OSCR

Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.

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 · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Differential analysis of state transition probability and energy ↔ ejc_bs_code/control/plotWeightedControlEnergyvsTransitionProbabilityTrange.R, the whole file · a weak match · score 0.61 · transition probability, transition energy, control energy, TP, brain

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 · 91 lines · 5 KB · no license · 1 match

  1. # plot weighted control energy vs. transition probabilities for a range of T values
  2. # compare to correlation between system-weighted interstate distance and transition probabilities
  3. # do this for the 7 Yeo cognitive systems
  4. # then also for whole brain control, with comparison to null
  6. args <- commandArgs(TRUE)
  7. name_root <- args[1]
  8. numClusters <- as.numeric(args[2])
  9. basedir <- args[3]
  10. c <- args[4]
  12. library(ggplot2)
  13. library(R.matlab)
  14. library(RColorBrewer)
  15. library(lm.beta)
  16. library(reshape2)
  17. library(viridis)
  18. library(gridExtra)
  20. masterdir <- paste(basedir,'results/',name_root,'/',sep='')
  21. source(paste(basedir,'code/plottingfxns/plottingfxns.R',sep=''))
  22. source(paste(basedir,'code/miscfxns/statfxns.R',sep=''))
  24. clusterNames <- readMat(paste(basedir,'results/',name_root,'/clusterAssignments/k',numClusters,name_root,'.mat',sep=''))
  25. clusterNames <- unlist(clusterNames$clusterAssignments[[1]][[5]])
  26. clusterColors <- getClusterColors(numClusters)
  27. RNcolors <- c('#005C9F','#FF8400')
  29. savedir <- paste(masterdir,'analyses/control_energy/EvsTP_plots/WeightedControl/',sep = '')
  30. dir.create(path = savedir,recursive = TRUE)
  32. ### load transition probabilities ###
  34. restTP <- readMat(paste(masterdir,'analyses/transitionprobabilities/RestCombTransitionProbabilitiesNoPersist_k',
  35. numClusters,name_root,'.mat',sep = ''))$transitionProbability
  36. restTP <- colMeans(restTP) # group average
  37. nbackTP <- readMat(paste(masterdir,'analyses/nbackblocks/TransProbsNoPersist2back_k',numClusters,name_root,'.mat',sep = ''))$BlockTransitionProbability
  38. nbackTP <- colMeans(nbackTP,na.rm=T)
  40. onDiag = (1:numClusters) + (numClusters*(0:(numClusters-1))) # construct indices to select only persistence probabilites
  41. offDiag = 1:(numClusters^2); offDiag <- offDiag[-onDiag] # construct indices to select only transition probabilites
  43. Centroids <- readMat(paste(masterdir,'analyses/centroids/OverallClusterCentroids_k',
  44. numClusters,name_root,'.mat',sep = ''))$kClusterCentroids
  45. xf <- Centroids[,rep(1:numClusters,times=numClusters)]
  46. x0 <- Centroids[,rep(1:numClusters,each=numClusters)]
  47. InterStateDistance <- colSums((xf - x0)^2)
  49. ### load weighted control energies ###
  51. yeo <- readMat(paste(basedir,'data/yeo7netlabelsLaus250.mat',sep=''))$network7labels
  52. yeo <- yeo[-length(yeo)] # remove brainstem
  53. Systems <- c('VIS','SOM','DAT','VAT','LIM','FPN','DMN')
  54. B.matrix <- lapply(1:length(Systems), function(S) sapply(1:numClusters^2, function(K) (1+(yeo==S))^2)) # for weighted control, reconstruct B matrix to use for weighted distance calculation
  55. names(B.matrix) <- Systems
  56. InterStateDistance.weighted <- lapply(Systems, function(S) colSums((xf-x0)^2/B.matrix[[S]])) # interstate distance weighted by input matrix
  57. names(InterStateDistance.weighted) <- Systems
  59. weightedcontrol <- lapply(Systems, function(Sys) readMat(paste(masterdir,'analyses/control_energy/model_task_inputs/TransitionEnergies_c',c,Sys,'WeightedControl_k',
  60. numClusters,'.mat',sep='')))
  61. names(weightedcontrol) <- Systems
  62. E.null <- weightedcontrol[['SOM']]$E.identity[offDiag,] # get whole brain control from a random system, doesn't matter bc E.identity is always the same.
  63. E.null <- array(E.null,dim=c(dim(E.null)[1],1,dim(E.null)[2])) # add a 3rd dimension
  64. E.null = list(WB=E.null)
  66. ### load whole brain control energies ###
  68. E.wholebrain <- readMat(paste(masterdir,'analyses/control_energy/Tsweep/c',c,'_k',numClusters,
  69. '/GroupAverageTransitionEnergies_k',numClusters,'.mat',sep=''))
  71. E.brain <- E.wholebrain$E.full[offDiag,]
  72. E.null.full <- list(BCT=E.wholebrain$E.BCTnull[offDiag,,])
  73. t.rng <- as.numeric(E.wholebrain$T.rng)
  75. for(method in c('pearson','spearman')){
  76. rest.plots.weighted <- lapply(Systems, function(S) p.Tsweep(as.numeric(weightedcontrol[[S]]$T.rng),weightedcontrol[[S]]$E.full[offDiag,],E.null,restTP[offDiag],
  77. InterStateDistance.weighted[[S]][offDiag],RNcolors[1],ttl=paste('Rest: ',S,'-Weighted',sep=''),method=method,leg=FALSE))
  78. # tack on whole brain control plot for rest
  79. rest.plots.uniform <- list(p.Tsweep(t.rng,E.brain,E.null.full,restTP[offDiag],InterStateDistance[offDiag],RNcolors[1],ttl='Rest: Uniform',method=method,leg = FALSE))
  80. rest.plots <- c(rest.plots.uniform,rest.plots.weighted)
  81. ggsave(plot = arrangeGrob(grobs=rest.plots,nrow=2),filename = paste(savedir,'AllTSweepsControlEnergyVsRestTP_c',c,method,'_k',numClusters,'.pdf',sep =''),
  82. units = 'cm',height = 9,width = 18)
  83. nback.plots.weighted <- lapply(Systems, function(S) p.Tsweep(as.numeric(weightedcontrol[[S]]$T.rng),weightedcontrol[[S]]$E.full[offDiag,],E.null,nbackTP[offDiag],
  84. InterStateDistance.weighted[[S]][offDiag],RNcolors[2],ttl=paste('2-back: ',S,'-Weighted',sep=''),method=method,leg=FALSE))
  85. # tack on whole brain control plot for nback
  86. nback.plots.uniform <- list(p.Tsweep(t.rng,E.brain,E.null.full,nbackTP[offDiag],InterStateDistance[offDiag],RNcolors[2],ttl='2-back: Uniform',method=method,leg = FALSE))
  87. nback.plots <- c(nback.plots.uniform,nback.plots.weighted)
  88. ggsave(plot = arrangeGrob(grobs=nback.plots,nrow=2),filename = paste(savedir,'AllTSweepsControlEnergyVs2BackTP_c',c,method,'_k',numClusters,'.pdf',sep =''),
  89. units = 'cm',height = 9,width = 18)
  90. }

