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A large-scale fMRI dataset for vision-language semantic association.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data preprocessing pipeline ↔ preprocess/surf_preprocess.sh, the whole file · a weak match · score 0.71 · FreeSurfer, mri_vol2surf, boundary, FSL, pipeline, resampling
  2. [2] § Methods › Data preprocessing pipeline ↔ preprocess/S3_preprocess.sh, lines 41–94 · score 0.67 · distortion corrected, fMRI, topup, FSL, Motion, Slice
  3. [3] § Methods › Data preprocessing pipeline ↔ preprocess/surf_preprocess.sh, the whole file · a weak match · score 0.56 · FreeSurfer, recon, pipeline, FSL, MRI, surfaces
  4. [4] § Technical Validation › Encoding model results ↔ encoding/layer_preference.py, lines 171–256 · score 0.51 · CLIP model, layer, BERT, linear, Encoding
  5. [5] § Data Records ↔ preprocess/S3_preprocess.sh, lines 41–94 · score 0.51 · distortion correction, fMRI, motion, slice, volume, gz
  6. [6] § Technical Validation › Head motion ↔ preprocess/generate_info.py, lines 108–147 · score 0.51 · framewise displacement, rotational, translational, FD
  7. [7] § Technical Validation › Encoding model results ↔ encoding/layer_preference.py, lines 171–256 · score 0.50 · weight decay parameter, optimized, Encoding, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Shell · 56 lines · 2.1 KB · CC-BY-NC-ND-4.0 · 2 matches

  1. module add apps/fsl/6.0
  2. source ~/anaconda3/etc/profile.d/conda.sh
  3. conda activate tats
  5. export FREESURFER_HOME=/public/home/lishr2022/freesurfer
  6. export SUBJECTS_DIR=/public_bme/data/lishr/Cross_modal/subjects
  7. export FSFAST_HOME=/public/home/lishr2022/freesurfer/fsfast
  8. export MNI_DIR=/public/home/lishr2022/freesurfer/mni
  9. export FS_LICENSE=/public/home/lishr2022/freesurfer/license.txt
  11. source $FSLDIR/etc/fslconf/fsl.sh
  12. source $FREESURFER_HOME/SetUpFreeSurfer.sh
  14. echo "Begin"
  15. date
  16. RootPath="/public/home/lishr2022/Project/Cross-modal/pipeline"
  18. echo "工作目录: $1"
  20. ResultsPath=$1 #ResultsPath是数据处理后最终保存的位置
  21. T1=$2
  22. OutputF2S="${ResultsPath}/F2S"
  23. OutputFL="${ResultsPath}/FL"
  24. OutputSURF="${ResultsPath}/SURF"
  26. AP_fl=${OutputFL}/AP_fMRIAfterfilterAddMean.nii.gz
  27. PA_fl=${OutputFL}/PA_fMRIAfterfilterAddMean.nii.gz
  28. RefAP=${OutputF2S}/AP_postvols/vol0_jac.nii.gz
  29. RefPA=${OutputF2S}/PA_postvols/vol0_jac.nii.gz
  31. echo "Recon all by FreeSurfer"
  32. date
  33. echo "########################################"
  34. subject_name=$3
  35. OutputREG="${ResultsPath}/REG"
  37. # recon-all -i $T1 -subjid $subject_name -all -openmp 8
  39. echo "Boundary-based register by FSL"
  40. date
  41. echo "########################################"
  42. mkdir -p ${OutputREG}
  43. bbregister --s ${subject_name} --mov $RefAP --init-fsl --reg ${OutputREG}/registerAP.dat --bold
  44. bbregister --s ${subject_name} --mov $RefPA --init-fsl --reg ${OutputREG}/registerPA.dat --bold
  46. echo "Resample the data onto the surface"
  47. date
  48. echo "########################################"
  50. mkdir -p ${OutputSURF}
  52. mri_vol2surf --mov ${AP_fl} --reg ${OutputREG}/registerAP.dat --projfrac 0.5 --interp nearest --hemi lh --o ${OutputSURF}/AP_lh_surf.mgh
  53. mri_vol2surf --mov ${AP_fl} --reg ${OutputREG}/registerAP.dat --projfrac 0.5 --interp nearest --hemi rh --o ${OutputSURF}/AP_rh_surf.mgh
  55. mri_vol2surf --mov ${PA_fl} --reg ${OutputREG}/registerPA.dat --projfrac 0.5 --interp nearest --hemi lh --o ${OutputSURF}/PA_lh_surf.mgh
  56. mri_vol2surf --mov ${PA_fl} --reg ${OutputREG}/registerPA.dat --projfrac 0.5 --interp nearest --hemi rh --o ${OutputSURF}/PA_rh_surf.mgh

