JOSS submission analytics
2025-10-08 09:02:38.209493
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$total_dois)## [1] 3186## Pull down all records from Crossref
papers <- rcrossref::cr_journals(issn, works = TRUE, cursor = "*",
cursor_max = joss_details$total_dois * 2) %>%
pluck("data")
## Only keep articles
papers <- papers %>%
dplyr::filter(type == "journal-article")
dim(papers)## [1] 3195 28dim(papers %>% distinct())## [1] 3195 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] 3194 28dim(papers %>% distinct())## [1] 3194 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] 3199 45length(unique(oa$doi))## [1] 3199papers <- papers %>% dplyr::left_join(
oa %>% dplyr::mutate(alternative.id = sub("https://doi.org/", "", doi)) %>%
dplyr::select(alternative.id, cited_by_count, id,
affil_countries_all, affil_countries_first) %>%
dplyr::rename(citation_count = cited_by_count,
openalex_id = id),
by = "alternative.id"
)
dim(papers)## [1] 3194 32dim(papers %>% distinct())## [1] 3194 32source_track <- c(source_track,
structure(rep("OpenAlex", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Pull down info from JOSS API
For each published paper, we use the JOSS API to get information about pre-review and review issue numbers, corresponding software repository etc.
joss_api <- list()
p <- 1
a0 <- NULL
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0 && !identical(a, a0)) {
joss_api <- c(joss_api, a)
p <- p + 1
a0 <- a
a <- tryCatch({
jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)},
error = function(e) return(numeric(0))
)
}
joss_api <- do.call(dplyr::bind_rows, lapply(joss_api, function(w) {
data.frame(api_title = w$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] 3201 12dim(joss_api %>% distinct())## [1] 3201 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] 3194 43dim(papers %>% distinct())## [1] 3194 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] 175 7head(issother)## title
## 1 Quickstart Tutorial and Testing Documentation
## 2 Installation Path Clarity - Non Blocking
## 3 Community Guidelines Enhancement - Non Blocking
## 4 Getting Started Document unavailable
## 5 [PRE-REVIEW]: Alpaga: A Python package for automated analysis of Second Harmonic Generation polarization experiments
## 6 re-compiling figures
## number state opened closed ncomments labels
## 1 8955 closed 2025-09-07T21:20:59Z 2025-09-07T21:21:02Z 1
## 2 8954 closed 2025-09-07T21:19:45Z 2025-09-07T21:19:47Z 1
## 3 8953 closed 2025-09-07T21:19:04Z 2025-09-07T21:19:06Z 1
## 4 8890 closed 2025-08-29T07:10:18Z 2025-08-29T07:10:20Z 1
## 5 8870 closed 2025-08-26T15:44:09Z 2025-08-26T15:44:11Z 1
## 6 8760 closed 2025-08-07T08:02:15Z 2025-08-07T08:02:18Z 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] 3194 59dim(papers %>% distinct())## [1] 3194 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/nomad-coe/greenX"
## [4] "https://github.com/bcgov/ssdtools"
## [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://github.com/landlab/landlab"
## [10] "https://github.com/symmy596/SurfinPy"
## [11] "https://github.com/julia-wrobel/registr"
## [12] "https://github.com/landlab/landlab"
## [13] "https://github.com/mlpack/mlpack"
## [14] "https://github.com/barbagroup/pygbe"
## [15] "https://github.com/pvlib/pvlib-python"is_github <- grepl("github", software_urls)
length(is_github)## [1] 3194sum(is_github)## [1] 3022software_urls[!is_github]## [1] "https://gitlab.kuleuven.be/ITSCreaLab/public-toolboxes/dyntapy"
