OSCR

An open multi-center MEG-EEG dataset for studying conscious visual perception.

Code ↔ Paper

12 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 12 matches
  1. [1] § Technical Validation ↔ coglib/meeg/qc/QC_processing_eeg.py, lines 121–177 · score 0.74 · Power Spectral Density, PREP pipeline, Maxwell filtering, noisy, PSD, SSS
  2. [2] § Technical Validation ↔ coglib/meeg/qc/QC_processing.py, lines 123–174 · score 0.74 · Power Spectral Density, PREP pipeline, Maxwell filtering, noisy, PSD, SSS
  3. [3] § Technical Validation ↔ coglib/ieeg/Preprocessing/PreprocessingHelperFunctions.py, lines 338–441 · score 0.65 · bad channel detection, Power Spectral Density, deviation, median, PSD, interval
  4. [4] § Technical Validation ↔ coglib/meeg/preprocessing/P01_maxwell_filtering.py, lines 120–158 · score 0.63 · Maxwell filtering, bad channel, grad, mag, SSS, PREP
  5. [5] § Methods › Participants ↔ coglib/fmri/logfiles_and_checks/01_exp1_create_events_tsv_file.py, lines 89–223 · score 0.63 · mini block, stimulus orientation, stimulus category, sequence, irrelevant, letters
  6. [6] § Methods › Experimental design ↔ coglib/fmri/logfiles_and_checks/01_exp1_create_events_tsv_file.py, lines 89–223 · score 0.63 · mini block, stimulus categories, identities, sequence, orientations, irrelevant
  7. [7] § Methods › Eye tracking ↔ coglib/beh_et/eyetracking/DataParser.py, lines 739–833 · score 0.58 · EyeLink, eye tracker, Plus, Pupil
  8. [8] § Data Records › BIDS data structure ↔ coglib/meeg/preprocessing/P01_maxwell_filtering.py, lines 120–158 · score 0.58 · Maxwell filtering, status, coils, crosstalk, SSS, magnetometer
  9. [9] § Methods › Data collection harmonization ↔ coglib/beh_et/eyetracking/DataParser.py, lines 739–833 · score 0.54 · EyeLink, eye tracker, Plus
  10. [10] § Technical Validation ↔ coglib/beh_et/eyetracking/ET_qc_manager.py, lines 99–221 · score 0.54 · quality checks, eye tracking, valuable, blocks, error, behavioral
  11. [11] § Data Records › BIDS data structure ↔ coglib/ieeg/data_preparation/mne_bids_converter.py, lines 283–330 · score 0.50 · BIDS converted, MNE BIDS, meeg, tsv, root
  12. [12] § Technical Validation ↔ coglib/beh_et/behavior/quality_checks.py, lines 833–903 · score 0.50 · alarm rate, hit rate, Behaviorally

