Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging.

Code ↔ Paper

13 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 13 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Method › Phantom and In Vivo Studies › Data Processing and Analysis ↔ tractseg/data/dataset_specific_utils.py, lines 13–74 · score 0.98 · ATR_left, ATR_right, AF_right, FPT_left, FPT_right, MLF_left
[2] § Theory ↔ Function/BMP_solution_ihMT.m, the whole file · a weak match · score 0.91 · local dipolar field, longitudinal relaxation rate, equilibrium magnetizations, exchange rates, dipolar relaxation, frequency offset
[3] § Theory ↔ Function/cal_RATIO_dosl_acquired.m, lines 40–147 · score 0.77 · absorption lineshape, super Lorentzian, frequency offset, transverse, spin lock, field
[4] § Method › Simulation Studies › Simulation Study 1: Accuracy of Quantification › Accuracy of Quantification ↔ Simulation_study_1/Accuracry_of_T1D_quantification/Simulation_study1_2.m, lines 31–83 · score 0.72 · 200–800 Hz, ground truth, 2–7 kHz, spin lock, TSL, pulses
[5] § Method › Acquisition Scheme ↔ Function/cal_RATIO_dosl_acquired.m, lines 40–147 · score 0.71 · ihMT, dual frequency, frequency offsets, spin lock, sequence, duration
[6] § Results › Simulation Studies ↔ Simulation_study_1/Accuracry_of_T1D_quantification/Simulation_study1_2.m, lines 31–83 · score 0.71 · 200–800 Hz, ground truth, frequency offset, 2–7 kHz, spin lock, duration
[7] § Method › Calculation of ↔ Simulation_stuty_2/Robustness_B1_B0/Simulation_Study2_2_B1_B0.m, lines 35–66 · score 0.70 · spin lock parameters, RF pulses, water pool, tissue parameters, simulated, model
[8] § Method › Simulation Studies › Simulation Study 2: Robustness of the Proposed Method › Robustness in Presence of B1 and B0 Inhomogeneity ↔ Simulation_stuty_2/Robustness_B1_B0/Simulation_Study2_2_B1_B0.m, lines 35–66 · score 0.64 · 0.7–1.3, tissue parameters, spin lock, inhomogeneities, robustness, B0
[9] § Method › Simulation Studies › Simulation Study 1: Accuracy of Quantification › Accuracy of Approximated ↔ Simulation_study_1/Accuracy_of_RATIOdosl/Simulation_study1_1a.m, lines 1–28 · score 0.60 · exact numerical solution, tissue parameters, accuracy, validate, sensitivity, analytical
[10] § Method › Simulation Studies › Simulation Study 1: Accuracy of Quantification › Accuracy of Approximated ↔ Simulation_study_1/Accuracy_of_RATIOdosl/Simulation_study1_1b.m, lines 1–41 · score 0.60 · exact numerical solution, tissue parameters, accuracy, sensitivity, analytical
[11] § Results › Simulation Studies ↔ Simulation_study_1/Accuracry_of_T1D_quantification/Simulation_study1_2_plot.m, lines 105–157 · score 0.54 · RF amplitude, ground truth, relative error, quantification, Simulation
[12] § Theory ↔ Function/BMP_solution_ihMT.m, the whole file · a weak match · score 0.54 · ihMT, relaxation rate, exchange, field, dipolar
[13] § Method › Simulation Studies › Simulation Study 2: Robustness of the Proposed Method › Robustness Against Noise ↔ Simulation_stuty_2/Robustness_noise/Simulation_stuty2_SNR.m, lines 1–32 · score 0.52 · noise ratio, SNR, signal, Simulated, Robustness

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 paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 51 lines · 2.1 KB · MIT · 2 matches

