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            f 1 { f 1 {
            2   "author": "Toulany, Nikan", 2   "author": "Toulany, Nikan",
            3   "author_email": "", 3   "author_email": "",
            4   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", 4   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            5   "doi": "10.48606/50", 5   "doi": "10.48606/50",
            6   "doi_date_published": "2023", 6   "doi_date_published": "2023",
            7   "doi_publisher": "", 7   "doi_publisher": "",
            8   "doi_status": "True", 8   "doi_status": "True",
            9   "groups": [], 9   "groups": [],
            10   "id": "dc7be277-653e-47a2-b594-1bd57ddaa4f8", 10   "id": "dc7be277-653e-47a2-b594-1bd57ddaa4f8",
            11   "isopen": false, 11   "isopen": false,
            12   "license_id": "CC BY 4.0 Attribution", 12   "license_id": "CC BY 4.0 Attribution",
            13   "license_title": "CC BY 4.0 Attribution", 13   "license_title": "CC BY 4.0 Attribution",
            14   "metadata_created": "2023-05-02T07:10:12.143895", 14   "metadata_created": "2023-05-02T07:10:12.143895",
            n 15   "metadata_modified": "2023-05-02T07:10:12.143902", n 15   "metadata_modified": "2023-08-04T08:49:49.639978",
            16   "name": "rdr-doi-10-48606-50", 16   "name": "rdr-doi-10-48606-50",
            17   "notes": "Abstract: This is the data repository for training and  17   "notes": "Abstract: This is the data repository for training and 
            18 testing the Twin Network. The imaging data repositories are divided  18 testing the Twin Network. The imaging data repositories are divided 
            19 into several packages based on independent experiments. The data  19 into several packages based on independent experiments. The data 
            20 comprises bright-field time-lapse images of zebrafish embryos acquired  20 comprises bright-field time-lapse images of zebrafish embryos acquired 
            21 in multiple batches within multi-well plates using an Acquifer Imaging  21 in multiple batches within multi-well plates using an Acquifer Imaging 
            22 Machine. Individual embryo segments were identified and extracted  22 Machine. Individual embryo segments were identified and extracted 
            23 using a trained neural network for object detection. Within these  23 using a trained neural network for object detection. Within these 
            24 experiment folders, data are organized by microscope position and  24 experiment folders, data are organized by microscope position and 
            25 embryo number.", 25 embryo number.",
            26   "num_resources": 0, 26   "num_resources": 0,
            27   "num_tags": 9, 27   "num_tags": 9,
            28   "orcid": "0000-0003-3505-7325", 28   "orcid": "0000-0003-3505-7325",
            29   "organization": { 29   "organization": {
            30     "approval_status": "approved", 30     "approval_status": "approved",
            31     "created": "2023-01-12T13:30:23.238233", 31     "created": "2023-01-12T13:30:23.238233",
            32     "description": "RADAR (Research Data Repository) is a  32     "description": "RADAR (Research Data Repository) is a 
            33 cross-disciplinary repository for archiving and publishing research  33 cross-disciplinary repository for archiving and publishing research 
            34 data from completed scientific studies and projects. The focus is on  34 data from completed scientific studies and projects. The focus is on 
            35 research data from subjects that do not yet have their own  35 research data from subjects that do not yet have their own 
            36 discipline-specific infrastructures for research data management. ", 36 discipline-specific infrastructures for research data management. ",
            37     "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", 37     "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9",
            38     "image_url": "radar-logo.svg", 38     "image_url": "radar-logo.svg",
            39     "is_organization": true, 39     "is_organization": true,
            40     "name": "radar", 40     "name": "radar",
            41     "state": "active", 41     "state": "active",
            42     "title": "RADAR", 42     "title": "RADAR",
            43     "type": "organization" 43     "type": "organization"
            44   }, 44   },
            45   "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", 45   "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9",
            46   "private": false, 46   "private": false,
            47   "production_year": "2020-2023", 47   "production_year": "2020-2023",
            48   "publication_year": "2023", 48   "publication_year": "2023",
            49   "publishers": [ 49   "publishers": [
            50     { 50     {
            51       "publisher": "University of Konstanz" 51       "publisher": "University of Konstanz"
            52     } 52     }
            53   ], 53   ],
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            54   "relationships_as_object": [], 226   "relationships_as_object": [],
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            56   "repository_name": "RADAR (Research Data Repository)", 228   "repository_name": "RADAR (Research Data Repository)",
            57   "resource_type": "Dataset - Overview of the Twin Network data  229   "resource_type": "Dataset - Overview of the Twin Network data 
            58 packages.", 230 packages.",
            59   "resources": [], 231   "resources": [],
            t t 232   "services_used_list": "",
