The Cxcl14 chemokine defines pioneer axon guidance and early circuit assembly in the inner ear.

Code ↔ Paper

2 matches 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 2 matches

[1] § Results › Pioneer SAG neurons share a transcriptional profile with aLL progenitors ↔ WT_14_18_22_30_48hpf_SAG.Rmd, lines 431 to 467 · score 0.83 · fstl1a, pou2f2a.1, irx1b, greb1l, irx1a, itga5 · Report a mismatch
[2] § Materials and methods › Single-cell RNA sequencing ↔ WT_14_18_22_30_48hpf_SAG.Rmd, lines 343 to 428 · score 0.73 · hoxb5b, FindAllMarkers, identity, logfc, threshold, Gene · Report a mismatch

Paper

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The authors' code

R Markdown · 478 lines · 14 KB · no license · 2 matches

---
title: "nechiporuk_10x_analysis"
author: "avn"
date: "9/16/2025 - 2/16/2026"
output:
html_document:
toc: yes
toc_depth: 2
toc_float: yes
toc_collapsed: yes
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, fig.align = 'center', out.width = '90%')
```
<style>
body .main-container {
max-width: 2160px;
}
</style>
# Set up
## Load libraries
```{r}
library(tidyverse)
library(Seurat)
library(patchwork)
library(MAST)
library(ggrepel)
library(RColorBrewer)
```
## Load data
```{r}
#download data sets from NCBI and rename each folder as follows
# "WT_14h" = "GSM8216056",
# "WT_18h" = "GSM8997253",
# "WT_22h" = "GSM8222579",
# "WT30h_1" = "GSM8244692",
# "WT30h_2" = "GSM7708237",
# "WT48h_1" = "GSM8222580"
#14h data set
data_folder1 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT_14h"
WT14_data <- Read10X(data.dir = data_folder1)
WT14 <- CreateSeuratObject(counts = WT14_data,
project = "WT14", min.cells = 3, min.features = 200)
#18h data set
data_folder2 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT_18h"
WT18_data <- Read10X(data.dir = data_folder2)
WT18 <- CreateSeuratObject(counts = WT18_data,
project = "WT18", min.cells = 3, min.features = 200)
# #22h data set
data_folder2 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT_22h"
WT22_data <- Read10X(data.dir = data_folder2)
WT22 <- CreateSeuratObject(counts = WT22_data,
project = "WT22", min.cells = 3, min.features = 200)
# #30h data set#1
data_folder3 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT30h_1"
WT_3_data <- Read10X(data.dir = data_folder3)
WT30_1 <- CreateSeuratObject(counts = WT_3_data,
project = "WT30_1", min.cells = 3, min.features = 200)
# #30h data set#2
data_folder4 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT30h_2"
WT_4_data <- Read10X(data.dir = data_folder4)
WT30_2 <- CreateSeuratObject(counts = WT_4_data,
project = "WT30_2", min.cells = 3, min.features = 200)
#48h data set
data_folder5 <- "/Users/nechipor/R_scripts/WT_14_18_22_30_48hpf/WT48h_1"
WT_5_data <- Read10X(data.dir = data_folder5)
WT48 <- CreateSeuratObject(counts = WT_5_data,
project = "WT48_1", min.cells = 3, min.features = 200)
```
```{r}
data.combined <- merge(x = WT14, y = c(WT18, WT22, WT30_1, WT30_2, WT48), add.cell.ids = c("WT14", "WT18", "WT22", "WT30_1", "WT30_2", "WT48"), project = "data.combined")
data.combined
data.combined[['condition']] <- str_split([email hidden]$orig.ident, pattern = '_', simplify = TRUE)[,1]
head(colnames(data.combined))
table(data.combined$orig.ident)
```
# 'Standard' Seurat analysis
## Assign mitochondrial percent
*NOTE (NLC):* zebrafish mitochondrial genes have a lowercase prefix, using the regex pattern '^mt-' appeared to work, however only 13 mitchondrial genes are present.
