{ "bsebench_status": "raw_mirror_pending_validation", "categories": [ "Spectroscopy", "Lithium Ion Battery", "Impedance" ], "contributors": [ { "first_name": "selcuk", "institution": "Eskisehir Osmangazi Universitesi", "last_name": "temiz" }, { "first_name": "Hasan", "institution": "San Jose State University", "last_name": "Kurban" }, { "first_name": "Salim", "institution": "Eskisehir Osmangazi Universitesi", "last_name": "Erol" }, { "first_name": "Mehmet", "institution": "Indiana University Bloomington", "last_name": "Dalkilic" } ], "description": "The experimental impedance data were collected in cell potentials as 3.2V, 3.4V, 3.6V, 3.8V, 4.0V, and 4.2V (corresponding state of charge values: 0%, 3%, 8%, 40%, 78%, 100%).\n\nML-regenerated-impedance-data.txt: Impedance data generated by a Machine Learning model. 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source filenames and bytes are preserved under source_files/.", "Per-file SHA-256 checksums matched the hashes published by Mendeley Data at download time." ], "published_at": "2022-10-10T19:13:16.282Z", "related_links": [ { "href": "https://www.sciencedirect.com/science/article/abs/pii/S2352152X22010246?via%3Dihub", "rel": "related_to", "type": "article" }, { "href": "https://github.com/hasankurban/Machine-Learning-Based-Regeneration-of-Li-ion-Battery-Impedance", "rel": "related_to", "type": "software" } ], "repository": { "id": "MENDELEY_DATA", "name": "Mendeley Data" }, "source": "Mendeley Data", "source_api_url": "https://data.mendeley.com/public-api/datasets/hr2ftv6gcn", "source_file_count": 7, "source_total_bytes": 44409, "source_url": "https://data.mendeley.com/datasets/hr2ftv6gcn/1", "title": "Lithium-ion battery impedance data set", "version": 1 }