Changes
On December 3, 2024 at 10:52:29 AM UTC, admin:
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Added resource Original Metadata to Predicted perovskites dataset
f | 1 | { | f | 1 | { |
2 | "access_rights": "", | 2 | "access_rights": "", | ||
3 | "author": "Achintha Ihalage", | 3 | "author": "Achintha Ihalage", | ||
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": "https://doi.org/10.1038/s41524-021-00536-2", | 7 | "defined_in": "https://doi.org/10.1038/s41524-021-00536-2", | ||
8 | "doi": "10.57702/w8zxv9n9", | 8 | "doi": "10.57702/w8zxv9n9", | ||
9 | "doi_date_published": "2024-12-03", | 9 | "doi_date_published": "2024-12-03", | ||
10 | "doi_publisher": "TIB", | 10 | "doi_publisher": "TIB", | ||
11 | "doi_status": true, | 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": "Yang Hao", | 15 | "extra_author": "Yang Hao", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
17 | } | 17 | } | ||
18 | ], | 18 | ], | ||
19 | "groups": [ | 19 | "groups": [ | ||
20 | { | 20 | { | ||
21 | "description": "", | 21 | "description": "", | ||
22 | "display_name": "Machine Learning", | 22 | "display_name": "Machine Learning", | ||
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24 | "image_display_url": "", | 24 | "image_display_url": "", | ||
25 | "name": "machine-learning", | 25 | "name": "machine-learning", | ||
26 | "title": "Machine Learning" | 26 | "title": "Machine Learning" | ||
27 | }, | 27 | }, | ||
28 | { | 28 | { | ||
29 | "description": "", | 29 | "description": "", | ||
30 | "display_name": "Materials Science", | 30 | "display_name": "Materials Science", | ||
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32 | "image_display_url": "", | 32 | "image_display_url": "", | ||
33 | "name": "materials-science", | 33 | "name": "materials-science", | ||
34 | "title": "Materials Science" | 34 | "title": "Materials Science" | ||
35 | } | 35 | } | ||
36 | ], | 36 | ], | ||
37 | "id": "11c9103e-2108-4d82-9b94-7cca258c0358", | 37 | "id": "11c9103e-2108-4d82-9b94-7cca258c0358", | ||
38 | "isopen": false, | 38 | "isopen": false, | ||
39 | "landing_page": "", | 39 | "landing_page": "", | ||
40 | "license_title": null, | 40 | "license_title": null, | ||
41 | "link_orkg": "http://orkg.org/orkg/resource/R747555", | 41 | "link_orkg": "http://orkg.org/orkg/resource/R747555", | ||
42 | "metadata_created": "2024-12-03T10:52:27.105252", | 42 | "metadata_created": "2024-12-03T10:52:27.105252", | ||
n | 43 | "metadata_modified": "2024-12-03T10:52:27.164470", | n | 43 | "metadata_modified": "2024-12-03T10:52:28.387225", |
44 | "name": "predicted-perovskites-dataset", | 44 | "name": "predicted-perovskites-dataset", | ||
45 | "notes": "A dataset of 10,790 predicted perovskites, along with | 45 | "notes": "A dataset of 10,790 predicted perovskites, along with | ||
46 | their 5 most similar experimental materials, used to test the | 46 | their 5 most similar experimental materials, used to test the | ||
47 | effectiveness of the VAE model.", | 47 | effectiveness of the VAE model.", | ||
n | 48 | "num_resources": 0, | n | 48 | "num_resources": 1, |
49 | "num_tags": 3, | 49 | "num_tags": 3, | ||
50 | "organization": { | 50 | "organization": { | ||
51 | "approval_status": "approved", | 51 | "approval_status": "approved", | ||
52 | "created": "2024-11-25T12:11:38.292601", | 52 | "created": "2024-11-25T12:11:38.292601", | ||
53 | "description": "", | 53 | "description": "", | ||
54 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 54 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
55 | "image_url": "", | 55 | "image_url": "", | ||
56 | "is_organization": true, | 56 | "is_organization": true, | ||
57 | "name": "no-organization", | 57 | "name": "no-organization", | ||
