Changes
On August 4, 2023 at 9:04:30 AM UTC, admin:
-
No fields were updated. See the metadata diff for more details.
f | 1 | { | f | 1 | { |
2 | "author": "Pilia, Nicolas", | 2 | "author": "Pilia, Nicolas", | ||
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": "10.35097/1425", | 5 | "doi": "10.35097/1425", | ||
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 | "extra_authors": [ | 9 | "extra_authors": [ | ||
10 | { | 10 | { | ||
11 | "extra_author": "Schuler, Steffen", | 11 | "extra_author": "Schuler, Steffen", | ||
12 | "orcid": "" | 12 | "orcid": "" | ||
13 | }, | 13 | }, | ||
14 | { | 14 | { | ||
15 | "extra_author": "Rees, Maike", | 15 | "extra_author": "Rees, Maike", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
17 | }, | 17 | }, | ||
18 | { | 18 | { | ||
19 | "extra_author": "Moik, Gerald", | 19 | "extra_author": "Moik, Gerald", | ||
20 | "orcid": "" | 20 | "orcid": "" | ||
21 | }, | 21 | }, | ||
22 | { | 22 | { | ||
23 | "extra_author": "Potyagaylo, Danila", | 23 | "extra_author": "Potyagaylo, Danila", | ||
24 | "orcid": "" | 24 | "orcid": "" | ||
25 | }, | 25 | }, | ||
26 | { | 26 | { | ||
27 | "extra_author": "D\u00f6ssel, Olaf", | 27 | "extra_author": "D\u00f6ssel, Olaf", | ||
28 | "orcid": "" | 28 | "orcid": "" | ||
29 | }, | 29 | }, | ||
30 | { | 30 | { | ||
31 | "extra_author": "Loewe, Axel", | 31 | "extra_author": "Loewe, Axel", | ||
32 | "orcid": "This dataset contains about 1.8 million body surface | 32 | "orcid": "This dataset contains about 1.8 million body surface | ||
33 | potentials (BSPs) simulated using 1000 heart models generated using a | 33 | potentials (BSPs) simulated using 1000 heart models generated using a | ||
34 | statistical shape model.\r\nIt has been used in [1,1a]. Here, only the | 34 | statistical shape model.\r\nIt has been used in [1,1a]. Here, only the | ||
35 | noise-free BSPs are provided.\r\n\r\nDue to its size, this is a | 35 | noise-free BSPs are provided.\r\n\r\nDue to its size, this is a | ||
36 | multi-part dataset.\r\nPart 1: | 36 | multi-part dataset.\r\nPart 1: | ||
37 | https://doi.org/10.5445/IR/1000156139\r\nPart 2: | 37 | https://doi.org/10.5445/IR/1000156139\r\nPart 2: | ||
38 | https://doi.org/10.5445/IR/1000156554\r\nPart 3: | 38 | https://doi.org/10.5445/IR/1000156554\r\nPart 3: | ||
39 | https://doi.org/10.5445/IR/1000156555\r\nPart 4: | 39 | https://doi.org/10.5445/IR/1000156555\r\nPart 4: | ||
40 | https://doi.org/10.5445/IR/1000156556\r\nPart 5: | 40 | https://doi.org/10.5445/IR/1000156556\r\nPart 5: | ||
41 | https://doi.org/10.5445/IR/1000156557\r\n\r\nEach archive | 41 | https://doi.org/10.5445/IR/1000156557\r\n\r\nEach archive | ||
42 | XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each | 42 | XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each | ||
43 | subdirectory within the archive contains:\r\n\r\n- heart.vtp:\r\n A | 43 | subdirectory within the archive contains:\r\n\r\n- heart.vtp:\r\n A | ||
44 | triangle mesh of the heart including the point data:\r\n - ab, rt, | 44 | triangle mesh of the heart including the point data:\r\n - ab, rt, | ||
45 | rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\r\n - | 45 | rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\r\n - | ||
46 | class: Boundary regions used as input for the computation of fiber | 46 | class: Boundary regions used as input for the computation of fiber | ||
47 | orientations [3].\r\n - trigger: 1-based indices of the ca. 600 foci | 47 | orientations [3].\r\n - trigger: 1-based indices of the ca. 600 foci | ||
48 | (-1000 if not a focus).\r\n\r\n- heart_transform_matrices.mat:\r\n A | 48 | (-1000 if not a focus).\r\n\r\n- heart_transform_matrices.mat:\r\n A | ||