Paper

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The authors' code

Python · 318 lines · 12 KB · MIT · 2 matches

  1. """
  2. ===================================
  3. 01. Maxwell filter using MNE-python
  4. ===================================
  6. The data are Maxwell filtered using tSSS/SSS.
  8. It is critical to mark bad channels before Maxwell filtering.
  10. @author: Oscar Ferrante [email hidden]
  12. """ # noqa: E501
  14. import os.path as op
  15. import os
  16. import numpy as np
  17. import pandas as pd
  18. import seaborn as sns
  19. import matplotlib.pyplot as plt
  20. import shutil
  22. from fpdf import FPDF
  23. import mne
  24. from mne.preprocessing import find_bad_channels_maxwell
  25. import mne_bids
  27. import sys
  28. sys.path.insert(1, op.dirname(op.dirname(os.path.abspath(__file__))))
  30. from config.config import bids_root
  33. def run_maxwell_filter(subject_id, visit_id, record="run"):
  35. # Prepare PDF report
  36. pdf = FPDF(orientation="P", unit="mm", format="A4")
  38. # Set path to preprocessing derivatives and create the related folders
  39. prep_deriv_root = op.join(bids_root, "derivatives", "preprocessing")
  40. if not op.exists(prep_deriv_root):
  41. os.makedirs(prep_deriv_root)
  42. prep_figure_root = op.join(prep_deriv_root,
  43. f"sub-{subject_id}",f"ses-{visit_id}","meg",
  44. "figures")
  45. if not op.exists(prep_figure_root):
  46. os.makedirs(prep_figure_root)
  47. prep_report_root = op.join(prep_deriv_root,
  48. f"sub-{subject_id}",f"ses-{visit_id}","meg",
  49. "reports")
  50. if not op.exists(prep_report_root):
  51. os.makedirs(prep_report_root)
  52. prep_code_root = op.join(prep_deriv_root,
  53. f"sub-{subject_id}",f"ses-{visit_id}","meg",
  54. "codes")
  55. if not op.exists(prep_code_root):
  56. os.makedirs(prep_code_root)
  58. print("Processing subject: %s" % subject_id)
  60. # Loop over runs
  61. data_path = os.path.join(bids_root,f"sub-{subject_id}",f"ses-{visit_id}","meg")
  63. for fname in sorted(os.listdir(data_path)):
  64. if fname.endswith(".json") and record in fname:
  66. # Set run
  67. if "run" in fname:
  68. run = f"{int(fname[-10]):02}"
  69. elif "rest" in fname:
  70. run = None
  71. print(" Run: %s" % run)
  73. # Set task
  74. if 'dur' in fname:
  75. bids_task = 'dur'
  76. elif 'vg' in fname:
  77. bids_task = 'vg'
  78. elif 'replay' in fname:
  79. bids_task = 'replay'
  80. elif "rest" in fname:
  81. bids_task = "rest"
  82. else:
  83. raise ValueError("Error: could not find the task for %s" % fname)
  85. # Set split
  86. if len([f for f in os.listdir(data_path) if op.splitext(fname)[0][:-3] in f and f.endswith(".fif")]) > 1:
  87. split = 1
  88. else:
  89. split = None
  91. # Set BIDS path
  92. bids_path = mne_bids.BIDSPath(
  93. root=bids_root,
  94. subject=subject_id,
  95. datatype='meg',
  96. task=bids_task,
  97. run=run,
  98. session=visit_id,
  99. split=split,
  100. extension='.fif')
  102. # Read raw data
  103. raw = mne_bids.read_raw_bids(bids_path)
  105. # Find initial head position
  106. if run in ["01", None]:
  107. destination = raw.info['dev_head_t']
  109. # Detect bad channels
  110. raw.info['bads'] = []
  111. raw_check = raw.copy()
  112. auto_noisy_chs, auto_flat_chs, auto_scores = find_bad_channels_maxwell(
  113. raw_check,
  114. cross_talk=bids_path.meg_crosstalk_fpath,
  115. calibration=bids_path.meg_calibration_fpath,
  116. return_scores=True,
  117. verbose=True)
  118. raw.info['bads'].extend(auto_noisy_chs + auto_flat_chs)
