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{
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{
            
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  "author": "Toulany, Nikan",
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  "author": "Toulany, Nikan",
            
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  "author_email": "",
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  "author_email": "",
            
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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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  "doi": "10.48606/50",
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  "doi": "10.48606/50",
            
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  "doi_date_published": "2023",
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  "doi_date_published": "2023",
            
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  "doi_publisher": "",
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  "doi_publisher": "",
            
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  "doi_status": "True",
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  "doi_status": "True",
            
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  "groups": [],
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  "groups": [],
            
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  "id": "dc7be277-653e-47a2-b594-1bd57ddaa4f8",
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  "id": "dc7be277-653e-47a2-b594-1bd57ddaa4f8",
            
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  "isopen": false,
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  "isopen": false,
            
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  "license_id": "CC BY 4.0 Attribution",
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  "license_id": "CC BY 4.0 Attribution",
            
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  "license_title": "CC BY 4.0 Attribution",
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  "license_title": "CC BY 4.0 Attribution",
            
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  "metadata_created": "2023-05-02T07:10:12.143895",
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  "metadata_created": "2023-05-02T07:10:12.143895",
            
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  "metadata_modified": "2023-08-04T08:49:49.639978",
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  "metadata_modified": "2023-08-04T08:51:38.055896",
            
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  "name": "rdr-doi-10-48606-50",
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  "name": "rdr-doi-10-48606-50",
            
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  "notes": "Abstract: This is the data repository for training and 
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  "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 
            
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into several packages based on independent experiments. The data 
19
into several packages based on independent experiments. The data 
            
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comprises bright-field time-lapse images of zebrafish embryos acquired 
20
comprises bright-field time-lapse images of zebrafish embryos acquired 
            
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in multiple batches within multi-well plates using an Acquifer Imaging 
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in multiple batches within multi-well plates using an Acquifer Imaging 
            
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Machine. Individual embryo segments were identified and extracted 
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Machine. Individual embryo segments were identified and extracted 
            
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using a trained neural network for object detection. Within these 
23
using a trained neural network for object detection. Within these 
            
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experiment folders, data are organized by microscope position and 
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experiment folders, data are organized by microscope position and 
            
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embryo number.",
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embryo number.",
            
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  "num_resources": 0,
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  "num_resources": 0,
            
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  "num_tags": 9,
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  "orcid": "0000-0003-3505-7325",
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  "orcid": "0000-0003-3505-7325",
            
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2023-01-12T13:30:23.238233",
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    "created": "2023-01-12T13:30:23.238233",
            
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    "description": "RADAR (Research Data Repository) is a 
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    "description": "RADAR (Research Data Repository) is a 
            
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cross-disciplinary repository for archiving and publishing research 
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cross-disciplinary repository for archiving and publishing research 
            
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data from completed scientific studies and projects. The focus is on 
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data from completed scientific studies and projects. The focus is on 
            
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research data from subjects that do not yet have their own 
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research data from subjects that do not yet have their own 
            
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discipline-specific infrastructures for research data management. ",
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discipline-specific infrastructures for research data management. ",
            
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    "image_url": "radar-logo.svg",
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    "image_url": "radar-logo.svg",
            
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    "is_organization": true,
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    "is_organization": true,
            
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    "name": "radar",
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    "name": "radar",
            
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    "state": "active",
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    "state": "active",
            
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    "title": "RADAR",
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    "title": "RADAR",
            
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    "type": "organization"
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    "type": "organization"
            
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  },
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  "private": false,
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  "production_year": "2020-2023",
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  "production_year": "2020-2023",
            
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  "publication_year": "2023",
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  "publication_year": "2023",
            
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  "publishers": [
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  "publishers": [
            
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    {
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    {
            
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      "publisher": "University of Konstanz"
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      "publisher": "University of Konstanz"
            
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    }
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    }
            
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  "resource_type": "Dataset - Overview of the Twin Network data 
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packages.",
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    }
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  ],
            
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  "tags": [
242
  "tags": [
            
243
    {
243
    {
            
244
      "display_name": "Twin Network",
244
      "display_name": "Twin Network",
            
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      "id": "577497b6-6d97-4c41-b0b2-dbaefb6702eb",
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      "id": "577497b6-6d97-4c41-b0b2-dbaefb6702eb",
            
