OSCR

Brain idiosyncrasy during biological-motion perception is amplified in autistic individuals with intellectual impairment.

Code ↔ Paper

5 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 5 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Brain behaviour correlations ↔ ScatterplotGAM.Rmd, lines 6 to 32 · score 0.69 · SNAP IV, VABS ABC, GAMs, score, CSS, GEC · Report a mismatch
  2. [2] § Methods › Idiosyncrasy ↔ CalculateVariability.R, the whole file · a weak match · score 0.67 · correlational distance, TDC reference, vector, ROI, metrics, variability · Report a mismatch
  3. [3] § Results › Transdiagnostic correlations between VariabilityWhole and cognitive/behavioural traits ↔ ScatterplotGAM.Rmd, lines 6 to 32 · score 0.58 · SNAP IV, VABS ABC, CSS, GEC, NVIQ, RBS · Report a mismatch
  4. [4] § Results › Transdiagnostic correlations between VariabilityWhole and cognitive/behavioural traits ↔ ScatterplotGAM.Rmd, lines 107 to 126 · score 0.58 · SNAP IV, VABS ABC, Scatterplots, fits, CSS, GEC · Report a mismatch
  5. [5] § Methods › Idiosyncrasy ↔ IdiosyncrasyUtilities.R, lines 24 to 45 · score 0.52 · correlational distance, ROI, matrix, metrics, diagnostic, ASC IA · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R Markdown · 159 lines · 6.1 KB · other · 3 matches

