OSCR

An open-access multi-site fMRI dataset for investigating conscious visual perception.

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
  1. [1] § Technical Validation › Functional scans ↔ qc/01_analyze_MRIQC_IQMs.py, lines 1–44 · score 0.82 · framewise displacement, temporal SNR, MRIQC, DVARS, tsnr, fd
  2. [2] § Methods › Eye tracking data acquisition ↔ coglib/beh_et/eyetracking/DataParser.py, lines 739–833 · score 0.64 · EyeLink, eye tracker, Plus, pupil, position
  3. [3] § Technical Validation › Quality control ↔ coglib/beh_et/behavior/quality_checks.py, lines 833–903 · score 0.56 · alarm rates, hit rate, behavioral, Quality
  4. [4] § Methods › Experiment design ↔ logfiles_and_checks/01_exp1_create_events_tsv_file.py, lines 89–223 · score 0.55 · stimulus orientation, stimulus category, identities, block, irrelevant, letters
  5. [5] § Methods › Site-specific equipment differences ↔ coglib/beh_et/eyetracking/ET_param_manager.py, lines 241–296 · score 0.53 · eye trackers, viewing distances, setup
  6. [6] § Methods › Experiment procedure ↔ coglib/beh_et/behavior/data_reader.py, lines 350–416 · score 0.52 · stimulus onset, task irrelevant, manipulated, behavioral, fixation, block
  7. [7] § Data Records › Bundles › Subject-specific data ↔ decoding/searchlight_orientation_decoding_subject_level.py, lines 46–86 · score 0.51 · Dur_run, stimulus orientation, tsv, ses, sub

