OSCR

IRAK4 constrains cellular plasticity during chemically-induced cell fate reprogramming into multiple lineages.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match
  1. [1] § Methods › Single-cell RNA-sequencing data processing ↔ R/featureCornerAxes.R, lines 1–64 · score 0.52 · scRNAtoolVis, t-SNE, Seurat, variable, genes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 288 lines · 8.4 KB · other · 1 match

  1. #' @name featureCornerAxes
  2. #' @author Junjun Lao
  3. #' @title Add corner axes on seurat UMAP/tSNE gene FeaturePlot function figures
  4. #'
  5. #' @param object object seurat object.
  6. #' @param reduction "string", reduction type (umap/tsne).
  7. #' @param features "string", the gene you want to plot.
  8. #' @param groupFacet "string", give the column name in seurat metadata to facet plot, if it is "NULL", facet plot only by gene.
  9. #' @param relLength "num", the corner axis line relative length to plot axis(0-1).
  10. #' @param relDist "num", the relative distance of corner axis label to axis.
  11. #' @param aspect.ratio "num", plot width and height ratio, default NULL.
  12. #' @param low "string", point color with low expression.
  13. #' @param high "string", point color with high expression.
  14. #' @param axes "string", show multiple corner axis or only one (mul/one), default "mul".
  15. #' @param legendPos "string", legend position same as ggplot theme function, default "right".
  16. #' @param stripCol "string", facet background color, defaults "white".
  17. #' @param pSize "num", point size.
  18. #' @param arrowType "string", arrow type (open/closed), default "closed".
  19. #' @param lineTextcol "string", facet background color, default "white".
  20. #' @param cornerTextSize "num", the corner label text size, default is 5.
  21. #' @param base_size "num", theme base size, default is 14.
  22. #' @param themebg Another theme style, default is "default", or "bwCorner".
  23. #' @param show.legend Whether show legend, default "TRUE".
  24. #' @param cornerVariable Which group corner axis to be added when "axes" set to "one", default is the first group.
  25. #' @param nLayout = NULL Similar to the ncol/nrow for the layout, default is the gene numbers.
  26. #' @param minExp Minimum expression value defined, default is NULL.
  27. #' @param maxExp Maxmum expression value defined, default is NULL.
  28. #' @return Return a ggplot.
  29. #' @export
  30. #' @examples
  31. #'
  32. #' test <- system.file("extdata", "seuratTest.RDS", package = "scRNAtoolVis")
  33. #'
  34. #' tmp <- readRDS(test)
  35. #'
  36. #' # umap
  37. #' featureCornerAxes(
  38. #' object = tmp, reduction = "umap",
  39. #' groupFacet = "orig.ident",
  40. #' relLength = 0.5, relDist = 0.2,
  41. #' features = c("Actb", "Ythdc1", "Ythdf2")
  42. #' )
  43. #'
  44. #' # one axes
  45. #' featureCornerAxes(
  46. #' object = tmp, reduction = "umap",
  47. #' groupFacet = "orig.ident",
  48. #' features = c("Actb", "Ythdc1", "Ythdf2"),
  49. #' relLength = 0.5, relDist = 0.2,
  50. #' axes = "one",
  51. #' lineTextcol = "grey50"
  52. #' )
  53. #'
  54. #' # tsne
  55. #' featureCornerAxes(
  56. #' object = tmp, reduction = "tsne",
  57. #' groupFacet = "orig.ident",
  58. #' relLength = 0.5, relDist = 0.2,
