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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Tri Nguyen",
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  "author": "Nguyen et al.",
            
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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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    "https://doi.org/10.48550/arXiv.2110.07752",

            
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    "https://doi.org/10.48550/arXiv.2208.13661",

            
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    "https://doi.org/10.48550/arXiv.2312.15503",

            
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    "https://doi.org/10.48550/arXiv.2205.03284",

            
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    "https://doi.org/10.48550/arXiv.2202.05144",

            
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    "https://doi.org/10.48550/arXiv.2311.02834",

            
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    "https://doi.org/10.48550/arXiv.2403.14541",

            
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    "https://doi.org/10.1109/IKT51791.2020.9345613",

            
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    "https://doi.org/10.48550/arXiv.2203.07735",

            
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    "https://doi.org/10.48550/arXiv.2404.13950",

            
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    "https://doi.org/10.48550/arXiv.1909.03716",

            
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    "https://doi.org/10.48550/arXiv.2303.16780",

            
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    "https://doi.org/10.48550/arXiv.2203.00537",

            
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    "https://doi.org/10.48550/arXiv.2101.06980",

            
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    "https://doi.org/10.48550/arXiv.2404.15103"

            
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  ],

            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.1145/3583780.3615123",
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  "defined_in": "https://doi.org/10.48550/arXiv.1805.02220",
            
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  "doi": "10.57702/y9ev8opk",
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  "doi": "10.57702/y9ev8opk",
            
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  "doi_date_published": "2024-11-25",
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  "doi_date_published": "2024-11-25",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "doi_status": true,
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  "doi_status": true,
            
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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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    {

            
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      "extra_author": "Mir Rosenberg",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Xia Song",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Jianfeng Gao",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Saurabh Tiwary",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Rangan Majumder",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Li Deng",

            
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      "orcid": ""

            
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    }

            
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  ],

            
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  "groups": [
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  "groups": [
            
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    {
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      "description": "",

            
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      "display_name": "Document Retrieval",

            
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      "id": "530cb507-0ba5-4237-8cef-47043758c987",

            
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      "image_display_url": "",

            
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      "name": "document-retrieval",

            
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      "title": "Document Retrieval"

            
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    },

            
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    {

            
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      "description": "",

            
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      "display_name": "Information Retrieval",

            
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      "id": "2193c32a-4b35-4438-b3a0-07c44d00abbc",

            
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      "image_display_url": "",

            
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      "name": "information-retrieval",

            
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      "title": "Information Retrieval"

            
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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": "Machine Reading Comprehension",
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      "display_name": "Machine Reading Comprehension",
            
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      "id": "d4bdf82e-a4db-400a-b364-18088a397a26",
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      "image_display_url": "",
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      "name": "machine-reading-comprehension",
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      "name": "machine-reading-comprehension",
            
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      "title": "Machine Reading Comprehension"
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      "title": "Machine Reading Comprehension"
            
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    },
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    {

            
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      "description": "",

            
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      "display_name": "Passage Ranking",

            
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      "id": "c7fd2228-f103-41f8-8aec-17d9f5584d44",

            
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      "image_display_url": "",

            
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      "name": "passage-ranking",

            
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      "title": "Passage Ranking"

            
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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": "Question Answering",
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      "display_name": "Question Answering",
            
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      "id": "01e9bd6b-910d-421d-bb31-799604023211",
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      "id": "01e9bd6b-910d-421d-bb31-799604023211",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "question-answering",
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      "name": "question-answering",
            
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      "title": "Question Answering"
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      "title": "Question Answering"
            
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    }
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  ],
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  "isopen": false,
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  "isopen": false,
            
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  "landing_page": "https://ai.stanford.edu/~suiyue/MARCO/",
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  "landing_page": "https://rajpurkar.github.io/SQuAD-explorer/",
            
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
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  "metadata_created": "2024-11-25T14:23:55.046003",
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  "metadata_modified": "2024-12-03T10:53:17.988763",
            
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  "name": "ms-marco",
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  "name": "ms-marco",
            
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  "notes": "Large-scale passage retrieval aims to fetch relevant 
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  "notes": "MS-MARCO dataset is a large-scale question answering 
            
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passages from a million- or billion-scale collection for a given query 
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dataset, focusing on real-world web data",
            
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to meet users\u2019 information needs, serving as an important role in 

            
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many downstream applications including open domain question answering, 

            
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search engine, and recommendation system.",

            
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  "num_resources": 1,
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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    "created": "2024-11-25T12:11:38.292601",
            
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    "type": "organization"
            
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      "created": "2024-12-03T00:20:35",

            
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      "data": [

            
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      ],

            
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      "description": "The json representation of the dataset with its 

            
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distributions based on DCAT.",

            
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      "format": "JSON",

            
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      "hash": "",

            
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      "id": "fa44de5a-4d21-4414-9aca-3fc4d351e3ec",

            
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      "last_modified": "2024-12-02T23:52:51.837607",

            
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      "mimetype": "application/json",

            
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      "name": "Original Metadata",

            
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      "display_name": "MS MARCO",
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317
      "state": "active",

            
318
      "vocabulary_id": null

            
319
    },

            
320
    {

            
321
      "display_name": "user click",

            
322
      "id": "2797776e-78c1-49ff-b055-b0e980f795eb",

            
323
      "name": "user click",

            
324
      "state": "active",

            
325
      "vocabulary_id": null

            
326
    },

            
327
    {

            
328
      "display_name": "zero-shot retrieval",

            
329
      "id": "c1e134bc-f820-427f-845b-acf9a5994f12",

            
330
      "name": "zero-shot retrieval",

            
331
      "state": "active",
88
      "state": "active",
            
332
      "vocabulary_id": null
89
      "vocabulary_id": null
            
333
    }
90
    }
            
334
  ],
91
  ],
            
t
335
  "title": "MS MARCO",
t
92
  "title": "MS-MARCO",
            
336
  "type": "dataset",
93
  "type": "dataset",
            
337
  "version": ""
94
  "version": ""
            
338
}
95
}