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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Ye Wang",
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  "author": "Ye Wang",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "citation": [],
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2204.06584",
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  "defined_in": "https://doi.org/10.48550/arXiv.2204.06584",
            
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  "doi": "10.57702/th3y0and",
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  "doi": "10.57702/th3y0and",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-03",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Huazheng Pan",
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      "extra_author": "Huazheng Pan",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Tao Zhang",
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      "extra_author": "Tao Zhang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Wen Wu",
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      "extra_author": "Wen Wu",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Wenxin Hu",
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      "extra_author": "Wenxin Hu",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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  ],
            
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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "Biomedical Document-Level Relation Extraction",
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      "display_name": "Biomedical Document-Level Relation Extraction",
            
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      "name": "biomedical-document-level-relation-extraction",
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      "name": "biomedical-document-level-relation-extraction",
            
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      "title": "Biomedical Document-Level Relation Extraction"
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      "title": "Biomedical Document-Level Relation Extraction"
            
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    },
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    },
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Biomedical Relation Extraction",
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      "display_name": "Biomedical Relation Extraction",
            
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      "name": "biomedical-relation-extraction",
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      "name": "biomedical-relation-extraction",
            
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      "title": "Biomedical Relation Extraction"
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      "title": "Biomedical Relation Extraction"
            
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    }
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  ],
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  "metadata_created": "2024-12-03T09:52:46.382009",
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  "metadata_modified": "2024-12-03T09:52:46.729455",
            
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  "name": "chemdisgene",
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  "name": "chemdisgene",
            
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  "notes": "ChemDisGene is a new dataset for training and evaluating 
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  "notes": "ChemDisGene is a new dataset for training and evaluating 
            
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multi-type multi-label biomedical relation extraction models. It 
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multi-type multi-label biomedical relation extraction models. It 
            
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contains a large automatically derived corpus with noisy relationship 
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contains a large automatically derived corpus with noisy relationship 
            
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labels and a cleaner manually curated dataset of 523 abstracts.",
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labels and a cleaner manually curated dataset of 523 abstracts.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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      "name": "multi-type multi-label",
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      "name": "multi-type multi-label",
            
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}