  1. ---
  2. ScatterplotGAM.Rmd
  3. This script is used to fit the generalized additive models (GAMs) and plot them with scatterplots. (Fig 4, Fig S3-7, Table S2, Table S4)
  4. ---
  6. ```{r}
  7. #loading data
  8. source('IdiosyncrasyUtilities.R')
  9. library(tibble)
  10. library(lsr)
  11. library(ggplot2)
  14. male.only <- FALSE #sensitivity analysis
  15. all.linear <- FALSE #should all behavioural regressors be treated as linear?
  18. #df.var.behav <- load.data("Schaefer200+Tian+Suit_spmT_0001_var_scores.csv", male.only)
  19. df.var.behav <- load.data('data/variability_behaviour.csv', male.only)
  22. var.type <- 'var.whole' #options: var.whole, var.within, var.tdc
  24. #psych.tests <- c('ADOS-2 CSS', 'NVIQ','VABS-ABC', 'SRS Total', 'RBS-R Total', 'SSP Total', 'BRIEF-GEC', 'SNAP-IV Total')
  25. psych.tests <- c('nviq','rbs.total','srs.t','ssp.total', 'ados', 'brief.gec', 'snap.t', 'vabs.total')
  28. test.labels <- c('ados'= 'ADOS-2 CSS', 'nviq' = 'NVIQ', 'vabs.total' = 'VABS-ABC', 'srs.t'= 'SRS Total', 'rbs.total' = 'RBS-R Total', 'ssp.total' = 'SSP Total', 'brief.gec' = 'BRIEF-GEC', 'snap.t' = 'SNAP-IV Total')
  29. var.labels <- c('var.whole' = 'Whole', 'var.within' = 'Within', 'var.tdc' = 'TDC')
  31. linear <- c('ados', 'snap.t', 'vabs.total', 'brief.gec')
  32. ```
  34. ```{r}
  35. #Statistical significance table (Table S2, S4)
  36. df.sig <- data.frame(behav.p = rep(NA, length(psych.tests)),
  37. age.p = rep(NA, length(psych.tests)),
  38. fd.p = rep(NA, length(psych.tests)))
  40. if (!male.only){
  41. add_column(df.sig, sex.p = rep(NA, length(psych.tests)), .after = 'age.p')
  43. }
  45. for (i in 1:length(psych.tests)){
  46. #define gam
  47. test <- psych.tests[i] #column name of psych test in df.var.behav
  48. current.data <- df.var.behav[!is.na(df.var.behav[test]),] #remove participants with missing values for current psych test
  50. current.gam <- fit.gam(test = test, linear = linear, male.only = male.only, current.data = current.data)
  53. if (test %in% linear){
  54. df.sig[i, 'behav.p'] <- summary(current.gam)$p.pv['get(test)']
  55. } else {
  56. df.sig[i, 'behav.p'] <- summary(current.gam)$s.table['s(get(test))', 'p-value']
  57. }
  59. df.sig[i, 'age.p'] <- summary(current.gam)$p.pv['age']
  60. if (!male.only){
  61. df.sig[i, 'sex.p'] <- summary(current.gam)$p.pv['sex1']
  62. }
  63. df.sig[i, 'fd.p'] <- summary(current.gam)$s.table['s(mean.fd)', 'p-value']
  64. df.sig[i, 'r2.adj'] <- summary(current.gam)$r.sq
  65. }
  67. n.vars <- 4 - male.only
  68. orig.vars <- colnames(df.sig)
  69. for (i in 1:n.vars){
  71. #T/F statistic
  72. if (orig.vars[i] == 'sex.p'){
  73. pred.name <- 'sex1'
  74. } else if (orig.vars[i] == 'fd.p'){
  75. pred.name <- 'mean.fd'
  76. } else{
  77. pred.name <- strsplit(orig.vars[i], '.', fixed = TRUE)[[1]][1]
  78. }
  80. tf.name <- paste(strsplit(orig.vars[i], '.', fixed = TRUE)[[1]][1], '.tf', sep = '')
  81. df.sig <- add_column(df.sig, '{tf.name}' := sapply(psych.tests, get.tf, linear = linear, var.type = var.type, predictor = pred.name, df = df.var.behav, male.only = male.only), .before = orig.vars[i])
  83. q.name <- paste(strsplit(orig.vars[i], '.', fixed = TRUE)[[1]][1], '.q', sep = '')
  84. df.sig <- add_column(df.sig, '{q.name}' := p.adjust(df.sig[,orig.vars[i]], method = 'BH'), .after = orig.vars[i])
  86. sig.name <- paste(strsplit(orig.vars[i], '.', fixed = TRUE)[[1]][1], '.sig', sep = '')
  87. df.sig <- add_column(df.sig, '{sig.name}' := df.sig[,q.name] <= 0.05, .after = q.name)
  88. }
  90. df.sig <- add_column(df.sig, test = psych.tests, .before = 'behav.tf')
  92. df.sig
  93. ```
  95. ```{r}
  96. #Write results to csv (Table S2, S4)
  98. if (male.only){
  99. csv.name <- paste('tables/table_s4/table_s4.csv')
  100. } else{
  101. csv.name <- paste('tables/table_s2/table_s2.csv')
  102. }
  104. write.csv(df.sig, csv.name, row.names = FALSE)
  105. ```
  107. ```{r}
  108. #Create single scatterplot
  110. test <- 'nviq' #options in test.labels
  111. current.data <- df.var.behav[!is.na(df.var.behav[test]),]
  112. current.gam <- fit.gam(test = test, linear = linear, male.only = male.only, current.data = current.data)
  114. #test.labels <- c('ados'= 'ADOS-2 CSS', 'nviq' = 'NVIQ', 'vabs.total' = 'VABS-ABC', 'srs.t'= 'SRS Total', 'rbs.total' = 'RBS-R Total', 'ssp.total' = 'SSP Total', 'brief.gec' = 'BRIEF-GEC', 'snap.t' = 'SNAP-IV Total')
  116. var.labels <- c('var.whole' = 'Whole', 'var.within' = 'Within', 'var.tdc' = 'TDC')
  118. group.colours <- c('TDC' = 'blue', 'ASC' = 'red')
  120. scatterplot <- fit.scatterplot(test = test, current.data = current.data, var.type = var.type, linear = linear, var.labels = var.labels, group.colours = group.colours, current.gam = current.gam)
  122. plot(scatterplot)
  124. #save as image
  125. #ggsave(filename = "name your plot", scatterplot, width=2400, height=1600, units = 'px', dpi=300)
  126. ```
  127. ```{r}
  128. group.colours <- c('TDC' = 'blue', 'ASC' = 'red')
  130. if (var.type == 'var.whole' & !all.linear){
  131. fig.num <- 'fig_4'
  132. } else if (var.type == 'var.within' & !all.linear){
  133. fig.num <- 'fig_s3'
  134. } else if (var.type == 'var.tdc' & !all.linear){
  135. fig.num <- 'fig_s4'
  136. } else if (var.type == 'var.whole' & all.linear){
  137. fig.num <- 'fig_s5'
  138. } else if (var.type == 'var.within' & all.linear){
  139. fig.num <- 'fig_s6'
  140. } else if (var.type == 'var.tdc' & all.linear){
  141. fig.num <- 'fig_s7'
  142. }
  144. for (test in names(test.labels)){
  145. current.data <- df.var.behav[!is.na(df.var.behav[test]),]
  146. if (!all.linear){
  147. current.gam <- fit.gam(test = test, linear = linear, male.only = male.only, current.data = current.data)
  148. scatterplot <- fit.scatterplot(test = test, current.data = current.data, var.type = var.type, linear = linear, var.labels = var.labels, group.colours = group.colours, current.gam = current.gam)
  149. } else{
  150. current.gam <- fit.gam(test = test, linear = psych.tests, male.only = male.only, current.data = current.data)
  151. scatterplot <- fit.scatterplot(test = test, current.data = current.data, var.type = var.type, linear = psych.tests, var.labels = var.labels, group.colours = group.colours, current.gam = current.gam) #all psych tests are linear
  152. }
  154. plot(scatterplot)
  155. scatterplot_path <- paste('figures/', fig.num, '/', test, '_scatterplot.tiff',sep = '')
  156. ggsave(filename = scatterplot_path, scatterplot, width=2400, height=1600, units = 'px', dpi=300)
  157. }
  158. ```