  120. # Mark bad channels in BIDS events
  121. mne_bids.mark_channels(ch_names=raw.info['bads'],
  122. bids_path=bids_path,
  123. status='bad',
  124. verbose=False)
  126. # Visualize the scoring used to classify channels as noisy or flat
  127. ch_type = 'grad'
  128. fig = viz_badch_scores(auto_scores, ch_type)
  129. fname_fig = op.join(prep_figure_root,
  130. "01_%sr%s_badchannels_%sscore.png" % (bids_task,run,ch_type))
  131. fig.savefig(fname_fig)
  132. plt.close(fig)
  133. ch_type = 'mag'
  134. fig = viz_badch_scores(auto_scores, ch_type)
  135. fname_fig = op.join(prep_figure_root,
  136. "01_%sr%s_badchannels_%sscore.png" % (bids_task,run,ch_type))
  137. fig.savefig(fname_fig)
  138. plt.close(fig)
  140. # Fix Elekta magnetometer coil types
  141. raw.fix_mag_coil_types()
  143. # Set coordinate frame
  144. if subject_id == 'empty':
  145. coord_frame = 'meg'
  146. else:
  147. coord_frame = 'head'
  149. # Perform tSSS/SSS and Maxwell filtering
  150. raw_sss = mne.preprocessing.maxwell_filter(
  151. raw,
  152. cross_talk=bids_path.meg_crosstalk_fpath,
  153. calibration=bids_path.meg_calibration_fpath,
  154. st_duration=None,
  155. origin='auto',
  156. destination=destination, #align head location to first run
  157. coord_frame=coord_frame,
  158. verbose=True)
  160. # Show original and filtered signals
  161. fig = raw.copy().pick(['meg']).plot(duration=5,
  162. start=100,
  163. butterfly=True)
  164. fname_fig = op.join(prep_figure_root,
  165. '01_%sr%s_plotraw.png' % (bids_task,run))
  166. fig.savefig(fname_fig)
  167. plt.close(fig)
  168. fig = raw_sss.copy().pick(['meg']).plot(duration=5,
  169. start=100,
  170. butterfly=True)
  171. fname_fig = op.join(prep_figure_root,
  172. '01_%sr%s_plotrawsss.png' % (bids_task,run))
  173. fig.savefig(fname_fig)
  174. plt.close(fig)
  176. # Show original and filtered power
  177. fig1 = raw.plot_psd(picks = ['meg'],fmin = 1,fmax = 100)
  178. fname_fig1 = op.join(prep_figure_root,
  179. '01_%sr%s_plot_psd_raw100.png' % (bids_task,run))
  180. fig1.savefig(fname_fig1)
  181. plt.close(fig1)
  182. fig2 = raw_sss.plot_psd(picks = ['meg'],fmin = 1,fmax = 100)
  183. fname_fig2 = op.join(prep_figure_root,
  184. '01_%sr%s_plot_psd_raw100sss.png' % (bids_task,run))
  185. fig2.savefig(fname_fig2)
  186. plt.close(fig2)
  188. # Add figures to report
  189. pdf.add_page()
  190. pdf.set_font('helvetica', 'B', 16)
  191. pdf.cell(0, 10, fname[:-8])
  192. pdf.ln(20)
  193. pdf.set_font('helvetica', 'B', 12)
  194. pdf.cell(0, 10, 'Power Spectrum of Raw MEG Data', 'B', ln=1)
  195. pdf.image(fname_fig1, 0, 45, pdf.epw)
  196. pdf.ln(120)
  197. pdf.cell(0, 10, 'Power Spectrum of Filtered MEG Data', 'B', ln=1)
  198. pdf.image(fname_fig2, 0, 175, pdf.epw)
  200. # Save filtered data
  201. bids_path_sss = bids_path.copy().update(
  202. root=prep_deriv_root,
  203. split=None,
  204. suffix="sss",
  205. check=False)
  206. if not op.exists(bids_path_sss):
  207. bids_path_sss.fpath.parent.mkdir(exist_ok=True, parents=True)
  209. raw_sss.save(bids_path_sss, overwrite=True)
  211. # Add note about reconstructed sensors to report
  212. pdf.add_page()
  213. pdf.set_font('helvetica', 'B', 16)
  214. pdf.cell(0, 10, "Reconstructed sensors:")
  215. pdf.ln(20)
  216. pdf.set_font('helvetica', 'B', 12)
  217. pdf.cell(0, 10, 'bad MEG sensors: %s' % raw.info['bads'], 'B', ln=1)
  219. # Save code
  220. shutil.copy(__file__, prep_code_root)
  222. # Save report
  223. if record == "rest":
  224. pdf.output(op.join(prep_report_root,
