OSCR

Visual information is broadcast among cortical areas in discrete channels.

Code ↔ Paper

4 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 4 matches
  1. [1] § Results › Functional connectivity is stable across stimuli ↔ NSC_compare.m, lines 274 to 342 · score 0.62 · linear predictor, error bars, naturalistic video, Variance explained, model, grating · Report a mismatch
  2. [2] § Materials and methods › Decompose neural activity ↔ FR_pupilAna.m, lines 37 to 161 · score 0.58 · pupil dynamics, ordinary regression, prediction, video, model, variance · Report a mismatch
  3. [3] § Materials and methods › Decompose neural activity ↔ FR_pupilAna.m, lines 37 to 161 · score 0.58 · pupil dynamics, residual population, subtracting, regression, model, stimulus · Report a mismatch
  4. [4] § Results › Tuning similarity is a major factor in the V1-HVA functional network ↔ NSCmean_vsDist.m, lines 354 to 452 · score 0.55 · error bar, neuron distance, signal correlation, FR, V1 · 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

MATLAB · 263 lines · 11 KB · GPL-3.0 · 2 matches

  1. load('pooldata_SinGrat.mat', 'pooldata','name1','areaID')
  2. load('pooldata_pupiltrack.mat')
  3. Dir = [0, 135, 270, 45, 180, 315, 90, 225];
  4. [DirSort, DirInd] = sort(Dir);
  5. stimPara = ...
  6. [8 0.16;
  7. 2 0.02;
  8. 8 0.02;
  9. 1 0.02;
  10. 1 0.16;
  11. 2 0.16;
  12. 8 0.04;
  13. 2 0.04;
  14. 1 0.04];
  15. stimflag = [repmat(Dir',9,1)/360*2*pi, reshape(repmat(stimPara(:,1),1,8)',[],1),...
  16. reshape(repmat(stimPara(:,2),1,8)',[],1)];
  17. [stimsort, stimInd] = sortrows(stimPara, [1 2]);
  18. TmFreq = [1,2,8];
  19. SpFreq = [0.02, 0.04, 0.16];
  20. % TmFreq = stimsort(:,1);
  21. % SpFreq = stimsort(:,2);
  22. Twindow = [0 2 4 6 8 10 12 14 16]; % s
  23. delay = 0;
  24. DirSortVect = [0 1; -sqrt(2)/2 sqrt(2)/2; -1 0;-sqrt(2)/2 -sqrt(2)/2; 0 -1;sqrt(2)/2 -sqrt(2)/2; 1 0;sqrt(2)/2 sqrt(2)/2];
  26. %%
  27. Npc = [];
  28. for k1 = 1:length(poolpupildata)
  29. if ~isempty(poolpupildata(k1).pupildata)
  30. pupildata = poolpupildata(k1).pupildata;
  31. if isfield(pupildata, 'pupilsync')
  32. Npc(k1,1) = pupildata.video_PCdim;
  33. Npc(k1,2) = pupildata.video_PCvar;
  34. end
  35. end
  36. end
  37. %%
  38. binWidth = 100;
  39. smoothSD = 2;
  40. popResp_var = [];
  41. for k1 = 1:length(poolpupildata)
  42. if ~isempty(poolpupildata(k1).pupildata)
  43. pupildata = poolpupildata(k1).pupildata;
  44. if isfield(pupildata, 'pupilsync')
  45. pupilsync = pupildata.pupilsync;
  46. sync2p = table2array(poolpupildata(k1).sync2p);
  48. l0 = find(pupilsync(:,3)==1,1);
  49. l1 = find(pupilsync(:,3)==1,1,'last');
  50. pupildyn = [pupildata.eyearea(l0:l1)',pupildata.eyecenter(l0:l1,:),pupildata.videoscore];
  51. pupildyn(pupildyn(:,1)==0,1:3) = nan;
  52. pupilPC(k1,1:2) = [pupildata.video_PCdim, pupildata.video_PCvar];
  53. pupildyn_t = pupilsync(l0:l1,1:2);
  54. pupildyn_t = bsxfun(@minus, pupildyn_t, pupildyn_t(1,:))+1;
  55. pupildyn_match2p = [];
  56. for j = 1:size(pupildyn,2)
  57. tmp = interp1(pupildyn_t(:,1)/pupildyn_t(end,1), pupildyn(:,j), sync2p(:,1)/sync2p(end,1));
  58. pupildyn_match2p = cat(2, pupildyn_match2p, tmp);
  59. end
  61. imgPara = pooldata(k1,1).imgPara;
  62. % pupil dynamics bined per direction (2 sec window)
  63. pupildyn_bin = [];
  64. for i = 1:imgPara.stimrep
  65. for j = 1:imgPara.stim_type
  66. tmp = pupildyn_match2p(sync2p(:,3)==i & sync2p(:,5)==j,:);