surf_preprocess.sh at commit 84b2db7, under CC-BY-NC-ND-4.0 · at the source

Overview

Authors: Shurui Li1,2,3, Zheyu Jin1,2, Shi Gu4,5, Ru-Yuan Zhang6,7,8,9, Yuanning Li1,2,3,10
ORCID iDs: Zheyu Jin
  1. School of Biomedical Engineering, ShanghaiTech University,Shanghai, China
  2. State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University,Shanghai, China
  3. Shanghai Artificial Intelligence Laboratory,Shanghai, China
  4. College of Computer Science and Technology, Zhejiang University,Hangzhou, China
  5. State Key Laboratory of Brain Machine Intelligence, Zhejiang University,Hangzhou, China
  6. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University,Beijing, China
  7. IDG/McGovern Institute for Brain Research, Peking University,Beijing, China
  8. Key Laboratory of Machine Perception (Ministry of Education), Peking University,Beijing, China
  9. Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology,Shanghai, China
  10. Shanghai Clinical Research and Trial Center,Shanghai, China
Institutions: ShanghaiTech University (China); Shanghai Artificial Intelligence Laboratory (China); Zhejiang University (China); Peking University (China); Shanghai Jiao Tong University (China); Shanghai Clinical Research Center (China)
Journal: Scientific data, volume 13, issue 1, article 905
Dates: received 27 November 2025; accepted 13 April 2026; published online 18 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07248-6 · PMID 42000762 · PMCID PMC13276023 · OpenAlex W7154824657
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
MeSH: Brain*, Language*, Magnetic Resonance Imaging*, Semantics*, Visual Perception*, Deep Learning, Humans (* major topic)
Journal subjects: Data Descriptor
Topic: Multimodal Machine Learning Applications (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: National Science and Technology Major Project (2025ZD0217000); National Natural Science Foundation of China (National Science Foundation of China) (32371154); Shanghai Science and Technology Committee (24QA2705500); Lin Gang Laboratory (LG-GG-202402-06)
Citations: not cited yet (Europe PMC); 64 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

lishurui0612/caption_scene_dataset

License: CC-BY-NC-ND-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 84b2db73278e346406242455f7e67d59d86b0919, 9 July 2026
Languages: Python (18), Shell (11), MATLAB (1)
Size: 48 files, 30 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (18 files), SciPy (13 files), NiBabel (12 files), Matplotlib (11 files), FreeSurfer (9 files), FSL (9 files), imageio (7 files), Nilearn (5 files), pandas (5 files), Pillow (3 files), PyTorch (3 files), h5py (2 files), OpenCV (2 files), scikit-learn (2 files), ANTs (1 file), dcm2niix (1 file), GLMsingle (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
32 files
At the source: github.com/lishurui0612/caption_scene_dataset

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/s41597-026-07248-6.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 30 scripts, each with its path and the digest of its content;
  • 7 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 paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41597-026-07248-6.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 MeSH terms, 4 funders, 60 references.

Cite

This paper

Li, S., Jin, Z., Gu, S., Zhang, R.-Y., & Li, Y. (2026). A large-scale fMRI dataset for vision-language semantic association. Scientific data, 13(1), 905. https://doi.org/10.1038/s41597-026-07248-6

BibTeX

@article{li2026large,
author = {Li, Shurui and Jin, Zheyu and Gu, Shi and Zhang, Ru-Yuan and Li, Yuanning},
title = {{A large-scale fMRI dataset for vision-language semantic association}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {905},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07248-6},
url = {https://doi.org/10.1038/s41597-026-07248-6},
pmid = {42000762},
pmcid = {PMC13276023}
}

RIS

TY - JOUR
AU - Li, Shurui
AU - Jin, Zheyu
AU - Gu, Shi
AU - Zhang, Ru-Yuan
AU - Li, Yuanning
TI - A large-scale fMRI dataset for vision-language semantic association
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/18
VL - 13
IS - 1
SP - 905
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07248-6
UR - https://doi.org/10.1038/s41597-026-07248-6
LA - en
ER -

CSL-JSON

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"date-parts": [
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