function M_t = BMP_solution_ihMT(t, M, R1a, R2a, dw, w1, w2, kab, kba, M0a, M0b, T2b, R1b, T1d, rfmt)
% BMP_SOLUTION_IHMT Solves the Bloch-McConnell-Provotorov equations for ihMT.
%
% dM/dt = A * M + C
%
% Inputs:
% t - Evolution time duration (s)
% M - Initial magnetization state vector [Mxa; Mya; Mza; Mzb; Md]
% R1a - Longitudinal relaxation rate of free pool (1/s)
% R2a - Transverse relaxation rate of free pool (1/s)
% dw - Frequency offset (rad/s)
% w1 - RF amplitude x-component (rad/s)
% w2 - RF amplitude y-component (rad/s)
% kab - Exchange rate from pool A to B (1/s)
% kba - Exchange rate from pool B to A (1/s)
% M0a - Equilibrium magnetization of free pool
% M0b - Equilibrium magnetization of bound pool
% T2b - T2 of bound pool, the lineshape prameter
% R1b - Longitudinal relaxation rate of bound pool (1/s)
% T1d - Dipolar relaxation time (s)
% rfmt - RF saturation rate for the bound pool (1/s)
%
% Output:
% M_t - Magnetization vector at time t
%% 1. Define Physical Constants
% Local dipolar field strength (D)
D = 1/sqrt(15)/T2b;
% Initial state
M_init = M;
%% 2. System Matrix A
% Represents relaxation, precession, exchange, and RF saturation interactions.
% State vector: [Mxa, Mya, Mza, Mzb, beta]'
A = [-R2a, dw, -w2, 0, 0; % Mxa
-dw, -R2a, w1, 0, 0; % Mya
w2, -w1, -(R1a + kab), kba, 0; % Mza
0, 0, kab, -(R1b + kba + rfmt), rfmt * dw; % Mzb
0, 0, 0, rfmt * dw / (D^2), -(1/T1d + rfmt * (dw / D)^2)]; % beta
%% 3. Recovery Vector C
% Drives the system towards thermal equilibrium.
C = [0; 0; R1a * M0a; R1b * M0b; 0];
%% 4. Analytical Solution
% Solves the linear differential equation dM/dt = A*M + C
% Solution: M(t) = exp(A*t) * (M_init - M_ss) + M_ss, where M_ss = -inv(A)*C
M_t = expm(A * t) * (M_init + A \ C) - A \ C;
end

BMP_solution_ihMT.m at commit 37033dc, under MIT · at the source

Overview

Authors: Zijian Gao¹, Qianxue Shan¹, Ziqin Zhou^1,2, Ziqiang Yu¹, Weitian Chen¹

ORCID iDs: Weitian Chen

Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong
MR Research Collaboration, Siemens Healthineers Ltd. Hong Kong

Institutions: Chinese University of Hong Kong (Hong Kong SAR China)

Journal: NMR in biomedicine, volume 39, issue 7, article e70331

Dates: received 5 March 2026; accepted 26 May 2026; published online 17 June 2026; in print July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/nbm.70331 · PMID 42309541 · PMCID PMC13275185 · OpenAlex W4415981657

Open access: hybrid, a free copy (OpenAlex)

Preprint: arxiv:2510.02847

Status: code verified

Categories: structural MRI / diffusion (modality), human (organism)

Methods: Connectivity, fMRI & imaging

Keywords: dipolar order, inhomogeneous magnetization transfer, spin‐lock

MeSH: Magnetic Resonance Imaging*, Spin Labels*, Algorithms, Brain, Humans, Phantoms, Imaging (* major topic)

Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Funding: Research Grants Council of the Hong Kong SAR (GRF 14213322)

Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

Inhomogeneous magnetization transfer (ihMT) is sensitive to dipolar order associated with motion‐restricted macromolecules and can be characterized by the dipolar relaxation time T1D. In this study, we propose a spin‐lock MRI framework for T1D quantification. Specifically, we introduce a T1D‐sensitive metric, RATIOdosl, derived from the distinct relaxation rate Rdosl, defined as the difference between dual‐frequency and single‐frequency R1ρ measurements. To enable dual‐frequency spin‐lock acquisition, we developed a dedicated rotary‐echo spin‐lock sequence. Based on this framework, we further estimated T1D and the macromolecular proton fraction (MPF) within a unified acquisition. The proposed method was evaluated using numerical simulations, phantom experiments, and in vivo imaging in the healthy human brain. Simulations demonstrated high sensitivity of RATIOdosl to T1D and supported the robustness of the proposed approach under the investigated conditions. Phantom experiments showed measurable ihMT contrast and supported the feasibility of T1D estimation using RATIOdosl. In vivo experiments demonstrated simultaneous T1D and MPF mapping using only three spin‐lock‐prepared images. Across 10 healthy volunteers, mean white matter T1D values ranged from approximately 3.70 to 4.80 ms. By requiring only three contrast‐prepared images, the proposed technique provides a rapid framework for simultaneous T1D and MPF mapping and may facilitate further investigation of dipolar‐order‐sensitive microstructural imaging in vivo.