246
      "name": "Twin Network",
246
      "name": "Twin Network",
            
247
      "state": "active",
247
      "state": "active",
            
248
      "vocabulary_id": null
248
      "vocabulary_id": null
            
249
    },
249
    },
            
250
    {
250
    {
            
251
      "display_name": "TwinNet",
251
      "display_name": "TwinNet",
            
252
      "id": "1ba52854-ef13-464f-ab07-2e57070491c3",
252
      "id": "1ba52854-ef13-464f-ab07-2e57070491c3",
            
253
      "name": "TwinNet",
253
      "name": "TwinNet",
            
254
      "state": "active",
254
      "state": "active",
            
255
      "vocabulary_id": null
255
      "vocabulary_id": null
            
256
    },
256
    },
            
257
    {
257
    {
            
258
      "display_name": "computational biology",
258
      "display_name": "computational biology",
            
259
      "id": "d65dd4ed-b2a9-41b0-9d19-cb77b1965ff1",
259
      "id": "d65dd4ed-b2a9-41b0-9d19-cb77b1965ff1",
            
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      "name": "computational biology",
260
      "name": "computational biology",
            
261
      "state": "active",
261
      "state": "active",
            
262
      "vocabulary_id": null
262
      "vocabulary_id": null
            
263
    },
263
    },
            
264
    {
264
    {
            
265
      "display_name": "deep learning",
265
      "display_name": "deep learning",
            
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      "id": "19e41883-3799-4184-9e0e-26c95795b119",
266
      "id": "19e41883-3799-4184-9e0e-26c95795b119",
            
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      "name": "deep learning",
267
      "name": "deep learning",
            
268
      "state": "active",
268
      "state": "active",
            
269
      "vocabulary_id": null
269
      "vocabulary_id": null
            
270
    },
270
    },
            
271
    {
271
    {
            
272
      "display_name": "developmental biology",
272
      "display_name": "developmental biology",
            
273
      "id": "4acec762-54a0-4ecf-884d-e65d76375c46",
273
      "id": "4acec762-54a0-4ecf-884d-e65d76375c46",
            
274
      "name": "developmental biology",
274
      "name": "developmental biology",
            
275
      "state": "active",
275
      "state": "active",
            
276
      "vocabulary_id": null
276
      "vocabulary_id": null
            
277
    },
277
    },
            
278
    {
278
    {
            
279
      "display_name": "embryogenesis",
279
      "display_name": "embryogenesis",
            
280
      "id": "b66e6284-edc5-4f9b-97c8-4699c36c7352",
280
      "id": "b66e6284-edc5-4f9b-97c8-4699c36c7352",
            
281
      "name": "embryogenesis",
281
      "name": "embryogenesis",
            
282
      "state": "active",
282
      "state": "active",
            
283
      "vocabulary_id": null
283
      "vocabulary_id": null
            
284
    },
284
    },
            
285
    {
285
    {
            
286
      "display_name": "high-throughput",
286
      "display_name": "high-throughput",
            
287
      "id": "4c7004f2-f511-4233-af6a-3c2f8674670b",
287
      "id": "4c7004f2-f511-4233-af6a-3c2f8674670b",
            
288
      "name": "high-throughput",
288
      "name": "high-throughput",
            
289
      "state": "active",
289
      "state": "active",
            
290
      "vocabulary_id": null
290
      "vocabulary_id": null
            
291
    },
291
    },
            
292
    {
292
    {
            
293
      "display_name": "machine learning",
293
      "display_name": "machine learning",
            
294
      "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b",
294
      "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b",
            
295
      "name": "machine learning",
295
      "name": "machine learning",
            
296
      "state": "active",
296
      "state": "active",
            
297
      "vocabulary_id": null
297
      "vocabulary_id": null
            
298
    },
298
    },
            
299
    {
299
    {
            
300
      "display_name": "zebrafish",
300
      "display_name": "zebrafish",
            
301
      "id": "98db8ebb-7bdf-40cb-adc7-4b6ad2ed20f7",
301
      "id": "98db8ebb-7bdf-40cb-adc7-4b6ad2ed20f7",
            
302
      "name": "zebrafish",
302
      "name": "zebrafish",
            
303
      "state": "active",
303
      "state": "active",
            
304
      "vocabulary_id": null
304
      "vocabulary_id": null
            
305
    }
305
    }
            
306
  ],
306
  ],
            
307
  "title": "Datasets for \"uncovering developmental time and tempo 
307
  "title": "Datasets for \"uncovering developmental time and tempo 
            
308
using deep learning\"",
308
using deep learning\"",
            
309
  "type": "vdataset",
309
  "type": "vdataset",
            
310
  "url": "https://doi.org/10.48606/50"
310
  "url": "https://doi.org/10.48606/50"
            
311
}
311
}