```{r}
data.combined[["percent.mt"]] <- PercentageFeatureSet(data.combined, pattern = "^mt-|^COX3$")
# Visualize QC metrics as a violin plot
VlnPlot(data.combined, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0)
```
## QC visualization
*Note:* FeatureScatter is typically used to visualize feature-feature relationships, but can be used for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
*Note:* zebrafish mitochondrial genes have a lowercase prefix,
Run this chunk to manually set cutoffs:
```{r, eval = TRUE}
nCount_RNA_limits <- c(1e3, 7.5e4)
nFeature_RNA_limits <- c(1900, 9000)
percent_mt_limits <- c(0, 5)
```
```{r}
FeatureScatter(data.combined, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident")+
geom_vline(xintercept = nCount_RNA_limits, linetype = 'dashed', size = 1.5)+
geom_hline(yintercept = percent_mt_limits, linetype = 'dashed', size = 1.5)
FeatureScatter(data.combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident")+
geom_vline(xintercept = nCount_RNA_limits, linetype = 'dashed', size = 1.5)+
geom_hline(yintercept = nFeature_RNA_limits, linetype = 'dashed', size = 1.5)
data.combined_metadata <- [email hidden]
ggplot(data.combined_metadata, aes(x = nCount_RNA, y = nFeature_RNA))+
geom_point(alpha = 0.1)+
geom_density2d()+
geom_vline(xintercept = nCount_RNA_limits, linetype = 'dashed', size = 1.5)+
geom_hline(yintercept = nFeature_RNA_limits, linetype = 'dashed', size = 1.5)+
scale_x_log10()
ggplot(data.combined_metadata, aes(x = nCount_RNA/nFeature_RNA, y = nFeature_RNA))+
geom_point(alpha = 0.1)+
geom_density2d()+
geom_hline(yintercept = nFeature_RNA_limits, linetype = 'dashed', size = 1.5)
```
## Subset data
```{r}
#Display counts before pruning
print('Cell count before pruning:')
table([email hidden]$orig.ident)
data.combined <- subset(data.combined, subset = nFeature_RNA > min(nFeature_RNA_limits) & nFeature_RNA < max(nFeature_RNA_limits) & nCount_RNA > min(nCount_RNA_limits) & nCount_RNA < max(nCount_RNA_limits) & percent.mt > min(percent_mt_limits) & percent.mt < max(percent_mt_limits))
#Display counts after pruning
print('Cell count after pruning:')
table([email hidden]$orig.ident)
```
## Ancor and normalize data
```{r}
data.combined.list <- SplitObject(data.combined, split.by = "orig.ident")
data.combined.list <- lapply(X = data.combined.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
anchors <- FindIntegrationAnchors(object.list = data.combined.list, dims = 1:25)
data.combined <- IntegrateData(anchorset = anchors, dims = 1:25)
#saveRDS(data.combined, file = "outputs/14_18_22_30_48h.rds")
```
## Identify variable features and scale data
```{r}
# Run the standard workflow for visualization and clustering
#data.combined <- readRDS("outputs/14_18_22_30_48h.rds")
DefaultAssay(data.combined) <- "integrated"
data.combined <- ScaleData(data.combined, features = row.names(data.combined), verbose = FALSE)
data.combined <- RunPCA(data.combined, npcs = 50, verbose = FALSE)
DimPlot(data.combined, reduction = 'pca')
ElbowPlot(data.combined, ndims = 50)+
geom_vline(xintercept = 30.5)
# t-SNE and Clustering
data.combined <- RunUMAP(data.combined, reduction = "pca", dims = 1:40)
data.combined <- FindNeighbors(data.combined, reduction = "pca", dims = 1:40)
data.combined <- FindClusters(data.combined, resolution = 2)
plot_14_48h <- DimPlot(data.combined, reduction = 'umap', label = T) + NoLegend()+
coord_equal()
ggsave("outputs/plot_14_48h.jpeg", plot_14_48h, dpi = 600)
DimPlot(data.combined, reduction = 'umap', group.by = 'orig.ident')+
coord_equal()
```
## color blind friendly UMAP
```{r}
my_colors <- c("5c5cc2",
"#a6bc3a",
"#733fa1",
"#59c66f",
"#b43996",
"#7cb041",
"#847deb",
"#c8aa34",
"#482070",
"#4cbe84",
"#d370d1",
"#4d7f29",
"#de4782",
"#43c29e",
"#9c1a54",
"#36dee6",
"#902a0e",
'deepskyblue2',
"#d28826",
"#353f8d",
"#9ca03c",
"#81438d",
"#7cbf76",
"#6d195d",
'darkmagenta',
"#851657",
"#3a8142",
"#e66bab",
"#255719",
"#c08adc",
"#dcae57",
"#5963ad",
"#cb692b",
"#7199e0",
"#d5533b",
"#db81c4",
"#a67724",
"#9b4486",
"#7b6a24",
"#b23679",
"#cca361",
"#6b1b4b",
"#dc8e5a",
"#6f193d",
"#d16a43",
"#a14e7c",
"#8d4d17",