  59. #' features = c("Actb", "Ythdc1", "Ythdf2")
  60. #' )
  61. #'
  62. #'
  63. # define variables
  64. globalVariables(c("x1", "y1", "linegrou", "angle", "lab", "gene_name", "scaledValue"))
  66. # define function
  67. featureCornerAxes <- function(
  68. object = NULL,
  69. reduction = "umap",
  70. features = NULL,
  71. groupFacet = "orig.ident",
  72. minExp = NULL,
  73. maxExp = NULL,
  74. relLength = 0.25,
  75. relDist = 0.1,
  76. aspect.ratio = NULL,
  77. low = "lightgrey",
  78. high = "red",
  79. axes = "mul",
  80. show.legend = TRUE,
  81. legendPos = "right",
  82. stripCol = "white",
  83. cornerVariable = NULL,
  84. nLayout = NULL,
  85. pSize = 1,
  86. arrowType = "closed",
  87. lineTextcol = "black",
  88. cornerTextSize = 3,
  89. base_size = 14,
  90. themebg = "default") {
  91. # make PC data
  92. reduc <- data.frame(Seurat::Embeddings(object, reduction = reduction))
  94. # metadata
  95. meta <- [email hidden]
  97. # combine
  98. pc12 <- cbind(reduc, meta)
  100. # get gene expression
  101. geneExp <- Seurat::FetchData(object = object, vars = features)
  103. # cbind
  104. mer <- cbind(pc12, geneExp)
  106. # merge data
  107. megredf <- reshape2::melt(
  108. mer,
  109. id.vars = colnames(pc12),
  110. variable.name = "gene_name",
  111. value.name = "scaledValue"
  112. )
  114. # data range
  115. range <- floor(min(min(pc12[, 1]), min(pc12[, 2])))
  117. # get bottom-left coord
  118. lower <- range - relDist * abs(range)
  120. # label reldist to axes
  121. labelRel <- relDist * abs(lower)
  123. # get relative line length
  124. linelen <- abs(relLength * lower) + lower
  126. # mid point
  127. mid <- abs(relLength * lower) / 2 + lower
  129. # give reduction type
  130. if (startsWith(reduction, "umap")) {
  131. axs_label <- paste("UMAP", 2:1, sep = "")
  132. } else if (startsWith(reduction, "tsne")) {
  133. axs_label <- paste("t-SNE", 2:1, sep = "")
  134. } else {
  135. print("Please give correct type(umap or tsne)!")
  136. }
  138. if (axes == "mul") {
  139. # axises data
  140. axes <- data.frame(
  141. "x1" = c(lower, lower, lower, linelen),
  142. "y1" = c(lower, linelen, lower, lower),
  143. "linegrou" = c(1, 1, 2, 2)
  144. )
  145. # axises label
  146. label <- data.frame(
  147. "lab" = c(axs_label),
  148. "angle" = c(90, 0),
  149. "x1" = c(lower - labelRel, mid),
  150. "y1" = c(mid, lower - labelRel)
  151. )
  152. } else if (axes == "one") {
  153. # add specific group corner
  154. if (is.null(cornerVariable)) {
  155. lev <- levels(pc12[, groupFacet])
  156. if (!is.null(lev)) {
  157. firstFacet <- factor(lev[1], levels = lev)
  158. } else {
  159. firstFacet <- unique(pc12[, groupFacet])[1]
  160. }
  161. } else {
  162. lev <- levels(pc12[, groupFacet])
  163. if (!is.null(lev)) {
  164. firstFacet <- factor(cornerVariable, levels = lev)
  165. } else {
  166. firstFacet <- cornerVariable
  167. }
  168. }
  170. # axises data
  171. axes <- data.frame(
  172. "x1" = c(lower, lower, lower, linelen),
  173. "y1" = c(lower, linelen, lower, lower),
  174. "linegrou" = c(1, 1, 2, 2),
  175. "group" = rep(firstFacet, 2)
  176. )
  177. # axises label
  178. label <- data.frame(
  179. "lab" = c(axs_label),
  180. angle = c(90, 0),