## [2] "https://gitlab.com/morikawa-lab-osakau/vibir-parallel-compute"
## [3] "https://gitlab.com/ENKI-portal/ThermoCodegen"
## [4] "https://bitbucket.org/orionmhdteam/orion2_release1/src/master/"
## [5] "https://gitlab.dune-project.org/copasi/dune-copasi"
## [6] "https://gitlab.com/bonsamurais/bonsai/util/ipcc"
## [7] "https://gite.lirmm.fr/doccy/RedOak"
## [8] "https://codebase.helmholtz.cloud/mussel/netlogo-northsea-species.git"
## [9] "https://gitlab.com/cosmograil/starred"
## [10] "https://gitlab.com/ffaucher/hawen"
## [11] "https://gitlab.com/cmbm-ethz/miop"
## [12] "https://gitlab.com/emd-dev/emd"
## [13] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [14] "https://gitlab.com/sails-dev/sails"
## [15] "https://gitlab.com/djsmithbham/cnearest"
## [16] "https://gitlab.com/mantik-ai/mantik"
## [17] "https://gitlab.kitware.com/LBM/lattice-boltzmann-solver"
## [18] "https://gitlab.com/dsbowen/conditional-inference"
## [19] "https://gitlab.com/soleil-data-treatment/soleil-software-projects/remote-desktop"
## [20] "https://bitbucket.org/berkeleylab/esdr-pygdh/"
## [21] "https://git.iws.uni-stuttgart.de/tools/frackit"
## [22] "https://gitlab.com/cmbm-ethz/pourbaix-diagrams"
## [23] "https://code.europa.eu/kada/mafw"
## [24] "https://bitbucket.org/ocellarisproject/ocellaris"
## [25] "https://gitlab.com/moorepants/skijumpdesign"
## [26] "https://gitlab.com/drti/basic-tools"
## [27] "https://bitbucket.org/cloopsy/android/"
## [28] "https://gitlab.com/pythia-uq/pythia"
## [29] "https://gitlab.com/fduchate/predihood"
## [30] "https://jugit.fz-juelich.de/compflu/swalbe.jl/"
## [31] "https://gitlab.dune-project.org/dorie/dorie"
## [32] "https://gitlab.com/dmt-development/dmt-core"
## [33] "https://gitlab.com/wpettersson/kep_solver"
## [34] "https://gitlab.com/myqueue/myqueue"
## [35] "https://gitlab.com/dlr-ve/esy/remix/framework"
## [36] "https://gitlab.com/gdetor/genetic_alg"
## [37] "https://gitlab.com/utopia-project/dantro"
## [38] "https://plmlab.math.cnrs.fr/lmrs/statistique/smmR"
## [39] "https://framagit.org/GustaveCoste/off-product-environmental-impact/"
## [40] "https://gitlab.com/InspectorCell/inspectorcell"
## [41] "https://gitlab.com/dlr-ve/esy/amiris/amiris"
## [42] "https://gitlab.com/dlr-dw/ontocode"
## [43] "https://bitbucket.org/glotzer/rowan"
## [44] "https://gitlab.com/fame-framework/fame-core"
## [45] "https://gitlab.com/fame-framework/fame-io"
## [46] "https://gitlab.com/thartwig/asloth"
## [47] "https://code.usgs.gov/umesc/quant-ecology/fishstan/"
## [48] "https://gitlab.com/habermann_lab/phasik"
## [49] "https://gitlab.com/dlr-ve/autumn/"
## [50] "https://gitlab.com/ags-data-format-wg/ags-python-library"
## [51] "https://zivgitlab.uni-muenster.de/ag-salinga/fastatomstruct"
## [52] "https://gitlab.com/tue-umphy/software/parmesan"
## [53] "https://gitlab.com/tesch1/cppduals"
## [54] "https://gitlab.com/datafold-dev/datafold/"
## [55] "https://gitlab.com/open-darts/open-darts"
## [56] "https://gitlab.com/materials-modeling/calorine"
## [57] "https://gitlab.com/celliern/scikit-fdiff/"
## [58] "https://gitlab.mpikg.mpg.de/curcuraci/bmiptools"
## [59] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [60] "https://gitlab.inria.fr/miet/miet"
## [61] "https://savannah.nongnu.org/projects/complot/"
## [62] "http://mutabit.com/repos.fossil/grafoscopio/"
## [63] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [64] "https://gitlab.com/cerfacs/batman"
## [65] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"
## [66] "https://gitlab.com/akantu/akantu"
## [67] "https://git.rwth-aachen.de/ants/sensorlab/imea"
## [68] "https://gitlab.com/marinvaders/marinvaders"
## [69] "https://gitlab.kuleuven.be/gelenslab/publications/pycline"
## [70] "https://bitbucket.org/bmskinner/nuclear_morphology"
## [71] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"
## [72] "https://bitbucket.org/sciencecapsule/sciencecapsule"
## [73] "https://codeberg.org/benmagill/deflake.rs"
