OSCR

Intracellular fluid accumulation underlies brain volume increases in early Alzheimer's disease.

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] § Materials and methods › Statistical analysis ↔ Create_Table_demographics.m, lines 80–158 · score 0.76 · AD signature, S100B, hippocampal volume, sTREM2, CL, IL6
  2. [2] § Results › Association of GM volume with amyloid-PET positivity, CSF Aβ42/40, IVIM parameters, and MD ↔ Scatterplots_DWI_metrics_vs_GMvolumes_PET_CL_cluster.m, lines 2–21 · score 0.61 · slow signal portion, fast DC, perfusion fraction, slow DC, PET, SSP
  3. [3] § Materials and methods › DWI quantifications ↔ Scatterplots_DWI_metrics_vs_GMvolumes_PET_CL_cluster.m, lines 2–21 · score 0.60 · slow signal portion, fast DC, slow DC, tissue, DWI, SSP
  4. [4] § Materials and methods › Statistical analysis ↔ Scatterplots_DWI_metrics_vs_GMvolumes_PET_CL_cluster.m, lines 32–86 · score 0.50 · stat maps, GM volume, Clusters, status, PET, variables

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

MATLAB · 86 lines · 3.3 KB · MIT · 3 matches

  2. %% Load data
  3. clear, clc, close all
  4. addpath('..')
  6. path_DWI = 'D:\Projects\2023_multib\MK_multi_b\Stats_group_level_297_subjs\Mediation_analysis\';
  8. list_phys_variables = {'slow_ADC'; 'fast_ADC'; 'slow_signal_portion'; 'tissueFraction'; 'meanDiffusivity'};
  9. list_phys_variables_long_name = { ...
  10. 'Slow DC (a.u.)'; ...
  11. 'Fast DC (a.u.)'; ...
  12. 'SSP (a.u.)'; ...
  13. 'Perfusion fraction (a.u.)'; ...
  14. 'Mean diffusivity (10^{-3} mm²/s)'};
  16. list_stat_maps = {'PET_CLs_group_p0.005.nii'; 'Ab4240_elecsys_continuous_p0.005.nii'};
  17. n_stat_maps = length(list_stat_maps);
  19. sample = 'all_subjs'; % 'A-T-' or 'all_subjs'
  20. load('Table_297_subjs_demographics_biomarkers_2024_05_03.mat')
  21. n_subjects = size(T_subjects,1);
  23. %% Load fluid biomarkers and attach missing fields
  24. path_fluid_biomarkers = '..\..\Biomarkers\f_bmk_long_09_05_23.xlsx';
  25. T_fluid_biomarkers = readtable(path_fluid_biomarkers);
  27. list_subject_IDs = str2double(T_subjects.Subject_ID);
  28. list_B = T_fluid_biomarkers.IdParticipante;
  29. [found, idx] = ismember(list_subject_IDs, list_B, 'rows');
  30. T_subjects.Ab42_elecsys = T_fluid_biomarkers.Ab42_CSFNC_RocheElecsys_BL(idx);
  32. %% Scatterplots (GM volume vs diffusion metrics)
  33. close all, clc
  35. id_stat_map = 4; % 1: Ab PET (group), 4: Ab CSF continuous (elecsys)
  36. disp(list_stat_maps{id_stat_map})
  38. path_DWI_stat = [path_DWI, 'DWI_pars_signif_ROIs_all_subjs_', list_stat_maps{id_stat_map}, '.mat'];
  39. load(path_DWI_stat) % loads T_DWI_pars
  41. % Group indices
  42. ind_AmTm = find(strcmp(T_subjects.AT_status,'A-T-'));
  43. ind_ApTm = find(strcmp(T_subjects.AT_status,'A+T-'));
  44. ind_ApTp = find(strcmp(T_subjects.AT_status,'A+T+'));
  46. T_Stats = nan(5,2); % [r, p] per parameter
  48. for i_phys = 1:5
  49. phys_variable = list_phys_variables{i_phys};
  50. fprintf('------------------------- \n')
  51. disp(phys_variable)
  53. % x: DWI parameter within significant clusters; y: GM volume
  54. eval(sprintf('x = T_DWI_pars.DWI_%s;', phys_variable))
  55. y = T_DWI_pars.GMvol;
  56. if i_phys == 5, x = x*1000; end % scale MD to 10^{-3} mm²/s
  58. % Remove outliers in x only (as in original)
  59. ind_outliers = isoutlier(x);
  60. x(ind_outliers) = nan;
  62. n_samples_test = length(x) - sum(isnan(x));
  63. fprintf('Sample size N: %d \n', n_samples_test)
  65. % Scatter + LS fit (overall)
  66. figure('Position',[652 692 328 283])
  67. scatter(x, y, 5, 'filled', 'k'); hold on
  68. xlabel(list_phys_variables_long_name{i_phys}, 'Interpreter','tex');
  69. ylabel('GM volume (a.u.)')
  70. h = lsline; h.LineWidth = 2; h.Color = [0.5 0.5 0.5];
  71. grid on
  73. [corr_r, corr_p] = corr(x, y, 'rows','complete');
  74. fprintf('Corr: %3.2f, p-value: %3.3f \n', corr_r, corr_p)
  75. T_Stats(i_phys,1) = corr_r;
  76. T_Stats(i_phys,2) = corr_p;
  78. % Overlay groups with shapes/colors
  79. par_size = 20; par_linewidth = 0.1; par_edge_colour = [0.2 0.2 0.2];
  80. scatter(x(ind_AmTm), y(ind_AmTm), par_size, [0.4660 0.6740 0.1880], 'filled', ...
  81. 'MarkerEdgeColor', par_edge_colour, 'LineWidth', par_linewidth);
  82. scatter(x(ind_ApTm), y(ind_ApTm), par_size, [0 0.4470 0.7410], 's', 'filled', ...
  83. 'MarkerEdgeColor', par_edge_colour, 'LineWidth', par_linewidth);
  84. scatter(x(ind_ApTp), y(ind_ApTp), par_size, [1 0 1], 'd', 'filled', ...
  85. 'MarkerEdgeColor', par_edge_colour, 'LineWidth', par_linewidth);
  86. end