  181. "x1" = c(lower - labelRel, mid),
  182. "y1" = c(mid, lower - labelRel),
  183. "group" = rep(firstFacet, 2)
  184. )
  186. # rename group name
  187. colnames(axes)[4] <- groupFacet
  188. colnames(label)[5] <- groupFacet
  189. } else {
  190. print("Please give correct args(mul or one)!")
  191. }
  193. ####################################
  194. # set color value range
  195. if (is.null(minExp) && is.null(maxExp)) {
  196. minexp <- 0
  197. maxexp <- round(max(megredf$scaledValue) + 1, digits = 0)
  198. } else {
  199. minexp <- minExp
  200. maxexp <- maxExp
  201. }
  203. ####################################################
  204. # plot
  205. pmain <- ggplot2::ggplot(
  206. megredf,
  207. ggplot2::aes(x = megredf[, 1], y = megredf[, 2])
  208. ) +
  209. ggplot2::geom_point(
  210. ggplot2::aes(color = scaledValue),
  211. size = pSize,
  212. show.legend = show.legend
  213. ) +
  214. ggplot2::theme_classic(base_size = base_size) +
  215. ggplot2::scale_color_gradient(
  216. name = "", low = low, high = high,
  217. limits = c(minexp, maxexp),
  218. na.value = high
  219. ) +
  220. ggplot2::labs(x = "", y = "") +
  221. ggplot2::geom_line(
  222. data = axes,
  223. ggplot2::aes(x = x1, y = y1, group = linegrou),
  224. color = lineTextcol,
  225. arrow = ggplot2::arrow(
  226. length = ggplot2::unit(0.1, "inches"),
  227. ends = "last",
  228. type = arrowType
  229. )
  230. ) +
  231. ggplot2::geom_text(
  232. data = label,
  233. ggplot2::aes(x = x1, y = y1, angle = angle, label = lab),
  234. fontface = "italic",
  235. color = lineTextcol,
  236. size = cornerTextSize
  237. ) +
  238. ggplot2::theme(
  239. strip.background = ggplot2::element_rect(colour = NA, fill = stripCol),
  240. strip.text = ggplot2::element_text(size = base_size),
  241. strip.text.y = ggplot2::element_text(angle = 0),
  242. aspect.ratio = aspect.ratio,
  243. legend.position = legendPos,
  244. plot.title = ggplot2::element_text(hjust = 0.5),
  245. axis.line = ggplot2::element_blank(),
  246. axis.ticks = ggplot2::element_blank(),
  247. axis.text = ggplot2::element_blank()
  248. )
  250. ######################################
  251. # plot layout
  252. if (is.null(nLayout)) {
  253. nLayout <- length(features)
  254. } else {
  255. nLayout <- nLayout
  256. }
  258. ######################################
  259. # facet plot
  260. if (is.null(groupFacet)) {
  261. p1 <- pmain +
  262. ggplot2::facet_wrap(facets = "gene_name", ncol = nLayout)
  263. } else {
  264. p1 <- pmain +
  265. # ggplot2::facet_grid(facets = c("gene_name", groupFacet))
  266. ggplot2::facet_grid(rows = ggplot2::vars(.data[["gene_name"]]),
  267. cols = ggplot2::vars(.data[[groupFacet]]))
  268. }
  270. ######################################
  271. # theme style
  272. if (themebg == "bwCorner") {
  273. p2 <- p1 +
  274. ggplot2::theme_bw(base_size = base_size) +
  275. ggplot2::theme(
  276. panel.grid = ggplot2::element_blank(),
  277. axis.text = ggplot2::element_blank(),
  278. axis.ticks = ggplot2::element_blank(),
  279. aspect.ratio = 1,
  280. strip.background = ggplot2::element_rect(colour = NA, fill = stripCol)
  281. )
  282. } else if (themebg == "default") {
  283. p2 <- p1
  284. }
  286. # output
  287. return(p2)
  288. }