## [74] "https://gitlab.ifremer.fr/resourcecode/resourcecode"
## [75] "https://gitlab.com/lheea/CN-AeroModels"
## [76] "https://gitlab.com/bioeconomy/forobs/biotrade/"
## [77] "https://gitlab.ruhr-uni-bochum.de/ee/cd2es"
## [78] "https://www.idpoisson.fr/fullswof/"
## [79] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [80] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"
## [81] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [82] "https://c4science.ch/source/tamaas/"
## [83] "https://git.mpib-berlin.mpg.de/castellum/castellum"
## [84] "https://gitlab.com/gims-developers/gims"
## [85] "https://gitlab.awi.de/sicopolis/sicopolis"
## [86] "https://gitlab.com/programgreg/tagginglatencyestimator"
## [87] "https://gitlab.eudat.eu/coccon-kit/proffastpylot"
## [88] "https://gitlab.com/ampere2/metalwalls"
## [89] "https://forgemia.inra.fr/migale/easy16s"
## [90] "https://gitlab.pasteur.fr/vlegrand/ROCK"
## [91] "https://git.ligo.org/asimov/asimov"
## [92] "https://bitbucket.org/berkeleylab/hardware-control/src/main/"
## [93] "https://gitlab.inria.fr/bcoye/game-engine-scheduling-simulation"
## [94] "https://gitlab.com/culturalcartography/text2map"
## [95] "https://gitlab.com/petsc/petsc"
## [96] "https://gitlab.com/jason-rumengan/pyarma"
## [97] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [98] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"
## [99] "https://forgemia.inra.fr/pherosensor/pherosensor-toolbox"
## [100] "https://bitbucket.org/sbarbot/motorcycle/src/master/"
## [101] "https://bitbucket.org/clhaley/Multitaper.jl"
## [102] "https://gitlab.com/project-dare/dare-platform"
## [103] "https://gitlab.com/remram44/taguette"
## [104] "https://gitlab.com/sissopp_developers/sissopp"
## [105] "https://git.geomar.de/digital-earth/dasf/dasf-messaging-python"
## [106] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [107] "https://gitlab.com/sigcorr/sigcorr"
## [108] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [109] "https://gitlab.inria.fr/mosaic/bvpy"
## [110] "https://bitbucket.org/dghoshal/frieda"
## [111] "https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy"
## [112] "https://gitlab.com/grogra/groimp-plugins/Pointcloud"
## [113] "https://gitlab.com/permafrostnet/teaspoon"
## [114] "https://gitlab.com/costrouc/pysrim"
## [115] "https://gitlab.com/free-astro/siril"
## [116] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [117] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [118] "https://gitlab.com/ComputationalScience/idinn"
## [119] "https://gitlab.com/jtagusari/hrisk-noisemodelling"
## [120] "https://gitlab.com/moerman1/fhi-cc4s"
## [121] "https://gitlab.com/pyFBS/pyFBS"
## [122] "https://gitlab.com/fibreglass/pivc"
## [123] "https://gitlab.com/libreumg/dataquier.git"
## [124] "https://bitbucket.org/manuela_s/hcp/"
## [125] "https://gitlab.com/ProjectRHEA/flowsolverrhea"
## [126] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"
## [127] "https://gitlab.com/jesseds/apav"
## [128] "https://gitlab.inria.fr/melissa/melissa"
## [129] "https://gitlab.com/cosapp/cosapp"
## [130] "https://gitlab.com/dlr-ve/esy/vencopy/vencopy"
## [131] "https://gitlab.com/qc-devs/aqcnes"
## [132] "https://gitlab.uliege.be/smart_grids/public/gboml"
## [133] "https://gitlab.com/vibes-developers/vibes"
## [134] "https://gricad-gitlab.univ-grenoble-alpes.fr/deformvis/insarviz"
## [135] "https://gitlab.com/eidheim/Simple-Web-Server"
## [136] "https://bitbucket.org/basicsums/basicsums"
## [137] "https://framagit.org/GustaveCoste/eldam"
## [138] "https://gitlab.com/cracklet/cracklet.git"
## [139] "https://git.ufz.de/despot/pysewer/"
## [140] "https://gitlab.com/EliseLei/easychem"
