Changes
On December 3, 2024 at 12:03:24 AM UTC,
-
Changed value of field
doi_status
toTrue
in AATTCT-IDS -
Changed value of field
doi_date_published
to2024-12-03
in AATTCT-IDS -
Added resource Original Metadata to AATTCT-IDS
f | 1 | { | f | 1 | { |
2 | "access_rights": "", | 2 | "access_rights": "", | ||
3 | "author": "Zhiyu Maa", | 3 | "author": "Zhiyu Maa", | ||
4 | "author_email": "", | 4 | "author_email": "", | ||
5 | "citation": [], | 5 | "citation": [], | ||
6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
7 | "defined_in": "", | 7 | "defined_in": "", | ||
8 | "doi": "10.57702/3mwtk19z", | 8 | "doi": "10.57702/3mwtk19z", | ||
n | 9 | "doi_date_published": null, | n | 9 | "doi_date_published": "2024-12-03", |
10 | "doi_publisher": "TIB", | 10 | "doi_publisher": "TIB", | ||
n | 11 | "doi_status": false, | n | 11 | "doi_status": true, |
12 | "domain": "https://service.tib.eu/ldmservice", | 12 | "domain": "https://service.tib.eu/ldmservice", | ||
13 | "extra_authors": [ | 13 | "extra_authors": [ | ||
14 | { | 14 | { | ||
15 | "extra_author": "Chen Lia", | 15 | "extra_author": "Chen Lia", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
17 | }, | 17 | }, | ||
18 | { | 18 | { | ||
19 | "extra_author": "Tianming Dua", | 19 | "extra_author": "Tianming Dua", | ||
20 | "orcid": "" | 20 | "orcid": "" | ||
21 | }, | 21 | }, | ||
22 | { | 22 | { | ||
23 | "extra_author": "Le Zhang", | 23 | "extra_author": "Le Zhang", | ||
24 | "orcid": "" | 24 | "orcid": "" | ||
25 | }, | 25 | }, | ||
26 | { | 26 | { | ||
27 | "extra_author": "Dechao Tanga", | 27 | "extra_author": "Dechao Tanga", | ||
28 | "orcid": "" | 28 | "orcid": "" | ||
29 | }, | 29 | }, | ||
30 | { | 30 | { | ||
31 | "extra_author": "Deguo Maa", | 31 | "extra_author": "Deguo Maa", | ||
32 | "orcid": "" | 32 | "orcid": "" | ||
33 | }, | 33 | }, | ||
34 | { | 34 | { | ||
35 | "extra_author": "Shanchuan Huanga", | 35 | "extra_author": "Shanchuan Huanga", | ||
36 | "orcid": "" | 36 | "orcid": "" | ||
37 | }, | 37 | }, | ||
38 | { | 38 | { | ||
39 | "extra_author": "Yan Liua", | 39 | "extra_author": "Yan Liua", | ||
40 | "orcid": "" | 40 | "orcid": "" | ||
41 | }, | 41 | }, | ||
42 | { | 42 | { | ||
43 | "extra_author": "Yihao Suna", | 43 | "extra_author": "Yihao Suna", | ||
44 | "orcid": "" | 44 | "orcid": "" | ||
45 | }, | 45 | }, | ||
46 | { | 46 | { | ||
47 | "extra_author": "Zhihao Chena", | 47 | "extra_author": "Zhihao Chena", | ||
48 | "orcid": "" | 48 | "orcid": "" | ||
49 | }, | 49 | }, | ||
50 | { | 50 | { | ||
51 | "extra_author": "Jin Yuana", | 51 | "extra_author": "Jin Yuana", | ||
52 | "orcid": "" | 52 | "orcid": "" | ||
53 | }, | 53 | }, | ||
54 | { | 54 | { | ||
55 | "extra_author": "Qianqing Niea", | 55 | "extra_author": "Qianqing Niea", | ||
56 | "orcid": "" | 56 | "orcid": "" | ||
57 | }, | 57 | }, | ||
58 | { | 58 | { | ||
59 | "extra_author": "Marcin Grzegorzekc", | 59 | "extra_author": "Marcin Grzegorzekc", | ||
60 | "orcid": "" | 60 | "orcid": "" | ||
61 | }, | 61 | }, | ||
62 | { | 62 | { | ||
63 | "extra_author": "Hongzan Sunb", | 63 | "extra_author": "Hongzan Sunb", | ||
64 | "orcid": "" | 64 | "orcid": "" | ||
65 | } | 65 | } | ||
66 | ], | 66 | ], | ||
67 | "groups": [ | 67 | "groups": [ | ||
68 | { | 68 | { | ||
69 | "description": "", | 69 | "description": "", | ||
70 | "display_name": "Image Denoising", | 70 | "display_name": "Image Denoising", | ||
71 | "id": "ed3be710-2314-4ae1-b51c-68427278372c", | 71 | "id": "ed3be710-2314-4ae1-b51c-68427278372c", | ||
72 | "image_display_url": "", | 72 | "image_display_url": "", | ||
73 | "name": "image-denoising", | 73 | "name": "image-denoising", | ||
74 | "title": "Image Denoising" | 74 | "title": "Image Denoising" | ||
75 | }, | 75 | }, | ||
76 | { | 76 | { | ||
77 | "description": "", | 77 | "description": "", | ||
78 | "display_name": "Medical Imaging", | 78 | "display_name": "Medical Imaging", | ||