  67. tmp1 = imresize(tmp, [8 size(tmp,2)]);
  68. pupildyn_bin = cat(1, pupildyn_bin, tmp1);
  69. end
  70. end
  71. X_pupildyn = pupildyn_bin;
  72. X_stim = repmat(stimflag, imgPara.stimrep, 1);
  73. for k2 = 1:4
  74. if ~isempty(pooldata(k1,k2).spiketrain) && length(pooldata(k1,k2).goodcell) > 5
  75. goodcell = pooldata(k1,k2).goodcell;
  76. spiketrain = pooldata(k1,k2).spiketrain(goodcell);
  77. num(k1,k2) = length(goodcell);
  78. spikecountAve = []; h = []; spikecountraw1 = [];
  79. spikecountraw2 = []; snr = [];
  80. for n = 1:length(goodcell)
  81. tmphist = [];
  82. stvec = [];
  83. for i = 1:imgPara.stimrep
  84. st1 = [];
  85. for j = 1:imgPara.stim_type
  86. st = spiketrain(n).st{i,j}*imgPara.dt;
  87. st(st<0) = [];
  88. st(st>imgPara.stim_time) = [];
  89. st1 = [st1, st+imgPara.stim_time*(j-1)];
  90. tmphist1 = histcounts(st, Twindow);
  91. tmphist = cat(1, tmphist, tmphist1');
  92. end
  93. stvec = vectCat(stvec, st1');
  94. end
  95. [rate , rateT] = myPSTH(stvec*1000, [0, imgPara.stim_time*imgPara.stim_type]*1000, binWidth, smoothSD);
  96. spikecountraw1(n,:) = tmphist;
  97. spikecountAve(n,:) = rate;
  98. end
  100. % total variance of population response
  101. Y = spikecountraw1';
  102. Y = bsxfun(@minus, Y, mean(Y,1));
  103. Y_totvar = var(Y(:));
  105. % variance explained by stimulus induced response
  106. Ym = mean(reshape(Y, 72,imgPara.stimrep,size(Y,2)), 2);
  107. Ym_var = var(Ym(:));
  108. Y_stimexp = Ym_var/Y_totvar;
  110. % residual population response after subtracting stimulus
  111. % induced portion (estimated by trial averaged response)
  112. Y = bsxfun(@minus, reshape(Y, 72,imgPara.stimrep,size(Y,2)), Ym);
  113. Y = reshape(Y,[],size(Y,3));
  114. Y_resvar = var(Y(:));
  116. % ordinary regression variance explained by pupil dynamics
  117. X = X_pupildyn;
  118. X(isnan(X)) = 0;
  119. % Bols = inv(X'*X)*X'*Y;
  120. % Yhat2 = X*Bols;
  121. % e = mean((Yhat2-Y).^2);
  122. % Y_pupildynexp_OLS = 1-mean(e./var(Y));
  124. e = []; d = [];
  125. for k = 1:size(X,2)
  126. [Br, Bols, e(k), d(k)] = RRR_bySVD(X, Y, k);
  127. end
  128. % Y_pupildynexp_RRR = 1 - min(e);
  129. [~, k] = min(e);
  130. [Br, Bols, ~, ~] = RRR_bySVD(X, Y, k);
  131. Yhat2 = X * Br;
  132. Y_pupildynexp_RRR = var(Yhat2(:))/Y_totvar;
  133. % residual network response factoring out pupil dynamics
  134. % correlated portion (graph model)
  135. Y_res = Y - Yhat2;
  136. r = []; ypred = [];
  137. for n = 1:size(Y,2)
  138. X = Y_res;
  139. X(:,n) = [];
  140. [c,s,l] = pca(X);
  141. pcscore = s(:,1:find(l<0.1,1));
  142. mdl2 = fitlm(pcscore, Y_res(:,n));
  143. % mdl2 = fitlm(X, Y_res(:,n));
  144. r(n) = mdl2.Rsquared.Ordinary;
  145. ypred(:,n) = predict(mdl2,pcscore);
  146. end
  147. Y_net = var(ypred(:))/Y_totvar;
  148. % Y_net = mean(r);
  149. popResp_var = cat(1, popResp_var,[Y_stimexp, Y_pupildynexp_RRR, Y_net,areaID(k1,k2), k]);
  150. end
  151. end
  153. end
  154. end
  155. end
  157. figure, boxplot(popResp_var(:,1:3), 'whisker', 10)
  158. box off
  159. set(gca, 'Xticklabels', {'Stimulus','Pupil dynamics','Network'})
  160. ylabel('Variance explained')
  161. title('Partition variance of population neuron response ')
  163. %% 1D pupil dynamics
  164. binWidth = 100;
  165. smoothSD = 2;
  166. ind = 1;
  167. for k1 = 1:length(poolpupildata)
  168. if ~isempty(poolpupildata(k1).pupildata)
  169. pupildata = poolpupildata(k1).pupildata;