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 13 matches between paragraphs and lines of code.

zjgao-spin/Dipolar-order-mapping

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 37033dc67cf77fd9c83797cdc1386149fdd08c7f, 27 June 2026

Languages: MATLAB (15)

Size: 19 files, 15 scripts

Software Heritage: not archived

Found in: the text, “Simulation Studies”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Optimization Toolbox (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

17 files

Function/BMP_solution_ihMT.m — MATLAB, 51 lines, 2 matches
Function/RF_MT.m — MATLAB, 32 lines
Function/cal_RATIO_dosl_acquired.m — MATLAB, 176 lines, 2 matches
Function/cal_RATIO_dosl_approx.m — MATLAB, 16 lines
Function/cal_RATIO_dosl_exact.m — MATLAB, 51 lines
Function/cal_T1d_analytical.m — MATLAB, 112 lines
Function/cal_T1d_dictionary.m — MATLAB, 17 lines
Simulation_study_1/Accuracry_of_T1D_quantification/Simulation_study1_2.m — MATLAB, 148 lines, 2 matches
Simulation_study_1/Accuracry_of_T1D_quantification/Simulation_study1_2_plot.m — MATLAB, 232 lines, 1 match
Simulation_study_1/Accuracy_of_RATIOdosl/Simulation_study1_1a.m — MATLAB, 257 lines, 1 match
Simulation_study_1/Accuracy_of_RATIOdosl/Simulation_study1_1b.m — MATLAB, 277 lines, 1 match
Simulation_stuty_2/Robustness_B1_B0/Simulation_Study2_1_B1_B0.m — MATLAB, 121 lines
Simulation_stuty_2/Robustness_B1_B0/Simulation_Study2_2_B1_B0.m — MATLAB, 212 lines, 2 matches
Simulation_stuty_2/Robustness_noise/Simulation_stuty2_SNR.m — MATLAB, 198 lines, 1 match
Simulation_stuty_2/Robustness_tissue_parameters/Simulation_study_2_tissue.m — MATLAB, 201 lines
LICENSE — License, 21 lines
README.md — Text, 11 lines

At the source: github.com/zjgao-spin/Dipolar-order-mapping

MIC-DKFZ/TractSeg

License: Apache-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: bff94975b0950385b4880050b8c9a095c7257adb, 23 February 2026

Languages: Python (102), Shell (3), Jupyter (1)

Size: 519 files, 106 scripts

Software Heritage: not archived

Found in: the text, “Data Processing and Analysis”

Holds: README, license file, environment (setup.py), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: NumPy (41 files), NiBabel (17 files), DIPY (10 files), PyTorch (10 files), SciPy (10 files), Matplotlib (3 files), MRtrix3 (3 files), scikit-learn (3 files), seaborn (3 files), FSL (2 files), pandas (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