## [141] "https://gitlab.com/materials-modeling/wulffpack"
## [142] "https://bitbucket.org/mituq/muq2.git"
## [143] "https://codeberg.org/JPHackstein/GREOPy"
## [144] "https://gitlab.com/energyincities/besos/"
## [145] "https://gitlab.com/robizzard/libcdict"
## [146] "https://bitbucket.org/robmoss/particle-filter-for-python/"
## [147] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
## [148] "https://bitbucket.org/meg/cbcbeat"
## [149] "https://gitlab.com/utopia-project/utopia"
## [150] "https://gitlab.inria.fr/bramas/tbfmm"
## [151] "https://bitbucket.org/hammurabicode/hamx"
## [152] "https://gitlab.com/davidwoodburn/itrm"
## [153] "https://gitlab.com/binary_c/binary_c-python/"
## [154] "https://gitlab.com/tum-ciip/elsa"
## [155] "https://gitlab.com/cosmology-ethz/ufig"
## [156] "https://gitlab.com/picos-api/picos"
## [157] "https://bitbucket.org/cdegroot/wediff"
## [158] "https://gitlab.com/QComms/cqptoolkit"
## [159] "https://gitlab.com/toposens/public/ros-packages"
## [160] "https://gitlab.com/pvst/asi"
## [161] "https://gitlab.inria.fr/azais/treex"
## [162] "https://gitlab.com/davidtourigny/dynamic-fba"
## [163] "https://gitlab.com/MartinBeseda/sa-oo-vqe-qiskit.git"
## [164] "https://gitlab.com/cosmograil/PyCS3"
## [165] "https://gitlab.com/chaver/choco-mining"
## [166] "https://bitbucket.org/cmutel/brightway2"
## [167] "https://bitbucket.org/likask/mofem-cephas"
## [168] "https://gitlab.com/geekysquirrel/bigx"
## [169] "https://gitlab.com/dglaeser/fieldcompare"
## [170] "https://gitlab.com/dlr-ve/esy/sfctools/framework/"
## [171] "https://gitlab.com/libreumg/dataquier.git"
## [172] "https://gitlab.com/davidwoodburn/r3f"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$repo_url))
dim(df)## [1] 1815 12## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$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… 2020-09-09 2025-09-24 2025-07-11 13 MATLAB
## 2 https://gi… 2022-05-30 2025-01-23 2023-10-01 8 Python
## 3 https://gi… 2018-06-21 2025-09-29 2025-10-03 11 Jupyter Note…
## 4 https://gi… 2023-05-09 2025-08-16 2025-08-16 6 R
## 5 https://gi… 2021-12-06 2025-08-06 2024-02-14 20 Python
## 6 https://gi… 2022-07-07 2024-07-27 2024-07-27 3 HTML
## # 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] 1364 12df <- dplyr::bind_rows(df, dfarchive)
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)## [1] 3179 12papers <- papers %>% dplyr::left_join(df, by = "repo_url")
dim(papers)## [1] 3194 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] 3194 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] 3194 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 1 row 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 1 row 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)## [1] 77172 8if (!is.null(citations) && is.data.frame(citations) && "oci" %in% colnames(citations)) {
citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci) &
citing != "")
tmpj <- rcrossref::cr_works(dois = unique(citations$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$citation_info_obtained))## [1] "2025-08-27"## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 1888## Number of JOSS papers with >0 citations according to OpenAlex
length(which(papers$citation_count > 0))## [1] 2381## Number of citations from Open Citations Corpus vs OpenAlex
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## Total citation count OpenAlex
sum(df0$citation_count, na.rm = TRUE)## [1] 103469## Total citation count Open Citations Corpus
sum(df0$n, na.rm = TRUE)## [1] 105690## Ratio of total citation count Open Citations Corpus/OpenAlex
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 1.021465ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()
## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))
## Number of journals citing JOSS papers
length(unique(citations$container.title))## [1] 11606length(unique(citations$issn))## [1] 8433Most citing journals
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.htmlplotly::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.07308 Journal of Open Source Software 2025-03-15 2025-03-15
## 2 10.21105/joss.01252 Journal of Open Source Software 2019-03-31 2019-11-17
## 3 10.21105/joss.01338 Journal of Open Source Software 2019-04-19 2019-11-17
## 4 10.21105/joss.01229 Journal of Open Source Software 2019-06-10 2024-07-19
## 5 10.21105/joss.01444 Journal of Open Source Software 2019-06-27 2022-09-22
## 6 10.21105/joss.00194 The Journal of Open Source Software 2017-03-07 2019-09-19
## published.print doi indexed issn issue issued
## 1 2025-03-15 10.21105/joss.07308 2025-03-16 2475-9066 107 2025-03-15
## 2 2019-03-31 10.21105/joss.01252 2025-02-21 2475-9066 35 2019-03-31
## 3 2019-04-19 10.21105/joss.01338 2025-02-21 2475-9066 36 2019-04-19
## 4 2019-06-10 10.21105/joss.01229 2025-02-21 2475-9066 38 2019-06-10
## 5 2019-06-27 10.21105/joss.01444 2025-02-21 2475-9066 38 2019-06-27
## 6 2017-03-07 10.21105/joss.00194 2025-02-21 2475-9066 11 2017-03-07
## member page prefix publisher score source reference.count
## 1 8722 7308 10.21105 The Open Journal 0 Crossref 9
## 2 8722 1252 10.21105 The Open Journal 0 Crossref 12
## 3 8722 1338 10.21105 The Open Journal 0 Crossref 5
## 4 8722 1229 10.21105 The Open Journal 0 Crossref 12
## 5 8722 1444 10.21105 The Open Journal 0 Crossref 11
## 6 8722 194 10.21105 The Open Journal 0 Crossref 6
## references.count is.referenced.by.count
## 1 9 0
## 2 12 6
## 3 5 0
## 4 12 3
## 5 11 1
## 6 6 1
## title
## 1 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 2 LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier
## 3 uJVM: Lightweight Java Virtual Machine for embedded systems
## 4 Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models
## 5 modelDown: automated website generator with interpretable documentation for predictive machine learning models
## 6 lawn: An R client for the Turf Javascript Library for Geospatial Analysis
## type url volume
## 1 journal-article https://doi.org/10.21105/joss.07308 10
## 2 journal-article https://doi.org/10.21105/joss.01252 4
## 3 journal-article https://doi.org/10.21105/joss.01338 4
## 4 journal-article https://doi.org/10.21105/joss.01229 4
## 5 journal-article https://doi.org/10.21105/joss.01444 4
## 6 journal-article https://doi.org/10.21105/joss.00194 2
## short.container.title
## 1 JOSS
## 2 JOSS
## 3 JOSS
## 4 JOSS
## 5 JOSS
## 6 JOSS
## author
## 1 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
## 2 https://orcid.org/0000-0002-9818-7752, https://orcid.org/0000-0003-1821-5037, FALSE, FALSE, Amir, Jalal, Mir, Nasiri, first, additional
## 3 https://orcid.org/0000-0003-3167-983X, https://orcid.org/0000-0002-4651-1696, https://orcid.org/0000-0002-6673-4053, https://orcid.org/0000-0003-3055-8104, FALSE, FALSE, FALSE, FALSE, Oleksandr, Taras, Vitalii, Oleg, Moliavko, Drozdovskyi, Petrychenko, Kopysov, first, additional, additional, additional
## 4 https://orcid.org/0000-0002-1559-9832, FALSE, Ellert, van der Velden, first
## 5 Kamil, Magda, Mateusz, Przemysław, Romaszko, Tatarynowicz, Urbański, Biecek, first, additional, additional, additional, NA, NA, NA, https://orcid.org/0000-0001-8423-1823, NA, NA, NA, FALSE
## 6 https://orcid.org/0000-0003-1444-9135, https://orcid.org/0000-0002-9254-9740, FALSE, FALSE, Scott, Jeffrey, Chamberlain, W. Hollister, first, additional
## citation_count openalex_id affil_countries_all
## 1 0 https://openalex.org/W4408479858 BR
## 2 9 