Scatterplots_DWI_metrics_vs_GMvolumes_PET_CL_cluster.m at commit b40e71a, under MIT · at the source

Overview

Authors: Michalis Kassinopoulos1, Paula Montesinos2, Carles Falcon1,3,4, Jordi Huguet1, Carolina Minguillon1,4, Karine Fauria1,4,5, Gwendlyn Kollmorgen6, Clara Quijano-Rubio7, José Luis Molinuevo1, Oriol Grau-Rivera1,4,5,8, Henrik Zetterberg9,10,11,12,13,14, Kaj Blennow9,10,15,16, Marc Suárez-Calvet1,4,5,8, Javier Sanchez-Gonzalez2, Juan Domingo Gispert1,3,4, ALFA study
ORCID iDs: Michalis Kassinopoulos, Oriol Grau-Rivera, Henrik Zetterberg
16 affiliations
  1. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona 08005, Spain
  2. Philips Healthcare Iberia, Madrid 28050, Spain
  3. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid 28029, Spain
  4. Hospital del Mar Research Institute, Barcelona 08003, Spain
  5. Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid 28029, Spain
  6. Roche Diagnostics GmbH, Penzberg 82377, Germany
  7. Roche Diagnostics International Ltd, Rotkreuz 6343, Switzerland
  8. Servei de Neurologia, Hospital del Mar, Barcelona 08003, Spain
  9. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 431 80, Sweden
  10. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal 431 80, Sweden
  11. UK Dementia Research Institute at University College London (UCL), London WC1N 3BG, UK
  12. Department of Neurodegenerative Disease, UCL Institute of Neurology, London WC1M 3BG, UK
  13. Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
  14. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison 53705, WI, USA
  15. Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris 75013, France
  16. Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei 230001, P.R. China
Institutions: Pasqual Maragall Foundation (Spain); Barcelonaβeta Brain Research Center (Spain); Philips (Spain) (Spain); Instituto de Salud Carlos III (Spain); Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (Spain); Centro de Investigación Biomédica en Red (Spain); Hospital del Mar Research Institute (Spain); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (Spain); Roche (Switzerland) (Switzerland); Roche Diagnostics International AG (Switzerland) (Switzerland); Hospital Del Mar (Spain); University of Wisconsin–Madison (United States); Sahlgrenska University Hospital (Sweden); UK Dementia Research Institute (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); University of Gothenburg (Sweden); University of Science and Technology of China (China); Sorbonne Université (France); Institut des Sciences Moléculaires (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France)
Journal: Brain communications, volume 8, issue 2, article fcag075
Dates: received 3 June 2025; accepted 9 March 2026; published online 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag075 · PMID 41884594 · PMCID PMC13009408 · OpenAlex W7134922349
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), PET / SPECT (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Connectivity, fMRI & imaging, Preprocessing
Keywords: neuroinflammation, brain volume, early AD, multi-shell DWI, amyloid PET
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Swedish Research Council (#2019-02397, #FO2017-0243, 948677, PI22/00456, #ALFGBG-71320, #ALZ2022-0006, 101053962, #2022-00732, #ADSF-21-831377-C, PI19/00155, #2017-00915, #22HLT07, #ADSF-21-831381-C, #AF-930351, #AF-968270, #2023-00356, 860197, JPND2019-466-236, SG-23-1038904 QC, #2022-01018, #201809-2016862, #AF-939721, #AF-994551, JPND2021-00694, #ADSF-21-831376-C, #ADSF-24-1284328-C, #ALFGBG-715986, #ALFGBG-965240, #FO2022-0270, ZEN-21-848495); "la Caixa" Foundation (TriBEKa-17-519007, ID 100010434, LCF/BQ/PR21/11840004, LCF/PR/GN17/50300004, 847648, SLT002/16/00201); UK Dementia Research Institute (2019-AARF-644568, UKDRI-1003); Spanish Ministry of Science, Innovation and Universities (IJC2020-043417-I); Ministry of Business and Knowledge of the Catalan Government (2021 SGR 00913); Universities and Research Secretariat; National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre
Citations: cited by 1 paper (Europe PMC); 40 references in the paper

Abstract

In the preclinical stages of Alzheimer’s disease, increased brain volume has been associated with amyloid-beta pathology, particularly in regions that undergo volume reductions as the disease progresses. Glial reactivity and water diffusion alterations have been linked to such macroscopic volumetric changes. Brain volume reductions have also been reported following amyloid-beta removal with anti-amyloid therapies with beneficial clinical effects, but it remains unclear whether these changes result from resolving amyloid-triggered neuroinflammation or neurodegeneration. Intravoxel incoherent motion modelling based on multi-shell diffusion-weighted imaging may provide a better understanding of the processes underlying these paradoxical changes. This study used intravoxel incoherent motion diffusion MRI to examine how alterations in cerebral water pools contribute to increased brain volume linked to amyloid-beta deposition and neuroinflammation in cognitively unimpaired individuals. We developed a three-compartment diffusion MRI model with four parameters of cerebral water diffusion: slow diffusion coefficient, fast diffusion coefficient, slow signal portion, and perfusion fraction. We computed these diffusion parameters in 297 cognitively unimpaired late middle-aged adults, 35% of whom showed evidence of amyloid deposition. We examined their correlation with demographic factors (age, sex, apolipoprotein E status), markers of Alzheimer’s disease pathology, neurodegeneration, neuroinflammation, and mean diffusivity. Then, we identified regions showing grey matter volume increases related to amyloid burden and examined the association between grey matter volume and diffusion parameters within these regions. We did not find evidence of associations between diffusion parameters and amyloid-related biomarkers, whether assessed by PET or cerebrospinal fluid measures. In contrast, the four diffusion parameters showed strong and widespread associations with biomarkers of neuroinflammation and neurodegeneration, particularly in frontoparietal and cingulate regions. Additionally, in grey matter regions where volume increases were related to amyloid levels, volumes were negatively correlated with the slow diffusion coefficient (P = 0.001), perfusion fraction (P = 0.036) and mean diffusivity (P = 0.047). These findings indicate that diffusion-derived measures are more sensitive to neuroinflammatory and neurodegenerative processes than to amyloid pathology in cognitively unimpaired individuals. Furthermore, the observed negative association between grey matter volume and slow diffusion coefficient in amyloid-related regions may reflect increased cellular complexity rather than intracellular water accumulation. This interpretation suggests that glial remodelling or microstructural changes could underlie brain volume increases in amyloid-positive individuals without overt neurodegeneration. These results underscore the value of intravoxel incoherent motion-derived metrics for gaining deeper insights into the pathophysiological mechanisms of Alzheimer’s disease, influencing brain volume changes as well as those resulting from the response to anti-amyloid therapies.