"#e480a8",
"#701e0f",
"#eb5573",
"#74171f",
"#e5836a",
"#72182e",
"#dd4e4f",
"#b1486e",
"#a3553a",
"#ad264f",
"#dd7a87",
"#a73830",
"#d55b75",
"#9d323b",
"#df6f70",
"#a34656",
"#c4394f")
plot_14_48h <- DimPlot(data.combined, reduction = 'umap', label = F, pt.size = 0.1, cols = my_colors) + NoLegend()
ggsave("outputs/plot_14_48h.jpeg", plot_14_48h, dpi = 600, width = 210,
height = 140, units = "mm")
```
# Map cranial sensory neurons
```{r}
DefaultAssay(data.combined) <- "RNA"
FeaturePlot(data.combined, features = c('irx1b', 'irx1a'), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("phox2bb",
"phox2a"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("neurod4",
"pax2a"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("isl2b",
"onecut1"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("ngfrb",
"ets1"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("uchl1",
"gng13b"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("kitb",
"cart3"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("vegfd",
"trarg1b"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
FeaturePlot(data.combined, features = c("pvalb5",
"dlx4b"), min.cutoff = 0,
max.cutoff = NA, order = TRUE)+
coord_equal()
```
# Analysis of clusters
```{r}
# all_markers <- FindAllMarkers(data.combined, only.pos = T, logfc.threshold = 0.2, min.diff.pct = 0.2)
# write_csv(all_markers, "outputs/all_markers")
```
## 'Automatic' subset and analysis a particular cluster
```{r}
#Set the subset cluster(s) that you're interested in
subset_cluster_id <- c(5,17,24)
DefaultAssay(data.combined) <- "integrated"
##
##The code below is designed to run automatically once the subset ids are selected above
##
print('Running automated ~analysis~ on the following clusters:')
print(subset_cluster_id)
#Pull the subset
data.combined_subset <- subset(data.combined, subset = seurat_clusters %in% subset_cluster_id)
#stash the original cluster identities
[email hidden]$original_seurat_clusters <- [email hidden]$seurat_clusters
#Perform relatively hands-free analysis of the selected clusters
data.combined_subset <- FindVariableFeatures(data.combined_subset)
data.combined_subset <- RunPCA(data.combined_subset)
ElbowPlot(data.combined_subset)
data.combined_subset <- FindNeighbors(data.combined_subset, dims = 1:10)
data.combined_subset <- FindClusters(data.combined_subset, resolution = 0.2)
data.combined_subset <- RunUMAP(data.combined_subset, dims = 1:10)
DimPlot(data.combined_subset, label = TRUE, label.size = 10, repel = 0.1, group.by = 'seurat_clusters')+
ggtitle('New clusters')
DimPlot(data.combined_subset, label = TRUE, label.size = 10, repel= 0.1, group.by = 'original_seurat_clusters')+
ggtitle('Original cluster identities')
my_colors <- c("#847deb",
"#c8aa34",
"#482070",
"#4cbe84",
"#d370d1",
"#4d7f29")
pLL_aLL_plot <- DimPlot(data.combined_subset, label = F, label.size = 10, pt.size = .5, repel = 0.1, group.by = 'seurat_clusters', cols = my_colors)
ggsave("outputs/pLL_aLL_plot.jpeg", pLL_aLL_plot, dpi = 600, width = 100,
height = 67, units = "mm")
#Identify anterior and posterior lateral line clusters
Idents(data.combined_subset) <- "seurat_clusters"
LL_markers <- FindAllMarkers(data.combined_subset, only.pos = T, logfc.threshold = 0.2, min.diff.pct = 0.2)
print (LL_markers %>% filter(cluster == 0) %>%
select(gene) %>% slice_head(n = 10), row.names = FALSE)
#select DE markers for dotplot
feat_list <- list(aLL_prog = c("uacab","greb1l","shisal1a","dchs1a"),
late_pLL_prog = c("hoxb5b","crabp1b","meis2a.1","hoxb1b"),
pLL_neurons = c("rab6ba","prph","nacad","ntrk3a"),
prolif_prog = c("her6","boc","ccnd2a", "pcna"),
early_pLL_prog = c("dla","b4galnt3b","cdh1","neurod4"),
aLL_neurons = c("ptgir","b3gat2","alcama","slc1a2b")
)
markers <- unique(unlist(feat_list))
markers <- markers[markers %in% rownames(data.combined_subset)]
Idents(data.combined_subset) <- "seurat_clusters"
all_LL_dotplot <- DotPlot(
data.combined_subset,
features = markers,
group.by = "seurat_clusters",
cols="RdBu",
assay = DefaultAssay(data.combined_subset)
) +
labs(x = NULL, y = NULL) +
guides(color = guide_colorbar(title = NULL), size = guide_legend(title = NULL)) +
