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Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report.

Code ↔ Paper

2 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 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data records ↔ dcm2bids/utils/scaffold.py, the whole file · a weak match · score 0.63 · participants.tsv, clinical information, variables, BIDS
  2. [2] § Methods › Data records ↔ dcm2bids/cli/dcm2bids_scaffold.py, lines 44–115 · score 0.59 · participants.json, participants.tsv, platform, BIDS

Paper

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The authors' code

Python · 214 lines · 6.6 KB · GPL-3.0 · 1 match

  1. # -*- coding: utf-8 -*-
  3. class bids_starter_kit(object):
  5. CHANGES = """Revision history for your dataset
  7. 1.0.0 DATE
  8. - Initialized study directory
  9. """
  11. dataset_description = """{
  12. "Name": "",
  13. "BIDSVersion": "BIDS_VERSION",
  14. "License": "",
  15. "Authors": [
  16. ""
  17. ],
  18. "Acknowledgments": "",
  19. "HowToAcknowledge": "",
  20. "Funding": [
  21. ""
  22. ],
  23. "ReferencesAndLinks": [
  24. ""
  25. ],
  26. "DatasetDOI": ""
  27. }
  28. """
  30. participants_json = """{
  31. "age": {
  32. "LongName": "",
  33. "Description": "age of the participant",
  34. "Units": "years"
  35. },
  36. "sex": {
  37. "LongName": "",
  38. "Description": "sex of the participant as reported by the participant",
  39. "Levels": {
  40. "M": "male",
  41. "F": "female"
  42. }
  43. },
  44. "group": {
  45. "LongName": "",
  46. "Description": "experimental group the participant belonged to",
  47. "Levels": {
  48. "control": "control",
  49. "patient": "patient"
  50. }
  51. }
  52. }
  53. """
  55. participants_tsv = """participant_id age sex group
  56. sub-01 34 M control
  57. sub-02 12 F control
  58. sub-03 33 F patient
  59. """
  61. README = """# README
  63. The README is usually the starting point for researchers using your data
  64. and serves as a guidepost for users of your data. A clear and informative
  65. README makes your data much more usable.
  67. In general you can include information in the README that is not captured by some other
  68. files in the BIDS dataset (dataset_description.json, events.tsv, ...).
  70. It can also be useful to also include information that might already be
  71. present in another file of the dataset but might be important for users to be aware of
  72. before preprocessing or analysing the data.
  74. If the README gets too long you have the possibility to create a `/doc` folder
  75. and add it to the `.bidsignore` file to make sure it is ignored by the BIDS validator.
  77. More info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3
  79. ## Details related to access to the data
  81. - [ ] Data user agreement
  83. If the dataset requires a data user agreement, link to the relevant information.
  85. - [ ] Contact person
  87. Indicate the name and contact details (email and ORCID) of the person responsible for additional information.
  89. - [ ] Practical information to access the data
  91. If there is any special information related to access rights or
  92. how to download the data make sure to include it.
  93. For example, if the dataset was curated using datalad,
  94. make sure to include the relevant section from the datalad handbook:
  95. http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset
  97. ## Overview
  99. - [ ] Project name (if relevant)
  101. - [ ] Year(s) that the project ran
  103. If no `scans.tsv` is included, this could at least cover when the data acquisition
  104. starter and ended. Local time of day is particularly relevant to subject state.
  106. - [ ] Brief overview of the tasks in the experiment
  108. A paragraph giving an overview of the experiment. This should include the
  109. goals or purpose and a discussion about how the experiment tries to achieve
  110. these goals.
  112. - [ ] Description of the contents of the dataset
  114. An easy thing to add is the output of the bids-validator that describes what type of
  115. data and the number of subject one can expect to find in the dataset.
  117. - [ ] Independent variables
  119. A brief discussion of condition variables (sometimes called contrasts
  120. or independent variables) that were varied across the experiment.
  122. - [ ] Dependent variables
  124. A brief discussion of the response variables (sometimes called the
  125. dependent variables) that were measured and or calculated to assess
  126. the effects of varying the condition variables. This might also include
  127. questionnaires administered to assess behavioral aspects of the experiment.
  129. - [ ] Control variables
  131. A brief discussion of the control variables --- that is what aspects
  132. were explicitly controlled in this experiment. The control variables might
  133. include subject pool, environmental conditions, set up, or other things
  134. that were explicitly controlled.
  136. - [ ] Quality assessment of the data
  138. Provide a short summary of the quality of the data ideally with descriptive statistics if relevant
  139. and with a link to more comprehensive description (like with MRIQC) if possible.
  141. ## Methods
  143. ### Subjects
  145. A brief sentence about the subject pool in this experiment.
  147. Remember that `Control` or `Patient` status should be defined in the `participants.tsv`
  148. using a group column.
  150. - [ ] Information about the recruitment procedure
  151. - [ ] Subject inclusion criteria (if relevant)
  152. - [ ] Subject exclusion criteria (if relevant)
  154. ### Apparatus
  156. A summary of the equipment and environment setup for the
  157. experiment. For example, was the experiment performed in a shielded room
  158. with the subject seated in a fixed position.
  160. ### Initial setup
  162. A summary of what setup was performed when a subject arrived.
  164. ### Task organization
  166. How the tasks were organized for a session.
  167. This is particularly important because BIDS datasets usually have task data
  168. separated into different files.)
  170. - [ ] Was task order counter-balanced?
  171. - [ ] What other activities were interspersed between tasks?
  173. - [ ] In what order were the tasks and other activities performed?
  175. ### Task details
  177. As much detail as possible about the task and the events that were recorded.
  179. ### Additional data acquired
  181. A brief indication of data other than the
  182. imaging data that was acquired as part of this experiment. In addition
  183. to data from other modalities and behavioral data, this might include
  184. questionnaires and surveys, swabs, and clinical information. Indicate
  185. the availability of this data.
  187. This is especially relevant if the data are not included in a `phenotype` folder.
  188. https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data
  190. ### Experimental location
  192. This should include any additional information regarding the
  193. the geographical location and facility that cannot be included
  194. in the relevant json files.
  196. ### Missing data
  198. Mention something if some participants are missing some aspects of the data.
  199. This can take the form of a processing log and/or abnormalities about the dataset.
  201. Some examples:
  203. - A brain lesion or defect only present in one participant
  204. - Some experimental conditions missing on a given run for a participant because
  205. of some technical issue.
  206. - Any noticeable feature of the data for certain participants
  207. - Differences (even slight) in protocol for certain participants.
  209. ### Notes
  211. Any additional information or pointers to information that
  212. might be helpful to users of the dataset. Include qualitative information
  213. related to how the data acquisition went.
  214. """

