ordr]
## Initialize vector for smoothed y-values
ys <- rep(NA, length(x))
## Calculate median y-value for each unique x-value
for (xs in setdiff(unique(x), NA)) {
## Get x-values in the window, and calculate median of corresponding y
j <- ((xs - xwindow/2) < x) & (x < (xs + xwindow/2))
ys[x == xs] <- median(y[j], na.rm = TRUE)
}
y <- ys
structure(list(x = x, y = y, f = approxfun(x, y)), class = "rollmed")
}
predict.rollmed <- function(mod, newdata, ...) {
setNames(mod$f(newdata$x), newdata$x)
}ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review",
caption = dcap) +
theme(axis.title = element_text(size = 15))
ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of review opening", y = "Number of days in review",
caption = dcap) +
theme(axis.title = element_text(size = 15))
ggplot(papers, aes(x = prerev_opened,
y = as.numeric(days_in_pre) + as.numeric(days_in_rev))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review + review",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Languages
Next, we consider the languages used by the submissions, both as reported by JOSS and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.
## Language information from JOSS
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
data.frame(language = l,
nbr_submissions_JOSS_API = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>%
setNames(c("language", "bytes")) %>%
dplyr::mutate(bytes = as.numeric(bytes)) %>%
dplyr::filter(!is.na(language)) %>%
dplyr::group_by(language) %>%
dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
nbr_repos_GitHub = length(bytes)) %>%
dplyr::arrange(desc(nbr_bytes_GitHub))
langs <- dplyr::full_join(langs, langbytes, by = "language")ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_JOSS_API)) %>%
dplyr::filter(nbr_submissions_JOSS_API > 10) %>%
dplyr::mutate(language = factor(language, levels = language)),
aes(x = language, y = nbr_submissions_JOSS_API)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15))
DT::datatable(
langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) +
geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() +
theme_bw() +
labs(x = "Number of repos using the language",
y = "Total number of bytes of code\nwritten in the language",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Association between number of citations and number of stars of the GitHub repo
ggplotly(
ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
label = title)) +
geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() +
theme_bw() +
labs(x = "OpenAlex citation count", y = "Number of stars, GitHub repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)Distribution of time between GitHub repo creation and JOSS submission
ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from repo creation to JOSS pre-review start",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Distribution of time between JOSS acceptance and last commit
ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)) +
facet_wrap(~ year(published.date), scales = "free_y")
Number of authors per paper
List the papers with the largest number of authors, and display the distribution of the number of authors per paper, for papers with at most 20 authors.
## Papers with largest number of authors
papers %>% dplyr::arrange(desc(nbr_authors)) %>%
dplyr::select(title, published.date, url, nbr_authors) %>%
as.data.frame() %>% head(10)## title
## 1 SunPy: A Python package for Solar Physics
## 2 ENZO: An Adaptive Mesh Refinement Code for Astrophysics (Version 2.6)
## 3 GEOS: A performance portable multi-physics simulation\nframework for subsurface applications
## 4 The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained
## 5 sbi reloaded: a toolkit for simulation-based inference workflows
## 6 GRChombo: An adaptable numerical relativity code for fundamental physics
## 7 DataLad: distributed system for joint management of code, data, and their relationship
## 8 PyBIDS: Python tools for BIDS datasets
## 9 Chaste: Cancer, Heart and Soft Tissue Environment
## 10 sourmash v4: A multitool to quickly search, compare,\nand analyze genomic and metagenomic data sets
## published.date url nbr_authors
## 1 2020-02-14 https://doi.org/10.21105/joss.01832 124
## 2 2019-10-03 https://doi.org/10.21105/joss.01636 55
## 3 2024-10-10 https://doi.org/10.21105/joss.06973 53
## 4 2021-02-21 https://doi.org/10.21105/joss.02807 38
## 5 2025-04-08 https://doi.org/10.21105/joss.07754 33
## 6 2021-12-10 https://doi.org/10.21105/joss.03703 32
## 7 2021-07-01 https://doi.org/10.21105/joss.03262 31
## 8 2019-08-12 https://doi.org/10.21105/joss.01294 31
## 9 2020-03-13 https://doi.org/10.21105/joss.01848 29
## 10 2024-06-28 https://doi.org/10.21105/joss.06830 29nbins <- max(papers$nbr_authors[papers$nbr_authors <= 20])
ggplot(papers %>% dplyr::filter(nbr_authors <= 20),