49 | 1 x 3 cell array containing 4 x 4 transformation matrices that\r\n | 49 | 1 x 3 cell array containing 4 x 4 transformation matrices that\r\n | ||
50 | describe the pose of the heart within the torso.\r\n Apply the matrix | 50 | describe the pose of the heart within the torso.\r\n Apply the matrix | ||
51 | from heart_transform_matrices.mat to the nodes in heart.vtp.\r\n\r\n- | 51 | from heart_transform_matrices.mat to the nodes in heart.vtp.\r\n\r\n- | ||
52 | actTimes.mat:\r\n A numNodes x numFoci matrix of activation times | 52 | actTimes.mat:\r\n A numNodes x numFoci matrix of activation times | ||
53 | computed using the fast iterative method [4,5]\r\n (conduction | 53 | computed using the fast iterative method [4,5]\r\n (conduction | ||
54 | velocity in fiber direction: 1 m/s, perpendicular to fiber direction: | 54 | velocity in fiber direction: 1 m/s, perpendicular to fiber direction: | ||
55 | 1/2.7 m/s).\r\n\r\n- bsp.mat:\r\n - bsp: A numElectrodes x | 55 | 1/2.7 m/s).\r\n\r\n- bsp.mat:\r\n - bsp: A numElectrodes x | ||
56 | numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by | 56 | numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by | ||
57 | aligning a transmembrane voltage template with\r\n scaled | 57 | aligning a transmembrane voltage template with\r\n scaled | ||
58 | activation times (see actTimeScalings.mat below) and solving the | 58 | activation times (see actTimeScalings.mat below) and solving the | ||
59 | second bidomain equation using the boundary element method [6].\r\n - | 59 | second bidomain equation using the boundary element method [6].\r\n - | ||
60 | bspEnd: Time index of the end of depolarization (largest scaled | 60 | bspEnd: Time index of the end of depolarization (largest scaled | ||
61 | activation time).\r\n\r\n\r\nThe archive general.tar contains | 61 | activation time).\r\n\r\n\r\nThe archive general.tar contains | ||
62 | heart-model-independent data and parameters used to generate the | 62 | heart-model-independent data and parameters used to generate the | ||
63 | individual heart models:\r\n\r\n- torso.vtp:\r\n A triangle mesh of | 63 | individual heart models:\r\n\r\n- torso.vtp:\r\n A triangle mesh of | ||
64 | the torso including the point data:\r\n - electrodes: 1-based indices | 64 | the torso including the point data:\r\n - electrodes: 1-based indices | ||
65 | of the 200 electrodes (-1000 if not an electrode).\r\n\r\n- | 65 | of the 200 electrodes (-1000 if not an electrode).\r\n\r\n- | ||
66 | heart_meanshape.vtp:\r\n A triangle mesh of the mean shape of the | 66 | heart_meanshape.vtp:\r\n A triangle mesh of the mean shape of the | ||
67 | statistical shape model [7,8]. \r\n\r\n- heart_shapemodel.mat:\r\n - | 67 | statistical shape model [7,8]. \r\n\r\n- heart_shapemodel.mat:\r\n - | ||
68 | pc: A 3*numNodes x numModes matrix of principal components (numModes = | 68 | pc: A 3*numNodes x numModes matrix of principal components (numModes = | ||
69 | 100).\r\n - var: A numModes x 1 vector of variances.\r\n - weights: | 69 | 100).\r\n - var: A numModes x 1 vector of variances.\r\n - weights: | ||
70 | A numModes x numModels matrix of weights used to generate the 1000 | 70 | A numModes x numModels matrix of weights used to generate the 1000 | ||
71 | heart models.\r\n\r\n- heart_alignment_matrix.mat:\r\n A 4 x 4 | 71 | heart models.\r\n\r\n- heart_alignment_matrix.mat:\r\n A 4 x 4 | ||
72 | transformation matrix describing the alignment of the mean shape with | 72 | transformation matrix describing the alignment of the mean shape with | ||
73 | the torso-specific heart. Only to be appleid to node coordinates in | 73 | the torso-specific heart. Only to be appleid to node coordinates in | ||
74 | within general.tar (already contained in | 74 | within general.tar (already contained in | ||
