OSCR

An artefact-resilient wide bandwidth bidirectional graphene neural interface.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › Device fabrication and characterization ↔ PhyREC/Calibration_old.py, lines 9–104 · score 0.54 · gate voltage, effective gate, offset, transfer, attribute, device
  2. [2] § Results › In vitro assessment of gSGFET recording performance during stimulation ↔ examples/spectrogram_plots.py, lines 1–15 · score 0.53 · power spectral density, baseline normalized, PSD, signals
  3. [3] § Methods › Data analysis ↔ examples/basic_plot.py, lines 1–21 · score 0.53 · PhyREC, Electrophysiological signals, library, Neo

Paper

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The authors' code

Python · 318 lines · 11 KB · no license · 1 match

  2. import sys
  3. from neo.core import AnalogSignal
  4. import numpy as np
  5. import quantities as pq
  6. from scipy.interpolate import interpolate
  7. from PhyREC.SignalProcess import sliding_window
  9. def CalcVgeff(Sig, Tchar, VgsExp=None, Regim='hole', CalType='interp'):
  10. """
  11. Calibrate signal to gate voltage using transfer characteristics.
  13. Convert measured current signal to effective gate voltage using interpolation
  14. or linear approximation based on device transfer characteristics.
  16. Parameters
  17. ----------
  18. Sig : neo.core.AnalogSignal
  19. Input current signal to calibrate.
  20. Tchar : object
  21. Device transfer characteristics object with methods GetVgs(), GetIds(),
  22. GetGM(), GetUd0(), and IsOK attribute.
  23. VgsExp : float or array-like, optional
  24. Gate voltage value(s) for linear calibration method.
  25. Regim : str, optional
  26. Device regime ('hole' or 'electron', default: 'hole').
  27. CalType : str, optional
  28. Calibration method: 'interp' for interpolation or 'linear' for linear
  29. approximation (default: 'interp').
  31. Returns
  32. -------
  33. neo.core.AnalogSignal
  34. Calibrated signal in volts with annotations including calibration status,
  35. bias current, offset current, and device parameters.
  37. Notes
  38. -----
  39. The function adds several annotations to the returned signal:
  40. - Calibrated: Boolean indicating successful calibration
  41. - Working: Same as Calibrated
  42. - IdsOff: Offset current
  43. - VgsCal: Mean calibrated gate voltage
  44. - IdsBias: Bias current
  45. - IsOK: Device transfer characteristics status
  46. - Iname: Original signal name
  47. - GM: Conductance in Siemens
  48. """
  49. Vgs = Tchar.GetVgs()
  50. vgs = np.linspace(np.min(Vgs), np.max(Vgs), 10000)
  52. if Regim == 'hole':
  53. Inds = np.where(vgs < Tchar.GetUd0())[1]
  54. else:
  55. Inds = np.where(vgs > Tchar.GetUd0())[1]
  57. Ids = Tchar.GetIds(Vgs=vgs[Inds]) * pq.A
  58. GM = Tchar.GetGM(Vgs=VgsExp) * pq.S
  60. IdsExp = Tchar.GetIds(Vgs=VgsExp) * pq.A
  61. IdsOff = np.mean(Sig) - IdsExp
  62. IdsBias = np.mean(Sig)
  64. Calibrated = np.array((True,))
  65. try:
  66. if CalType == 'interp':
  67. fgm = interpolate.interp1d(Ids[:, 0], vgs[Inds])
  68. st = fgm(np.clip(Sig, np.min(Ids), np.max(Ids))) * pq.V
  69. elif CalType == 'linear':
  70. st = Sig / GM
  71. else:
  72. print('Calibration Not defined')
  73. except:
  74. print(Sig.name, "Calibration error:", sys.exc_info()[0])
  75. st = np.zeros(Sig.shape)
  76. Calibrated = np.array((False,))
  78. print(str(Sig.name), '-> ',
  79. 'IdsBias', IdsBias,
  80. 'IdsOff', IdsOff,
  81. 'Vgs', np.mean(st),
  82. Tchar.IsOK)
  84. annotations = {'Calibrated': Calibrated,
  85. 'Working': Calibrated,
  86. 'IdsOff': IdsOff.flatten()[0],
  87. 'VgsCal': np.mean(st),
  88. 'IdsBias': IdsBias.flatten()[0],
  89. 'IsOK': Tchar.IsOK,
  90. 'Iname': Sig.name,