ScatterplotGAM.Rmd at commit e1b8523, under other · at the source

Overview

Authors: Michael Cheng1,2, Colin Hawco1,3,4, Daniel Yang5, Hsing-Chang Ni6,7, Chun-Hung Yeh6,8, Jung-Chi Chang9, En-Nien Tu10,11, Mei-Yun Hsu12, Yu-Yu Wu12, Tai-Li Chou13, Susan Shur-Fen Gau9, Hsiang-Yuan Lin1,2,4
ORCID iDs: Chun-Hung Yeh, Hsiang-Yuan Lin
13 affiliations
  1. Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON Canada
  2. Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 1025 Queen St West – 3314, Toronto, ON M6J 1H4 Canada
  3. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON Canada
  4. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON Canada
  5. Independent Scholar (with George Washington University, USA While This Study Was Being Conducted), Washington D.C., USA
  6. Department of Psychiatry, Chang, Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
  7. College of Medicine, Chang Gung University, Taoyuan, Taiwan
  8. Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
  9. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
  10. Department of Psychiatry, University of Oxford, Oxford, UK
  11. Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
  12. YuNing Clinic, Taipei, Taiwan
  13. Department of Psychology, National Taiwan University, Taipei, Taiwan
Institutions: Centre for Addiction and Mental Health (Canada); University of Toronto (Canada); Campbell Family Mental Health Research Institute (Canada); George Washington University (United States); Chang Gung University (Taiwan); Chang Gung Memorial Hospital (Taiwan); Linkou Chang Gung Memorial Hospital (Taiwan); National Taiwan University (Taiwan); National Taiwan University Hospital (Taiwan); University of Oxford (United Kingdom); Keelung Chang Gung Memorial Hospital (Taiwan)
Journal: Journal of neurodevelopmental disorders, volume 18, issue 1, article 31
Dates: received 24 August 2025; accepted 25 February 2026; published online 25 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s11689-026-09683-3 · PMID 41882534 · PMCID PMC13217712 · OpenAlex W7140299285
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), other condition (population), autism (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: Autism spectrum condition, Intellectual impairment, Brain idiosyncrasy, Biological motion, FMRI, Heterogeneity, Social cognition
MeSH: Autism Spectrum Disorder*, Autistic Disorder*, Brain*, Intellectual Disability*, Motion Perception*, Adolescent, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Branch Out Neurological Foundation (Undergraduate Award); Department of Psychiatry, University of Toronto (Academic Scholar Award); Victoria College, University of Toronto (Dr Lorus J. Milne and Dr Margery J. Milne Award); Ministry of Science and Technology, Taiwan (105-2628-B-002-035-MY3)
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

Background: Neuroimaging research in autism spectrum condition (ASC) often overlooks brain idiosyncrasy by focusing on group averages and frequently excludes individuals with co-occurring intellectual impairment (II).

Methods: We investigated functional MRI correlates of passive biological motion (BM) perception, comparing typically developing controls (TDC; n = 33), autistic individuals without II (intellectually able; ASC-IA; n = 28), and autistic individuals with II (ASC-II; n = 19; defined by IQ or adaptive function < 85).