            60   "source_metadata_created": "2023", 233   "source_metadata_created": "2023",
            61   "source_metadata_modified": "", 234   "source_metadata_modified": "",
            62   "state": "active", 235   "state": "active",
            63   "subject_areas": [ 236   "subject_areas": [
            64     { 237     {
            65       "subject_area_additional": "", 238       "subject_area_additional": "",
            66       "subject_area_name": "Biology" 239       "subject_area_name": "Biology"
            67     } 240     }
            68   ], 241   ],
            69   "tags": [ 242   "tags": [
            70     { 243     {
            71       "display_name": "Twin Network", 244       "display_name": "Twin Network",
            72       "id": "577497b6-6d97-4c41-b0b2-dbaefb6702eb", 245       "id": "577497b6-6d97-4c41-b0b2-dbaefb6702eb",
            73       "name": "Twin Network", 246       "name": "Twin Network",
            74       "state": "active", 247       "state": "active",
            75       "vocabulary_id": null 248       "vocabulary_id": null
            76     }, 249     },
            77     { 250     {
            78       "display_name": "TwinNet", 251       "display_name": "TwinNet",
            79       "id": "1ba52854-ef13-464f-ab07-2e57070491c3", 252       "id": "1ba52854-ef13-464f-ab07-2e57070491c3",
            80       "name": "TwinNet", 253       "name": "TwinNet",
            81       "state": "active", 254       "state": "active",
            82       "vocabulary_id": null 255       "vocabulary_id": null
            83     }, 256     },
            84     { 257     {
            85       "display_name": "computational biology", 258       "display_name": "computational biology",
            86       "id": "d65dd4ed-b2a9-41b0-9d19-cb77b1965ff1", 259       "id": "d65dd4ed-b2a9-41b0-9d19-cb77b1965ff1",
            87       "name": "computational biology", 260       "name": "computational biology",
            88       "state": "active", 261       "state": "active",
            89       "vocabulary_id": null 262       "vocabulary_id": null
            90     }, 263     },
            91     { 264     {
            92       "display_name": "deep learning", 265       "display_name": "deep learning",
            93       "id": "19e41883-3799-4184-9e0e-26c95795b119", 266       "id": "19e41883-3799-4184-9e0e-26c95795b119",
            94       "name": "deep learning", 267       "name": "deep learning",
            95       "state": "active", 268       "state": "active",
            96       "vocabulary_id": null 269       "vocabulary_id": null
            97     }, 270     },
            98     { 271     {
            99       "display_name": "developmental biology", 272       "display_name": "developmental biology",
            100       "id": "4acec762-54a0-4ecf-884d-e65d76375c46", 273       "id": "4acec762-54a0-4ecf-884d-e65d76375c46",
            101       "name": "developmental biology", 274       "name": "developmental biology",
            102       "state": "active", 275       "state": "active",
            103       "vocabulary_id": null 276       "vocabulary_id": null
            104     }, 277     },
            105     { 278     {
            106       "display_name": "embryogenesis", 279       "display_name": "embryogenesis",
            107       "id": "b66e6284-edc5-4f9b-97c8-4699c36c7352", 280       "id": "b66e6284-edc5-4f9b-97c8-4699c36c7352",
            108       "name": "embryogenesis", 281       "name": "embryogenesis",
            109       "state": "active", 282       "state": "active",
            110       "vocabulary_id": null 283       "vocabulary_id": null
            111     }, 284     },
            112     { 285     {
            113       "display_name": "high-throughput", 286       "display_name": "high-throughput",
            114       "id": "4c7004f2-f511-4233-af6a-3c2f8674670b", 287       "id": "4c7004f2-f511-4233-af6a-3c2f8674670b",
            115       "name": "high-throughput", 288       "name": "high-throughput",
            116       "state": "active", 289       "state": "active",
            117       "vocabulary_id": null 290       "vocabulary_id": null
            118     }, 291     },
            119     { 292     {
            120       "display_name": "machine learning", 293       "display_name": "machine learning",
            121       "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b", 294       "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b",
            122       "name": "machine learning", 295       "name": "machine learning",
            123       "state": "active", 296       "state": "active",
            124       "vocabulary_id": null 297       "vocabulary_id": null
            125     }, 298     },
            126     { 299     {
            127       "display_name": "zebrafish", 300       "display_name": "zebrafish",
            128       "id": "98db8ebb-7bdf-40cb-adc7-4b6ad2ed20f7", 301       "id": "98db8ebb-7bdf-40cb-adc7-4b6ad2ed20f7",
            129       "name": "zebrafish", 302       "name": "zebrafish",
            130       "state": "active", 303       "state": "active",
            131       "vocabulary_id": null 304       "vocabulary_id": null
            132     } 305     }
            133   ], 306   ],
            134   "title": "Datasets for \"uncovering developmental time and tempo  307   "title": "Datasets for \"uncovering developmental time and tempo 
            135 using deep learning\"", 308 using deep learning\"",
            136   "type": "vdataset", 309   "type": "vdataset",
            137   "url": "https://doi.org/10.48606/50" 310   "url": "https://doi.org/10.48606/50"
            138 } 311 }