aes(x = nbr_authors)) +
geom_histogram(bins = nbins, fill = "lightgrey", color = "grey50") +
theme_bw() +
facet_wrap(~ year(published.date), scales = "free_y") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Number of authors",
y = "Number of publications with\na given number of authors",
caption = dcap)
ggplot(papers %>%
dplyr::mutate(nbr_authors = replace(nbr_authors, nbr_authors > 5, ">5")) %>%
dplyr::mutate(nbr_authors = factor(nbr_authors, levels = c("1", "2", "3",
"4", "5", ">5"))) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(year = factor(year)) %>%
dplyr::group_by(year, nbr_authors, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = nbr_authors)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Number of\nauthors",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)
Number of reviewers per paper
Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
ggplot(papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)),
aes(x = nbr_reviewers)) + geom_bar() +
facet_wrap(~ year) + theme_bw() +
labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)
Most active reviewers
Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
All time
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Past 5 years
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::filter(as.Date(published.date) >= (lubridate::today() - 5 * 365.25)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Past year
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::filter(as.Date(published.date) >= (lubridate::today() - 365.25)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Number of papers per editor and year
ggplot(papers %>%
dplyr::mutate(year = year(published.date),
`r/pyOpenSci` = factor(
grepl("rOpenSci|pyOpenSci", prerev_labels),
levels = c("TRUE", "FALSE"))),
aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) +
theme_bw() + facet_wrap(~ year, ncol = 1) +
scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Editor", y = "Number of submissions", caption = dcap)
Distribution of software repo licenses
all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
grep("bsd", all_licenses, value = TRUE),
grep("mit", all_licenses, value = TRUE),
grep("gpl", all_licenses, value = TRUE),
grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>%
dplyr::mutate(repo_license = factor(repo_license,
levels = license_levels)),
aes(x = repo_license)) +
geom_bar() +
theme_bw() +
labs(x = "Software license", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~ year(published.date), scales = "free_y")
## For plots below, replace licenses present in less
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::count() %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = n, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Number of submissions", caption = dcap)
ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)
Most common GitHub repo topics
a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)
colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE,
colors = colors, scale = c(10, 0.1), random.color = TRUE,
ordered.colors = FALSE, vfont = c("serif", "plain")
)
DT::datatable(as.data.frame(topicfreq) %>%
dplyr::rename(topic = a, nbr_repos = Freq) %>%
dplyr::arrange(desc(nbr_repos)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))Citation analysis
Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.
Get citing papers for each submission
## Split into several queries
## Randomize the splitting since a whole query may fail if one ID is not recognized
papidx <- seq_len(nrow(papers))
idxL <- split(sample(papidx, length(papidx), replace = FALSE), ceiling(papidx / 50))
citationsL <- lapply(idxL, function(idx) {
tryCatch({
citecorp::oc_coci_cites(doi = papers$alternative.id[idx]) %>%
dplyr::distinct() %>%
dplyr::mutate(citation_info_obtained = as.character(lubridate::today()))
}, error = function(e) {
NULL
})
})
citationsL <- citationsL[vapply(citationsL, function(df) !is.null(df) && nrow(df) > 0, FALSE)]
if (length(citationsL) > 0) {
citations <- do.call(dplyr::bind_rows, citationsL)
} else {
citations <- NULL
}
dim(citations)## NULLif (!is.null(citations) && is.data.frame(citations) && "oci" %in% colnames(citations)) {
citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci))
tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
contains("type"), contains("publisher"), contains("prefix"))
citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume
## that they can be
## identified from the prefix 10.1101 - set the container.title
## for these records manually; we may or may not want to count these
## (would it count citations twice, both preprint and publication?)
citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
## JOSS is represented by 'The Journal of Open Source Software' as well as
## 'Journal of Open Source Software'
citations$container.title[citations$container.title ==
"Journal of Open Source Software"] <-
"The Journal of Open Source Software"
## Remove real self citations (cited DOI = citing DOI)
citations <- citations %>% dplyr::filter(cited != citing)
## Merge with the archive
citations <- dplyr::bind_rows(citations, citations_archive)
} else {
citations <- citations_archive
if (is.null(citations[["citation_info_obtained"]])) {
citations$citation_info_obtained <- NA_character_
}
}
citations$citation_info_obtained[is.na(citations$citation_info_obtained)] <-
"2021-08-11"
write.table(citations, file = "joss_submission_citations.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)Summary statistics
## Latest successful update of new citation data
max(as.Date(citations$citation_info_obtained))## [1] "2025-04-12"## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 1844## Number of JOSS papers with >0 citations according to OpenAlex
length(which(papers$citation_count > 0))## [1] 2267## Number of citations from Open Citations Corpus vs OpenAlex
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## Total citation count OpenAlex
sum(df0$citation_count, na.rm = TRUE)## [1] 95652## Total citation count Open Citations Corpus
sum(df0$n, na.rm = TRUE)## [1] 92735## Ratio of total citation count Open Citations Corpus/OpenAlex
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 0.969504ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()
## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))
## Number of journals citing JOSS papers
length(unique(citations$container.title))## [1] 10467length(unique(citations$issn))## [1] 7585Most citing journals
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)write.table(topcit, file = "joss_submission_citations_byjournal.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)Save object
The tibble object with all data collected above is serialized to a file that can be downloaded and reused.
head(papers) %>% as.data.frame()## alternative.id container.title created deposited
## 1 10.21105/joss.03596 Journal of Open Source Software 2022-02-10 2022-02-10
## 2 10.21105/joss.06880 Journal of Open Source Software 2024-08-16 2024-08-16
## 3 10.21105/joss.03082 Journal of Open Source Software 2021-05-09 2021-05-09
## 4 10.21105/joss.07308 Journal of Open Source Software 2025-03-15 2025-03-15
## 5 10.21105/joss.06825 Journal of Open Source Software 2024-10-13 2024-10-13
## 6 10.21105/joss.01970 Journal of Open Source Software 2020-01-22 2020-01-22
## published.print doi indexed issn issue issued
## 1 2022-02-10 10.21105/joss.03596 2025-03-19 2475-9066 70 2022-02-10
## 2 2024-08-16 10.21105/joss.06880 2024-08-17 2475-9066 100 2024-08-16
## 3 2021-05-09 10.21105/joss.03082 2024-06-11 2475-9066 61 2021-05-09
## 4 2025-03-15 10.21105/joss.07308 2025-03-16 2475-9066 107 2025-03-15
## 5 2024-10-13 10.21105/joss.06825 2024-10-14 2475-9066 102 2024-10-13
## 6 2020-01-23 10.21105/joss.01970 2025-02-21 2475-9066 45 2020-01-23
## member page prefix publisher score source reference.count
## 1 8722 3596 10.21105 The Open Journal 0 Crossref 26
## 2 8722 6880 10.21105 The Open Journal 0 Crossref 10
## 3 8722 3082 10.21105 The