featureCornerAxes.R at commit c99eaa8, under other · at the source

Overview

Authors: Chuanshu Huang1,2, Xiaoyun Han2, Tao Wang3, Yang Zhao4, Jun Li1,2
ORCID iDs: Chuanshu Huang, Xiaoyun Han, Tao Wang, Yang Zhao, Jun Li
  1. Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, 325035 Wenzhou, Zhejiang China
  2. Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325035 China
  3. Key Laboratory of Laboratory Medicine, Ministry of Education, Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325035 China
  4. State Key Laboratory of Natural and Biomimetic Drugs, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing Advanced Center of Cellular Homeostasis and Aging-Related Diseases, Institute of Advanced Clinical Medicine, Peking University, Beijing, 100871 China
Institutions: Wenzhou Medical University (China); Peking University (China); State Key Laboratory of Natural and Biomimetic Drugs (China)
Journal: EMBO reports, volume 27, issue 16, pages 4903-4934
Dates: received 1 September 2025; accepted 19 June 2026; published online 15 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44319-026-00855-9 · PMID 42458070 · PMCID PMC13503836 · OpenAlex W7168414402
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Cell Cycle, Chromatin, Transcription & Genomics, Stem Cells & Regenerative Medicine
MeSH: Cell Lineage*, Cell Plasticity*, Cellular Reprogramming*, Interleukin-1 Receptor-Associated Kinases*, Animals, Cell Cycle, Cell Differentiation, Endoderm, Fibroblasts, Hepatocytes, Mice, Neurons (* major topic)
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: MOST | National Natural Science Foundation of China (NSFC) (32400603); Fundamental Research Foundation of wenzhou medical university (KYYW202319 and KYYW202401); MOST | NSFC | NSFC-Zhejiang Joint Fund | | Natural Science Foundation of Zhejiang Province (ZJNSF) (LQ24H110004); MOST | NSFC | NSFC-Zhejiang Joint Fund | 浙 江 省 科 学 技 术 厅| Natural Science Foundation of Zhejiang Province (LQ24H110004)
Citations: not cited yet (Europe PMC); 61 references in the paper
Research resources: Anti-FOXA2 RRID:AB_11157157, RRID:AB_141607, Anti-vGlut2 RRID:AB_2187539, Anti-Tuj1 RRID:AB_2256751, RRID:AB_2535853, Anti-Syn1 RRID:AB_2616578, Goat anti-Rabbit IgG H&L RRID:AB_2630356, Anti-CYP3A4 RRID:AB_2882414, RRID:AB_3068539, RRID:AB_3073507, Anti-hSOX17 RRID:AB_355060, Anti-GATA-4 RRID:AB_627667, Anti-Sall4 RRID:AB_777810

Abstract

Chemical reprogramming holds transformative potential for regenerative medicine. However, the regulatory mechanisms governing cell fate transitions are not well understood. Here, we identify Interleukin-1 Receptor-Associated Kinase 4 (IRAK4) as a barrier to multi-lineage reprogramming. Pharmacological inhibition of IRAK4 enhances the reprogramming of mouse embryonic fibroblasts (MEFs) through a chemically activated multi-lineage priming (CaMP) state and extraembryonic endoderm (XEN)-like intermediates, increasing colony formation, and the expression of core XEN regulators (Sox17, Gata4, Sall4, and Foxa2). Genetic knockdown of Irak4 similarly accelerates reprogramming, whereas its overexpression blocks cell fate transitions. IRAK4 inhibition enhances chromatin accessibility and reshapes cell cycle dynamics, characterized by G0/G1 shortening and G2/M lengthening, potentially contributing to multi-lineage state establishment. Furthermore, IRAK4 suppression enhances the direct conversion of MEFs to neuron-like and hepatocyte-like cells, which exhibit enhanced functional maturity, including increased glycogen storage and improved detoxification capacity. Our findings establish IRAK4 as a regulator that constrains cellular plasticity potentially by coordinating chromatin accessibility and cell cycle dynamics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

junjunlab/scRNAtoolVis

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c99eaa820f28c0f192a67853d86aabbea40e3ddd, 18 April 2026
Languages: R (12)
Size: 43 files, 12 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (DESCRIPTION), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: ggplot2 (10 files), tidyverse (9 files), Seurat (7 files), reshape2 (3 files), circlize (2 files), ComplexHeatmap (1 file), cowplot (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/junjunlab/scRNAtoolVis

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Other data links

Data availability

Supplemental sequencing data are available in the Gene Expression Omnibus database under accession numbers GSE303071 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303071), GSE303072 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303072), GSE303073 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303073), GSE303074 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303074), and GSE327913 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE327913). All microscopy images of this paper are collected in the following database record: biostudies: S-BIAD3324.

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44319-026-00855-9 (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44319-026-00855-9).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 12 MeSH terms, 4 funders, 60 references, 13 RRIDs.

