OSCR

Multimodal laminar characterization of visual areas along the cortical hierarchy.

Code ↔ Paper

14 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 14 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Statistical analysis on microscopy laminar profiles ↔ AHEAD/04_Laminar_profiles/12_compue_stats.py, lines 44–86 · score 0.80 · Wilcoxon signed rank, Bonferroni correction, post hoc, ANOVA, laminar profiles, correlation
  2. [2] § Results › Comparing the microscopic cortical architecture of visual areas ↔ AHEAD/04_Laminar_profiles/12_compue_stats.py, lines 44–86 · score 0.80 · Wilcoxon signed rank, Bonferroni correction, post hoc, Pairwise comparisons, ANOVA, correlates
  3. [3] § Materials and Methods › Data acquisition › In-vivo MRI dataset ↔ stimulus_scripts/hMT_Localiser/Main/MQ_locMt_Huk_AP.py, lines 1–43 · score 0.77 · fixation cross, Siemens, slab, GE, localizer, scanning
  4. [4] § Materials and Methods › Cortical mapping › Tissue segmentation ↔ AHEAD/05_qMRI/03_ants_registration.sh, the whole file · a weak match · score 0.67 · rim polish, qMRI, ITK, smoothing, global, voronoi
  5. [5] § Materials and Methods › Cortical mapping › Region-of-interest definition ↔ Anat/4_create_subcorticalMask.sh, the whole file · a weak match · score 0.62 · subcortical masking, BrainVoyager, ACPC, hemisphere, UNI, Segmentation
  6. [6] § Materials and Methods › Data acquisition › In-vivo MRI dataset ↔ stimulus_scripts/MotionQuartet/ambiguous.py, lines 1–48 · score 0.58 · pulses, fMRI, scanning, stimuli, distance, TR
  7. [7] § Materials and Methods › Data preprocessing for laminar signal extraction › Post-mortem dataset › Microscopy data › Intensity normalization ↔ AHEAD/04_Laminar_profiles/00_polish_2D_slices.py, lines 38–87 · score 0.57 · outer gray matter, deviations, kernel, thickness, slice, Filter
  8. [8] § Materials and Methods › Data preprocessing for laminar signal extraction › Post-mortem dataset › Microscopy data › Intensity normalization ↔ AHEAD/03_post_CBA/optional_to_polish_segmentation/2D_final_polishing.py, lines 26–67 · score 0.57 · outer gray matter, deviations, kernel, thickness, Filter, voxel
  9. [9] § Materials and Methods › Data acquisition › Post-mortem dataset ↔ AHEAD/05_qMRI/01_extractROI.py, lines 12–25 · score 0.57 · proton density, qMRI, R1, reconstructed, ROI, brain
  10. [10] § Materials and Methods › Data preprocessing for laminar signal extraction › In-vivo dataset › Resting-state fMRI ↔ fMRI_Processing/LOC/1_preprocessFMR.py, lines 10–41 · score 0.55 · high pass filter, motion correction, preprocessing, temporal, slice
  11. [11] § Materials and Methods › Cortical mapping › Region-of-interest definition ↔ Anat/2_read_VMR_export_nifti.py, lines 1–13 · score 0.52 · subcortical masking, BrainVoyager, Pipeline, anatomical
  12. [12] § Materials and Methods › Data preprocessing for laminar signal extraction › In-vivo dataset › Anatomical MRI ↔ fMRI_Processing/PRF/02_ants_apply_registration_tseries.sh, the whole file · a weak match · score 0.52 · transformation matrix, co registration, resampled, echo, MRI
  13. [13] § Materials and Methods › Geometric cortical layers ↔ AHEAD/05_qMRI/cutting_effect/02_compute_streamlines.sh, the whole file · a weak match · score 0.51 · LN2_LAYERS, Cutting Angle, computational, equivol, segmentation
  14. [14] § Materials and Methods › Data preprocessing for laminar signal extraction › In-vivo dataset › Resting-state fMRI ↔ in_vivo_qMRI/resting_state/Preprocessing/03_read_fmr_export_nifti.py, the whole file · a weak match · score 0.50 · motion correction, BrainVoyager, TOPUP, preprocessing, slice, resolution