Paper

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The authors' code

Python · 221 lines · 8.7 KB · MIT · 1 match

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. The code is used for QC motion threshold.
  5. It checks MRI data quality using IQMs from MRIQC.
  7. Takes MRIQC outputs, assuming bids specifications (bids_dir).
  8. Calculates mean FD, FD_perc, DVARS and tsnr per run. Uses FD_perc and DVARS
  9. to reject bad participants exceeding X SD above (FD_perc, DVARS) the group
  10. mean (X defined in SdThreshold).
  12. Utilized metrics are specified in more detail here:
  13. https://mriqc.readthedocs.io/en/latest/measures.html
  14. FD = Framewise Displacement: expresses instantaneous head-motion.
  15. MRIQC reports the average FD, labeled as fd_mean.
  16. Rotational displacements are calculated as the displacement on the surface
  17. of a sphere of radius 50 mm [Power2012]:
  18. FD_perc = the percent of FDs above the FD threshold w.r.t. the full timeseries
  19. FD threshold is set at 0.20mm.
  20. DVARS = D: temporal derivative of timecourses. VARS: RMS variance over voxels
  21. ([Power2012] dvars_nstd). Indexes the rate of change of BOLD signal across
  22. the entire brain at each frame of data.
  23. TSNR = Temporal SNR is a simplified interpretation of the tSNR definition
  24. [Kruger2001]. Reports the median value of the tSNR map calculated by:
  25. average BOLD signal (across time) divided by the corresponding temporal
  26. standard-deviation map.
  28. Writes 3 output csv files:
  29. 2 csv files with subjects rejected per session, averaged over runs
  30. (IQM-summary-ses).
  31. 1 csv file with rows per run, allowing for run specific rejection (IQM-allRuns)
  32. Each csv file contains subject IDs, session labels if applicable and 4 IQMs
  33. (FD, FD_perc, DVARS and tsnr), and a rejection flag, indicating if the
  34. run/subject should be rejected from analysis. Outputs are written to the MRIQC
  35. directory (mriQcSubdir).
  37. @author: David Richter, first created 01/27/2021
  38. @ Modified by Urszula Gorska ([email hidden])
  39. Last modified 06/15/2023
  40. """
  42. import os, sys, json
  43. import numpy as np
  44. import pandas as pd
  47. #%% Paths and Parameters
  49. # root project path
  50. root_dir = '/mnt/beegfs/XNAT/COGITATE/fMRI/phase_2/processed'
  52. # threshold to mark subjects as rejected if they exceed X SD above group mean
  53. SdThreshold = 2
  55. ###############################################################################
  57. # bids path
  58. bids_dir = root_dir + '/bids'
  60. # mri qc sub dir
  61. mriQcSubdir = '/derivatives/mriqc'
  62. # sub dir pattern with sub and ses key-value pairs
  63. dataDirPattern = bids_dir + mriQcSubdir + os.sep + '%(sub)s' + os.sep + '%(ses)s' + os.sep + 'func' + os.sep
  65. # session labels
  66. sesLabels = ['ses-V1']
  68. participants_dir = bids_dir
  69. subject_list_type = 'demo'
  71. # load helper functions / code dir
  72. code_dir_with_helperfunctions = bids_dir + '/coglib/fmri'
  73. sys.path.append(code_dir_with_helperfunctions)
  74. from helper_functions_MRI import get_subject_list
  77. # %% support functions
  78. def getJsonFnames(fPath):
  79. """
  80. Get MRIQC output json files for current subject and session
  81. fPath: file path + name
  82. Returns: jsonFiles per run
  83. """
  84. jsonFiles = []
  85. for root, dirs, files in os.walk(fPath):
  86. for file in files:
  87. if file.endswith(".json"):
  88. jsonFiles.append(os.path.join(root, file))
  89. return jsonFiles
  91. def saveIQMs(df, ses=None):
  92. """
  93. Save summary IQM as csv file, either per session with data averaged across
  94. runs if ses arg is passed or for all runs separately if ses arg is not
  95. passed
  96. df: data frame with IQMs to be written as csv file
  97. ses: session label
  98. """
  99. outputDir = bids_dir + mriQcSubdir
  100. # outputDir = bids_dir + mriQcSubdir
  101. if ses is None:
  102. fname = outputDir + os.sep + 'IQM-perRun_' + subject_list_type + '.csv'
  103. else:
  104. fname = outputDir + os.sep + 'IQM-summary_' + ses + '_' + subject_list_type + '.csv'
  105. df.to_csv(fname, sep=',', index=False)
  108. # %% data processing functions
  109. def getDataFromJson(jsonFiles):
  110. """
  111. Get relevant IQM data from json files (output of MRIQC) per run
  112. jsonFiles: json file paths + names
  113. Returns: df_sub pandas data frame with relevant IQMs per run
  114. """
  115. sub_id = []
  116. ses_label = []
  117. run_label = []
  118. fd_mean = []
  119. fd_perc = []
  120. dvars_nstd = []
  121. tsnr = []