  91. 'GM': GM,
  92. }
  94. CalSig = AnalogSignal(st,
  95. units='V',
  96. t_start=Sig.t_start,
  97. sampling_rate=Sig.sampling_rate,
  98. name=str(Sig.name),
  99. file_origin=Sig.file_origin)
  101. # CalSig.annotate(**annotations)
  102. CalSig.array_annotate(**annotations)
  104. return CalSig
  107. def CalcVgeff2(Sig, Tchar, VgsExp, Regim='hole', CalType='interp'):
  108. """
  109. Calibrate signal to gate voltage using transfer characteristics (alternative).
  111. Convert measured current signal to effective gate voltage using interpolation
  112. or linear approximation. This variant flattens arrays and handles units differently
  113. than CalcVgeff.
  115. Parameters
  116. ----------
  117. Sig : neo.core.AnalogSignal
  118. Input current signal to calibrate.
  119. Tchar : object
  120. Device transfer characteristics object with methods GetVgs(), GetIds(),
  121. GetGM(), GetUd0(), and IsOK attribute.
  122. VgsExp : float or array-like
  123. Gate voltage value(s) for calibration (required, not optional).
  124. Regim : str, optional
  125. Device regime ('hole' or 'electron', default: 'hole').
  126. CalType : str, optional
  127. Calibration method: 'interp' for interpolation or 'linear' for linear
  128. approximation (default: 'interp').
  130. Returns
  131. -------
  132. neo.core.AnalogSignal
  133. Calibrated signal in volts with array annotations including calibration
  134. status, bias current, offset current, and device parameters.
  136. Notes
  137. -----
  138. Similar to CalcVgeff but uses flattened arrays and stores annotations as
  139. Python floats rather than quantities objects. Used for compatibility with
  140. specific analysis workflows.
  141. """
  142. Vgs = Tchar.GetVgs()
  143. vgs = np.linspace(np.min(Vgs), np.max(Vgs), 10000)
  145. if Regim == 'hole':
  146. Inds = np.where(vgs < Tchar.GetUd0())[1]
  147. else:
  148. Inds = np.where(vgs > Tchar.GetUd0())[1]
  150. IdsBias = np.array((np.nan,)) * pq.A
  151. IdsOff = np.array((np.nan,)) * pq.A
  152. GM = np.nan * pq.S
  153. try:
  154. Ids = Tchar.GetIds(Vgs=vgs[Inds]).flatten()
  155. GM = Tchar.GetGM(Vgs=VgsExp).flatten()
  157. IdsExp = Tchar.GetIds(Vgs=VgsExp).flatten()
  158. IdsOff = np.mean(Sig) - IdsExp
  159. IdsBias = np.mean(Sig)
  161. Calibrated = np.array((True,))
  163. if CalType == 'interp':
  164. fgm = interpolate.interp1d(Ids, vgs[Inds])
  165. st = fgm(np.clip(Sig, np.min(Ids), np.max(Ids))) * pq.V
  166. elif CalType == 'linear':
  167. st = Sig / GM
  168. else:
  169. print('Calibration Not defined')
  170. except:
  171. print(Sig.name, "Calibration error:", sys.exc_info()[0])
  172. st = np.zeros(Sig.shape) * pq.V
  173. Calibrated = np.array((False,))
  175. print(str(Sig.name), '-> ',
  176. 'IdsBias', IdsBias,
  177. 'IdsOff', IdsOff,
  178. 'Vgs', np.mean(st),
  179. 'GM', GM,
  180. Tchar.IsOK)
  182. annotations = {'Calibrated': Calibrated[0],
  183. 'Working': Calibrated[0],
  184. 'IdsOff': float(IdsOff.flatten()[0].magnitude),
  185. 'VgsCal': float(np.mean(st).magnitude),
  186. 'IdsBias': float(IdsBias.flatten()[0].magnitude),
  187. 'IsOK': Tchar.IsOK,
  188. 'Iname': Sig.name,
  189. 'GM': float(GM.magnitude),
  190. }
  192. CalSig = AnalogSignal(st,
  193. units='V',
  194. t_start=Sig.t_start,
  195. sampling_rate=Sig.sampling_rate,
  196. name=str(Sig.name),
  197. file_origin=Sig.file_origin)
  199. CalSig.array_annotate(**annotations)
  200. # CalSig.array_annotate(**annotations)
  202. return CalSig
  205. def CalcVgeffNoInterp(Sig, Tchar, VgsExp=None, Regim='hole'):
  206. """
  207. Calibrate signal to gate voltage using linear approximation only.
  209. Convert measured current signal to effective gate voltage using conductance-based