79 | "id": "b86e8f52-a230-44ce-b290-7823c9f6a877", | 79 | "id": "b86e8f52-a230-44ce-b290-7823c9f6a877", | ||
80 | "image_display_url": "", | 80 | "image_display_url": "", | ||
81 | "name": "medical-imaging", | 81 | "name": "medical-imaging", | ||
82 | "title": "Medical Imaging" | 82 | "title": "Medical Imaging" | ||
83 | }, | 83 | }, | ||
84 | { | 84 | { | ||
85 | "description": "", | 85 | "description": "", | ||
86 | "display_name": "Radiomics", | 86 | "display_name": "Radiomics", | ||
87 | "id": "cb3ced12-5b14-4d68-9916-9d52ea60eecc", | 87 | "id": "cb3ced12-5b14-4d68-9916-9d52ea60eecc", | ||
88 | "image_display_url": "", | 88 | "image_display_url": "", | ||
89 | "name": "radiomics", | 89 | "name": "radiomics", | ||
90 | "title": "Radiomics" | 90 | "title": "Radiomics" | ||
91 | }, | 91 | }, | ||
92 | { | 92 | { | ||
93 | "description": "", | 93 | "description": "", | ||
94 | "display_name": "Semantic Segmentation", | 94 | "display_name": "Semantic Segmentation", | ||
95 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | 95 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | ||
96 | "image_display_url": "", | 96 | "image_display_url": "", | ||
97 | "name": "semantic-segmentation", | 97 | "name": "semantic-segmentation", | ||
98 | "title": "Semantic Segmentation" | 98 | "title": "Semantic Segmentation" | ||
99 | } | 99 | } | ||
100 | ], | 100 | ], | ||
101 | "id": "a9bc39db-85fb-4ecb-902c-e567c8720cec", | 101 | "id": "a9bc39db-85fb-4ecb-902c-e567c8720cec", | ||
102 | "isopen": false, | 102 | "isopen": false, | ||
103 | "landing_page": | 103 | "landing_page": | ||
104 | "https://figshare.com/articles/dataset/AATTCT-IDS/23807256", | 104 | "https://figshare.com/articles/dataset/AATTCT-IDS/23807256", | ||
105 | "license_title": null, | 105 | "license_title": null, | ||
106 | "link_orkg": "", | 106 | "link_orkg": "", | ||
107 | "metadata_created": "2024-12-03T00:03:23.196550", | 107 | "metadata_created": "2024-12-03T00:03:23.196550", | ||
n | 108 | "metadata_modified": "2024-12-03T00:03:23.196555", | n | 108 | "metadata_modified": "2024-12-03T00:03:23.862526", |
109 | "name": "aattct-ids", | 109 | "name": "aattct-ids", | ||
110 | "notes": "A benchmark Abdominal Adipose Tissue CT Image Dataset | 110 | "notes": "A benchmark Abdominal Adipose Tissue CT Image Dataset | ||
111 | (AATTCT-IDS) for image denoising, semantic segmentation, and radiomics | 111 | (AATTCT-IDS) for image denoising, semantic segmentation, and radiomics | ||
112 | evaluation.", | 112 | evaluation.", | ||
n | 113 | "num_resources": 0, | n | 113 | "num_resources": 1, |
114 | "num_tags": 5, | 114 | "num_tags": 5, | ||
115 | "organization": { | 115 | "organization": { | ||
116 | "approval_status": "approved", | 116 | "approval_status": "approved", | ||
117 | "created": "2024-11-25T12:11:38.292601", | 117 | "created": "2024-11-25T12:11:38.292601", | ||
118 | "description": "", | 118 | "description": "", | ||
119 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 119 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
120 | "image_url": "", | 120 | "image_url": "", | ||
121 | "is_organization": true, | 121 | "is_organization": true, | ||
122 | "name": "no-organization", | 122 | "name": "no-organization", | ||
123 | "state": "active", | 123 | "state": "active", | ||
124 | "title": "No Organization", | 124 | "title": "No Organization", | ||
125 | "type": "organization" | 125 | "type": "organization" | ||
126 | }, | 126 | }, | ||
127 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 127 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
128 | "private": false, | 128 | "private": false, | ||
129 | "relationships_as_object": [], | 129 | "relationships_as_object": [], | ||
130 | "relationships_as_subject": [], | 130 | "relationships_as_subject": [], | ||
t | 131 | "resources": [], | t | 131 | "resources": [ |
132 | { | ||||
133 | "cache_last_updated": null, | ||||
134 | "cache_url": null, | ||||
135 | "created": "2024-12-03T00:20:35", | ||||