  225. os.path.basename(__file__) + '-report_rest.pdf'))
  226. else:
  227. pdf.output(op.join(prep_report_root,
  228. os.path.basename(__file__) + '-report.pdf'))
  231. def viz_badch_scores(auto_scores, ch_type):
  232. fig, ax = plt.subplots(1, 4, figsize=(12, 8))
  233. fig.suptitle(f'Automated noisy/flat channel detection: {ch_type}',
  234. fontsize=16, fontweight='bold')
  236. #### Noisy channels ####
  237. ch_subset = auto_scores['ch_types'] == ch_type
  238. ch_names = auto_scores['ch_names'][ch_subset]
  239. scores = auto_scores['scores_noisy'][ch_subset]
  240. limits = auto_scores['limits_noisy'][ch_subset]
  241. bins = auto_scores['bins'] #the windows that were evaluated
  243. # Label each segment by its start and stop time (3 digits / 1 ms precision)
  244. bin_labels = [f'{start:3.3f} - {stop:3.3f}'
  245. for start, stop in bins]
  247. # Store data in DataFrame
  248. data_to_plot = pd.DataFrame(data=scores,
  249. columns=pd.Index(bin_labels, name='Time (s)'),
  250. index=pd.Index(ch_names, name='Channel'))
  252. # First, plot the raw scores
  253. sns.heatmap(data=data_to_plot,
  254. cmap='Reds',
  255. cbar=False,
  256. # cbar_kws=dict(label='Score'),
  257. ax=ax[0])
  258. [ax[0].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')
  259. for x in range(1, len(bins))]
  260. ax[0].set_title('Noisy: All Scores', fontweight='bold')
  262. # Second, highlight segments that exceeded the 'noisy' limit
  263. sns.heatmap(data=data_to_plot,
  264. vmin=np.nanmin(limits),
  265. cmap='Reds',
  266. cbar=True,
  267. # cbar_kws=dict(label='Score'),
  268. ax=ax[1])
  269. [ax[1].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')
  270. for x in range(1, len(bins))]
  271. ax[1].set_title('Noisy: Scores > Limit', fontweight='bold')
  273. #### Flat channels ####
  274. ch_subset = auto_scores['ch_types'] == ch_type
  275. ch_names = auto_scores['ch_names'][ch_subset]
  276. scores = auto_scores['scores_flat'][ch_subset]
  277. limits = auto_scores['limits_flat'][ch_subset]
  278. bins = auto_scores['bins'] #the windows that were evaluated
  280. # Label each segment by its start and stop time (3 digits / 1 ms precision)
  281. bin_labels = [f'{start:3.3f} - {stop:3.3f}'
  282. for start, stop in bins]
  284. # Store data in DataFrame
  285. data_to_plot = pd.DataFrame(data=scores,
  286. columns=pd.Index(bin_labels, name='Time (s)'),
  287. index=pd.Index(ch_names, name='Channel'))
  289. # First, plot the raw scores
  290. sns.heatmap(data=data_to_plot,
  291. cmap='Reds',
  292. cbar=False,
  293. # cbar_kws=dict(label='Score'),
  294. ax=ax[2])
  295. [ax[2].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')
  296. for x in range(1, len(bins))]
  297. ax[2].set_title('Flat: All Scores', fontweight='bold')
  299. # Second, highlight segments that exceeded the 'noisy' limit
  300. sns.heatmap(data=data_to_plot,
  301. vmax=np.nanmax(limits),
  302. cmap='Reds',
  303. cbar=True,
  304. # cbar_kws=dict(label='Score'),
  305. ax=ax[3])
  306. [ax[3].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')
  307. for x in range(1, len(bins))]
  308. ax[3].set_title('Flat: Scores > Limit', fontweight='bold')
  310. # Fit figure title to not overlap with the subplots
  311. fig.tight_layout(rect=[0, 0.03, 1, 0.95])
  312. return fig
  315. if __name__ == '__main__':
  316. subject_id = input("Type the subject ID (e.g., SA101)\n>>> ")
  317. visit_id = input("Type the visit ID (V1 or V2)\n>>> ")
  318. run_maxwell_filter(subject_id, visit_id)