75 | heart_transform_matrices.mat).\r\n\r\n- | 75 | heart_transform_matrices.mat).\r\n\r\n- | ||
76 | heart_transform_params.mat:\r\n A struct containing roll, pitch, yaw | 76 | heart_transform_params.mat:\r\n A struct containing roll, pitch, yaw | ||
77 | angles and x, y, z translations used to generate the | 77 | angles and x, y, z translations used to generate the | ||
78 | heart_transform_matrices.mat (see above).\r\n\r\n- | 78 | heart_transform_matrices.mat (see above).\r\n\r\n- | ||
79 | fiber_angles.mat:\r\n - alphaEndo: numModels x 1 vector of | 79 | fiber_angles.mat:\r\n - alphaEndo: numModels x 1 vector of | ||
80 | endocardial fiber angles used to generate fiber orientations.\r\n - | 80 | endocardial fiber angles used to generate fiber orientations.\r\n - | ||
81 | alphaEpi: numModels x 1 vector of epicardial fiber angles used to | 81 | alphaEpi: numModels x 1 vector of epicardial fiber angles used to | ||
82 | generate fiber orientations.\r\n\r\n- actTimeScalings.mat:\r\n - A | 82 | generate fiber orientations.\r\n\r\n- actTimeScalings.mat:\r\n - A | ||
83 | numModels x numFoci matrix of factors used to scale the activation | 83 | numModels x numFoci matrix of factors used to scale the activation | ||
84 | times.\r\n\r\n- tmv_template.mat:\r\n The transmembrane voltage time | 84 | times.\r\n\r\n- tmv_template.mat:\r\n The transmembrane voltage time | ||
85 | course used to compute BSPs.\r\n\r\n- heart_classes.vtp:\r\n A coarse | 85 | course used to compute BSPs.\r\n\r\n- heart_classes.vtp:\r\n A coarse | ||
86 | triangle mesh of the mean shape used for fuzzy | 86 | triangle mesh of the mean shape used for fuzzy | ||
87 | classification.\r\n\r\n- heart_classes_subdiv.vtp:\r\n A subdivided | 87 | classification.\r\n\r\n- heart_classes_subdiv.vtp:\r\n A subdivided | ||
88 | version of the coarse triangle mesh of the mean shape used to convert | 88 | version of the coarse triangle mesh of the mean shape used to convert | ||
89 | between Cobiveco and barycentric coordinates.\r\n\r\n\r\n[1] | 89 | between Cobiveco and barycentric coordinates.\r\n\r\n\r\n[1] | ||
90 | .2209.08095\r\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\r\n[2] | 90 | .2209.08095\r\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\r\n[2] | ||
91 | https://doi.org/10.1016/j.media.2021.102247\r\n[3] | 91 | https://doi.org/10.1016/j.media.2021.102247\r\n[3] | ||
92 | https://github.com/KIT-IBT/LDRB_Fibers\r\n[4] | 92 | https://github.com/KIT-IBT/LDRB_Fibers\r\n[4] | ||
93 | https://github.com/KIT-IBT/FIM_Eikonal\r\n[5] | 93 | https://github.com/KIT-IBT/FIM_Eikonal\r\n[5] | ||
94 | https://doi.org/10.1137/120881956\r\n[6] | 94 | https://doi.org/10.1137/120881956\r\n[6] | ||
95 | https://doi.org/10.1016/j.cmpb.2007.09.004\r\n[7] | 95 | https://doi.org/10.1016/j.cmpb.2007.09.004\r\n[7] | ||
96 | https://doi.org/10.5281/zenodo.4506463\r\n[8] | 96 | https://doi.org/10.5281/zenodo.4506463\r\n[8] | ||
97 | https://doi.org/10.1016/j.media.2015.08.009" | 97 | https://doi.org/10.1016/j.media.2015.08.009" | ||
98 | } | 98 | } | ||
99 | ], | 99 | ], | ||
100 | "groups": [], | 100 | "groups": [], | ||
101 | "id": "b44284b7-3551-413e-af35-fc4eba5dc60f", | 101 | "id": "b44284b7-3551-413e-af35-fc4eba5dc60f", | ||
102 | "isopen": false, | 102 | "isopen": false, | ||
103 | "license_id": "CC BY-NC-SA 4.0 | 103 | "license_id": "CC BY-NC-SA 4.0 | ||
104 | Attribution-NonCommercial-ShareAlike", | 104 | Attribution-NonCommercial-ShareAlike", | ||
105 | "license_title": "CC BY-NC-SA 4.0 | 105 | "license_title": "CC BY-NC-SA 4.0 | ||
106 | Attribution-NonCommercial-ShareAlike", | 106 | Attribution-NonCommercial-ShareAlike", | ||
107 | "metadata_created": "2023-08-04T08:51:11.848903", | 107 | "metadata_created": "2023-08-04T08:51:11.848903", | ||
