{"dataset":{"id":"61185","dataset_id":"on006720","name":"Alpha power indexes working memory load for durations","description":"This dataset contains anonymized raw magnetoencephalography (MEG) recordings from 23 healthy adult participants performing an n-item delayed temporal reproduction task, designed to probe working memory for temporal durations. Alongside task MEG data, the dataset includes EOG and ECG recordings, digitized sensor and fiducial coordinates, a resting-state MEG recording, and individual high-resolution structural MRI scans for source reconstruction. The data support research on neural dynamics underlying working memory, auditory processing, and motor responses in the healthy adult brain.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on006720","concept_doi":"10.82901/nemar.on006720","latest_version_doi":"10.82901/nemar.on006720.v1.0.0","created_at":"2026-06-29 08:31:45","updated_at":"2026-08-19 00:27:13","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Alpha power indexes working memory load for durations\",\n  \"description\": \"This dataset contains anonymized raw magnetoencephalography (MEG) recordings from 23 healthy adult participants performing an n-item delayed temporal reproduction task, designed to probe working memory for temporal durations. Alongside task MEG data, the dataset includes EOG and ECG recordings, digitized sensor and fiducial coordinates, a resting-state MEG recording, and individual high-resolution structural MRI scans for source reconstruction. The data support research on neural dynamics underlying working memory, auditory processing, and motor responses in the healthy adult brain.\",\n  \"methods_description\": \"MEG data were recorded using a whole-head Elekta Neuromag Vector View 306 MEG system (102 triple-sensor elements) in a magnetically shielded room, sampled at 1 kHz with 0.03 Hz high-pass and 330 Hz low-pass filters. EOG and ECG were recorded via external electrodes, and EEG electrode, HPI coil, and fiducial positions were digitized with a Polhemus 3D digitizer. Eight task runs (~10 min each) were recorded, followed by a two-minute eyes-open resting-state recording used for noise covariance estimation. Structural MRI scans were acquired on a separate day using a Siemens 3T Magnetom Prisma Fit scanner (slice thickness 1 mm, TR = 2300 ms, TE = 2.98 ms, flip angle = 9°). Data were formatted into BIDS using MNE-Python (v1.8.0) and MNE-BIDS (v1.6.0).\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Sophie K. Herbst [1]\": {},\n    \"Izem Mangione [1]\": {},\n    \"Charbel-Raphaël Segerie [2]\": {},\n    \"Richard Höchenberger [2]\": {},\n    \"Tadeusz Kononowicz [1, 3, 4]\": {},\n    \"Alexandre Gramfort [2]\": {},\n    \"Virginie van Wassenhove [1]\": {},\n    \"[1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"working memory\"\n    },\n    {\n      \"term\": \"temporal reproduction\"\n    },\n    {\n      \"term\": \"Magnetic Resonance Imaging\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D008279\"\n    },\n    {\n      \"term\": \"alpha power\"\n    },\n    {\n      \"term\": \"neural dynamics\"\n    },\n    {\n      \"term\": \"resting state\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1101/2025.05.12.653390\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementTo\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on006720\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on006720\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/sdata.2018.110\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds006720.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Agence Nationale de la Recherche\",\n      \"award_number\": \"ANR-16-CE37-0004-04\",\n      \"award_title\": \"AutoTime\"\n    },\n    {\n      \"funder_name\": \"Agence Nationale de la Recherche\",\n      \"award_number\": \"ANR-19-DATA-0023\",\n      \"award_title\": \"meegBIDS.fr\"\n    },\n    {\n      \"funder_name\": \"European Union's Horizon 2020 research and innovation program\",\n      \"award_number\": \"101017727\",\n      \"award_title\": \"FET Experience\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"146.5 GB (248 files)\"\n  ],\n  \"formats\": [\n    \".dat\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"f680cc2b3fc2fee290c9c33fc172ceb29903a2bb391715372f535fcca5c05291\"\n}","last_activity_at":"2026-06-29 08:31:45","source":"openneuro","source_id":"ds006720","subject_count":24,"modalities":"anat,meg","age_min":null,"age_max":null,"file_size":146513562254,"total_files":1014,"tasks":"noise,rest,tiwm","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Sophie K. Herbst [1], Izem Mangione [1], Charbel-Raphaël Segerie [2], Richard Höchenberger [2], Tadeusz Kononowicz [1, 3, 4], Alexandre Gramfort [2], Virginie van Wassenhove [1], [1] Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin, 91191 Gif/Yvette, France [2] Inria, CEA, Université Paris-Saclay, Palaiseau, France [3] Institute of Psychology, The Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland [4] Institut NeuroPSI - UMR9197 CNRS Université Paris-Saclay","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006720-blue)](https://doi.org/10.82901/nemar.on006720)\n\nThe data set contains anonymized raw magnetoencephalography (MEG) recordings of 23 healthy adult participants, performed at Neurospin, Gif sur Yvette, France. Participants performed an n-item delayed temporal reproduction task: They were presented with a sequence of one or three “empty” intervals (see cover figure), delimited by short pure tones. They had to maintain the sequence in memory (retention), and, upon a prompt, reproduce the whole sequence by pressing a button for each tone. Eight task runs were recorded (~ 10 min each). The dataset also contains recordings of the electro-occulogram (EOG, horizontal and vertical eye movements) and -cardiogram (ECG), and the 3D coordinates of the EEG electrodes, four head-position indicator coils, and three fiducial points (nasion, left and right pre-auricular areas). A two-minute-long resting state recording (eyes open) was performed after the task. To improve the spatial resolution of the source reconstruction, individual high-resolution structural Magnetic Resonance Imaging (MRI) recordings were acquired. The data are reusable for researchers with a dedicated interest in the neural dynamics of working memory, but also to a broader community interested in neural dynamics in the healthy adult brain, in relation to auditory stimuli, motor responses, and during periods of rest. \n\nThe data were formatted in BIDS and anonymized using the following software: \n\nMNE Python version 1.8.0\nMNE-BIDS version 1.6.0\t\n\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110\n\nMEG recording:\n\nBefore undergoing the MEG recording, participants were equipped with external electrodes, positioned to record the electro-occulogram (EOG, horizontal and vertical eye movements) and -cardiogram (ECG). The positions of the EEG electrodes, four head-position indicator coils, and three fiducial points (nasion, left and right pre-auricular areas) were digitized using a 3D digitizer (Polhemus, US/Canada) for subsequent co-registration with the individual's anatomical MRI. The MEG recordings took place in a magnetically shielded chamber, where the participant was seated in an armchair under the MEG helmet. The electromagnetic brain activity was recorded using a whole-head Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD, Helsinki) with 102 triple-sensors elements (two orthogonal planar gradiometers, and one magnetometer per sensor location). Participants were instructed to fixate their gaze on a screen positioned in front of them, at about one meter distance. The chamber was dimly lit. Their head position was measured before each recording run (8 in total) using the head-position indicator coils. MEG recordings were sampled online at 1 kHz, high-pass filtered at 0.03 Hz, and low-pass filtered at 330 Hz. A two-minute-long resting state recording (eyes open) was performed after the task, used to compute the noise covariance matrix for source reconstruction.\n\nAnatomical MRI recordings:\n\nTo improve the spatial resolution of the source reconstruction, individual high-resolution structural Magnetic Resonance Imaging (MRI) recordings were used. These were recorded on another day, using a Siemens 3 T Magnetom Prisma Fit MRI scanner. Parameters of the sequence were: slice thickness: 1 mm, repetition time TR = 2300 ms, echo time TE = 2.98 ms, and flip angle = 9 degrees.\n","bids_version":"1.6.0","sessions_count":16,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 136.5 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":223,"zarr_failure_count":223,"zarr_deterministic":0,"zarr_failed_at":"2026-08-24 13:00:13","num_dataset_citations":0,"num_datapaper_citations":1,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":146501928293,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 00:26:54\",\"metadata_updated_at\":\"2026-08-19 00:27:12\",\"archive_checked_at\":\"2026-06-29 08:41:29\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-29 08:42:38\",\"citations_updated_at\":\"2026-09-08 03:00:53\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 05:36:56\",\"data_checked_at\":\"2026-08-29 03:00:56\",\"availability_report_at\":\"2026-07-23 01:29:57\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:47:59\"}","participants":24,"num_citations":1,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"136 GB","zarr_data_failures":{"count":223,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}