theme_bw() +
theme(
plot.title = element_blank(),
strip.background = element_blank(),
strip.text = element_blank()
)+
RotatedAxis()+ theme(axis.text.x = element_text(size = 18))
ggsave("outputs/all_LL_dotplot.jpeg", all_LL_dotplot, dpi = 600, width = 300,
height = 100, units = "mm")
```
```{r}
#plot 18hpf
Idents(data.combined_subset) <- "condition"
cell_values <- c("WT18")
data.combined_subset_18h <- subset(x = data.combined_subset, idents = cell_values, subset = seurat_clusters != 5, invert = FALSE)
#DE analysis for aLL at 18 hpf
Idents(data.combined_subset_18h) <- "seurat_clusters"
aLL_markers <- FindAllMarkers(data.combined_subset_18h, only.pos = T, logfc.threshold = 0.2, min.diff.pct = 0.2)
#write.csv(aLL_markers, "outputs/aLL_markers.csv", row.names = TRUE)
#dot plot top markers
DefaultAssay(data.combined_subset_18h) <- "RNA"
ear_markers <- c("fstl1a", "greb1l", "irx1a", "irx1b", "itga5", "pou2f2a.1")
dotplot_18h <- DotPlot(data.combined_subset_18h, features = ear_markers, dot.scale=10, cols="RdBu")+
labs(x = NULL, y = NULL) +
guides(color = guide_colorbar(title = NULL), size = guide_legend(title = NULL)) +
theme_bw() +
theme(
plot.title = element_blank(),
strip.background = element_blank(),
strip.text = element_blank()
)+
RotatedAxis()+ theme(axis.text.x = element_text(size = 20))
ggsave("outputs/dotplot_18h.jpeg", dotplot_18h, dpi = 600, width = 200,
height = 100, units = "mm")
#plot irx1a/b
DefaultAssay(data.combined_subset_18h) <- "RNA"
irx1 <- FeaturePlot(data.combined_subset_18h, features = c('irx1a', 'irx1b'), min.cutoff = 0.1, pt.size = 1,
max.cutoff = NA, order = TRUE)
ggsave("outputs/irx1.jpeg", irx1, dpi = 600, width = 300,
height = 100, units = "mm")
```
# SessionInfo
```{r}
sessionInfo()
```

WT_14_18_22_30_48hpf_SAG.Rmd at commit f2c8b7a, no license · at the source

Overview

Authors: Mireia Rumbo¹, Aitor Bañón¹, Alex V Nechiporuk², Berta Alsina¹

ORCID iDs: Mireia Rumbo, Aitor Bañón, Alex V Nechiporuk, Berta Alsina

Department of Medicine and Life Sciences, Universitat Pompeu Fabra-Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA

Journal: The Journal of cell biology, volume 226, issue 2, article e202604185

Dates: received 27 May 2026; accepted 21 October 2026; published online 11 November 2026; in print February 2027

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1083/jcb.202604185 · PMID 43012745 · PMCID PMC13753874

Status: code verified (how the code is found and verified)

Categories: mouse (organism)

Methods: Statistics, Evoked potentials, fMRI & imaging

MeSH: Axon Guidance*, Axons*, Chemokines, CXC*, Ear, Inner*, Animals, Cell Movement, Mice (* major topic)

Funding: Agencia Estatal de Investigación (CEX2018-000792-M, PRE2021-097774); Universitat Pompeu Fabra; NIH HHS (NS143805); NINDS NIH HHS; Ministerio de Ciencia e Innovación (PID2023-146798NB-I00, PID2020-117662GB-I00); Spanish Ministerio de Universidades (FPU17/03287)

Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

The nervous system wiring requires the precise coordination of axon guidance, neuronal migration, and target cell recognition. Here, we show that inner ear circuit formation relies on pioneer cells extending an axonal scaffold that selectively targets early-born hair cells. High spatiotemporal imaging of pioneer axons reveals how they navigate through the cranial environment, establishing dynamic cell–cell contacts with other axons to finally stabilize in target cells. These pioneer axons are required not only for follower axon growth but also for coordinated migration of follower neurons, revealing a cellular hierarchy underlying circuit assembly. We identify the chemokine Cxcl14 as a novel instructive guidance cue regulating pioneer axon extension, turning, and fasciculation at discrete cellular decision points. Loss of Cxcl14 disrupts axonal navigation, compromises synaptic organization in hair cells, and impairs mechanosensory-based behavior. Together, our findings establish a new chemokine-based mechanism linking pioneer axon guidance to early sensory circuit assembly necessary for building mechanosensory networks.