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.

mkassinopoulos/2026_multib_paper_AlfaPLUS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b40e71aedb3fdaccfdea2985c8d6b11689cbd16c, 22 January 2026
Languages: MATLAB (15)
Size: 17 files, 15 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (10 files), Tools for NIfTI and ANALYZE image (MATLAB) (3 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
17 files
At the source: github.com/mkassinopoulos/2026_multib_paper_AlfaPLUS

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;
  • 15 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

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

Data availability

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Code used for the analyses is available at the following GitHub repository: https://github.com/mkassinopoulos/2026_multib_paper_AlfaPLUS.

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, 16 authors, 5 keywords, 7 funders, 40 references.

Cite

This paper

Kassinopoulos, M., Montesinos, P., Falcon, C., Huguet, J., Minguillon, C., Fauria, K., Kollmorgen, G., Quijano-Rubio, C., Molinuevo, J. L., Grau-Rivera, O., Zetterberg, H., Blennow, K., Suárez-Calvet, M., Sanchez-Gonzalez, J., Gispert, J. D., & ALFA study. (2026). Intracellular fluid accumulation underlies brain volume increases in early Alzheimer's disease. Brain communications, 8(2), fcag075. https://doi.org/10.1093/braincomms/fcag075

BibTeX

@article{kassinopoulos2026intracellular,
author = {Kassinopoulos, Michalis and Montesinos, Paula and Falcon, Carles and Huguet, Jordi and Minguillon, Carolina and Fauria, Karine and Kollmorgen, Gwendlyn and Quijano-Rubio, Clara and Molinuevo, José Luis and Grau-Rivera, Oriol and Zetterberg, Henrik and Blennow, Kaj and Suárez-Calvet, Marc and Sanchez-Gonzalez, Javier and Gispert, Juan Domingo and {ALFA study}},
title = {{Intracellular fluid accumulation underlies brain volume increases in early Alzheimer's disease}},
journal = {Brain communications},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {fcag075},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag075},
url = {https://doi.org/10.1093/braincomms/fcag075},
pmid = {41884594},
pmcid = {PMC13009408}
}