t | 108 | "metadata_modified": "2023-08-04T08:53:48.771449", | t | 108 | "metadata_modified": "2023-08-04T09:04:30.376226", |
109 | "name": "rdr-doi-10-35097-1425", | 109 | "name": "rdr-doi-10-35097-1425", | ||
110 | "notes": "Abstract: 1.8 million ECGs derived from multiscale | 110 | "notes": "Abstract: 1.8 million ECGs derived from multiscale | ||
111 | simulations of cardiac electrophysiology of ventricular extrasystoles. | 111 | simulations of cardiac electrophysiology of ventricular extrasystoles. | ||
112 | 1000 anatomical variants of a bi-ventricular mesh x 600 excitation | 112 | 1000 anatomical variants of a bi-ventricular mesh x 600 excitation | ||
113 | origins x 3 heart positions in the torso.\r\nTechnicalRemarks: This | 113 | origins x 3 heart positions in the torso.\r\nTechnicalRemarks: This | ||
114 | dataset contains about 1.8 million body surface potentials (BSPs) | 114 | dataset contains about 1.8 million body surface potentials (BSPs) | ||
115 | simulated using 1000 heart models generated using a statistical shape | 115 | simulated using 1000 heart models generated using a statistical shape | ||
116 | model.\r\nIt has been used in [1,1a]. Here, only the noise-free BSPs | 116 | model.\r\nIt has been used in [1,1a]. Here, only the noise-free BSPs | ||
117 | are provided.\r\n\r\nDue to its size, this is a multi-part | 117 | are provided.\r\n\r\nDue to its size, this is a multi-part | ||
118 | dataset.\r\nPart 1: https://doi.org/10.5445/IR/1000156139\r\nPart 2: | 118 | dataset.\r\nPart 1: https://doi.org/10.5445/IR/1000156139\r\nPart 2: | ||
119 | https://doi.org/10.5445/IR/1000156554\r\nPart 3: | 119 | https://doi.org/10.5445/IR/1000156554\r\nPart 3: | ||
120 | https://doi.org/10.5445/IR/1000156555\r\nPart 4: | 120 | https://doi.org/10.5445/IR/1000156555\r\nPart 4: | ||
121 | https://doi.org/10.5445/IR/1000156556\r\nPart 5: | 121 | https://doi.org/10.5445/IR/1000156556\r\nPart 5: | ||
122 | https://doi.org/10.5445/IR/1000156557\r\n\r\nEach archive | 122 | https://doi.org/10.5445/IR/1000156557\r\n\r\nEach archive | ||
123 | XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each | 123 | XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each | ||
124 | subdirectory within the archive contains:\r\n\r\n- heart.vtp:\r\n A | 124 | subdirectory within the archive contains:\r\n\r\n- heart.vtp:\r\n A | ||
125 | triangle mesh of the heart including the point data:\r\n - ab, rt, | 125 | triangle mesh of the heart including the point data:\r\n - ab, rt, | ||
126 | rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\r\n - | 126 | rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].\r\n - | ||
127 | class: Boundary regions used as input for the computation of fiber | 127 | class: Boundary regions used as input for the computation of fiber | ||
128 | orientations [3].\r\n - trigger: 1-based indices of the ca. 600 foci | 128 | orientations [3].\r\n - trigger: 1-based indices of the ca. 600 foci | ||
129 | (-1000 if not a focus).\r\n\r\n- heart_transform_matrices.mat:\r\n A | 129 | (-1000 if not a focus).\r\n\r\n- heart_transform_matrices.mat:\r\n A | ||
130 | 1 x 3 cell array containing 4 x 4 transformation matrices that\r\n | 130 | 1 x 3 cell array containing 4 x 4 transformation matrices that\r\n | ||
131 | describe the pose of the heart within the torso.\r\n Apply the matrix | 131 | describe the pose of the heart within the torso.\r\n Apply the matrix | ||
132 | from heart_transform_matrices.mat to the nodes in heart.vtp.\r\n\r\n- | 132 | from heart_transform_matrices.mat to the nodes in heart.vtp.\r\n\r\n- | ||
133 | actTimes.mat:\r\n A numNodes x numFoci matrix of activation times | 133 | actTimes.mat:\r\n A numNodes x numFoci matrix of activation times | ||