plotWeightedControlEnergyvsTransitionProbabilityTrange.R at commit 47cd3d2, no license · at the source

Overview

Authors: Jinpeng Niu1,2, Jie Xia1,2, Qingjin Liu1,2, Yaohui He1,2, Wei Li1,2, Kangjia Chen1,2, Xi Zhang1,2,3, Jiang Qiu4, Huafu Chen1,2, Jiao Li1,2, Wei Liao1,2
ORCID iDs: Jinpeng Niu, Jiang Qiu, Huafu Chen, Wei Liao
  1. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China,Chengdu, 611731 P.R. China
  2. Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province, Chengdu, 611731 P. R. China
  3. Department of Radiology, Xinqiao Hospital, Army Medical University,Chongqing, 400037 P.R. China
  4. Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University,Chongqing, 400715 P.R. China
Institutions: University of Electronic Science and Technology of China (China); Army Medical University (China); Southwest University (China)
Journal: Translational psychiatry, volume 16, issue 1, article 270
Dates: received 4 December 2025; accepted 24 March 2026; published online 6 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04025-2 · PMID 41942448 · PMCID PMC13183912 · OpenAlex W7150982754
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), depression (population)
Methods: Statistics, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Psychiatric disorders, Neuroscience
MeSH: Brain*, Energy Metabolism*, Major Depressive Disorder*, Nerve Net*, Adult, Female, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China (National Science Foundation of China)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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

netneurolab/neuromaps

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ffcc2e0f657943ce00a1b6a968396f32250e495c, 4 June 2026
Languages: Python (45), Shell (3)
Size: 90 files, 48 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file, environment (Dockerfile, pyproject.toml, requirements.txt, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: neuromaps (29 files), NumPy (19 files), NiBabel (8 files), SciPy (7 files), Nilearn (5 files), scikit-learn (3 files), Matplotlib (2 files), BrainSMASH (1 file), BrainSpace (1 file), pandas (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
50 files
At the source: github.com/netneurolab/neuromaps

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (93 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/

isebenius/MIND

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0d334454eaac62e49a197801446ce7750256c882, 12 March 2024
Languages: Python (4), Jupyter (2)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), NiBabel (3 files), SciPy (3 files), Matplotlib (2 files), AFNI (1 file), FreeSurfer (1 file), Nipype (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/isebenius/MIND