108 files

examples/.ipynb_checkpoints/plot_tractometry_results-checkpoint.ipynb — Jupyter, 68 lines
resources/utility_scripts/__init__.py — Python, 1 line
resources/utility_scripts/apply_brain_mask.py — Python, 15 lines
resources/utility_scripts/dwi_preprocessing.sh — Shell, 173 lines
resources/utility_scripts/process_subjects.py — Python, 38 lines
resources/utility_scripts/trk_2_binary.py — Python, 102 lines
setup.py — Python, 45 lines
tests.sh — Shell, 61 lines
tests/__init__.py — Python, 1 line
tests/reference_files/create_toy_streamlines.py — Python, 74 lines
tests/reference_files/create_toy_streamlines_offset_and_invert.py — Python, 85 lines
tests/test_end_to_end.py — Python, 203 lines
tests/test_functions.py — Python, 21 lines
tractseg/__init__.py — Python, 1 line
tractseg/data/DLDABG_standalone.py — Python, 157 lines
tractseg/data/__init__.py — Python, 1 line
tractseg/data/check_data.py — Python, 52 lines
tractseg/data/custom_transformations.py — Python, 120 lines
tractseg/data/data_loader_inference.py — Python, 184 lines
tractseg/data/data_loader_precomputed.py — Python, 87 lines
tractseg/data/data_loader_training.py — Python, 433 lines
tractseg/data/data_loader_training_3D.py — Python, 159 lines
tractseg/data/dataset_specific_utils.py — Python, 615 lines, 1 match
tractseg/data/preprocessing.py — Python, 122 lines
tractseg/data/spatial_transform_custom.py — Python, 224 lines
tractseg/data/spatial_transform_peaks.py — Python, 318 lines
tractseg/data/subjects.py — Python, 117 lines
tractseg/experiments/__init__.py — Python, 1 line
tractseg/experiments/backup/TractSeg_BX.py — Python, 27 lines
tractseg/experiments/backup/TractSeg_HR_DAug_Fast_TEST.py — Python, 23 lines
tractseg/experiments/base.py — Python, 121 lines
tractseg/experiments/base_legacy/__init__.py — Python, 1 line
tractseg/experiments/base_legacy/dm_reg_legacy.py — Python, 18 lines
tractseg/experiments/base_legacy/dm_reg_lowres_legacy.py — Python, 9 lines
tractseg/experiments/base_legacy/endings_seg_legacy.py — Python, 16 lines
tractseg/experiments/base_legacy/endings_seg_lowres_legacy.py — Python, 9 lines
tractseg/experiments/base_legacy/peak_reg_legacy.py — Python, 20 lines
tractseg/experiments/base_legacy/peak_reg_lowres_legacy.py — Python, 9 lines
tractseg/experiments/base_legacy/tract_seg_legacy.py — Python, 13 lines
tractseg/experiments/base_legacy/tract_seg_lowres_legacy.py — Python, 9 lines
tractseg/experiments/custom/my_custom_experiment.py — Python, 12 lines
tractseg/experiments/dm_reg.py — Python, 10 lines
tractseg/experiments/dm_reg_lowres.py — Python, 9 lines
tractseg/experiments/endings_seg.py — Python, 13 lines
tractseg/experiments/endings_seg_lowres.py — Python, 9 lines
tractseg/experiments/peak_reg.py — Python, 20 lines
tractseg/experiments/peak_reg_angle.py — Python, 14 lines
tractseg/experiments/peak_reg_lowres.py — Python, 9 lines
tractseg/experiments/pretrained_models/DmReg.py — Python, 9 lines
tractseg/experiments/pretrained_models/DmReg_All_BXTensAg_aPTX_platLR20_noMiss.py — Python, 29 lines
tractseg/experiments/pretrained_models/DmReg_All_xtract_PeakRot4.py — Python, 29 lines
tractseg/experiments/pretrained_models/EndingsSeg_PeakRot4.py — Python, 10 lines
tractseg/experiments/pretrained_models/Peaks_AngL.py — Python, 8 lines
tractseg/experiments/pretrained_models/TractSeg72_888.py — Python, 7 lines
tractseg/experiments/pretrained_models/TractSeg_All_BXTensAg_aPTX_platLR20.py — Python, 26 lines
tractseg/experiments/pretrained_models/TractSeg_All_xtract_PeakRot4.py — Python, 25 lines
tractseg/experiments/pretrained_models/TractSeg_BXTensAg.py — Python, 16 lines
tractseg/experiments/pretrained_models/TractSeg_HR_3D_DAug.py — Python, 26 lines
tractseg/experiments/pretrained_models/TractSeg_PeakRot4.py — Python, 15 lines
tractseg/experiments/pretrained_models/TractSeg_T1_125mm_DAugAll.py — Python, 8 lines
tractseg/experiments/pretrained_models/TractSeg_T1_12g90g270g_125mm_DAugAll.py — Python, 11 lines
tractseg/experiments/pretrained_models/__init__.py — Python, 1 line
tractseg/experiments/pretrained_models/old_1/EndingsSeg_12g90g270g_125mm_DAugAll.py — Python, 9 lines
tractseg/experiments/pretrained_models/old_1/Peaks20_12g90g270g_125mm.py — Python, 12 lines
tractseg/experiments/pretrained_models/old_1/Peaks20_12g90g270g_125mm_DS_DAugAll.py — Python, 12 lines
tractseg/experiments/pretrained_models/old_1/Peaks20_270g_125mm.py — Python, 9 lines
tractseg/experiments/pretrained_models/old_1/Peaks_12g90g270g_125mm_DS_DAugAll.py — Python, 12 lines
tractseg/experiments/pretrained_models/old_1/TractSeg_12g90g270g_125mm_DAugAll_Dropout.py — Python, 10 lines
tractseg/experiments/pretrained_models/old_1/__init__.py — Python, 1 line
tractseg/experiments/pretrained_models/old_2/DmReg_12g90g270g_125mm_DAugAll.py — Python, 10 lines
tractseg/experiments/pretrained_models/old_2/EndingsSeg_12g90g270g_125mm_DS_DAugAll.py — Python, 6 lines
tractseg/experiments/pretrained_models/old_2/TractSeg_12g90g270g_125mm_DS_DAugAll.py — Python, 7 lines
tractseg/experiments/pretrained_models/old_2/TractSeg_12g90g270g_125mm_DS_DAugAll_Dropout.py — Python, 8 lines
tractseg/experiments/pretrained_models/old_2/__init__.py — Python, 1 line
tractseg/experiments/run_multiple.sh — Shell, 39 lines
tractseg/experiments/tract_seg.py — Python, 10 lines
tractseg/experiments/tract_seg_lowres.py — Python, 9 lines
tractseg/libs/AFQ_MultiCompCorrection.py — Python, 186 lines
tractseg/libs/__init__.py — Python, 1 line
tractseg/libs/create_endpoints_mask_with_clustering.py — Python, 147 lines
tractseg/libs/data_utils.py — Python, 231 lines
tractseg/libs/direction_merger.py — Python, 112 lines
tractseg/libs/exp_utils.py — Python, 182 lines
tractseg/libs/fiber_utils.py — Python, 486 lines
tractseg/libs/img_utils.py — Python, 708 lines
tractseg/libs/metric_utils.py — Python, 382 lines
tractseg/libs/peak_utils.py — Python, 277 lines
tractseg/libs/plot_utils.py — Python, 495 lines
tractseg/libs/preprocessing.py — Python, 212 lines
tractseg/libs/pytorch_einsum.py — Python, 393 lines
tractseg/libs/pytorch_utils.py — Python, 327 lines
tractseg/libs/system_config.py — Python, 104 lines
tractseg/libs/tracking.py — Python, 259 lines
tractseg/libs/tractometry.py — Python, 201 lines
tractseg/libs/tractseg_prob_tracking.py — Python, 311 lines
tractseg/libs/trainer.py — Python, 378 lines
tractseg/libs/utils.py — Python, 180 lines
tractseg/libs/vtk_utils.py — Python, 155 lines
tractseg/models/__init__.py — Python, 1 line
tractseg/models/base_model.py — Python, 285 lines
tractseg/models/unet3d_pytorch_deepsup_sm.py — Python, 110 lines
tractseg/models/unet_pytorch.py — Python, 111 lines
tractseg/models/unet_pytorch_deepsup.py — Python, 127 lines
tractseg/models/unet_pytorch_deepsup_test.py — Python, 127 lines
tractseg/python_api.py — Python, 253 lines
tractseg/resources/__init__.py — Python, 1 line
LICENSE — License, 201 lines
Readme.md — Text, 419 lines