  122. for fname in jsonFiles:
  123. sub_id.append(fname[fname.find('sub-')+4:fname.find('sub-')+9])
  124. ses_label.append(fname[fname.find('ses-')+4:fname.find('ses-')+6])
  125. run_label.append(fname[fname.find('task-')+5:fname.find('run-')+5])
  126. with open(fname) as json_file:
  127. data = json.load(json_file)
  128. fd_mean.append(data['fd_mean'])
  129. fd_perc.append(data['fd_perc'])
  130. dvars_nstd.append(data['dvars_nstd'])
  131. tsnr.append(data['tsnr'])
  132. df_sub = pd.DataFrame({"sub_id":sub_id, "session":ses_label, "run":run_label, "fd_mean":fd_mean, "fd_perc":fd_perc, "dvars_nstd":dvars_nstd, "tsnr":tsnr, "rejected":[False]*len(sub_id)})
  133. return df_sub
  135. def processQMs(df):
  136. """
  137. Process IQMs by adding the mean and adding a rejection flag per subject
  138. based on the subject's IQM being x SD (defined in SdThreshold) worse than
  139. the group mean.
  140. IQMs used for rejection are:
  141. fd_perc = the percent of FDs above the FD threshold w.r.t. the full
  142. timeseries FD threshold is set at 0.20mm (MRI QC default).
  143. dvars_nstd = D: temporal derivative of timecourses. VARS: RMS variance over
  144. voxels ([Power2012] dvars_nstd). Indexes the rate of change of BOLD
  145. signal across the entire brain at each frame of data.
  146. tsnr = Temporal SNR is a simplified interpretation of the tSNR definition
  147. [Kruger2001]. Reports the median value of the tSNR map calculated by:
  148. average BOLD signal (across time) divided by the corresponding
  149. temporal standard-deviation map.
  150. df: data frame to be processed containing the IQMs
  151. Returns df with added mean and rejection flags (rejected == 1 indicating
  152. rejected participants/runs)
  153. """
  154. # add mean
  155. sd = df.std().copy()
  156. df.loc['mean'] = df.mean().copy()
  157. df.loc['mean', ['subIDs']]='average'
  158. # add rejection flag for 'bad' subjects if any IQM does not pass check
  159. # fd percentage
  160. rejThresh_fd_perc = df.fd_perc['mean'] + SdThreshold * sd['fd_perc']
  161. df.loc[df.fd_perc > rejThresh_fd_perc,['rejected']] = 1
  162. # dvars
  163. rejThresh_dvars = df.dvars_nstd['mean'] + SdThreshold * sd['dvars_nstd']
  164. df.loc[df.dvars_nstd > rejThresh_dvars,['rejected']] = 1
  165. ## tsnr (add lines below back in if tsnr is also to be used to reject data)
  166. #rejThresh_tsnr = df.tsnr['mean'] - SdThreshold * sd['tsnr']
  167. #df.loc[df.tsnr < rejThresh_tsnr,['rejected']] = 1
  168. return df
  170. #%%
  171. if __name__ == '__main__':
  172. """
  173. Gather IQMs (image quality metrics) from MRIQC output per subject
  174. (averaging over runs) and session.
  175. Write output csv file containing IQMs of interest
  176. subjects: list of subjects to be processed (MRI QC must be finished for all
  177. sessions for these subjects)
  178. """
  179. # subjects = get_subject_list(bids_dir,subject_list_type)
  180. subjects = get_subject_list(participants_dir,subject_list_type)
  181. df_all = pd.DataFrame()
  183. # loop over sessions
  184. for sesIdx in range(len(sesLabels)):
  185. ses = sesLabels[sesIdx]
  186. subIDs = []
  187. av_fd_mean = []
  188. av_fd_perc = []
  189. av_dvars_nstd =[]
  190. av_tsnr = []
  192. # loop over subjects
  193. for sub in subjects:
  194. print('Processing | subject: ' + sub + ' | session: ' + ses)
  195. fPath = dataDirPattern%{'sub':sub, 'ses':ses}
  196. # get json files
  197. jsonFiles = getJsonFnames(fPath)
  198. print(fPath)
  199. # check if json files exist; otherwise throw warning and skip sub
  200. if not jsonFiles:
  201. print('! CAUTION: No MRIQC json files found for subject: ' + sub + ' | session: ' + ses + ' ! Skipping ! Make sure to run MRIQC for all subjects first !')
  202. continue
  203. df_sub = getDataFromJson(jsonFiles)
  204. # append sub df to df with all runs and sessions
  205. df_all = df_all.append(df_sub, ignore_index=True)
  206. # calc mean over runs and add to list
  207. av_fd_mean.append(np.mean(df_sub['fd_mean']))
  208. av_fd_perc.append(np.mean(df_sub['fd_perc']))
  209. av_dvars_nstd.append(np.mean(df_sub['dvars_nstd']))
  210. av_tsnr.append(np.mean(df_sub['tsnr']))
  211. subIDs.append(sub)
  213. # gather data to df (QMs averaged over runs per subject)
  214. df = pd.DataFrame({"subIDs":subIDs, "fd_mean":av_fd_mean, "fd_perc":av_fd_perc, "dvars_nstd":av_dvars_nstd, "tsnr":av_tsnr, "rejected":[False]*len(av_tsnr)})
  215. df = processQMs(df)
  216. saveIQMs(df, ses)
  218. # process data frame containing all subjects & session IQMs per run
  219. df_all = processQMs(df_all)
  220. saveIQMs(df_all)