NeuroenergeticsLab/control_costs

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: e006cbf445f8c8c90b0e666020cf26a35451afb6, 19 January 2026
Languages: Python (14)
Size: 57 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (13 files), Matplotlib (11 files), neuromaps (9 files), pandas (9 files), seaborn (9 files), Nilearn (8 files), Pingouin (5 files), SciPy (5 files), Brain Connectivity Toolbox (1 file), NiBabel (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
15 files
At the source: github.com/NeuroenergeticsLab/control_costs

smweinst.github.io/nest-method

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: smweinst.github.io/nest-method/

singlesp/energy_landscape

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 47cd3d2347e7ec24eec7f274551a264070b5b997, 8 February 2023
Languages: MATLAB (186), R (23), Python (3)
Size: 283 files, 212 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, CITATION.cff
Not found: license file, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (40 files), ggplot2 (21 files), Tools for NIfTI and ANALYZE image (MATLAB) (13 files), reshape2 (11 files), Brain Connectivity Toolbox (3 files), NumPy (3 files), FreeSurfer (2 files), FSL (2 files), Matplotlib (2 files), NiBabel (2 files), SciPy (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
213 files
At the source: github.com/singlesp/energy_landscape

rmarkello/pyls

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d8a19d564cc5804249527b68c937df3a5fd8c7cc, 4 November 2019
Languages: Python (32)
Size: 65 files, 32 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (18 files), scikit-learn (4 files), h5py (2 files), pandas (2 files), SciPy (2 files), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
34 files
At the source: github.com/rmarkello/pyls

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-04025-2.

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:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,310 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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-04025-2.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 11 MeSH terms, 2 funders, 79 references.

Cite

This paper

Niu, J., Xia, J., Liu, Q., He, Y., Li, W., Chen, K., Zhang, X., Qiu, J., Chen, H., Li, J., & Liao, W. (2026). Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective. Translational psychiatry, 16(1), 270. https://doi.org/10.1038/s41398-026-04025-2

BibTeX

@article{niu2026brain,
author = {Niu, Jinpeng and Xia, Jie and Liu, Qingjin and He, Yaohui and Li, Wei and Chen, Kangjia and Zhang, Xi and Qiu, Jiang and Chen, Huafu and Li, Jiao and Liao, Wei},
title = {{Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective}},
journal = {Translational psychiatry},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {270},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04025-2},
url = {https://doi.org/10.1038/s41398-026-04025-2},
pmid = {41942448},
pmcid = {PMC13183912}
}

RIS

TY - JOUR
AU - Niu, Jinpeng
AU - Xia, Jie
AU - Liu, Qingjin
AU - He, Yaohui
AU - Li, Wei
AU - Chen, Kangjia
AU - Zhang, Xi
AU - Qiu, Jiang
AU - Chen, Huafu
AU - Li, Jiao
AU - Liao, Wei
TI - Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/04/06
VL - 16
IS - 1
SP - 270
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04025-2
UR - https://doi.org/10.1038/s41398-026-04025-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04025-2",
"type": "article-journal",
"title": "Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Niu",
"given": "Jinpeng"
},
{
"family": "Xia",
"given": "Jie"
},
{
"family": "Liu",
"given": "Qingjin"
},
{
"family": "He",
"given": "Yaohui"
},
{
"family": "Li",
"given": "Wei"
},
{
"family": "Chen",
"given": "Kangjia"
},
{
"family": "Zhang",
"given": "Xi"
},
{
"family": "Qiu",
"given": "Jiang"
},
{
"family": "Chen",
"given": "Huafu"
},
{
"family": "Li",
"given": "Jiao"
},
{
"family": "Liao",
"given": "Wei"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "270",
"DOI": "10.1038/s41398-026-04025-2",
"PMID": "41942448",
"PMCID": "PMC13183912",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04025-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
6
]
]
}
}

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/s41467-026-76452-0 [code]
Music evokes shared neural representations of imagined narratives across sensory modalities.
Journal: Nature communications
In common: tedana, Connectome Workbench, AFNI, 19 other tools, 3 references
[2] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: tedana, neuromaps, Connectome Workbench, 19 other tools, 2 references
[3] doi:10.1038/s42003-026-10276-y [code]
The cellular correlates and adolescent reorganisation of cortical myelination networks in the common marmoset.
Journal: Communications biology
In common: BrainSMASH, BrainSpace, Nipype, 15 other tools, 5 references
[4] doi:10.1038/s41467-026-73668-y [code]
Convergent and divergent brain-cognition development in early adolescence.
Journal: Nature communications
In common: tedana, Connectome Workbench, AFNI, 19 other tools, 3 references
[5] doi:10.3389/fnagi.2026.1742371 [code]
Cross-sectional and longitudinal functional network alterations associated with subthreshold depressive symptoms in healthy older adults.
Journal: Frontiers in aging neuroscience
In common: tedana, Connectome Workbench, AFNI, 16 other tools, depression, 4 references
[6] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: BrainSpace, Brain Connectivity Toolbox, Connectome Workbench, 15 other tools, 6 references
[7] doi:10.1038/s41586-026-10631-3 [code]
A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: BrainSMASH, neuromaps, BrainSpace, 18 other tools, 2 references
[8] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: Nipype, Brain Connectivity Toolbox, Connectome Workbench, 17 other tools, 3 references
[9] doi:10.1038/s41467-026-71270-w [code]
Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan.
Journal: Nature communications
In common: BrainSpace, Connectome Workbench, GIfTI library for MATLAB, 13 other tools, 5 references, author Jiang Qiu
[10] doi:10.1016/j.isci.2026.116903 [code]
Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.
Journal: iScience
In common: tedana, Connectome Workbench, AFNI, 16 other tools, 3 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.