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

Python · 86 lines · 4 KB · no license · 2 matches

  1. import numpy as np
  2. import pandas as pd
  3. from scipy.stats import shapiro, friedmanchisquare, f_oneway
  4. from scipy.stats import wilcoxon
  6. # Step 1: Data input
  7. data = {
  8. 'Region Pair': ['V1-V2', 'V1-V3', 'V2-V3', 'V1-hMT+', 'V2-hMT+', 'V3-hMT+',
  9. 'V1-V2', 'V1-V3', 'V2-V3', 'V1-hMT+', 'V2-hMT+', 'V3-hMT+'],
  10. 'Hemisphere': ['Left', 'Left', 'Left', 'Left', 'Left', 'Left', 'Right', 'Right', 'Right', 'Right', 'Right',
  11. 'Right'],
  12. 'Bielschowsky': [0.994, 0.997, 0.996, 0.993, 0.995, 0.996, 0.999, 0.993, 0.996, 0.998, 0.998, 0.995],
  13. 'Thionin': [0.994, 0.985, 0.996, 0.961, 0.963, 0.948, 0.999, 0.998, 0.999, 0.988, 0.989, 0.989],
  14. 'Parvalbumin': [0.997, 0.989, 0.991, 0.942, 0.921, 0.917, 0.995, 0.978, 0.974, 0.938, 0.925, 0.981]
  15. }
  17. df = pd.DataFrame(data)
  19. # Step 2: Fisher Z-transformation
  20. def fisher_z_transform(r):
  21. return 0.5 * np.log((1 + r) / (1 - r))
  23. df['Bielschowsky_Z'] = df['Bielschowsky'].apply(fisher_z_transform)
  24. df['Thionin_Z'] = df['Thionin'].apply(fisher_z_transform)
  25. df['Parvalbumin_Z'] = df['Parvalbumin'].apply(fisher_z_transform)
  27. # Step 3: Compute descriptive statistics (means and standard deviations)
  28. means = df[['Bielschowsky_Z', 'Thionin_Z', 'Parvalbumin_Z']].mean()
  29. std_devs = df[['Bielschowsky_Z', 'Thionin_Z', 'Parvalbumin_Z']].std()
  31. print("Means:\n", means)
  32. print("\nStandard Deviations:\n", std_devs)
  34. # Step 4: Check for normality with Shapiro-Wilk test
  35. _, p_biel = shapiro(df['Bielschowsky_Z'])
  36. _, p_thio = shapiro(df['Thionin_Z'])
  37. _, p_parv = shapiro(df['Parvalbumin_Z'])
  39. print("\nShapiro-Wilk p-values:")
  40. print(f"Bielschowsky: {p_biel}, Thionin: {p_thio}, Parvalbumin: {p_parv}")
  42. # Step 5: Perform the appropriate statistical test
  44. # If normal, run ANOVA
  45. if p_biel > 0.05 and p_thio > 0.05 and p_parv > 0.05:
  46. print("\nData is normally distributed. Running ANOVA...")
  47. F_stat, p_anova = f_oneway(df['Bielschowsky_Z'], df['Thionin_Z'], df['Parvalbumin_Z'])
  48. print(f"ANOVA result: F = {F_stat}, p = {p_anova}")
  50. # Post-hoc analysis (if significant)
  51. if p_anova < 0.05:
  52. print("Significant result, performing post-hoc analysis...")
  53. print("Pairwise comparisons using Wilcoxon signed-rank test (non-parametric post-hoc)...")
  54. _, p_b_t = wilcoxon(df['Bielschowsky_Z'], df['Thionin_Z'])
  55. _, p_b_p = wilcoxon(df['Bielschowsky_Z'], df['Parvalbumin_Z'])
  56. _, p_t_p = wilcoxon(df['Thionin_Z'], df['Parvalbumin_Z'])
  57. print(f"Bielschowsky vs Thionin: p = {p_b_t}")
  58. print(f"Bielschowsky vs Parvalbumin: p = {p_b_p}")
  59. print(f"Thionin vs Parvalbumin: p = {p_t_p}")
  61. # Apply Bonferroni correction (multiply p-values by 3)
  62. p_b_t_corr = min(p_b_t * 3, 1.0) # Ensure that the corrected p-value is <= 1
  63. p_b_p_corr = min(p_b_p * 3, 1.0)
  64. p_t_p_corr = min(p_t_p * 3, 1.0)
  66. # Print corrected p-values
  67. print(f"Bielschowsky vs Thionin: p (uncorrected) = {p_b_t}, p (Bonferroni corrected) = {p_b_t_corr}")
  68. print(f"Bielschowsky vs Parvalbumin: p (uncorrected) = {p_b_p}, p (Bonferroni corrected) = {p_b_p_corr}")
  69. print(f"Thionin vs Parvalbumin: p (uncorrected) = {p_t_p}, p (Bonferroni corrected) = {p_t_p_corr}")
  71. # If not normal, run Friedman test
  72. else:
  73. print("\nData is not normally distributed. Running Friedman test...")
  74. stat, p_friedman = friedmanchisquare(df['Bielschowsky_Z'], df['Thionin_Z'], df['Parvalbumin_Z'])
  75. print(f"Friedman result: chi-square = {stat}, p = {p_friedman}")
  77. # Post-hoc analysis (if significant)
  78. if p_friedman < 0.05:
  79. print("Significant result, performing post-hoc analysis...")
  80. print("Pairwise comparisons using Wilcoxon signed-rank test (non-parametric post-hoc)...")
  81. _, p_b_t = wilcoxon(df['Bielschowsky_Z'], df['Thionin_Z'])
  82. _, p_b_p = wilcoxon(df['Bielschowsky_Z'], df['Parvalbumin_Z'])
  83. _, p_t_p = wilcoxon(df['Thionin_Z'], df['Parvalbumin_Z'])
  84. print(f"Bielschowsky vs Thionin: p = {p_b_t}")
  85. print(f"Bielschowsky vs Parvalbumin: p = {p_b_p}")
  86. print(f"Thionin vs Parvalbumin: p = {p_t_p}")