  210. linear calibration. This simplified method does not use interpolation.
  212. Parameters
  213. ----------
  214. Sig : neo.core.AnalogSignal
  215. Input current signal to calibrate.
  216. Tchar : object
  217. Device transfer characteristics object with method GetGM() and IsOK attribute.
  218. VgsExp : float or array-like, optional
  219. Gate voltage value(s) for conductance calculation.
  220. Regim : str, optional
  221. Device regime ('hole' or 'electron', default: 'hole'). Not currently used.
  223. Returns
  224. -------
  225. neo.core.AnalogSignal
  226. Calibrated signal in volts with array annotations including calibration
  227. status, mean gate voltage, and device status.
  229. Notes
  230. -----
  231. This is a simplified calibration that only uses conductance (GM) for linear
  232. scaling. No interpolation is performed on transfer characteristics.
  233. """
  234. gm = Tchar.GetGM(Vgs=VgsExp)
  236. Calibrated = np.array((True,))
  237. try:
  238. st = Sig.magnitude / gm
  239. except:
  240. print(Sig.name, "Calibration error:", sys.exc_info()[0])
  241. st = np.zeros(Sig.shape)
  242. Calibrated = np.array((False,))
  244. print(str(Sig.name), '-> ', 'GM', gm, 'Vgs', np.mean(st), Tchar.IsOK)
  245. annotations = {'Calibrated': Calibrated,
  246. 'Working': Calibrated,
  247. # 'IdsOff': IdsOff.flatten(),
  248. 'VgsCal': np.array((np.mean(st),)),
  249. 'IsOK': np.array((Tchar.IsOK,)),
  250. 'Iname': np.array((Sig.name,)),
  251. }
  253. CalSig = AnalogSignal(st * pq.V,
  254. units='V',
  255. t_start=Sig.t_start,
  256. sampling_rate=Sig.sampling_rate,
  257. name=str(Sig.name),
  258. file_origin=Sig.file_origin)
  260. # CalSig.annotate(**annotations)
  261. CalSig.array_annotate(**annotations)
  263. return CalSig
  266. def NoiseBlanking(sig, NoiseLimitRMS=20 * pq.uV, MinNoiseFreeTime=5 * pq.s, timewidth=1 * pq.s, Value=0):
  267. """
  268. Remove high-noise sections from signal by blanking with a specified value.
  270. Identify periods where signal RMS exceeds a threshold and replace them with
  271. a constant value. Maintains breaks between noise blocks if they exceed minimum
  272. duration.
  274. Parameters
  275. ----------
  276. sig : neo.core.AnalogSignal
  277. Input signal to process.
  278. NoiseLimitRMS : quantities.Quantity, optional
  279. RMS threshold for noise detection (default: 20 µV).
  280. MinNoiseFreeTime : quantities.Quantity, optional
  281. Minimum time between noise blocks required to split into separate blocks
  282. (default: 5 s).
  283. timewidth : quantities.Quantity, optional
  284. Time window for sliding RMS calculation (default: 1 s).
  285. Value : float, optional
  286. Value to replace noise sections with (default: 0).
  288. Returns
  289. -------
  290. neo.core.AnalogSignal
  291. Signal with noise regions blanked with the specified value.
  293. Notes
  294. -----
  295. The function uses sliding window RMS analysis to identify noisy periods.
  296. Consecutive noise blocks separated by less than MinNoiseFreeTime are merged.
  297. """
  298. sRMS = sliding_window(sig, timewidth)
  300. noisets = sRMS.times[np.where(sRMS > NoiseLimitRMS)[0]]
  302. if len(noisets) == 0:
  303. return sig
  305. inds = np.where(np.diff(noisets) > MinNoiseFreeTime)[0]
  307. NoiseBlocks = []
  308. NoiseBlocks.append((noisets[0], noisets[inds[0]]))
  309. for ic, ind in enumerate(inds[:-1]):
  310. NoiseBlocks.append((noisets[ind + 1], noisets[inds[ic + 1]]))
  311. NoiseBlocks.append((noisets[inds[-1] + 1], noisets[-1]))
  313. for ti, te in NoiseBlocks:
  314. i1 = sig.time_index(ti)
  315. i2 = sig.time_index(te)
  316. sig[i1:i2] = np.ones((i2 - i1, 1)) * Value * sig.units
  318. return sig