Cite

This paper

Huang, C., Han, X., Wang, T., Zhao, Y., & Li, J. (2026). IRAK4 constrains cellular plasticity during chemically-induced cell fate reprogramming into multiple lineages. EMBO reports, 27(16), 4903-4934. https://doi.org/10.1038/s44319-026-00855-9

BibTeX

@article{huang2026irak4,
author = {Huang, Chuanshu and Han, Xiaoyun and Wang, Tao and Zhao, Yang and Li, Jun},
title = {{IRAK4 constrains cellular plasticity during chemically-induced cell fate reprogramming into multiple lineages}},
journal = {EMBO reports},
year = {2026},
month = jul,
volume = {27},
number = {16},
pages = {4903--4934},
publisher = {Nature Publishing Group},
issn = {1469-221X},
doi = {10.1038/s44319-026-00855-9},
url = {https://doi.org/10.1038/s44319-026-00855-9},
pmid = {42458070},
pmcid = {PMC13503836}
}

RIS

TY - JOUR
AU - Huang, Chuanshu
AU - Han, Xiaoyun
AU - Wang, Tao
AU - Zhao, Yang
AU - Li, Jun
TI - IRAK4 constrains cellular plasticity during chemically-induced cell fate reprogramming into multiple lineages
T2 - EMBO reports
J2 - EMBO Rep
PY - 2026
DA - 2026/07/15
VL - 27
IS - 16
SP - 4903
EP - 4934
SN - 1469-221X
PB - Nature Publishing Group
DO - 10.1038/s44319-026-00855-9
UR - https://doi.org/10.1038/s44319-026-00855-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44319-026-00855-9",
"type": "article-journal",
"title": "IRAK4 constrains cellular plasticity during chemically-induced cell fate reprogramming into multiple lineages",
"container-title": "EMBO reports",
"author": [
{
"family": "Huang",
"given": "Chuanshu"
},
{
"family": "Han",
"given": "Xiaoyun"
},
{
"family": "Wang",
"given": "Tao"
},
{
"family": "Zhao",
"given": "Yang"
},
{
"family": "Li",
"given": "Jun"
}
],
"container-title-short": "EMBO Rep",
"volume": "27",
"issue": "16",
"page": "4903-4934",
"DOI": "10.1038/s44319-026-00855-9",
"PMID": "42458070",
"PMCID": "PMC13503836",
"ISSN": "1469-221X",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s44319-026-00855-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
15
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41586-026-10512-9 [code]
Astrocyte glucocorticoid receptor signalling restricts neuronal plasticity.
Journal: Nature
In common: circlize, ComplexHeatmap, Seurat, 4 other tools, mouse, cellular / molecular, 6 references
[2] doi:10.1101/gr.281113.125 [code]
Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.
Journal: Genome research
In common: circlize, ComplexHeatmap, Seurat, 5 other tools, mouse, cellular / molecular, 3 references
[3] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: circlize, ComplexHeatmap, Seurat, 5 other tools, cellular / molecular, 2 references
[4] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: circlize, Seurat, cowplot, 4 other tools, mouse, cellular / molecular, 3 references
[5] doi:10.1016/j.celrep.2026.117073 [code]
Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging.
Journal: Cell reports
In common: Seurat, cowplot, reshape2, 3 other tools, mouse, cellular / molecular, 4 references
[6] doi:10.1038/s41380-026-03629-w [code]
Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes.
Journal: Molecular psychiatry
In common: circlize, ComplexHeatmap, Seurat, 5 other tools, mouse, cellular / molecular, 1 reference
[7] doi:10.1093/nar/gkag294 [code]
Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila.
Journal: Nucleic acids research
In common: cowplot, reshape2, patchwork, 2 other tools, 6 references
[8] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: circlize, ComplexHeatmap, Seurat, 5 other tools, mouse, 1 reference
[9] doi:10.1038/s41593-026-02367-0 [code]
A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.
Journal: Nature neuroscience
In common: circlize, ComplexHeatmap, Seurat, 5 other tools, cellular / molecular, 1 reference
[10] doi:10.1002/ejp.70277 [code]
Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons.
Journal: European journal of pain (London, England)
In common: circlize, ComplexHeatmap, cowplot, 4 other tools, mouse, cellular / molecular, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.