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On November 28, 2024 at 1:15:35 PM UTC, admin:
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Changed value of field
extra_authors
to[{'extra_author': 'Huber, Julian', 'familyName': 'Huber', 'givenName': 'Julian', 'orcid': ''}, {'extra_author': 'Weinhardt, Christof', 'familyName': 'Weinhardt', 'givenName': 'Christof', 'orcid': ''}]
in Estimation of marginal co2 emission factors in germany 2017
f | 1 | { | f | 1 | { |
2 | "author": "Lohmann, Kai", | 2 | "author": "Lohmann, Kai", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
n | n | 4 | "citation": [], | ||
4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 5 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
5 | "doi": "10.35097/1202", | 6 | "doi": "10.35097/1202", | ||
6 | "doi_date_published": "2023", | 7 | "doi_date_published": "2023", | ||
7 | "doi_publisher": "", | 8 | "doi_publisher": "", | ||
8 | "doi_status": "True", | 9 | "doi_status": "True", | ||
9 | "extra_authors": [ | 10 | "extra_authors": [ | ||
10 | { | 11 | { | ||
11 | "extra_author": "Huber, Julian", | 12 | "extra_author": "Huber, Julian", | ||
n | n | 13 | "familyName": "Huber", | ||
14 | "givenName": "Julian", | ||||
12 | "orcid": "" | 15 | "orcid": "" | ||
13 | }, | 16 | }, | ||
14 | { | 17 | { | ||
15 | "extra_author": "Weinhardt, Christof", | 18 | "extra_author": "Weinhardt, Christof", | ||
n | n | 19 | "familyName": "Weinhardt", | ||
20 | "givenName": "Christof", | ||||
16 | "orcid": "" | 21 | "orcid": "" | ||
17 | } | 22 | } | ||
18 | ], | 23 | ], | ||
n | n | 24 | "familyName": "Lohmann", | ||
25 | "givenName": "Kai", | ||||
19 | "groups": [], | 26 | "groups": [], | ||
20 | "id": "ae22a4bc-746e-4e9f-9c88-c425d18c8768", | 27 | "id": "ae22a4bc-746e-4e9f-9c88-c425d18c8768", | ||
21 | "isopen": false, | 28 | "isopen": false, | ||
22 | "license_id": "CC BY-NC-SA 4.0 | 29 | "license_id": "CC BY-NC-SA 4.0 | ||
23 | Attribution-NonCommercial-ShareAlike", | 30 | Attribution-NonCommercial-ShareAlike", | ||
24 | "license_title": "CC BY-NC-SA 4.0 | 31 | "license_title": "CC BY-NC-SA 4.0 | ||
25 | Attribution-NonCommercial-ShareAlike", | 32 | Attribution-NonCommercial-ShareAlike", | ||
26 | "metadata_created": "2023-08-04T08:50:16.749905", | 33 | "metadata_created": "2023-08-04T08:50:16.749905", | ||
t | 27 | "metadata_modified": "2023-08-04T09:28:56.814574", | t | 34 | "metadata_modified": "2024-11-28T13:15:35.774474", |
28 | "name": "rdr-doi-10-35097-1202", | 35 | "name": "rdr-doi-10-35097-1202", | ||
29 | "notes": "Abstract: This data set covers total load, hourly | 36 | "notes": "Abstract: This data set covers total load, hourly | ||
30 | generation and electric efficiency of power plants >100MW in Germany | 37 | generation and electric efficiency of power plants >100MW in Germany | ||
31 | 2017. Based on this data, the four jupyter-notebooks derive average | 38 | 2017. Based on this data, the four jupyter-notebooks derive average | ||
32 | carbon dioxide emissions factors for each hour and marginal emission | 39 | carbon dioxide emissions factors for each hour and marginal emission | ||
33 | factors for twenty load levels. The third script contains a case study | 40 | factors for twenty load levels. The third script contains a case study | ||
34 | for scheduling battery electric vehicles based on short-term forecasts | 41 | for scheduling battery electric vehicles based on short-term forecasts | ||
35 | of marginal emission factors.\r\nTechnicalRemarks: This data set | 42 | of marginal emission factors.\r\nTechnicalRemarks: This data set | ||
36 | covers total load, hourly generation and electric efficiency of power | 43 | covers total load, hourly generation and electric efficiency of power | ||
37 | plants >100MW in Germany 2017. Based on this data, the four | 44 | plants >100MW in Germany 2017. Based on this data, the four | ||
38 | jupyter-notebooks derive average carbon dioxide emissions factors for | 45 | jupyter-notebooks derive average carbon dioxide emissions factors for | ||