Open Journal 0 Crossref 6
## 4 8722 7308 10.21105 The Open Journal 0 Crossref 9
## 5 8722 6825 10.21105 The Open Journal 0 Crossref 8
## 6 8722 1970 10.21105 The Open Journal 0 Crossref 8
## references.count is.referenced.by.count
## 1 26 4
## 2 10 0
## 3 6 3
## 4 9 0
## 5 8 0
## 6 8 3
## title
## 1 Nempy: A Python package for modelling the Australian National Electricity Market dispatch procedure
## 2 ChainoPy: A Python Library for Discrete Time Markov\nChain Based Stochastic Analysis
## 3 toqito -- Theory of quantum information toolkit: A Python package for studying quantum information
## 4 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 5 An Open-Source Tool for Generating Domain-Specific\nAccelerators for Resource-Constrained Computing
## 6 MOAFS: A Massive Online Analysis library for feature selection in data streams
## type url volume
## 1 journal-article https://doi.org/10.21105/joss.03596 7
## 2 journal-article https://doi.org/10.21105/joss.06880 9
## 3 journal-article https://doi.org/10.21105/joss.03082 6
## 4 journal-article https://doi.org/10.21105/joss.07308 10
## 5 journal-article https://doi.org/10.21105/joss.06825 9
## 6 journal-article https://doi.org/10.21105/joss.01970 5
## short.container.title
## 1 JOSS
## 2 JOSS
## 3 JOSS
## 4 JOSS
## 5 JOSS
## 6 JOSS
## author
## 1 Nicholas, Anna, Iain, Gorman, Bruce, MacGill, first, additional, additional
## 2 Aadya A., Chinubhai, first
## 3 Vincent, Russo, first
## 4 https://orcid.org/0009-0004-9334-5936, https://orcid.org/0000-0001-9054-8659, NA, NA, FALSE, FALSE, NA, NA, Juan Pablo P., José Fernando, Diogo D., Ricardo, de Aquino, de M. Firmino, Moreira, de S. Job, first, additional, additional, additional, Instituto Federal de Educação Ciência e Tecnologia da Paraíba - IFPB, Brazil, Universidade Federal da Paraíba - UFPB, Brazil, Instituto Federal de Educação Ciência e Tecnologia da Paraíba - IFPB, Brazil, Instituto Federal de Educação Ciência e Tecnologia da Paraíba - IFPB, Brazil
## 5 David T, Tosiron, Kerns, Adegbija, first, additional
## 6 https://orcid.org/0000-0002-9485-0334, https://orcid.org/0000-0002-6520-9740, FALSE, FALSE, Matheus, André, de Moraes, Gradvohl, first, additional
## citation_count openalex_id affil_countries_all
## 1 4 https://openalex.org/W4211164564 AU
## 2 0 https://openalex.org/W4401633516 IN
## 3 6 https://openalex.org/W3161015373
## 4 0 https://openalex.org/W4408479858 BR
## 5 0 https://openalex.org/W4403373919
## 6 3 https://openalex.org/W3001158079 BR
## affil_countries_first
## 1 AU
## 2 IN
## 3
## 4 BR
## 5
## 6 BR
## api_title
## 1 Nempy: A Python package for modelling the Australian National Electricity Market dispatch procedure
## 2 ChainoPy: A Python Library for Discrete Time Markov Chain Based Stochastic Analysis
## 3 toqito -- Theory of quantum information toolkit: A Python package for studying quantum information
## 4 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 5 An Open-Source Tool for Generating Domain-Specific Accelerators for Resource-Constrained Computing
## 6 MOAFS: A Massive Online Analysis library for feature selection in data streams
## api_state
## 1 accepted
## 2 accepted
## 3 accepted
## 4 accepted
## 5 accepted
## 6 accepted
## author_affiliations
## 1 School of Photovoltaics and Renewable Energy Engineering, University of New South Wales, Australia, Collaboration on Energy and Environmental Markets (CEEM), University of New South Wales, Australia;School of Electrical Engineering and Telecommunications, University of New South Wales, Australia, Collaboration on Energy and Environmental Markets (CEEM), University of New South Wales, Australia
