{"dataset":{"id":"41110","dataset_id":"on004395","name":"Penn Electrophysiology of Encoding and Retrieval Study (PEERS)","description":"The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) is a large-scale investigation of the behavioral and electrophysiological correlates of memory encoding and retrieval. The dataset comprises EEG recordings from over 300 subjects across three experiments (ltpFR, ltpFR2, and VFFR), totaling more than 7,000 ninety-minute memory testing sessions. Data were acquired using either 129-channel Geodesic Sensor Net or 128-channel BioSemi systems, providing a comprehensive resource for studying neural mechanisms of human memory.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004395","concept_doi":"10.82901/nemar.on004395","latest_version_doi":"10.82901/nemar.on004395.v1.0.0","created_at":"2026-06-17 14:01:08","updated_at":"2026-07-10 22:30:48","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Penn Electrophysiology of Encoding and Retrieval Study (PEERS)\",\n  \"description\": \"The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) is a large-scale investigation of the behavioral and electrophysiological correlates of memory encoding and retrieval. The dataset comprises EEG recordings from over 300 subjects across three experiments (ltpFR, ltpFR2, and VFFR), totaling more than 7,000 ninety-minute memory testing sessions. Data were acquired using either 129-channel Geodesic Sensor Net or 128-channel BioSemi systems, providing a comprehensive resource for studying neural mechanisms of human memory.\",\n  \"methods_description\": \"Electroencephalogram (EEG) data were recorded using either a 129-channel Geodesic Sensor Net (GSN 200 or HydroCel GSN model) with Netstation acquisition software (Electrical Geodesics, Inc.; EGI) or a 128-channel BioSemi headcap with the Biosemi ActiveTwo acquisition system. Electrode coordinates reflect generic layouts for each headcap type rather than subject-specific anatomical positions.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Michael J. Kahana\": {},\n    \"Joseph H. Rudoler\": {},\n    \"Lynn J. Lohnas\": {},\n    \"Karl Healey\": {},\n    \"Ada Aka\": {},\n    \"Adam Broitman\": {},\n    \"Elizabeth Crutchley\": {},\n    \"Patrick Crutchley\": {},\n    \"Kylie H. Alm\": {},\n    \"Brandon S. Katerman\": {},\n    \"Nicole E. Miller\": {},\n    \"Joel R. Kuhn\": {},\n    \"Yuxuan Li\": {},\n    \"Nicole M. Long\": {},\n    \"Jonathan Miller\": {},\n    \"Madison D. Paron\": {},\n    \"Jesse K. Pazdera\": {},\n    \"Isaac Pedisich\": {},\n    \"Christoph T. Weidemann\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"memory encoding\"\n    },\n    {\n      \"term\": \"memory retrieval\"\n    },\n    {\n      \"term\": \"electrophysiology\"\n    },\n    {\n      \"term\": \"human cognition\"\n    },\n    {\n      \"term\": \"neuroimaging\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on004395\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on004395\",\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/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004395.v2.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Neuroimaging Dataset\",\n  \"modalities\": [\n    \"beh\",\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"9.6 TB (6495 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".ttf\",\n    \".txt\",\n    \".yml\"\n  ],\n  \"source_hash\": \"2e40ee62fc564126a69f03b23cb35de61c04731344c408910a24fbc0ba764d6d\"\n}","last_activity_at":"2026-06-17 14:01:08","source":"openneuro","source_id":"ds004395","subject_count":364,"modalities":"beh,eeg","age_min":17,"age_max":86,"file_size":9581115955881,"total_files":6495,"tasks":"VFFR,ltpFR,ltpFR2","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Michael J. Kahana, Joseph H. Rudoler, Lynn J. Lohnas, Karl Healey, Ada Aka, Adam Broitman, Elizabeth Crutchley, Patrick Crutchley, Kylie H. Alm, Brandon S. Katerman, Nicole E. Miller, Joel R. Kuhn, Yuxuan Li, Nicole M. Long, Jonathan Miller, Madison D. Paron, Jesse K. Pazdera, Isaac Pedisich, Christoph T. Weidemann","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004395-blue)](https://doi.org/10.82901/nemar.on004395)\n\nThe Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 90 minute memory testing sessions with recorded EEG data.\n\nSee the Computational Memory Lab's [wiki page](https://memory.psych.upenn.edu/PEERS) for more detailed information, and [this paper](https://psyarxiv.com/bu5x8/) for a discussion of the main findings and lessons learned from this large-scale study.\n\nThis dataset contains 3 experiments:\n* ltpFR (a.k.a. PEERS1-3)\n* ltpFR2 (a.k.a. PEERS4)\n* VFFR (a.k.a. PEERS5)\n\nElectroencephalogram (EEG) data were recorded with either a 129-channel Geodesic Sensor Net (either GSN 200 model or HydroCel GSN model) using the Netstation acquisition environment (Electrical Geodesics, Inc.; EGI) or with a 128-channel BioSemi headcap using the Biosemi ActiveTwo acquisition system. \n**Note:** subject-specific electrode layouts were NOT recorded. Despite being labeled as \"CapTrak\" space, the coordinates reflect a generic electrode layout for a given headcap and do NOT represent any individual's head shape. \n\n\nReferences\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\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n\n","bids_version":"1.6.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"pending","zarr_converted_at":"2026-06-20 16:04:19","zarr_store_count":5266,"zarr_index_etag":"ebff42376e1283f42400e654340c5f7a","zarr_source_commit":"d849d6f12cf3c3135291b11391af0d7053678865","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 8925.2 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":null,"zarr_failure_count":null,"zarr_deterministic":null,"zarr_failed_at":null,"num_dataset_citations":9,"num_datapaper_citations":0,"n_channels":125,"electrode_system":"egi-geodesic","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":null,"data_complete":null,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"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":500,"power_line_frequency":60,"eeg_reference":"Cz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-17 19:49:09\",\"metadata_updated_at\":\"2026-06-17 19:49:09\",\"archive_checked_at\":\"2026-06-17 19:52:20\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-17 20:01:12\",\"citations_updated_at\":\"2026-09-08 03:00:49\",\"channel_montage_checked_at\":\"2026-06-28 23:24:52\",\"hed_checked_at\":\"2026-06-30 04:58:24\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:18:40\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:12:22\"}","participants":364,"num_citations":9,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"8.71 TB","zarr_data_failures":null,"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}}