P01_maxwell_filtering.py at commit e60764d, under MIT · at the source

Overview

Authors: Ling Liu1,2,3, Oscar Ferrante4,5, Tara Ghafari4,6,7, Dorottya Hetenyi4,8, Shujun Yang9, Rony Hirschhorn10, Urszula Gorska-Klimowska11, Praveen Sripad12, Fatemeh Taheriyan12, Tanya Brown12, Diptyajit Das12, Kyle Kahraman12,13, Niccolò Bonacchi14,15, Michael Pitts16, Liad Mudrik10,17,18, Ole Jensen4,6,7, Huan Luo3, Lucia Melloni12,18,19,20
ORCID iDs: Ling Liu, Oscar Ferrante, Tara Ghafari, Rony Hirschhorn, Praveen Sripad, Tanya Brown, Niccolò Bonacchi, Liad Mudrik, Ole Jensen, Huan Luo
20 affiliations
  1. Cognitive Science and Allied Health School, Beijing Language and Culture University,Beijing, 100875 China
  2. Speech and Hearing Impairment and Brain Computer Interface LAB, Beijing Language and Culture University,Beijing, 100875 China
  3. School of Psychological and Cognitive Sciences, Peking University,Beijing, 100871 China
  4. Centre for Human Brain Health, School of Psychology, University of Birmingham,Birmingham, B15 2TT UK
  5. School of Psychology, University of Surrey,Guildford, GU2 7XH United Kingdom
  6. Department of Experimental Psychology, University of Oxford,Oxford, OX2 6GG UK
  7. Oxford Centre for Human Brain Activity (OHBA), Oxford Centre for Integrative Neuroimaging (OxCIN), Department of Psychiatry, University of Oxford,Oxford, OX3 7JX UK
  8. Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London,WC1N 3AR London, UK
  9. Department of Psychology, University of Amsterdam,Amsterdam, Netherlands
  10. Sagol School of Neuroscience, Tel Aviv University,Tel Aviv, 6997801 Israel
  11. Department of Neurology, University of Wisconsin-Madison,Madison, WI 53705 USA
  12. Neural Circuits, Consciousness and Cognition Research Group, Max Planck Institute for Empirical Aesthetics,Frankfurt am Main, 60322 Germany
  13. Interdisciplinary Center for Neuroscience Frankfurt, Heinrich-Hoffmann-Straße 7, 60528 Frankfurt am Main, Germany
  14. William James Center for Research, ISPA - Instituto Universitario,Lisbon, 1149-041 Portugal
  15. Champalimaud Research, Lisbon, 1400-038 Portugal
  16. Psychology Department, Reed College,Portland, OR 97202 USA
  17. School of Psychological Sciences, Tel Aviv University,Tel Aviv, 69978 Israel
  18. Program for Brain, Mind, and Consciousness, Canadian Institute for Advanced Research,Toronto, Ontario Canada
  19. Department of Neurology, New York University Grossman School of Medicine,New York, NY 10016 USA
  20. Predictive Brain Department, Research Center One Health Ruhr, University Research Alliance, Faculty of Psychology, Ruhr University Bochum,Bochum, 44801 Germany
Institutions: Beijing Language and Culture University (China); Peking University (China); University of Birmingham (United Kingdom); University of Surrey (United Kingdom); University of Oxford (United Kingdom); University College London (United Kingdom); University of Amsterdam (Netherlands); Tel Aviv University (Israel); University of Wisconsin–Madison (United States); Max Planck Institute for Empirical Aesthetics (Germany); ISPA - Instituto Universitário (Portugal); Reed College (United States); Canadian Institute for Advanced Research (Canada); New York University (United States); Ruhr University Bochum (Germany)
Journal: Scientific data, volume 13, issue 1, article 799
Dates: received 5 August 2025; accepted 23 April 2026; published online 29 May 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07350-9 · PMID 42215489 · PMCID PMC13221470 · OpenAlex W7162795515
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), MEG (modality), human (organism)
Methods: Spectral & time-frequency, Physiology & signal measures
Keywords: Consciousness, Object vision
MeSH: Consciousness*, Electroencephalography*, Magnetoencephalography*, Visual Perception*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Data Descriptor
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Templeton World Charity Foundation (Templeton World Charity Foundation, Inc.) (TWCF0389,TWCF0486); Max Planck Society; Wellcome Trust (Wellcome) (grant number 227420); National Institute for Health (NIHR203316)
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.

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 12 matches between paragraphs and lines of code.

cogitate-consortium/cogitate-experiment-code

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6b935f0cea81acc66c1d9cb6a9b35cf044faa743, 9 September 2025
Size: 2 files, 0 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/Cogitate-consortium/cogitate-experiment-code/tree/MEEG-Exp1

cogitate-consortium/cogitate-msp1

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e60764df21ab63d39ffa726a0dd38c2b6f1e3e9a, 29 August 2026
Languages: Python (211), MATLAB (31), Shell (16), Jupyter (2), R (2)
Size: 771 files, 262 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (coglib/bayesFactor/requirements.txt, coglib/xnat/requirements_xnat.txt, coglib/ieeg/plotting_uniformization/requirements.txt), tests, 2 notebooks
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (134 files), pandas (89 files), Matplotlib (84 files), MNE-Python (78 files), SciPy (60 files), MNE-BIDS (38 files), seaborn (28 files), scikit-learn (22 files), NiBabel (15 files), Nilearn (12 files), statsmodels (12 files), scikit-image (8 files), Pingouin (6 files), MNE-Connectivity (5 files), autoreject (4 files), FreeSurfer (4 files), PyPREP (4 files), SPM (4 files), afex (2 files), BayesFactor (2 files), easystats (2 files), emmeans (2 files), FSL (2 files), ggplot2 (2 files), lmerTest (2 files), tidyverse (2 files), xarray (2 files), AFNI (1 file), car (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
264 files
At the source: github.com/Cogitate-consortium/cogitate-msp1/tree/main/coglib/meeg