RIS

TY - JOUR
AU - Kassinopoulos, Michalis
AU - Montesinos, Paula
AU - Falcon, Carles
AU - Huguet, Jordi
AU - Minguillon, Carolina
AU - Fauria, Karine
AU - Kollmorgen, Gwendlyn
AU - Quijano-Rubio, Clara
AU - Molinuevo, José Luis
AU - Grau-Rivera, Oriol
AU - Zetterberg, Henrik
AU - Blennow, Kaj
AU - Suárez-Calvet, Marc
AU - Sanchez-Gonzalez, Javier
AU - Gispert, Juan Domingo
AU - ALFA study
TI - Intracellular fluid accumulation underlies brain volume increases in early Alzheimer's disease
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/03/10
VL - 8
IS - 2
SP - fcag075
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag075
UR - https://doi.org/10.1093/braincomms/fcag075
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag075",
"type": "article-journal",
"title": "Intracellular fluid accumulation underlies brain volume increases in early Alzheimer's disease",
"container-title": "Brain communications",
"author": [
{
"family": "Kassinopoulos",
"given": "Michalis"
},
{
"family": "Montesinos",
"given": "Paula"
},
{
"family": "Falcon",
"given": "Carles"
},
{
"family": "Huguet",
"given": "Jordi"
},
{
"family": "Minguillon",
"given": "Carolina"
},
{
"family": "Fauria",
"given": "Karine"
},
{
"family": "Kollmorgen",
"given": "Gwendlyn"
},
{
"family": "Quijano-Rubio",
"given": "Clara"
},
{
"family": "Molinuevo",
"given": "José Luis"
},
{
"family": "Grau-Rivera",
"given": "Oriol"
},
{
"family": "Zetterberg",
"given": "Henrik"
},
{
"family": "Blennow",
"given": "Kaj"
},
{
"family": "Suárez-Calvet",
"given": "Marc"
},
{
"family": "Sanchez-Gonzalez",
"given": "Javier"
},
{
"family": "Gispert",
"given": "Juan Domingo"
},
{
"literal": "ALFA study"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "2",
"page": "fcag075",
"DOI": "10.1093/braincomms/fcag075",
"PMID": "41884594",
"PMCID": "PMC13009408",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag075",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

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:10.1093/brain/awaf413 [code]
Estimating the time course of biomarker changes in Alzheimer's disease.
Journal: Brain : a journal of neurology
In common: PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion, 3 references, author Henrik Zetterberg
[2] doi:10.1002/alz.71609
Cortical gray-white matter contrast alterations precede amyloid-β positivity and macrostructural changes in older adults without dementia.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion, 5 references
[3] doi:10.1126/sciadv.aee2305 [code]
Prediction of mild cognitive impairment progression using time-sensitive multimodal biomarkers.
Journal: Science advances
In common: SPM, PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion, 1 reference, author Henrik Zetterberg
[4] doi:10.1038/s41467-026-71682-8 [code]
GWAS meta-analysis of cerebrospinal fluid Alzheimer's biomarkers reveals loci regulating lipids, brain volume and autophagy.
Journal: Nature communications
In common: Alzheimer's / dementia, structural MRI / diffusion, 1 reference, author Henrik Zetterberg
[5] doi:10.1073/pnas.2536792123 [code]
Head-to-head comparison of brain-derived pTau217 and total pTau217 for brain amyloid and tau pathology classification.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: PET / SPECT, Alzheimer's / dementia, 1 reference, author Henrik Zetterberg
[6] doi:10.1038/s41593-026-02363-4 [code]
Cortical thickness changes precede high levels of amyloid by at least 7 years.
Journal: Nature neuroscience
In common: PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion, 3 references
[7] doi:10.1002/alz.71711 [code]
Exploring longitudinal relationships among Alzheimer's disease biomarkers.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: PET / SPECT, Alzheimer's / dementia, 4 references
[8] doi:10.1038/s41386-026-02426-x [code]
A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.
Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
In common: Tools for NIfTI and ANALYZE image (MATLAB), SPM, Statistics and Machine Learning Toolbox, PET / SPECT, Alzheimer's / dementia, structural MRI / diffusion
[9] doi:10.1038/s41467-026-71732-1 [code]
Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, PET / SPECT, Alzheimer's / dementia, 3 references
[10] doi:10.1093/braincomms/fcag074 [code]
Plasma p-tau217 and glucose metabolism correlate in neocortical association areas in Alzheimer's disease.
Journal: Brain communications
In common: PET / SPECT, Alzheimer's / dementia, cellular / molecular, author Henrik Zetterberg

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.