Calibration_old.py at commit f469409, no license · at the source

Overview

Authors: Michał Prokop1, Martín Esparza-Iaizzo2,3, Eduard Masvidal-Codina1,4,5, Xavi Illa4,6, Neela K Codadu2, Daman Rathore2, Nicola Ria1, Kostas Kostarelos1,5,7,8, Elena del Corro1, Ramon Garcia-Cortadella1,9, Rob C Wykes2,8,10, Anton Guimerà-Brunet4,6, Jose A Garrido1,7
ORCID iDs: Eduard Masvidal-Codina, Xavi Illa, Neela K Codadu, Daman Rathore, Nicola Ria, Elena del Corro, Ramon Garcia-Cortadella, Rob C Wykes, Anton Guimerà-Brunet, Jose A Garrido
  1. Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
  2. University College London, Queen Square Institute of Neurology, London, UK
  3. INBRAIN Neuroelectronics SL, Barcelona, Spain
  4. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
  5. Institute of Neurosciences, UAB Medical School, Bellaterra, Spain
  6. Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Campus UAB, Bellaterra, Spain
  7. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
  8. Centre for Nanotechnology in Medicine, Faculty of Biology Medicine & Health, AV Hill Building, University of Manchester, Manchester, UK
  9. Bernstein Center for Computational Neuroscience Munich, Faculty of Medicine, Ludwig-Maximilians Universität München, Planegg-Martinsried, Munich, Germany
  10. Division of Neuroscience, School of Biological Sciences, University of Manchester, Manchester, UK
Institutions: Institut Català de Nanociència i Nanotecnologia (Spain); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); Universitat Autònoma de Barcelona (Spain); Instituto de Salud Carlos III (Spain); Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (Spain); Centro de Investigación Biomédica en Red (Spain); Consejo Superior de Investigaciones Científicas (Spain); Centro Nacional de Microelectrónica (Spain); Institut de Microelectrònica de Barcelona (Spain); Institució Catalana de Recerca i Estudis Avançats (Spain); University of Manchester (United Kingdom); Bernstein Center for Computational Neuroscience Munich (Germany); Ludwig-Maximilians-Universität München (Germany)
Journal: Nature communications, volume 17, issue 1, article 6536
Dates: received 24 August 2025; accepted 20 May 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73790-x · PMID 42203801 · PMCID PMC13379384 · OpenAlex W4410533608
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.15.653988
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality)
Methods: Connectivity
Keywords: Electronic properties and devices, Biomedical engineering, Electrical and electronic engineering
MeSH: Brain*, Graphite*, Neurons*, Animals, Artifacts, Local Field Potential Measurement, Microelectrodes, Transistors, Electronic (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministerio de Ciencia, Innovación y Universidades (10.13039.501100011033, CEX2023, CEX2023-001397-M, PID2021-126117NA-I00, 100010434, PID2020‐113663RB‐I00, CEX2021‐001214‐S, PID2020, PLEC2022-009232, 10.13039, 13039/501100011033, SEV‐2017‐0706, 501100011033); Centres de Recerca de Catalunya (501100011033); Institut Català de Nanociència i Nanotecnologia (CEX2021-001214-S); Graphene Flagship (881603, Core3); European Regional Development Fund (CEX2021-001214-S, 100010434, 101130650, 13039/501100011033, 501100011033, HORIZON2020, 881603, 101070865, SEV-2017-0706, ID 100010434, CEX2023-001397-M); Generalitat de Catalunya (SEV-2017-0706, 501100011033, CEX2021-001214-S); Ministerio de Ciencia e Innovación (SEV‐2017‐0706, 13039/501100011033, ID100010434, 10.13039, / AEI/10, PID2021-126117NA-I00, 10.13039/501100011033/, 100010434, 501100011033, PID2020, CEX2021‐001214‐S); Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CB06/01/0049); Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona (ID 100010434, 100010434); HORIZON EUROPE Framework Programme (881603, 101070865); Instituto de Salud Carlos III (CB06/01/0049); Agencia Estatal de Investigación (CEX2023-001397-M, 501100011033, 13039, CEX2021‐001214‐S, 13039/501100011033, 10.13039, SEV‐2017‐0706, AEI/10, PID2020-113663RB-I00)
Citations: not cited yet (Europe PMC); 67 references in the paper
Research resources: RRID:SCR_000903, RRID:SCR_001622

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.