  170. if isfield(pupildata, 'pupilsync')
  171. pupilsync = pupildata.pupilsync;
  172. sync2p = table2array(poolpupildata(k1).sync2p);
  174. l0 = find(pupilsync(:,3)==1,1);
  175. l1 = find(pupilsync(:,3)==1,1,'last');
  176. pupildyn = [pupildata.eyearea(l0:l1)',pupildata.eyecenter(l0:l1,:),pupildata.videoscore];
  177. pupildyn(pupildyn(:,1)==0,1:3) = nan;
  178. pupilPC(k1,1:2) = [pupildata.video_PCdim, pupildata.video_PCvar];
  179. pupildyn_t = pupilsync(l0:l1,1:2);
  180. pupildyn_t = bsxfun(@minus, pupildyn_t, pupildyn_t(1,:))+1;
  181. pupildyn_match2p = [];
  182. for j = 1:size(pupildyn,2)
  183. tmp = interp1(pupildyn_t(:,1)/pupildyn_t(end,1), pupildyn(:,j), sync2p(:,1)/sync2p(end,1));
  184. pupildyn_match2p = cat(2, pupildyn_match2p, tmp);
  185. end
  187. imgPara = pooldata(k1,1).imgPara;
  188. % pupil dynamics bined per direction (2 sec window)
  189. pupildyn_bin = [];
  190. for i = 1:imgPara.stimrep
  191. for j = 1:imgPara.stim_type
  192. tmp = pupildyn_match2p(sync2p(:,3)==i & sync2p(:,5)==j,:);
  193. tmp1 = imresize(tmp, [8 size(tmp,2)]);
  194. pupildyn_bin = cat(1, pupildyn_bin, tmp1);
  195. end
  196. end
  197. X_pupildyn = pupildyn_bin;
  198. X_stim = repmat(stimflag, imgPara.stimrep, 1);
  199. for k2 = 1:4
  200. if ~isempty(pooldata(k1,k2).spiketrain) && length(pooldata(k1,k2).goodcell) > 5
  201. goodcell = pooldata(k1,k2).goodcell;
  202. spiketrain = pooldata(k1,k2).spiketrain(goodcell);
  203. num(k1,k2) = length(goodcell);
  204. spikecountAve = []; h = []; spikecountraw1 = [];
  205. spikecountraw2 = []; snr = [];
  206. for n = 1:length(goodcell)
  207. tmphist = [];
  208. stvec = [];
  209. for i = 1:imgPara.stimrep
  210. st1 = [];
  211. for j = 1:imgPara.stim_type
  212. st = spiketrain(n).st{i,j}*imgPara.dt;
  213. st(st<0) = [];
  214. st(st>imgPara.stim_time) = [];
  215. st1 = [st1, st+imgPara.stim_time*(j-1)];
  216. tmphist1 = histcounts(st, Twindow);
  217. tmphist = cat(1, tmphist, tmphist1');
  218. end
  219. stvec = vectCat(stvec, st1');
  220. end
  221. [rate , rateT] = myPSTH(stvec*1000, [0, imgPara.stim_time*imgPara.stim_type]*1000, binWidth, smoothSD);
  222. spikecountraw1(n,:) = tmphist;
  223. spikecountAve(n,:) = rate;
  224. end
  226. % total variance of population response
  227. Y = spikecountraw1';
  228. Y = bsxfun(@minus, Y, mean(Y,1));
  229. Y_totvar = var(Y(:));
  231. % variance explained by stimulus induced response
  232. Ym = mean(reshape(Y, 72,imgPara.stimrep,size(Y,2)), 2);
  233. Ym_var = var(Ym(:));
  234. Y_stimexp = Ym_var/Y_totvar;
  236. % residual population response after subtracting stimulus
  237. % induced portion (estimated by trial averaged response)
  238. Y = bsxfun(@minus, reshape(Y, 72,imgPara.stimrep,size(Y,2)), Ym);
  239. Y = reshape(Y,[],size(Y,3));
  240. Y_resvar = var(Y(:));
  242. % ordinary regression variance explained by pupil dynamics
  243. X = X_pupildyn;
  244. X(isnan(X)) = 0;
  245. [Br, Bols, ~, ~] = RRR_bySVD(X, Y, 1);
  246. Yhat2 = X * Br;
  247. Y_pupildynexp_RRR = var(Yhat2(:))/Y_totvar;
  249. popResp_var(ind, 6) = Y_pupildynexp_RRR;
  250. ind = ind+1;
  251. end
  252. end
  254. end
  255. end
  256. end
  259. figure, boxplot(popResp_var(:,[1,2,6,3]), 'whisker', 10)
  260. box off
  261. set(gca, 'Xticklabels', {'Stimulus','Pupil dynamics','1D Pupil dynamics','Network'})
  262. ylabel('Variance explained')
  263. title('Partition variance of population neuron response ')