01_analyze_MRIQC_IQMs.py at commit 179b917, under MIT · at the source

Overview

Authors: Aya Khalaf1, David Richter2,3, Yamil Vidal2, Urszula Gorska-Klimowska4, Rony Hirschhorn5, Diptyajit Das6, Kyle Sinan Taylan Kahraman6, Praveen Sripad6, Fatemeh Taheriyan6, Liad Mudrik5,7,8, Michael Pitts9, Hal Blumenfeld10, Floris P de Lange2, Niccolò Bonacchi11, Tanya Brown6, Lucia Melloni6,7,12
ORCID iDs: David Richter, Yamil Vidal, Rony Hirschhorn, Kyle Sinan Taylan Kahraman, Praveen Sripad, Liad Mudrik, Niccolò Bonacchi, Tanya Brown
  1. Department of Neurology, Yale School of Medicine, New Haven, CT USA
  2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
  3. Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
  4. Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719 USA
  5. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
  6. Neural Circuits, Consciousness and Cognition Research Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, 60322 Germany
  7. Program for Brain, Mind, and Consciousness, Canadian Institute for Advanced Research, Toronto, Ontario Canada
  8. School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
  9. Psychology Department, Reed College, Portland, OR 97202 USA
  10. Departments of Neurology, Neuroscience and Neurosurgery, Yale University School of Medicine, New Haven, CT USA
  11. William James Center for Research (WJCR), ISPA - Instituto Universitário, Rua Jardim do Tabaco, 34, 1149-041 Lisbon, Portugal
  12. Department of Neurology, New York University Grossman School of Medicine, New York, NY 10016 USA
Institutions: Yale University (United States); Radboud University Nijmegen (Netherlands); Universidad de Granada (Spain); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Centro de Investigación Mente, Cerebro y Comportamiento (Spain); University of Wisconsin–Madison (United States); Tel Aviv University (Israel); Max Planck Institute for Empirical Aesthetics (Germany); Canadian Institute for Advanced Research (Canada); Reed College (United States); ISPA - Instituto Universitário (Portugal); William James Center for Research (Portugal); New York University (United States)
Journal: Scientific data, volume 13, issue 1, article 779
Dates: received 25 August 2025; accepted 28 April 2026; published online 7 May 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07377-y · PMID 42115661 · PMCID PMC13213011 · OpenAlex W7160823495
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Physiology & signal measures, Statistics
Keywords: Perception, Consciousness
MeSH: Consciousness*, Magnetic Resonance Imaging*, Visual Perception*, Brain, Humans (* major topic)
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Max-Planck-Gesellschaft; Templeton World Charity Foundation (TWCF0389)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

We present a functional magnetic resonance (fMRI) dataset collected as part of an adversarial collaboration aimed at arbitrating between the Global Neuronal Workspace theory (GNWT) and the Integrated Information Theory (IIT) of consciousness. Participants (N = 118) were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) with three orientations (front, left, right view), and three durations (0.5, 1.0, 1.5 seconds). Participants were asked to identify infrequent targets that changed in each block, thereby rendering two categories task-relevant and two task-irrelevant. The simplicity of the experimental design and of the task given to the participants ensures that these data are broadly reusable. Besides testing predictions from other theories of consciousness, these data can be used to examine various aspects of visual processing. The anonymized data were converted to Brain Imaging Data Structure (BIDS), and can be easily accessed through a web platform or an API. The dataset contains quality reports, demographics, behavioral performance, and eye-tracking data. We also provide code for preprocessing and analyzing the data.

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 7 matches between paragraphs and lines of code.

cogitate-consortium/cogitate-experiment-code

License: MIT
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Commit: 6b935f0cea81acc66c1d9cb6a9b35cf044faa743, 9 September 2025
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At the source: github.com/Cogitate-consortium/cogitate-experiment-code/tree/FMRI-Exp1

Cogitate-consortium/cogitate-fmri-analysis

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Languages: Python (44), MATLAB (16), Shell (1)
Size: 128 files, 61 scripts
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Holds: README, license file
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Tools: NumPy (36 files), pandas (23 files), Nilearn (12 files), NiBabel (11 files), Matplotlib (6 files), scikit-learn (6 files), SciPy (5 files), MNE-Python (4 files), SPM (4 files), FSL (2 files), Pingouin (2 files), AFNI (1 file)
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At the source: github.com/Cogitate-consortium/cogitate-fmri-analysis

cogitate-consortium/cogitate-msp1

License: MIT
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Languages: Python (211), MATLAB (31), Shell (16), Jupyter (2), R (2)
Size: 771 files, 262 scripts
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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)
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At the source: github.com/Cogitate-consortium/cogitate-msp1/tree/main/coglib/fmri#sample-data-and-demo

Zenodo 14020043

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Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (36 files), pandas (23 files), Nilearn (12 files), NiBabel (11 files), Matplotlib (6 files), scikit-learn (6 files), SciPy (5 files), MNE-Python (4 files), SPM (4 files), FSL (2 files), Pingouin (2 files), AFNI (1 file)
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At the source:

Code availability

The codebase is implemented in Python and MATLAB. For an overview of the code and environment setup instructions, please refer to the README file in the repository. The code can be accessed through the following links:

• Experiment Code50: GitHub - Experiment Code (https://github.com/Cogitate-consortium/cogitate-experiment-code/tree/FMRI-Exp1)

• Preprocessing and Analysis Code51: GitHub - Analysis Code (https://github.com/Cogitate-consortium/cogitate-fmri-analysis)

For more information, please visit the COGITATE Data wiki (https://cogitate-consortium.github.io/cogitate-data), and the COGITATE website (https://www.arc-cogitate.com/).