## 2 School of Engineering and Applied Sciences (SEAS), Ahmedabad University, Ahmedabad, Gujarat, India
## 3 ISARA Corporation, Modellicity Inc.
## 4 Instituto Federal de Educação Ciência e Tecnologia da Paraíba - IFPB, Brazil;Universidade Federal da Paraíba - UFPB, Brazil
## 5 Department of Electrical & Computer Engineering, University of Arizona, Tucson, Arizona, USA
## 6 Faculty of Technology, University of Campinas
## editor reviewers nbr_reviewers
## 1 @timtroendle @noah80,@robinroche 2
## 2 @mooniean @braniii,@MichalisPanayides 2
## 3 @poulson @rurz,@jameshclrk,@marwahaha 3
## 4 @vissarion @saaikrishnan,@louiseadennis 2
## 5 @gkthiruvathukal @manuel-g-castro,@abhishektiwari 2
## 6 @VivianePons @sptennak,@DARSakthi 2
## repo_url review_issue_id
## 1 https://github.com/UNSW-CEEM/nempy 3596
## 2 https://github.com/aadya940/chainopy 6880
## 3 https://github.com/vprusso/toqito 3082
## 4 https://github.com/youngkaneda/DemeterWatch 7308
## 5 https://github.com/dtkerns/d2 6825
## 6 https://github.com/mbdemoraes/moafs 1970
## prereview_issue_id languages
## 1 3576 Python
## 2 6675 Jupyter Notebook,Python,Cython
## 3 3033 Python
## 4 7209 Java,JavaScript
## 5 6111 Python,C
## 6 1814 Java
## archive_doi
## 1 https://doi.org/10.5281/zenodo.5989170
## 2 https://doi.org/10.5281/zenodo.13305155
## 3 https://doi.org/10.5281/zenodo.4743211
## 4 https://doi.org/10.5281/zenodo.15012171
## 5 https://doi.org/10.5281/zenodo.13926674
## 6 https://doi.org/10.6084/m9.figshare.11663307.v2
## review_title
## 1 Nempy: A Python package for modelling the Australian National Electricity Market dispatch procedure
## 2 ChainoPy: A Python Library for Discrete Time Markov Chain based stochastic analysis
## 3 toqito -- Theory of quantum information toolkit: A Python package for studying quantum information
## 4 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 5 An Open-Source Tool for Generating Domain-Specific Accelerators for Resource-Constrained Computing
## 6 MOAFS: A Massive Online Analysis library for featureselection in data streams
## review_number review_state review_opened review_closed review_ncomments
## 1 3596 closed 2021-08-10 2022-02-10 83
## 2 6880 closed 2024-06-12 2024-08-16 98
## 3 3082 closed 2021-03-03 2021-05-09 45
## 4 7308 closed 2024-10-02 2025-03-15 70
## 5 6825 closed 2024-06-02 2024-10-13 66
## 6 1970 closed 2019-12-19 2020-01-23 60
## review_labels
## 1 accepted,Python,recommend-accept,published
## 2 accepted,recommend-accept,published,Track: 5 (DSAIS)
## 3 accepted,TeX,Python,recommend-accept,published
## 4 accepted,TeX,Java,JavaScript,recommend-accept,published,Track: 7 (CSISM)
## 5 accepted,Shell,Python,C++,recommend-accept,published,Track: 7 (CSISM)
## 6 accepted,recommend-accept,published
## prerev_title
## 1 Nempy: A Python package for modelling the Australian National Electricity Market dispatch procedure
## 2 ChainoPy: A Python Library for Discrete Time Markov Chain based stochastic analysis
## 3 toqito -- Theory of quantum information toolkit: A Python package for studying quantum information
## 4 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 5 An Open-Source Tool for Generating Domain-Specific Accelerators for Resource-Constrained Computing
## 6 MOAFS: A Massive Online Analysis library for featureselection in data streams
## prerev_state prerev_opened prerev_closed prerev_ncomments
## 1 closed 2021-08-06 2021-08-10 23
## 2 closed 2024-04-24 2024-06-12 35
## 3 closed 2021-02-12 2021-03-03 49
## 4 closed 2024-09-10 2024-10-02 35
## 5 closed 2023-12-02 2024-06-02 70
## 6 closed 2019-10-16 2019-12-19 31
## prerev_labels days_in_pre days_in_rev to_review
## 1 Python,waitlisted 4 days 184 days TRUE