FR_pupilAna.m at commit 4da24c0, under GPL-3.0 · at the source

Overview

Authors: Yiyi Yu1, Jeffery N Stirman1, Christopher R Dorsett1, Spencer LaVere Smith1,2
ORCID iDs: Yiyi Yu, Spencer LaVere Smith
  1. Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, United States
  2. Dynamical Neurosciences, University of California, Santa Barbara, Santa Barbara, United States
Institutions: University of California, Santa Barbara (United States)
Journal: eLife, volume 13, article RP97848
Dates: published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.97848 · PMID 41874539 · PMCID PMC13012721 · OpenAlex W4399364508
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Mouse
MeSH: Nerve Net*, Neurons*, Primary Visual Cortex*, Visual Cortex*, Visual Pathways*, Animals, Calcium, Mice (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH HHS (R01NS091335, T32NS007431, R01EY024294); Simons Foundation (SCGB325407); NSF (1450824, 1707287); NEI NIH HHS (R01 EY035378); NINDS NIH HHS (R01 NS121919)
Citations: cited by 3 papers (Europe PMC); 74 references in the paper

Abstract

Among brain areas, axonal projections carry channels of information that can be mixed to varying degrees. Here, we assess the rules for the network consisting of the primary visual cortex and higher visual areas (V1-HVA) in mice. We use large field-of-view two-photon calcium imaging to measure correlated variability (i.e. noise correlations, NCs) among thousands of neurons, forming over a million unique pairs, distributed across multiple cortical areas simultaneously. The amplitude of NCs is proportional to functional connectivity in the network, and we find that they are robust, reproducible statistical measures and are remarkably similar across stimuli, thus providing effective constraints to network models. We used these NCs to measure the statistics of functional connectivity among tuning classes of neurons in V1 and HVAs. Using a data-driven clustering approach, we identify approximately 60 distinct tuning classes found in V1 and HVAs. We find that NCs are higher between neurons from the same tuning class, both within and across cortical areas. Thus, in the V1-HVA network, mixing of channels is avoided. Instead, distinct channels of visual information are broadcast within and across cortical areas, at both the micron and millimeter length scales. This principle for the functional organization and correlation structure at the individual neuron level across multiple cortical areas can inform and constrain computational theories of neocortical 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 4 matches between paragraphs and lines of code.

yuyiyi/Broadcast_channel

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4da24c0febfb9591ac9cd683920095e79cb429bb, 27 February 2026
Languages: MATLAB (5)
Size: 26 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), Image Processing Toolbox (3 files), Curve Fitting Toolbox (2 files), shadedErrorBar (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
7 files
At the source: github.com/yuyiyi/Broadcast_channel

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;
  • 5 scripts, each with its path and the digest of its content;
  • 4 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

Data availability

Data and code generated in the current study are available on github https://github.com/yuyiyi/Broadcast_channel (copy archived at Yu, 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 1 keyword, 8 MeSH terms, 5 funders, 72 references.