Results: While standard group-average analyses revealed the expected BM-sensitive regions (e.g., bilateral posterior superior temporal sulci, cuneus) in the TDC and ASC-IA groups, the ASC-II group showed no consistent group-level activation pattern and exhibited greater activation in the right intraparietal sulcus compared to the ASC-IA group. Using a correlational distance-based metric, we quantified brain idiosyncrasy (“whole-sample brain variability”, VariabilityWhole), representing the deviance of an individual's activation pattern from others. Brain activity in the ASC-II group was significantly more idiosyncratic than the ASC-IA and TDC groups. Furthermore, VariabilityWhole showed significant transdiagnostic correlations with multiple cognitive and behavioural domains relevant to autism, including social difficulties, repetitive behaviours, non-verbal IQ, executive function, sensory hyper/hyposensitivity, ADHD symptoms, and adaptive function.

Conclusions: Key limitations include the cross-sectional design and the use of a passive viewing task without a concurrent behavioral measure to directly link brain findings to task performance. These findings highlight substantial brain heterogeneity within the autism spectrum, particularly in the understudied ASC-II subgroup, and suggest that individual differences in brain processing patterns, rather than solely group-average differences, are critically linked to clinical and cognitive phenotypes.

Supplementary Information: The online version contains supplementary material available at 10.1186/s11689-026-09683-3.

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 5 matches between paragraphs and lines of code.

chichael-meng/Brain-idiosyncrasy-in-autism

License: other
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e1b8523bf1151729ee7a9561e931506866bf4069, 26 June 2026
Languages: R (6)
Size: 13 files, 6 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (3 files), tidyverse (3 files), ggpubr (1 file), mgcv (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/chichael-meng/Brain-idiosyncrasy-in-autism

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;
  • 6 scripts, each with its path and the digest of its content;
  • 5 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

No dataset and no data link were found in the paper.

Data availability

The data that support the findings of this study are available from the corresponding author, HYL, upon reasonable request. Public availability of the raw data is restricted by the Research Ethics Committee of the National Taiwan University Hospital in the interest of participant confidentiality. The analysis scripts (R code) used for measuring brain idiosyncrasy and performing statistical analyses are available at https://github.com/chichael-meng/Brain-idiosyncrasy-in-autism.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 7 keywords, 13 MeSH terms, 4 funders, 92 references.

Cite

This paper

Cheng, M., Hawco, C., Yang, D., Ni, H.-C., Yeh, C.-H., Chang, J.-C., Tu, E.-N., Hsu, M.-Y., Wu, Y.-Y., Chou, T.-L., Gau, S. S.-F., & Lin, H.-Y. (2026). Brain idiosyncrasy during biological-motion perception is amplified in autistic individuals with intellectual impairment. Journal of neurodevelopmental disorders, 18(1), 31. https://doi.org/10.1186/s11689-026-09683-3

BibTeX

@article{cheng2026brain,
author = {Cheng, Michael and Hawco, Colin and Yang, Daniel and Ni, Hsing-Chang and Yeh, Chun-Hung and Chang, Jung-Chi and Tu, En-Nien and Hsu, Mei-Yun and Wu, Yu-Yu and Chou, Tai-Li and Gau, Susan Shur-Fen and Lin, Hsiang-Yuan},
title = {{Brain idiosyncrasy during biological-motion perception is amplified in autistic individuals with intellectual impairment}},
journal = {Journal of neurodevelopmental disorders},
year = {2026},
month = mar,
volume = {18},
number = {1},
pages = {31},
publisher = {BMC},
issn = {1866-1947},
doi = {10.1186/s11689-026-09683-3},
url = {https://doi.org/10.1186/s11689-026-09683-3},
pmid = {41882534},
pmcid = {PMC13217712}
}