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 2 matches between paragraphs and lines of code.

anechipor/Rumbo_et_al_2026

License: none: the authors keep all their rights

State: the link answers, verified on 12 November 2026

Evidence: files inventoried

Commit: f2c8b7aaa182c59a0689c69694c8759978609b28, 17 February 2026

Languages: R (1)

Size: 1 file, 1 script

Software Heritage: not archived

Found in: the text, “Single-cell RNA sequencing”

Holds: 1 notebook

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: patchwork (1 file), Seurat (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 12 November 2026: the link answers

12 November 2026: the link answers

1 file

WT_14_18_22_30_48hpf_SAG.Rmd, R, 478 lines, 2 matches

At the source: github.com/anechipor/Rumbo_et_al_2026

The paper's code and data availability statement is in the Data section.

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;
1 script, each with its path and the digest of its content;
2 matches 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

geo:GSE266312, at NCBI GEO; found in the text, “Single-cell RNA sequencing”

Data availability

The data supporting the findings of this study are available within the published article and its online supplementary material. The data underlying Fig. 1 are openly available in GitHub at https://github.com/anechipor/Rumbo_et_al_2026.

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, 12 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 MeSH terms, 6 funders, 66 references.

Cite

This paper

Rumbo, M., Bañón, A., Nechiporuk, A. V., & Alsina, B. (2026). The Cxcl14 chemokine defines pioneer axon guidance and early circuit assembly in the inner ear. The Journal of cell biology, 226(2), e202604185. https://doi.org/10.1083/jcb.202604185

BibTeX

@article{rumbo2026cxcl14, author = {Rumbo, Mireia and Bañón, Aitor and Nechiporuk, Alex V and Alsina, Berta}, title = {{The Cxcl14 chemokine defines pioneer axon guidance and early circuit assembly in the inner ear}}, journal = {The Journal of cell biology}, year = {2026}, month = nov, volume = {226}, number = {2}, pages = {e202604185}, publisher = {The Rockefeller University Press}, issn = {0021-9525}, doi = {10.1083/jcb.202604185}, url = {https://doi.org/10.1083/jcb.202604185}, pmid = {43012745}, pmcid = {PMC13753874} }

RIS

TY - JOUR AU - Rumbo, Mireia AU - Bañón, Aitor AU - Nechiporuk, Alex V AU - Alsina, Berta TI - The Cxcl14 chemokine defines pioneer axon guidance and early circuit assembly in the inner ear T2 - The Journal of cell biology J2 - J Cell Biol PY - 2026 DA - 2026/11/11 VL - 226 IS - 2 SP - e202604185 SN - 0021-9525 PB - The Rockefeller University Press DO - 10.1083/jcb.202604185 UR - https://doi.org/10.1083/jcb.202604185 LA - en ER -

CSL-JSON

{ "id": "10.1083/jcb.202604185", "type": "article-journal", "title": "The Cxcl14 chemokine defines pioneer axon guidance and early circuit assembly in the inner ear", "container-title": "The Journal of cell biology", "author": [ { "family": "Rumbo", "given": "Mireia" }, { "family": "Bañón", "given": "Aitor" }, { "family": "Nechiporuk", "given": "Alex V" }, { "family": "Alsina", "given": "Berta" } ], "container-title-short": "J Cell Biol", "volume": "226", "issue": "2", "page": "e202604185", "DOI": "10.1083/jcb.202604185", "PMID": "43012745", "PMCID": "PMC13753874", "ISSN": "0021-9525", "publisher": "The Rockefeller University Press", "URL": "https://doi.org/10.1083/jcb.202604185", "language": "en", "issued": { "date-parts": [ [ 2026, 11, 11 ] ] } }

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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.

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If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

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reStructuredText

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   :alt: OSCR

.. oscr-badge:end

HTML

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On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

anechipor/Rumbo_et_al_2026 on GitHub (add a README there)

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

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Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

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Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

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