Cite

This paper

Yu, Y., Stirman, J. N., Dorsett, C. R., & Smith, S. L. (2026). Visual information is broadcast among cortical areas in discrete channels. eLife, 13, RP97848. https://doi.org/10.7554/elife.97848

BibTeX

@article{yu2026visual,
author = {Yu, Yiyi and Stirman, Jeffery N and Dorsett, Christopher R and Smith, Spencer LaVere},
title = {{Visual information is broadcast among cortical areas in discrete channels}},
journal = {eLife},
year = {2026},
month = mar,
volume = {13},
pages = {RP97848},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.97848},
url = {https://doi.org/10.7554/elife.97848},
pmid = {41874539},
pmcid = {PMC13012721}
}

RIS

TY - JOUR
AU - Yu, Yiyi
AU - Stirman, Jeffery N
AU - Dorsett, Christopher R
AU - Smith, Spencer LaVere
TI - Visual information is broadcast among cortical areas in discrete channels
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/03/24
VL - 13
SP - RP97848
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.97848
UR - https://doi.org/10.7554/elife.97848
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.97848",
"type": "article-journal",
"title": "Visual information is broadcast among cortical areas in discrete channels",
"container-title": "eLife",
"author": [
{
"family": "Yu",
"given": "Yiyi"
},
{
"family": "Stirman",
"given": "Jeffery N"
},
{
"family": "Dorsett",
"given": "Christopher R"
},
{
"family": "Smith",
"given": "Spencer LaVere"
}
],
"container-title-short": "eLife",
"volume": "13",
"page": "RP97848",
"DOI": "10.7554/elife.97848",
"PMID": "41874539",
"PMCID": "PMC13012721",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.97848",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
24
]
]
}
}

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:10.7554/elife.105213 [code]
Mesoscale functional architecture in medial posterior parietal cortex.
Journal: eLife
In common: mouse, 12 references, 2 authors
[2] doi:10.1038/s41592-023-02098-1
The Cousa objective: a long-working distance air objective for multiphoton imaging in vivo.
Journal: Nature methods
In common: 7 references, 2 authors
[3] doi:10.1371/journal.pcbi.1011667 [code]
Modeling the diverse effects of divisive normalization on noise correlations.
Journal: PLoS computational biology
In common: Statistics and Machine Learning Toolbox, mouse, 14 references
[4] doi:10.1038/s41467-024-50563-y [code]
Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex.
Journal: Nature communications
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse, 12 references
[5] doi:10.1117/1.nph.11.2.024307 [code]
Comprehensive software suite for functional analysis and synaptic input mapping of dendritic spines imaged <i>in vivo</i>.
Journal: Neurophotonics
In common: shadedErrorBar, Curve Fitting Toolbox, Image Processing Toolbox, 1 other tool, 5 references, author Yiyi Yu
[6] doi:10.1038/s41467-022-29656-z [code]
Diversity of spatiotemporal coding reveals specialized visual processing streams in the mouse cortex.
Journal: Nature communications
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse, 10 references
[7] doi:10.1371/journal.pone.0271136 [code]
Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data.
Journal: PloS one
In common: shadedErrorBar, Statistics and Machine Learning Toolbox, 10 references
[8] doi:10.7554/elife.67258 [code]
A general decoding strategy explains the relationship between behavior and correlated variability.
Journal: eLife
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, 10 references
[9] doi:10.1126/sciadv.abg5244 [code]
Internally generated population activity in cortical networks hinders information transmission.
Journal: Science advances
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, 10 references
[10] doi:10.1126/sciadv.adz9632 [code]
Population coding under the scale invariance of high-dimensional noise.
Journal: Science advances
In common: mouse, 11 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/10.7554/elife.97848.svg)](https://openscicode.org/badge/doi/10.7554/elife.97848)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.7554/elife.97848.svg
   :target: https://openscicode.org/badge/doi/10.7554/elife.97848
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.7554/elife.97848"><img src="https://openscicode.org/badge/doi/10.7554/elife.97848.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.