134 | computed using the fast iterative method [4,5]\r\n (conduction | 134 | computed using the fast iterative method [4,5]\r\n (conduction | ||
135 | velocity in fiber direction: 1 m/s, perpendicular to fiber direction: | 135 | velocity in fiber direction: 1 m/s, perpendicular to fiber direction: | ||
136 | 1/2.7 m/s).\r\n\r\n- bsp.mat:\r\n - bsp: A numElectrodes x | 136 | 1/2.7 m/s).\r\n\r\n- bsp.mat:\r\n - bsp: A numElectrodes x | ||
137 | numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by | 137 | numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by | ||
138 | aligning a transmembrane voltage template with\r\n scaled | 138 | aligning a transmembrane voltage template with\r\n scaled | ||
139 | activation times (see actTimeScalings.mat below) and solving the | 139 | activation times (see actTimeScalings.mat below) and solving the | ||
140 | second bidomain equation using the boundary element method [6].\r\n - | 140 | second bidomain equation using the boundary element method [6].\r\n - | ||
141 | bspEnd: Time index of the end of depolarization (largest scaled | 141 | bspEnd: Time index of the end of depolarization (largest scaled | ||
142 | activation time).\r\n\r\n\r\nThe archive general.tar contains | 142 | activation time).\r\n\r\n\r\nThe archive general.tar contains | ||
143 | heart-model-independent data and parameters used to generate the | 143 | heart-model-independent data and parameters used to generate the | ||
144 | individual heart models:\r\n\r\n- torso.vtp:\r\n A triangle mesh of | 144 | individual heart models:\r\n\r\n- torso.vtp:\r\n A triangle mesh of | ||
145 | the torso including the point data:\r\n - electrodes: 1-based indices | 145 | the torso including the point data:\r\n - electrodes: 1-based indices | ||
146 | of the 200 electrodes (-1000 if not an electrode).\r\n\r\n- | 146 | of the 200 electrodes (-1000 if not an electrode).\r\n\r\n- | ||
147 | heart_meanshape.vtp:\r\n A triangle mesh of the mean shape of the | 147 | heart_meanshape.vtp:\r\n A triangle mesh of the mean shape of the | ||
148 | statistical shape model [7,8]. \r\n\r\n- heart_shapemodel.mat:\r\n - | 148 | statistical shape model [7,8]. \r\n\r\n- heart_shapemodel.mat:\r\n - | ||
149 | pc: A 3*numNodes x numModes matrix of principal components (numModes = | 149 | pc: A 3*numNodes x numModes matrix of principal components (numModes = | ||
150 | 100).\r\n - var: A numModes x 1 vector of variances.\r\n - weights: | 150 | 100).\r\n - var: A numModes x 1 vector of variances.\r\n - weights: | ||
151 | A numModes x numModels matrix of weights used to generate the 1000 | 151 | A numModes x numModels matrix of weights used to generate the 1000 | ||
152 | heart models.\r\n\r\n- heart_alignment_matrix.mat:\r\n A 4 x 4 | 152 | heart models.\r\n\r\n- heart_alignment_matrix.mat:\r\n A 4 x 4 | ||
153 | transformation matrix describing the alignment of the mean shape with | 153 | transformation matrix describing the alignment of the mean shape with | ||
154 | the torso-specific heart. Only to be appleid to node coordinates in | 154 | the torso-specific heart. Only to be appleid to node coordinates in | ||
155 | within general.tar (already contained in | 155 | within general.tar (already contained in | ||
156 | heart_transform_matrices.mat).\r\n\r\n- | 156 | heart_transform_matrices.mat).\r\n\r\n- | ||
157 | heart_transform_params.mat:\r\n A struct containing roll, pitch, yaw | 157 | heart_transform_params.mat:\r\n A struct containing roll, pitch, yaw | ||
158 | angles and x, y, z translations used to generate the | 158 | angles and x, y, z translations used to generate the | ||
159 | heart_transform_matrices.mat (see above).\r\n\r\n- | 159 | heart_transform_matrices.mat (see above).\r\n\r\n- | ||