scaffold.py at commit 7f5ecf1, under GPL-3.0 · at the source

Overview

Authors: Elena Filimonova1,2,3, Anton Pashkov1,2,4, Galina Moysak1,2,3, Azniv Martirosyan1, Vladimir Kurilov3, Aleksandra Poptsova1, Renata Morozova1,3, Jamil Rzaev1,2,3
ORCID iDs: Vladimir Kurilov
  1. Federal Neurosurgical Center, Novosibirsk, Russia
  2. Novosibirsk State Medical University, Novosibirsk, Russia
  3. Novosibirsk State University, Novosibirsk, Russia
  4. Novosibirsk State Technical University, Novosibirsk, Russia
Institutions: Novosibirsk State Medical University (Russia); Novosibirsk State University (Russia); Federal Neurosurgical Center Novosibirsk (Russia); Novosibirsk State Technical University (Russia)
Journal: Frontiers in radiology, volume 6, article 1809871
Dates: received 12 February 2026; accepted 20 April 2026; published online 8 May 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.3389/fradi.2026.1809871 · PMID 42182936 · PMCID PMC13194104 · OpenAlex W7160491334
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), other condition (population)
Methods: Spectral & time-frequency, fMRI & imaging
Keywords: dataset, DTI, fMRI, multimodal MRI, olfactory groove meningioma
Topic: Meningioma and schwannoma management (Epidemiology, Medicine), according to OpenAlex
Funding: Russian Science Foundation
Citations: not cited yet (Europe PMC); 14 references in the paper