Code availability

The MATLAB code utilized for conducting the experimental task can be retrieved from https://github.com/Cogitate-consortium/cogitate-experiment-code/tree/MEEG-Exp1. The code for preprocessing pipeline and analysis scripts is accessible at https://github.com/Cogitate-consortium/cogitate-msp1/tree/main/coglib/meeg. All code is written in Python using MNE-Python29. The README file in the repository offers an overview of the codebase and detailed instructions for setting up the computational environment, including the specific software versions and dependencies required to reproduce the analysis. While the README provides a general guide, we refer users to the Cogitate Data Release Wiki (https://cogitate-consortium.github.io/cogitate-data/) for detailed usage instructions. This wiki documentation demonstrates how to download the data from our repository and execute the described analyses. It also hosts the Standardized Operating Procedures (SOPs) for data acquisition, along with further guidance on interacting with the dataset, such as selecting specific conditions and customization options.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 262 scripts, each with its path and the digest of its content;
  • 12 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

The datasets described in this Data Descriptor are available in the Cogitate data repository (hosted by the Max Planck Society). The originally formatted data can be accessed via 10.17617/1.WQA3-WK71, and the BIDS-formatted data can be accessed via 10.17617/1.nzwp-7j89.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 2 keywords, 10 MeSH terms, 4 funders, 29 references.

Cite

This paper

Liu, L., Ferrante, O., Ghafari, T., Hetenyi, D., Yang, S., Hirschhorn, R., Gorska-Klimowska, U., Sripad, P., Taheriyan, F., Brown, T., Das, D., Kahraman, K., Bonacchi, N., Pitts, M., Mudrik, L., Jensen, O., Luo, H., & Melloni, L. (2026). An open multi-center MEG-EEG dataset for studying conscious visual perception. Scientific data, 13(1), 799. https://doi.org/10.1038/s41597-026-07350-9

BibTeX

@article{liu2026open,
author = {Liu, Ling and Ferrante, Oscar and Ghafari, Tara and Hetenyi, Dorottya and Yang, Shujun and Hirschhorn, Rony and Gorska-Klimowska, Urszula and Sripad, Praveen and Taheriyan, Fatemeh and Brown, Tanya and Das, Diptyajit and Kahraman, Kyle and Bonacchi, Niccolò and Pitts, Michael and Mudrik, Liad and Jensen, Ole and Luo, Huan and Melloni, Lucia},
title = {{An open multi-center MEG-EEG dataset for studying conscious visual perception}},
journal = {Scientific data},
year = {2026},
month = may,
volume = {13},
number = {1},
pages = {799},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07350-9},
url = {https://doi.org/10.1038/s41597-026-07350-9},
pmid = {42215489},
pmcid = {PMC13221470}
}

RIS

TY - JOUR
AU - Liu, Ling
AU - Ferrante, Oscar
AU - Ghafari, Tara
AU - Hetenyi, Dorottya
AU - Yang, Shujun
AU - Hirschhorn, Rony
AU - Gorska-Klimowska, Urszula
AU - Sripad, Praveen
AU - Taheriyan, Fatemeh
AU - Brown, Tanya
AU - Das, Diptyajit
AU - Kahraman, Kyle
AU - Bonacchi, Niccolò
AU - Pitts, Michael
AU - Mudrik, Liad
AU - Jensen, Ole
AU - Luo, Huan
AU - Melloni, Lucia
TI - An open multi-center MEG-EEG dataset for studying conscious visual perception
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/05/29
VL - 13
IS - 1
SP - 799
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07350-9
UR - https://doi.org/10.1038/s41597-026-07350-9
LA - en
ER -

CSL-JSON

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"container-title": "Scientific data",
"author": [
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"family": "Liu",
"given": "Ling"
},
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"given": "Oscar"
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{
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{
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{
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{
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},
{
"family": "Brown",
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{
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"given": "Diptyajit"
},
{
"family": "Kahraman",
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{
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},
{
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{
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}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "799",
"DOI": "10.1038/s41597-026-07350-9",
"PMID": "42215489",
"PMCID": "PMC13221470",
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}

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