RIS

TY - JOUR
AU - Cheng, Michael
AU - Hawco, Colin
AU - Yang, Daniel
AU - Ni, Hsing-Chang
AU - Yeh, Chun-Hung
AU - Chang, Jung-Chi
AU - Tu, En-Nien
AU - Hsu, Mei-Yun
AU - Wu, Yu-Yu
AU - Chou, Tai-Li
AU - Gau, Susan Shur-Fen
AU - Lin, Hsiang-Yuan
TI - Brain idiosyncrasy during biological-motion perception is amplified in autistic individuals with intellectual impairment
T2 - Journal of neurodevelopmental disorders
J2 - J Neurodev Disord
PY - 2026
DA - 2026/03/25
VL - 18
IS - 1
SP - 31
SN - 1866-1947
PB - BMC
DO - 10.1186/s11689-026-09683-3
UR - https://doi.org/10.1186/s11689-026-09683-3
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s11689-026-09683-3",
"type": "article-journal",
"title": "Brain idiosyncrasy during biological-motion perception is amplified in autistic individuals with intellectual impairment",
"container-title": "Journal of neurodevelopmental disorders",
"author": [
{
"family": "Cheng",
"given": "Michael"
},
{
"family": "Hawco",
"given": "Colin"
},
{
"family": "Yang",
"given": "Daniel"
},
{
"family": "Ni",
"given": "Hsing-Chang"
},
{
"family": "Yeh",
"given": "Chun-Hung"
},
{
"family": "Chang",
"given": "Jung-Chi"
},
{
"family": "Tu",
"given": "En-Nien"
},
{
"family": "Hsu",
"given": "Mei-Yun"
},
{
"family": "Wu",
"given": "Yu-Yu"
},
{
"family": "Chou",
"given": "Tai-Li"
},
{
"family": "Gau",
"given": "Susan Shur-Fen"
},
{
"family": "Lin",
"given": "Hsiang-Yuan"
}
],
"container-title-short": "J Neurodev Disord",
"volume": "18",
"issue": "1",
"page": "31",
"DOI": "10.1186/s11689-026-09683-3",
"PMID": "41882534",
"PMCID": "PMC13217712",
"ISSN": "1866-1947",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s11689-026-09683-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
25
]
]
}
}

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:10.1186/s13229-024-00614-4
Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography.
Journal: Molecular autism
In common: autism, cognitive, 13 references
[2] doi:10.1186/s13229-022-00517-2 [code]
Atypical gaze patterns in autistic adults are heterogeneous across but reliable within individuals.
Journal: Molecular autism
In common: autism, 10 references
[3] doi:10.1186/s13229-022-00512-7 [code]
Attentional influences on neural processing of biological motion in typically developing children and those on the autism spectrum.
Journal: Molecular autism
In common: autism, 9 references
[4] doi:10.1162/imag_a_00048 [code]
Incidental visual processing of spatiotemporal cues in communicative interactions: An fMRI investigation.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, cognitive, 7 references
[5] doi:10.1186/s13229-026-00707-2 [code]
Reduced inter-subject functional connectivity during movies in autism: replicability across cross-national fMRI datasets.
Journal: Molecular autism
In common: autism, fMRI, 7 references
[6] doi:10.1002/hbm.70052 [code]
Neural Mechanisms of Social Interaction Perception: Observing Interpersonal Synchrony Modulates Action Observation Network Activation and Is Spared in Autism.
Journal: Human brain mapping
In common: ggpubr, ggplot2, tidyverse, autism, fMRI, cognitive, 4 references
[7] doi:10.7554/elife.56257 [code]
Functional connectivity subtypes associate robustly with ASD diagnosis.
Journal: eLife
In common: autism, fMRI, other condition, 7 references
[8] doi:10.1038/s41593-026-02287-z [code]
Autism subtypes identified using cross-species functional connectivity analyses.
Journal: Nature neuroscience
In common: ggpubr, ggplot2, tidyverse, autism, fMRI, 5 references
[9] doi:10.1016/j.bpsgos.2026.100760
Prediction and Prediction Error in Autism: A Meta-Analysis of Functional Magnetic Resonance Imaging Results.
Journal: Biological psychiatry global open science
In common: autism, fMRI, cognitive, 5 references
[10] doi:10.1503/jpn.230135 [code]
Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.
Journal: Journal of psychiatry & neuroscience : JPN
In common: ggpubr, ggplot2, tidyverse, fMRI, 4 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.

Add the badge to the code's README

OSCR: the badge of this paper

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
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3.svg)](https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3.svg
   :target: https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3"><img src="https://openscicode.org/badge/doi/10.1186/s11689-026-09683-3.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

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

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.

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: 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.

Report a reproduction failure (signed in)

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.