136 | "data": [ | ||||
137 | "dcterms:title", | ||||
138 | "dcterms:accessRights", | ||||
139 | "dcterms:creator", | ||||
140 | "dcterms:description", | ||||
141 | "dcterms:issued", | ||||
142 | "dcterms:language", | ||||
143 | "dcterms:identifier", | ||||
144 | "dcat:theme", | ||||
145 | "dcterms:type", | ||||
146 | "dcat:keyword", | ||||
147 | "dcat:landingPage", | ||||
148 | "dcterms:hasVersion", | ||||
149 | "dcterms:format", | ||||
150 | "mls:task" | ||||
151 | ], | ||||
152 | "description": "The json representation of the dataset with its | ||||
153 | distributions based on DCAT.", | ||||
154 | "format": "JSON", | ||||
155 | "hash": "", | ||||
156 | "id": "8bc6f7a2-73e1-472a-94a0-0b47ba752837", | ||||
157 | "last_modified": "2024-12-03T00:03:23.854177", | ||||
158 | "metadata_modified": "2024-12-03T00:03:23.865294", | ||||
159 | "mimetype": "application/json", | ||||
160 | "mimetype_inner": null, | ||||
161 | "name": "Original Metadata", | ||||
162 | "package_id": "a9bc39db-85fb-4ecb-902c-e567c8720cec", | ||||
163 | "position": 0, | ||||
164 | "resource_type": null, | ||||
165 | "size": 1044, | ||||
166 | "state": "active", | ||||
167 | "url": | ||||
168 | resource/8bc6f7a2-73e1-472a-94a0-0b47ba752837/download/metadata.json", | ||||
169 | "url_type": "upload" | ||||
170 | } | ||||
171 | ], | ||||
132 | "services_used_list": "", | 172 | "services_used_list": "", | ||
133 | "state": "active", | 173 | "state": "active", | ||
134 | "tags": [ | 174 | "tags": [ | ||
135 | { | 175 | { | ||
136 | "display_name": "Abdominal Adipose Tissue", | 176 | "display_name": "Abdominal Adipose Tissue", | ||
137 | "id": "e8d782d1-0c84-4428-86c5-66950dd743ea", | 177 | "id": "e8d782d1-0c84-4428-86c5-66950dd743ea", | ||
138 | "name": "Abdominal Adipose Tissue", | 178 | "name": "Abdominal Adipose Tissue", | ||
139 | "state": "active", | 179 | "state": "active", | ||
140 | "vocabulary_id": null | 180 | "vocabulary_id": null | ||
141 | }, | 181 | }, | ||
142 | { | 182 | { | ||
143 | "display_name": "CT Image Dataset", | 183 | "display_name": "CT Image Dataset", | ||
144 | "id": "34ac96b4-2509-4bf8-9e30-ed0bae85ec21", | 184 | "id": "34ac96b4-2509-4bf8-9e30-ed0bae85ec21", | ||
145 | "name": "CT Image Dataset", | 185 | "name": "CT Image Dataset", | ||
146 | "state": "active", | 186 | "state": "active", | ||
147 | "vocabulary_id": null | 187 | "vocabulary_id": null | ||
148 | }, | 188 | }, | ||
149 | { | 189 | { | ||
150 | "display_name": "Image Denoising", | 190 | "display_name": "Image Denoising", | ||
151 | "id": "97c2b24e-d13c-490c-aad2-94ea75d52d15", | 191 | "id": "97c2b24e-d13c-490c-aad2-94ea75d52d15", | ||
152 | "name": "Image Denoising", | 192 | "name": "Image Denoising", | ||
153 | "state": "active", | 193 | "state": "active", | ||
154 | "vocabulary_id": null | 194 | "vocabulary_id": null | ||
155 | }, | 195 | }, | ||
156 | { | 196 | { | ||
157 | "display_name": "Radiomics", | 197 | "display_name": "Radiomics", | ||
158 | "id": "d6d6f4ed-eaff-4ec2-b3dd-d0700adb6404", | 198 | "id": "d6d6f4ed-eaff-4ec2-b3dd-d0700adb6404", | ||
159 | "name": "Radiomics", | 199 | "name": "Radiomics", | ||
160 | "state": "active", | 200 | "state": "active", | ||
161 | "vocabulary_id": null | 201 | "vocabulary_id": null | ||
162 | }, | 202 | }, | ||
163 | { | 203 | { | ||
164 | "display_name": "Semantic Segmentation", | 204 | "display_name": "Semantic Segmentation", | ||
165 | "id": "809ad6af-28cd-43bd-974d-055a5c0f2973", | 205 | "id": "809ad6af-28cd-43bd-974d-055a5c0f2973", | ||
166 | "name": "Semantic Segmentation", | 206 | "name": "Semantic Segmentation", | ||
167 | "state": "active", | 207 | "state": "active", | ||
168 | "vocabulary_id": null | 208 | "vocabulary_id": null | ||
169 | } | 209 | } | ||
170 | ], | 210 | ], | ||
171 | "title": "AATTCT-IDS", | 211 | "title": "AATTCT-IDS", | ||
172 | "type": "dataset", | 212 | "type": "dataset", | ||
173 | "version": "" | 213 | "version": "" | ||
174 | } | 214 | } |