12_compue_stats.py at commit 90b326d, no license · at the source

Overview

Authors: Alessandra Pizzuti1,2, Pierre-Louis Bazin3, Dimo Ivanov1, Sebastian Dresbach1,4, Judith C Peters1, Rainer Goebel1,2, Omer Faruk Gulban1,2
ORCID iDs: Alessandra Pizzuti, Pierre-Louis Bazin, Dimo Ivanov, Sebastian Dresbach, Judith C Peters, Rainer Goebel, Omer Faruk Gulban
  1. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
  2. Brain Innovation B.V., Maastricht, Netherlands
  3. Full brain picture Analytics, Leiden, Netherlands
  4. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Institutions: Maastricht University (Netherlands); Brain Innovation (Netherlands) (Netherlands); Max Planck Institute for Human Cognitive and Brain Sciences (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1279
Dates: received 18 November 2024; accepted 21 May 2026; published online 14 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1279 · PMID 42459498 · PMCID PMC13370752 · OpenAlex W4404500731
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.11.18.624072
Status: code verified
Categories: histology / microscopy (modality), systems (subfield)
Methods: Connectivity, Statistics, Preprocessing, Machine learning, fMRI & imaging
Keywords: cortical layers, 7T, microscopy, qMRI, R2*, vision
Topic: Infrared Target Detection Methodologies (Aerospace Engineering, Engineering), according to OpenAlex
Funding: H2020 Excellent Science (H2020-860563, 945539)
Citations: not cited yet (Europe PMC); 90 references in the paper