Abstract

Olfactory groove meningiomas are uncommon skull base tumors that often present at advanced stages and may cause persistent cognitive and behavioral disturbances despite successful surgical treatment. Neuroimaging studies of this tumor entity have been limited, and publicly available multimodal MRI datasets remain scarce. Here, we present a prospective, single-center dataset comprising multimodal magnetic resonance imaging and longitudinal clinical data from patients with olfactory groove meningiomas acquired before and after surgical intervention. The dataset includes high-resolution structural MRI, diffusion MRI with tensor-derived metrics, resting-state functional MRI, tumor and peritumoral edema segmentation masks, and detailed clinical and neuropsychological assessments. Imaging data were acquired using a standardized protocol and processed with reproducible pipelines, including quality control and de-identification procedures, and organized in a BIDS format. This dataset is intended to support reproducible research and secondary analyses focused on tumor-related brain alterations, imaging biomarker development, and postoperative recovery in neuro-oncology.

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 2 matches between paragraphs and lines of code.

UNFmontreal/Dcm2Bids

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 7f5ecf18775444d16685624f6c3906ec8b36acf8, 16 September 2026
Languages: Python (30)
Size: 119 files, 30 scripts
Software Heritage: not archived
Found in: the text, “MRI preprocessing”
Holds: README, license file, CITATION.cff, environment (Dockerfile, environment.yml, pyproject.toml, uv.lock), tests, continuous integration, documentation
Tools: Dcm2Bids (19 files), dcm2niix (1 file), PyBIDS (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
32 files
At the source: github.com/UNFmontreal/Dcm2Bids

Code availability

We utilized openly available code to run FSL's topup and eddy, as well as MRIQC without generating any specific scripts for the data analysis.

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

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;
  • 30 scripts, each with its path and the digest of its content;
  • 2 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 availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://openneuro.org/datasets/ds007345.

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 Russian Science Foundation

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 14 references.

Cite

This paper

Filimonova, E., Pashkov, A., Moysak, G., Martirosyan, A., Kurilov, V., Poptsova, A., Morozova, R., & Rzaev, J. (2026). Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report. Frontiers in radiology, 6, 1809871. https://doi.org/10.3389/fradi.2026.1809871

BibTeX

@article{filimonova2026multimodal,
author = {Filimonova, Elena and Pashkov, Anton and Moysak, Galina and Martirosyan, Azniv and Kurilov, Vladimir and Poptsova, Aleksandra and Morozova, Renata and Rzaev, Jamil},
title = {{Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report}},
journal = {Frontiers in radiology},
year = {2026},
month = may,
volume = {6},
pages = {1809871},
publisher = {Frontiers Media SA},
issn = {2673-8740},
doi = {10.3389/fradi.2026.1809871},
url = {https://doi.org/10.3389/fradi.2026.1809871},
pmid = {42182936},
pmcid = {PMC13194104}
}

RIS

TY - JOUR
AU - Filimonova, Elena
AU - Pashkov, Anton
AU - Moysak, Galina
AU - Martirosyan, Azniv
AU - Kurilov, Vladimir
AU - Poptsova, Aleksandra
AU - Morozova, Renata
AU - Rzaev, Jamil
TI - Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report
T2 - Frontiers in radiology
J2 - Front Radiol
PY - 2026
DA - 2026/05/08
VL - 6
SP - 1809871
SN - 2673-8740
PB - Frontiers Media SA
DO - 10.3389/fradi.2026.1809871
UR - https://doi.org/10.3389/fradi.2026.1809871
LA - en
ER -

CSL-JSON

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

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