Sample data and code demo. Sample data from four participants (two per testing site) can be found here (https://github.com/Cogitate-consortium/cogitate-msp1/tree/main/coglib/fmri#sample-data-and-demo). We provide bids converted data (./bids/) as well as preprocessed data (./bids/derivatives/fmriprep/ and ./bids/derivatives/freesurfer/). We also provide data quality measures that can be found in ./bids/derivatives/mriqc/. At the link, instructions can be found to try out the analyses using these data.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 384 scripts, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The data can be accessed through the following links:

• Data Terms of Use: PDF file (https://s.gwdg.de/NMyO8Y)

• Raw47 and BIDS48 fMRI Data (Bundle Format): Data Bundles (https://www.arc-cogitate.com/data-bundles-active)

• Raw and BIDS fMRI Data (XNAT49): XNAT Portal (https://cogitate-data.ae.mpg.de/)

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 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 2 keywords, 5 MeSH terms, 2 funders, 44 references.

Cite

This paper

Khalaf, A., Richter, D., Vidal, Y., Gorska-Klimowska, U., Hirschhorn, R., Das, D., Kahraman, K. S. T., Sripad, P., Taheriyan, F., Mudrik, L., Pitts, M., Blumenfeld, H., de Lange, F. P., Bonacchi, N., Brown, T., & Melloni, L. (2026). An open-access multi-site fMRI dataset for investigating conscious visual perception. Scientific data, 13(1), 779. https://doi.org/10.1038/s41597-026-07377-y

BibTeX

@article{khalaf2026open,
author = {Khalaf, Aya and Richter, David and Vidal, Yamil and Gorska-Klimowska, Urszula and Hirschhorn, Rony and Das, Diptyajit and Kahraman, Kyle Sinan Taylan and Sripad, Praveen and Taheriyan, Fatemeh and Mudrik, Liad and Pitts, Michael and Blumenfeld, Hal and de Lange, Floris P and Bonacchi, Niccolò and Brown, Tanya and Melloni, Lucia},
title = {{An open-access multi-site fMRI dataset for investigating conscious visual perception}},
journal = {Scientific data},
year = {2026},
month = may,
volume = {13},
number = {1},
pages = {779},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07377-y},
url = {https://doi.org/10.1038/s41597-026-07377-y},
pmid = {42115661},
pmcid = {PMC13213011}
}

RIS

TY - JOUR
AU - Khalaf, Aya
AU - Richter, David
AU - Vidal, Yamil
AU - Gorska-Klimowska, Urszula
AU - Hirschhorn, Rony
AU - Das, Diptyajit
AU - Kahraman, Kyle Sinan Taylan
AU - Sripad, Praveen
AU - Taheriyan, Fatemeh
AU - Mudrik, Liad
AU - Pitts, Michael
AU - Blumenfeld, Hal
AU - de Lange, Floris P
AU - Bonacchi, Niccolò
AU - Brown, Tanya
AU - Melloni, Lucia
TI - An open-access multi-site fMRI dataset for investigating conscious visual perception
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/05/07
VL - 13
IS - 1
SP - 779
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07377-y
UR - https://doi.org/10.1038/s41597-026-07377-y
LA - en
ER -

CSL-JSON

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},
{
"family": "Taheriyan",
"given": "Fatemeh"
},
{
"family": "Mudrik",
"given": "Liad"
},
{
"family": "Pitts",
"given": "Michael"
},
{
"family": "Blumenfeld",
"given": "Hal"
},
{
"family": "de Lange",
"given": "Floris P"
},
{
"family": "Bonacchi",
"given": "Niccolò"
},
{
"family": "Brown",
"given": "Tanya"
},
{
"family": "Melloni",
"given": "Lucia"
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "779",
"DOI": "10.1038/s41597-026-07377-y",
"PMID": "42115661",
"PMCID": "PMC13213011",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07377-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
7
]
]
}
}

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