At the source: github.com/MIC-DKFZ/TractSeg

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
121 scripts, each with its path and the digest of its content;
13 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 Statement

The data that support 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.

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

Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 6 MeSH terms, 1 funder, 45 references.

Cite

This paper

Gao, Z., Shan, Q., Zhou, Z., Yu, Z., & Chen, W. (2026). Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging. NMR in biomedicine, 39(7), e70331. https://doi.org/10.1002/nbm.70331

BibTeX

@article{gao2026dipolar, author = {Gao, Zijian and Shan, Qianxue and Zhou, Ziqin and Yu, Ziqiang and Chen, Weitian}, title = {{Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging}}, journal = {NMR in biomedicine}, year = {2026}, month = jul, volume = {39}, number = {7}, pages = {e70331}, publisher = {Wiley}, issn = {0952-3480}, doi = {10.1002/nbm.70331}, url = {https://doi.org/10.1002/nbm.70331}, pmid = {42309541}, pmcid = {PMC13275185} }

RIS

TY - JOUR AU - Gao, Zijian AU - Shan, Qianxue AU - Zhou, Ziqin AU - Yu, Ziqiang AU - Chen, Weitian TI - Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging T2 - NMR in biomedicine J2 - NMR Biomed PY - 2026 DA - 2026/07/01 VL - 39 IS - 7 SP - e70331 SN - 0952-3480 PB - Wiley DO - 10.1002/nbm.70331 UR - https://doi.org/10.1002/nbm.70331 LA - en ER -

CSL-JSON

{ "id": "10.1002/nbm.70331", "type": "article-journal", "title": "Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging", "container-title": "NMR in biomedicine", "author": [ { "family": "Gao", "given": "Zijian" }, { "family": "Shan", "given": "Qianxue" }, { "family": "Zhou", "given": "Ziqin" }, { "family": "Yu", "given": "Ziqiang" }, { "family": "Chen", "given": "Weitian" } ], "container-title-short": "NMR Biomed", "volume": "39", "issue": "7", "page": "e70331", "DOI": "10.1002/nbm.70331", "PMID": "42309541", "PMCID": "PMC13275185", "ISSN": "0952-3480", "publisher": "Wiley", "URL": "https://doi.org/10.1002/nbm.70331", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 1 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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.