Abstract

Understanding how brain structure gives rise to function remains a central challenge in neuroscience. Post-mortem histology provides unparalleled microstructural insight into cytoarchitecture, myeloarchitecture, and cell-type distributions, yet lacks information on functional coupling. Conversely, in-vivo MRI can reveal functional dynamics but with limited microstructural specificity. Bridging these modalities is, therefore, essential for advancing our understanding of cortical organization. Here, we investigate laminar organization across the human visual hierarchy by integrating post-mortem and in-vivo imaging. Specifically, we combined post-mortem histology and quantitative MRI (qMRI) from the Alkemade and colleagues’ dataset (Alkemade et al., 2022) with in-vivo ultra-high resolution q T2* MRI (0.35 mm isotropic) and resting-state layer-fMRI (0.8 mm isotropic), focusing on areas V1, V2, V3, and hMT+. Among post-mortem measures, parvalbumin (PV) interneuron distributions across cortical layers best discriminated visual areas, outperforming cell body density (Nissl) and fiber density (Bielschowsky). Furthermore, comparing laminar R2* profiles across modalities revealed systematic differences between post-mortem and in-vivo MRI, attributable to the absence of vascular contributions in post-mortem data. Extending these laminar analyses to resting-state fMRI acquisition (here only the temporal mean is used for the laminar profiles) represents a first step toward linking structural and functional profiles across layers. Finally, we make our analysis framework publicly available to enable broader exploration of laminar organization across cortical systems using the Alkemade et al. (2022) dataset. This integrative approach sets the stage for future frameworks that unite microstructural and functional data, advancing the development of next-generation models of cortical computation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

27-apizzuti/multimodal_layers

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 90b326dbede25de4f8f7d4601ef773ff570712c1, 15 November 2024
Languages: Python (62), Shell (5), MATLAB (4)
Size: 83 files, 71 scripts
Software Heritage: not archived
Found in: the text, “Materials and Methods”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (61 files), NiBabel (54 files), Matplotlib (14 files), SciPy (9 files), FSL (5 files), ANTs (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
72 files
At the source: github.com/27-apizzuti/multimodal_layers

27-apizzuti/meso_motionquartet

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4c453809a2d9a15807f087b3781d31e826499849, 4 February 2025
Languages: Python (129), Shell (10)
Size: 164 files, 139 scripts
Software Heritage: not archived
Found in: the text, “In-vivo MRI dataset”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (117 files), NiBabel (84 files), SciPy (26 files), Matplotlib (25 files), FSL (10 files), PsychoPy (8 files), pandas (7 files), ANTs (6 files), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
140 files
At the source: github.com/27-apizzuti/meso_MotionQuartet/blob/main/Protocols/sub-07_sess-01.pdf

ofgulban/segmentator

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 01f46b36152734d67495417528fcc384eb567732, 7 June 2023
Languages: Python (21), C (1)
Size: 38 files, 22 scripts
Software Heritage: archived
Found in: the text, “Anatomical MRI”
Holds: README, license file, CITATION.cff, environment (requirements.txt, setup.py), tests, continuous integration
Not found: documentation
Tools: NumPy (14 files), Matplotlib (10 files), NiBabel (9 files), SciPy (7 files), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
24 files
At the source: github.com/ofgulban/segmentator/wiki/Deriche-filter

figshare 26045911

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 232 scripts, each with its path and the digest of its content;
  • 14 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 and Code Availability

Analysis code is available on GitHub: https://github.com/27-apizzuti/multimodal_layers.git. Refer to Alkemade et al. (2022) for raw post-mortem data. In-vivo anatomical data are shared in Zenodo: https://doi.org/10.5281/zenodo.14147820. Raw resting-state laminar-fMRI data are shared in Zenodo: https://doi.org/10.5281/zenodo.14164885. Preprocessed data from both post-mortem and in-vivo datasets are shared in Zenodo: https://doi.org/10.5281/zenodo.14164885

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 2, 28 September 2026

  • Funding: added H2020 Excellent Science: H2020-860563, 945539

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 89 references.