## 2 Track: 5 (DSAIS) 49 days 65 days TRUE
## 3 TeX,Python 19 days 67 days TRUE
## 4 TeX,Java,JavaScript,Track: 7 (CSISM) 22 days 164 days TRUE
## 5 Shell,Python,C++,Track: 7 (CSISM) 183 days 133 days TRUE
## 6 TeX,Java 64 days 35 days TRUE
## repo_created repo_updated repo_pushed repo_nbr_stars repo_language
## 1 2020-04-14 2025-06-22 2025-03-07 59 Python
## 2 2024-02-09 2025-05-13 2024-08-16 17 Jupyter Notebook
## 3 2020-01-22 2025-06-23 2025-06-23 256 Python
## 4 2021-07-24 2025-03-15 2025-03-15 0 Java
## 5 2023-04-10 2024-10-13 2024-10-13 1 C++
## 6 2019-10-08 2025-03-07 2020-01-23 4 Java
## repo_languages_bytes
## 1 Python:892252
## 2 Jupyter Notebook:64982,Python:57310,Cython:5683,TeX:3669
## 3 Python:1233922,TeX:2054
## 4 Java:219015,TeX:5505,JavaScript:2668,HTML:2231
## 5 C++:24062,Python:18014,Shell:16085,TeX:12456,Makefile:2121,C:1601
## 6 Java:44560,TeX:3239
## repo_topics
## 1
## 2 bayesian-data-analysis,bayesian-inference,bayesian-statistics,data-analysis,deep-learning,forecasting,machine-learning,markov-chain,markov-model,markov-process,time-series,time-series-analysis,data-science
## 3 quantum-computing,python,quantum-information,matrix-analysis,python-3,unitaryhack,physics,quantum,convex-optimization,linear-algebra,python3,quantum-information-science,quantum-information-theory,quantum-physics,quantum-programming,quantum-programming-language,research,nonlocal-game,linear,semidefinite-programming
## 4
## 5
## 6
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 bsd-3-clause 7 5 2025-06-25
## 2 bsd-2-clause 2 1 2025-06-25
## 3 mit 53 39 2025-06-25
## 4 gpl-3.0 4 3 2025-06-25
## 5 mit 3 2 2025-06-25
## 6 gpl-3.0 2 1 2025-06-25
## published.date halfyear nbr_authors
## 1 2022-02-10 2022H1 3
## 2 2024-08-16 2024H2 1
## 3 2021-05-09 2021H1 1
## 4 2025-03-15 2025H1 4
## 5 2024-10-13 2024H2 2
## 6 2020-01-23 2020H1 2saveRDS(papers, file = "joss_submission_analytics.rds")To read the current version of this file directly from GitHub, use the following code:
papers <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true")))Session info
sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] openalexR_2.0.1 stringr_1.5.1 gt_1.0.0 rworldmap_1.3-8
## [5] sp_2.2-0 readr_2.1.5 citecorp_0.3.0 plotly_4.11.0
## [9] DT_0.33 jsonlite_2.0.0 purrr_1.0.4 gh_1.5.0
## [13] lubridate_1.9.4 ggplot2_3.5.2 tidyr_1.3.1 dplyr_1.1.4
## [17] rcrossref_1.2.009 tibble_3.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2 viridis_0.6.5
## [5] urltools_1.7.3.1 fields_16.3.1 fastmap_1.2.0 lazyeval_0.2.2
## [9] promises_1.3.3 digest_0.6.37 dotCall64_1.2 timechange_0.3.0
## [13] mime_0.13 lifecycle_1.0.4 terra_1.8-54 magrittr_2.0.3
## [17] compiler_4.5.1 rlang_1.1.6 sass_0.4.10 tools_4.5.1
## [21] wordcloud_2.6 utf8_1.2.6 yaml_2.3.10 data.table_1.17.6
## [25] knitr_1.50 labeling_0.4.3 fauxpas_0.5.2 htmlwidgets_1.6.4
## [29] bit_4.6.0 curl_6.4.0 plyr_1.8.9 xml2_1.3.8
## [33] RColorBrewer_1.1-3 httpcode_0.3.0 miniUI_0.1.2 withr_3.0.2
## [37] triebeard_0.4.1 grid_4.5.1 xtable_1.8-4 gitcreds_0.1.2
## [41] scales_1.4.0 crul_1.5.0 cli_3.6.5 rmarkdown_2.29
## [45] crayon_1.5.3 generics_0.1.4 httr_1.4.7 tzdb_0.5.0
## [49] cachem_1.1.0 splines_4.5.1 maps_3.4.3 parallel_4.5.1
## [53] vctrs_0.6.5 Matrix_1.7-3 hms_1.1.3 bit64_4.6.0-1
## [57] crosstalk_1.2.1 jquerylib_0.1.4 glue_1.8.0 spam_2.11-1
## [61] codetools_0.2-20 stringi_1.8.7 gtable_0.3.6 later_1.4.2
## [65] raster_3.6-32 pillar_1.10.2 rappdirs_0.3.3 htmltools_0.5.8.1
## [69] R6_2.6.1 httr2_1.1.2 vroom_1.6.5 evaluate_1.0.4
## [73] shiny_1.10.0 lattice_0.22-7 httpuv_1.6.16 bslib_0.9.0
## [77] Rcpp_1.0.14 gridExtra_2.3 nlme_3.1-168 mgcv_1.9-3
## [81] whisker_0.4.1 xfun_0.52 pkgconfig_2.0.3