https://openalex.org/W2926133036 IR
## 3 0 https://openalex.org/W2938572431
## 4 4 https://openalex.org/W2964188552 AU
## 5 3 https://openalex.org/W2953447938 PL
## 6 1 https://openalex.org/W2593495758
## affil_countries_first
## 1 BR
## 2
## 3
## 4 AU
## 5 PL
## 6
## api_title
## 1 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 2 LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier
## 3 uJVM: Lightweight Java Virtual Machine for embedded systems
## 4 Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models
## 5 modelDown: automated website generator with interpretable documentation for predictive machine learning models
## 6 lawn: An R client for the Turf Javascript Library for Geospatial Analysis
## api_state
## 1 accepted
## 2 accepted
## 3 retracted
## 4 accepted
## 5 accepted
## 6 accepted
## author_affiliations
## 1 Instituto Federal de Educação Ciência e Tecnologia da Paraíba - IFPB, Brazil;Universidade Federal da Paraíba - UFPB, Brazil
## 2 Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
## 3
## 4 Centre for Astrophysics and Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia, ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
## 5 Faculty of Mathematics and Information Science, Warsaw University of Technology
## 6 rOpenSci;US Environmental Protection Agency, Atlantic Ecology Division
## editor reviewers nbr_reviewers
## 1 @vissarion @saaikrishnan,@louiseadennis 2
## 2 @cMadan @stefanv @rougier 1
## 3 @gkthiruvathukal @morganericsson,@hainesr 2
## 4 @arokem @fonnesbeck 1
## 5 @alexhanna @terrytangyuan,@pdwaggoner 2
## 6 @arfon @arfon 1
## repo_url review_issue_id
## 1 https://github.com/youngkaneda/DemeterWatch 7308
## 2 https://github.com/mir-am/LightTwinSVM 1252
## 3 https://github.com/Samsung/uJVM/ 1338
## 4 https://github.com/1313e/PRISM 1229
## 5 https://github.com/MI2DataLab/modelDown 1444
## 6 https://github.com/ropensci/lawn 194
## prereview_issue_id languages
## 1 7209 Java,JavaScript
## 2 1155 Python,C++,PowerShell
## 3 1268 C,C++,Assembly
## 4 1200 Python
## 5 1439 R
## 6 193 R
## archive_doi
## 1 https://doi.org/10.5281/zenodo.15012171
## 2 https://doi.org/10.5281/zenodo.2619436
## 3 https://doi.org/10.5281/zenodo.2647414
## 4 https://doi.org/10.5281/zenodo.2572736
## 5 https://doi.org/10.5281/zenodo.3247303
## 6 https://doi.org/10.5281/zenodo.345991
## review_title
## 1 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 2 LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier
## 3 uJVM: Lightweight Java Virtual Machine for embedded systems
## 4 Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models
## 5 modelDown: automated website generator with interpretable documentation for predictive machine learning models
## 6 lawn: An R client for the Turf Javascript Library for Geospatial Analysis
## review_number review_state review_opened review_closed review_ncomments
## 1 7308 closed 2024-10-02 2025-03-15 70
## 2 1252 closed 2019-02-15 2019-03-31 62
## 3 1338 closed 2019-03-20 2019-04-19 83
## 4 1229 closed 2019-02-04 2019-06-10 103
## 5 1444 closed 2019-05-12 2019-06-27 51
## 6 194 closed 2017-03-04 2017-03-07 7
## review_labels
## 1 accepted,TeX,Java,JavaScript,recommend-accept,published,Track: 7 (CSISM)
## 2 accepted,recommend-accept,published
## 3 accepted,recommend-accept,published
## 4 accepted,AAS,recommend-accept,published
## 5 accepted,recommend-accept,published
## 6 accepted,rOpenSci,recommend-accept,published
## prerev_title
## 1 DemeterWatch: A Java tool to detect Law of Demeter violations in Java collections
## 2 LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier
## 3 uJVM: Lightweight Java Virtual Machine for embedded systems
## 4 Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models