Cite

This paper

Pizzuti, A., Bazin, P.-L., Ivanov, D., Dresbach, S., Peters, J. C., Goebel, R., & Gulban, O. F. (2026). Multimodal laminar characterization of visual areas along the cortical hierarchy. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1279. https://doi.org/10.1162/imag.a.1279

BibTeX

@article{pizzuti2026multimodal,
author = {Pizzuti, Alessandra and Bazin, Pierre-Louis and Ivanov, Dimo and Dresbach, Sebastian and Peters, Judith C and Goebel, Rainer and Gulban, Omer Faruk},
title = {{Multimodal laminar characterization of visual areas along the cortical hierarchy}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1279},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1279},
url = {https://doi.org/10.1162/imag.a.1279},
pmid = {42459498},
pmcid = {PMC13370752}
}

RIS

TY - JOUR
AU - Pizzuti, Alessandra
AU - Bazin, Pierre-Louis
AU - Ivanov, Dimo
AU - Dresbach, Sebastian
AU - Peters, Judith C
AU - Goebel, Rainer
AU - Gulban, Omer Faruk
TI - Multimodal laminar characterization of visual areas along the cortical hierarchy
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/14
VL - 4
SP - IMAG.a.1279
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1279
UR - https://doi.org/10.1162/imag.a.1279
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1279",
"type": "article-journal",
"title": "Multimodal laminar characterization of visual areas along the cortical hierarchy",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Pizzuti",
"given": "Alessandra"
},
{
"family": "Bazin",
"given": "Pierre-Louis"
},
{
"family": "Ivanov",
"given": "Dimo"
},
{
"family": "Dresbach",
"given": "Sebastian"
},
{
"family": "Peters",
"given": "Judith C"
},
{
"family": "Goebel",
"given": "Rainer"
},
{
"family": "Gulban",
"given": "Omer Faruk"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1279",
"DOI": "10.1162/imag.a.1279",
"PMID": "42459498",
"PMCID": "PMC13370752",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1279",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
14
]
]
}
}

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:10.1162/imag.a.1197 [code]
Blood volume-sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, FSL, systems, 10 references
[2] doi:10.1162/imag.a.1212 [code]
Intracortical microstructure profiling: A cross-modal method for indexing cortical lamination.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, FSL, NiBabel, 3 other tools, histology / microscopy, 7 references
[3] doi:10.1038/s41467-026-73366-9 [code]
Cortical and white matter myelination proceed in concert during early infancy.
Journal: Nature communications
In common: scikit-image, NiBabel, statsmodels, 4 other tools, 6 references
[4] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: ANTs, FSL, scikit-image, 6 other tools, systems, 2 references
[5] doi:10.1038/s41597-026-06869-1 [code]
Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.
Journal: Scientific data
In common: PsychoPy, ANTs, FSL, 6 other tools, 1 reference
[6] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: ANTs, FSL, NiBabel, 5 other tools, 2 references
[7] doi:10.1038/s41467-026-72605-3 [code]
Resolving mesoscale brainstem-prefrontal-striatal pathways underlying decisions upon salient events using submillimeter-resolution fMRI.
Journal: Nature communications
In common: PsychoPy, ANTs, statsmodels, 4 other tools, 2 references
[8] doi:10.1002/alz.71649 [code]
Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: ANTs, FSL, scikit-image, 6 other tools, histology / microscopy
[9] doi:10.21203/rs.3.rs-9326213/v1 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: Research Square (preprint)
In common: PsychoPy, ANTs, FSL, 5 other tools, 1 reference
[10] doi:10.64898/2026.03.09.710558 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: bioRxiv (preprint)
In common: PsychoPy, ANTs, FSL, 5 other tools, 1 reference

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.