58 | "state": "active", | 58 | "state": "active", | ||
59 | "title": "No Organization", | 59 | "title": "No Organization", | ||
60 | "type": "organization" | 60 | "type": "organization" | ||
61 | }, | 61 | }, | ||
62 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 62 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
63 | "private": false, | 63 | "private": false, | ||
64 | "relationships_as_object": [], | 64 | "relationships_as_object": [], | ||
65 | "relationships_as_subject": [], | 65 | "relationships_as_subject": [], | ||
t | 66 | "resources": [], | t | 66 | "resources": [ |
67 | { | ||||
68 | "cache_last_updated": null, | ||||
69 | "cache_url": null, | ||||
70 | "created": "2024-12-03T11:50:18", | ||||
71 | "data": [ | ||||
72 | "dcterms:title", | ||||
73 | "dcterms:accessRights", | ||||
74 | "dcterms:creator", | ||||
75 | "dcterms:description", | ||||
76 | "dcterms:issued", | ||||
77 | "dcterms:language", | ||||
78 | "dcterms:identifier", | ||||
79 | "dcat:theme", | ||||
80 | "dcterms:type", | ||||
81 | "dcat:keyword", | ||||
82 | "dcat:landingPage", | ||||
83 | "dcterms:hasVersion", | ||||
84 | "dcterms:format", | ||||
85 | "mls:task", | ||||
86 | "datacite:isDescribedBy" | ||||
87 | ], | ||||
88 | "description": "The json representation of the dataset with its | ||||
89 | distributions based on DCAT.", | ||||
90 | "format": "JSON", | ||||
91 | "hash": "", | ||||
92 | "id": "6ed0e54b-81fd-4a2a-8318-05e4bf879a39", | ||||
93 | "last_modified": "2024-12-03T10:52:28.380448", | ||||
94 | "metadata_modified": "2024-12-03T10:52:28.389976", | ||||
95 | "mimetype": "application/json", | ||||
96 | "mimetype_inner": null, | ||||
97 | "name": "Original Metadata", | ||||
98 | "package_id": "11c9103e-2108-4d82-9b94-7cca258c0358", | ||||
99 | "position": 0, | ||||
100 | "resource_type": null, | ||||
101 | "size": 733, | ||||
102 | "state": "active", | ||||
103 | "url": | ||||
104 | resource/6ed0e54b-81fd-4a2a-8318-05e4bf879a39/download/metadata.json", | ||||
105 | "url_type": "upload" | ||||
106 | } | ||||
107 | ], | ||||
67 | "services_used_list": "", | 108 | "services_used_list": "", | ||
68 | "state": "active", | 109 | "state": "active", | ||
69 | "tags": [ | 110 | "tags": [ | ||
70 | { | 111 | { | ||
71 | "display_name": "VAE", | 112 | "display_name": "VAE", | ||
72 | "id": "3d09fca3-2b24-425e-b566-6847f82c700d", | 113 | "id": "3d09fca3-2b24-425e-b566-6847f82c700d", | ||
73 | "name": "VAE", | 114 | "name": "VAE", | ||
74 | "state": "active", | 115 | "state": "active", | ||
75 | "vocabulary_id": null | 116 | "vocabulary_id": null | ||
76 | }, | 117 | }, | ||
77 | { | 118 | { | ||
78 | "display_name": "machine learning", | 119 | "display_name": "machine learning", | ||
79 | "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b", | 120 | "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b", | ||
80 | "name": "machine learning", | 121 | "name": "machine learning", | ||
81 | "state": "active", | 122 | "state": "active", | ||
82 | "vocabulary_id": null | 123 | "vocabulary_id": null | ||
83 | }, | 124 | }, | ||
84 | { | 125 | { | ||
85 | "display_name": "perovskites", | 126 | "display_name": "perovskites", | ||
86 | "id": "42942b7c-1478-4ca2-b151-c0502eef022b", | 127 | "id": "42942b7c-1478-4ca2-b151-c0502eef022b", | ||
87 | "name": "perovskites", | 128 | "name": "perovskites", | ||
88 | "state": "active", | 129 | "state": "active", | ||
89 | "vocabulary_id": null | 130 | "vocabulary_id": null | ||
90 | } | 131 | } | ||
91 | ], | 132 | ], | ||
92 | "title": "Predicted perovskites dataset", | 133 | "title": "Predicted perovskites dataset", | ||
93 | "type": "dataset", | 134 | "type": "dataset", | ||
94 | "version": "" | 135 | "version": "" | ||
95 | } | 136 | } |