160 | fiber_angles.mat:\r\n - alphaEndo: numModels x 1 vector of | 160 | fiber_angles.mat:\r\n - alphaEndo: numModels x 1 vector of | ||
161 | endocardial fiber angles used to generate fiber orientations.\r\n - | 161 | endocardial fiber angles used to generate fiber orientations.\r\n - | ||
162 | alphaEpi: numModels x 1 vector of epicardial fiber angles used to | 162 | alphaEpi: numModels x 1 vector of epicardial fiber angles used to | ||
163 | generate fiber orientations.\r\n\r\n- actTimeScalings.mat:\r\n - A | 163 | generate fiber orientations.\r\n\r\n- actTimeScalings.mat:\r\n - A | ||
164 | numModels x numFoci matrix of factors used to scale the activation | 164 | numModels x numFoci matrix of factors used to scale the activation | ||
165 | times.\r\n\r\n- tmv_template.mat:\r\n The transmembrane voltage time | 165 | times.\r\n\r\n- tmv_template.mat:\r\n The transmembrane voltage time | ||
166 | course used to compute BSPs.\r\n\r\n- heart_classes.vtp:\r\n A coarse | 166 | course used to compute BSPs.\r\n\r\n- heart_classes.vtp:\r\n A coarse | ||
167 | triangle mesh of the mean shape used for fuzzy | 167 | triangle mesh of the mean shape used for fuzzy | ||
168 | classification.\r\n\r\n- heart_classes_subdiv.vtp:\r\n A subdivided | 168 | classification.\r\n\r\n- heart_classes_subdiv.vtp:\r\n A subdivided | ||
169 | version of the coarse triangle mesh of the mean shape used to convert | 169 | version of the coarse triangle mesh of the mean shape used to convert | ||
170 | between Cobiveco and barycentric coordinates.\r\n\r\n\r\n[1] | 170 | between Cobiveco and barycentric coordinates.\r\n\r\n\r\n[1] | ||
171 | .2209.08095\r\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\r\n[2] | 171 | .2209.08095\r\n[1a]https://doi.org/10.1016/j.artmed.2023.102619\r\n[2] | ||
172 | https://doi.org/10.1016/j.media.2021.102247\r\n[3] | 172 | https://doi.org/10.1016/j.media.2021.102247\r\n[3] | ||
173 | https://github.com/KIT-IBT/LDRB_Fibers\r\n[4] | 173 | https://github.com/KIT-IBT/LDRB_Fibers\r\n[4] | ||
174 | https://github.com/KIT-IBT/FIM_Eikonal\r\n[5] | 174 | https://github.com/KIT-IBT/FIM_Eikonal\r\n[5] | ||
175 | https://doi.org/10.1137/120881956\r\n[6] | 175 | https://doi.org/10.1137/120881956\r\n[6] | ||
176 | https://doi.org/10.1016/j.cmpb.2007.09.004\r\n[7] | 176 | https://doi.org/10.1016/j.cmpb.2007.09.004\r\n[7] | ||
177 | https://doi.org/10.5281/zenodo.4506463\r\n[8] | 177 | https://doi.org/10.5281/zenodo.4506463\r\n[8] | ||
178 | https://doi.org/10.1016/j.media.2015.08.009", | 178 | https://doi.org/10.1016/j.media.2015.08.009", | ||
179 | "num_resources": 0, | 179 | "num_resources": 0, | ||
180 | "num_tags": 3, | 180 | "num_tags": 3, | ||
181 | "orcid": "", | 181 | "orcid": "", | ||
182 | "organization": { | 182 | "organization": { | ||
183 | "approval_status": "approved", | 183 | "approval_status": "approved", | ||
184 | "created": "2023-01-12T13:30:23.238233", | 184 | "created": "2023-01-12T13:30:23.238233", | ||
185 | "description": "RADAR (Research Data Repository) is a | 185 | "description": "RADAR (Research Data Repository) is a | ||
186 | cross-disciplinary repository for archiving and publishing research | 186 | cross-disciplinary repository for archiving and publishing research | ||
187 | data from completed scientific studies and projects. The focus is on | 187 | data from completed scientific studies and projects. The focus is on | ||
188 | research data from subjects that do not yet have their own | 188 | research data from subjects that do not yet have their own | ||
189 | discipline-specific infrastructures for research data management. ", | 189 | discipline-specific infrastructures for research data management. ", | ||