39 | each hour and marginal emission factors for twenty load levels. The | 46 | each hour and marginal emission factors for twenty load levels. The | ||
40 | third script contains a case study for scheduling battery electric | 47 | third script contains a case study for scheduling battery electric | ||
41 | vehicles based on short-term forecasts of marginal emission factors.", | 48 | vehicles based on short-term forecasts of marginal emission factors.", | ||
42 | "num_resources": 0, | 49 | "num_resources": 0, | ||
43 | "num_tags": 0, | 50 | "num_tags": 0, | ||
44 | "orcid": "", | 51 | "orcid": "", | ||
45 | "organization": { | 52 | "organization": { | ||
46 | "approval_status": "approved", | 53 | "approval_status": "approved", | ||
47 | "created": "2023-01-12T13:30:23.238233", | 54 | "created": "2023-01-12T13:30:23.238233", | ||
48 | "description": "RADAR (Research Data Repository) is a | 55 | "description": "RADAR (Research Data Repository) is a | ||
49 | cross-disciplinary repository for archiving and publishing research | 56 | cross-disciplinary repository for archiving and publishing research | ||
50 | data from completed scientific studies and projects. The focus is on | 57 | data from completed scientific studies and projects. The focus is on | ||
51 | research data from subjects that do not yet have their own | 58 | research data from subjects that do not yet have their own | ||
52 | discipline-specific infrastructures for research data management. ", | 59 | discipline-specific infrastructures for research data management. ", | ||
53 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 60 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
54 | "image_url": "radar-logo.svg", | 61 | "image_url": "radar-logo.svg", | ||
55 | "is_organization": true, | 62 | "is_organization": true, | ||
56 | "name": "radar", | 63 | "name": "radar", | ||
57 | "state": "active", | 64 | "state": "active", | ||
58 | "title": "RADAR", | 65 | "title": "RADAR", | ||
59 | "type": "organization" | 66 | "type": "organization" | ||
60 | }, | 67 | }, | ||
61 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 68 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
62 | "private": false, | 69 | "private": false, | ||
63 | "production_year": "2019", | 70 | "production_year": "2019", | ||
64 | "publication_year": "2023", | 71 | "publication_year": "2023", | ||
65 | "publishers": [ | 72 | "publishers": [ | ||
66 | { | 73 | { | ||
67 | "publisher": "Karlsruhe Institute of Technology" | 74 | "publisher": "Karlsruhe Institute of Technology" | ||
68 | } | 75 | } | ||
69 | ], | 76 | ], | ||
70 | "relationships_as_object": [], | 77 | "relationships_as_object": [], | ||
71 | "relationships_as_subject": [], | 78 | "relationships_as_subject": [], | ||
72 | "repository_name": "RADAR (Research Data Repository)", | 79 | "repository_name": "RADAR (Research Data Repository)", | ||
73 | "resources": [], | 80 | "resources": [], | ||
74 | "services_used_list": "", | 81 | "services_used_list": "", | ||
75 | "source_metadata_created": "2023", | 82 | "source_metadata_created": "2023", | ||
76 | "source_metadata_modified": "", | 83 | "source_metadata_modified": "", | ||
77 | "state": "active", | 84 | "state": "active", | ||
78 | "subject_areas": [ | 85 | "subject_areas": [ | ||
79 | { | 86 | { | ||
80 | "subject_area_additional": "", | 87 | "subject_area_additional": "", | ||
81 | "subject_area_name": "Economics" | 88 | "subject_area_name": "Economics" | ||
82 | } | 89 | } | ||
83 | ], | 90 | ], | ||
84 | "tags": [], | 91 | "tags": [], | ||
85 | "title": "Estimation of marginal co2 emission factors in germany | 92 | "title": "Estimation of marginal co2 emission factors in germany | ||
86 | 2017", | 93 | 2017", | ||
87 | "type": "vdataset", | 94 | "type": "vdataset", | ||
88 | "url": "https://doi.org/10.35097/1202" | 95 | "url": "https://doi.org/10.35097/1202" | ||
89 | } | 96 | } |