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aguimera/PhyREC

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f469409926f1f1acc91e7edf3a950d7e10114e65, 28 May 2026
Languages: Python (38), JavaScript (13)
Size: 294 files, 51 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (pyproject.toml, requirements.txt, setup.py, docs/requirements.txt), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: NumPy (33 files), Matplotlib (30 files), Neo (30 files), SciPy (7 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
52 files
At the source: github.com/aguimera/PhyREC

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Version 2, 28 September 2026

  • Funding: added Ministerio de Ciencia, Innovación y Universidades: 10.13039.501100011033, CEX2023, CEX2023-001397-M, PID2021-126117NA-I00, 100010434, PID2020‐113663RB‐I00, CEX2021‐001214‐S, PID2020, PLEC2022-009232, 10.13039, 13039/501100011033, SEV‐2017‐0706, 501100011033; Centres de Recerca de Catalunya: 501100011033; Institut Català de Nanociència i Nanotecnologia: CEX2021-001214-S; Graphene Flagship: 881603, Core3; European Commission: CEX2021-001214-S, 100010434, 101130650, 13039/501100011033, 501100011033, HORIZON2020, 881603, 101070865, SEV-2017-0706, ID 100010434, CEX2023-001397-M; Generalitat de Catalunya: SEV-2017-0706, 501100011033, CEX2021-001214-S; Ministerio de Ciencia e Innovación: SEV‐2017‐0706, 13039/501100011033, ID100010434, 10.13039, / AEI/10, PID2021-126117NA-I00, 10.13039/501100011033/, 100010434, 501100011033, PID2020, CEX2021‐001214‐S; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina: CB06/01/0049; Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona: ID 100010434, 100010434; HORIZON EUROPE Framework Programme: 881603, 101070865; Instituto de Salud Carlos III: CB06/01/0049; Agencia Estatal de Investigación: CEX2023-001397-M, 501100011033, 13039, CEX2021‐001214‐S, 13039/501100011033, 10.13039, SEV‐2017‐0706, AEI/10, PID2020-113663RB-I00

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 8 MeSH terms, 62 references, 2 RRIDs.

Cite

This paper

Prokop, M., Esparza-Iaizzo, M., Masvidal-Codina, E., Illa, X., Codadu, N. K., Rathore, D., Ria, N., Kostarelos, K., del Corro, E., Garcia-Cortadella, R., Wykes, R. C., Guimerà-Brunet, A., & Garrido, J. A. (2026). An artefact-resilient wide bandwidth bidirectional graphene neural interface. Nature communications, 17(1), 6536. https://doi.org/10.1038/s41467-026-73790-x

BibTeX

@article{prokop2026artefact,
author = {Prokop, Michał and Esparza-Iaizzo, Martín and Masvidal-Codina, Eduard and Illa, Xavi and Codadu, Neela K and Rathore, Daman and Ria, Nicola and Kostarelos, Kostas and del Corro, Elena and Garcia-Cortadella, Ramon and Wykes, Rob C and Guimerà-Brunet, Anton and Garrido, Jose A},
title = {{An artefact-resilient wide bandwidth bidirectional graphene neural interface}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6536},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73790-x},
url = {https://doi.org/10.1038/s41467-026-73790-x},
pmid = {42203801},
pmcid = {PMC13379384}
}

RIS

TY - JOUR
AU - Prokop, Michał
AU - Esparza-Iaizzo, Martín
AU - Masvidal-Codina, Eduard
AU - Illa, Xavi
AU - Codadu, Neela K
AU - Rathore, Daman
AU - Ria, Nicola
AU - Kostarelos, Kostas
AU - del Corro, Elena
AU - Garcia-Cortadella, Ramon
AU - Wykes, Rob C
AU - Guimerà-Brunet, Anton
AU - Garrido, Jose A
TI - An artefact-resilient wide bandwidth bidirectional graphene neural interface
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/28
VL - 17
IS - 1
SP - 6536
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73790-x
UR - https://doi.org/10.1038/s41467-026-73790-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73790-x",
"type": "article-journal",
"title": "An artefact-resilient wide bandwidth bidirectional graphene neural interface",
"container-title": "Nature communications",
"author": [
{
"family": "Prokop",
"given": "Michał"
},
{
"family": "Esparza-Iaizzo",
"given": "Martín"
},
{
"family": "Masvidal-Codina",
"given": "Eduard"
},
{
"family": "Illa",
"given": "Xavi"
},
{
"family": "Codadu",
"given": "Neela K"
},
{
"family": "Rathore",
"given": "Daman"
},
{
"family": "Ria",
"given": "Nicola"
},
{
"family": "Kostarelos",
"given": "Kostas"
},
{
"family": "del Corro",
"given": "Elena"
},
{
"family": "Garcia-Cortadella",
"given": "Ramon"
},
{
"family": "Wykes",
"given": "Rob C"
},
{
"family": "Guimerà-Brunet",
"given": "Anton"
},
{
"family": "Garrido",
"given": "Jose A"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6536",
"DOI": "10.1038/s41467-026-73790-x",
"PMID": "42203801",
"PMCID": "PMC13379384",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73790-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
28
]
]
}
}

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

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