190 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 190 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
191 | "image_url": "radar-logo.svg", | 191 | "image_url": "radar-logo.svg", | ||
192 | "is_organization": true, | 192 | "is_organization": true, | ||
193 | "name": "radar", | 193 | "name": "radar", | ||
194 | "state": "active", | 194 | "state": "active", | ||
195 | "title": "RADAR", | 195 | "title": "RADAR", | ||
196 | "type": "organization" | 196 | "type": "organization" | ||
197 | }, | 197 | }, | ||
198 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 198 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
199 | "private": false, | 199 | "private": false, | ||
200 | "production_year": "2023", | 200 | "production_year": "2023", | ||
201 | "publication_year": "2023", | 201 | "publication_year": "2023", | ||
202 | "publishers": [ | 202 | "publishers": [ | ||
203 | { | 203 | { | ||
204 | "publisher": "Karlsruhe Institute of Technology" | 204 | "publisher": "Karlsruhe Institute of Technology" | ||
205 | } | 205 | } | ||
206 | ], | 206 | ], | ||
207 | "relationships_as_object": [], | 207 | "relationships_as_object": [], | ||
208 | "relationships_as_subject": [], | 208 | "relationships_as_subject": [], | ||
209 | "repository_name": "RADAR (Research Data Repository)", | 209 | "repository_name": "RADAR (Research Data Repository)", | ||
210 | "resources": [], | 210 | "resources": [], | ||
211 | "services_used_list": "", | 211 | "services_used_list": "", | ||
212 | "source_metadata_created": "2023", | 212 | "source_metadata_created": "2023", | ||
213 | "source_metadata_modified": "", | 213 | "source_metadata_modified": "", | ||
214 | "state": "active", | 214 | "state": "active", | ||
215 | "subject_areas": [ | 215 | "subject_areas": [ | ||
216 | { | 216 | { | ||
217 | "subject_area_additional": "", | 217 | "subject_area_additional": "", | ||
218 | "subject_area_name": "Engineering" | 218 | "subject_area_name": "Engineering" | ||
219 | } | 219 | } | ||
220 | ], | 220 | ], | ||
221 | "tags": [ | 221 | "tags": [ | ||
222 | { | 222 | { | ||
223 | "display_name": "ECG", | 223 | "display_name": "ECG", | ||
224 | "id": "ccfae47f-089b-4315-bf52-aac716b895bb", | 224 | "id": "ccfae47f-089b-4315-bf52-aac716b895bb", | ||
225 | "name": "ECG", | 225 | "name": "ECG", | ||
226 | "state": "active", | 226 | "state": "active", | ||
227 | "vocabulary_id": null | 227 | "vocabulary_id": null | ||
228 | }, | 228 | }, | ||
229 | { | 229 | { | ||
230 | "display_name": "extrasystoles", | 230 | "display_name": "extrasystoles", | ||
231 | "id": "05abe71d-d7c0-4b7b-9c94-bdfbf25a28fb", | 231 | "id": "05abe71d-d7c0-4b7b-9c94-bdfbf25a28fb", | ||
232 | "name": "extrasystoles", | 232 | "name": "extrasystoles", | ||
233 | "state": "active", | 233 | "state": "active", | ||
234 | "vocabulary_id": null | 234 | "vocabulary_id": null | ||
235 | }, | 235 | }, | ||
236 | { | 236 | { | ||
237 | "display_name": "in silico", | 237 | "display_name": "in silico", | ||
238 | "id": "4b4814b7-88ed-4d87-8b53-5d47c24ec060", | 238 | "id": "4b4814b7-88ed-4d87-8b53-5d47c24ec060", | ||
239 | "name": "in silico", | 239 | "name": "in silico", | ||
240 | "state": "active", | 240 | "state": "active", | ||
241 | "vocabulary_id": null | 241 | "vocabulary_id": null | ||
242 | } | 242 | } | ||
243 | ], | 243 | ], | ||
244 | "title": "In silico electrocardiograms of 1.8 million ventricular | 244 | "title": "In silico electrocardiograms of 1.8 million ventricular | ||
245 | extrasystoles and corresponding activation maps (part 1)", | 245 | extrasystoles and corresponding activation maps (part 1)", | ||
246 | "type": "vdataset", | 246 | "type": "vdataset", | ||
247 | "url": "https://doi.org/10.35097/1425" | 247 | "url": "https://doi.org/10.35097/1425" | ||
248 | } | 248 | } |