## 5 modelDown: automated website generator with interpretable documentation for predictive machine learning models
## 6 lawn: An R client for the Turf Javascript Library for Geospatial Analysis
## prerev_state prerev_opened prerev_closed prerev_ncomments
## 1 closed 2024-09-10 2024-10-02 35
## 2 closed 2019-01-05 2019-02-15 63
## 3 closed 2019-02-20 2019-03-20 52
## 4 closed 2019-01-25 2019-02-04 19
## 5 closed 2019-05-09 2019-05-12 19
## 6 closed 2017-03-04 2017-03-04 8
## prerev_labels days_in_pre days_in_rev to_review
## 1 TeX,Java,JavaScript,Track: 7 (CSISM) 22 days 164 days TRUE
## 2 Shell,Python,C++ 41 days 44 days TRUE
## 3 Makefile,C++,C 28 days 30 days TRUE
## 4 TeX,Python 10 days 126 days TRUE
## 5 TeX,R 3 days 46 days TRUE
## 6 rOpenSci 0 days 3 days TRUE
## repo_created repo_updated repo_pushed repo_nbr_stars repo_language
## 1 2021-07-24 2025-03-15 2025-03-15 0 Java
## 2 2018-05-18 2024-08-15 2019-12-29 12 Python
## 3 <NA> <NA> <NA> NA <NA>
## 4 2018-02-06 2024-11-03 2021-06-28 46 Python
## 5 2018-05-27 2025-10-06 2022-07-08 120 R
## 6 2015-03-04 2025-03-22 2022-05-10 55 R
## repo_languages_bytes
## 1 Java:219015,TeX:5505,JavaScript:2668,HTML:2231
## 2 Python:81199,Shell:8739,C++:7752,TeX:3994,PowerShell:3450
## 3 <NA>
## 4 Python:911942,Jupyter Notebook:78845,TeX:6351
## 5 R:20459,TeX:2849
## 6 R:263262,TeX:1378,Makefile:654
## repo_topics
## 1
## 2 svm-classifier,machine-learning,twinsvm,clippdcd,pattern-recognition,classification,code,implementation,classifier,python
## 3 <NA>
## 4 model,analysis-pipeline,model-analysis,emulator,regression,python3,python
## 5
## 6 geojson,geospatial,r,rstats,r-package,turf,turfjs
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 gpl-3.0 4 3 2025-10-08
## 2 gpl-3.0 1 1 2025-10-08
## 3 <NA> NA NA <NA>
## 4 bsd-3-clause 1 1 2025-10-08
## 5 <NA> 8 7 2025-10-08
## 6 other 5 4 2025-10-08
## published.date halfyear nbr_authors
## 1 2025-03-15 2025H1 4
## 2 2019-03-31 2019H1 2
## 3 2019-04-19 2019H1 4
## 4 2019-06-10 2019H1 1
## 5 2019-06-27 2019H1 4
## 6 2017-03-07 2017H1 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 Sequoia 15.6.1
##
## 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_1.5.2 gt_1.1.0 rworldmap_1.3-8
## [5] sp_2.2-0 readr_2.1.5 citecorp_0.3.0 plotly_4.11.0
## [9] DT_0.34.0 jsonlite_2.0.0 purrr_1.1.0 gh_1.5.0
## [13] lubridate_1.9.4 ggplot2_4.0.0 tidyr_1.3.1 dplyr_1.1.4
## [17] rcrossref_1.2.1 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_17.1 S7_0.2.0 fastmap_1.2.0
## [9] lazyeval_0.2.2 promises_1.3.3 digest_0.6.37 dotCall64_1.2
## [13] timechange_0.3.0 mime_0.13 lifecycle_1.0.4 terra_1.8-70
## [17] magrittr_2.0.4 compiler_4.5.1 rlang_1.1.6 sass_0.4.10
## [21] tools_4.5.1 wordcloud_2.6 utf8_1.2.6 yaml_2.3.10
## [25] data.table_1.17.8 knitr_1.50 labeling_0.4.3 fauxpas_0.5.2
## [29] htmlwidgets_1.6.4 bit_4.6.0 curl_7.0.0 plyr_1.8.9
## [33] xml2_1.4.0 RColorBrewer_1.1-3 httpcode_0.3.0 miniUI_0.1.2
## [37] withr_3.0.2 triebeard_0.4.1 grid_4.5.1 xtable_1.8-4
## [41] gitcreds_0.1.2 scales_1.4.0 crul_1.6.0 cli_3.6.5
## [45] rmarkdown_2.30 crayon_1.5.3 generics_0.1.4 httr_1.4.7
## [49] tzdb_0.5.0 cachem_1.1.0 splines_4.5.1 maps_3.4.3
## [53] parallel_4.5.1 vctrs_0.6.5 Matrix_1.7-3 hms_1.1.3
## [57] bit64_4.6.0-1 crosstalk_1.2.2 jquerylib_0.1.4 glue_1.8.0
## [61] spam_2.11-1 codetools_0.2-20 stringi_1.8.7 gtable_0.3.6
## [65] later_1.4.4 raster_3.6-32 pillar_1.11.1 rappdirs_0.3.3
## [69] htmltools_0.5.8.1 httr2_1.2.1 R6_2.6.1 vroom_1.6.6
## [73] evaluate_1.0.5 shiny_1.11.1 lattice_0.22-7 httpuv_1.6.16
## [77] bslib_0.9.0 Rcpp_1.1.0 gridExtra_2.3 nlme_3.1-168
## [81] mgcv_1.9-3 whisker_0.4.1 xfun_0.53 fs_1.6.6
## [85] pkgconfig_2.0.3