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  "author": "[{\"affiliation\": \"WSL-Institut f\\u00fcr Schnee- und 
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  "author": "[{\"affiliation\": \"WSL-Institut f\\u00fcr Schnee- und 
            
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Lawinenforschung SLF\", \"affiliation_02\": \"\", \"affiliation_03\": 
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Lawinenforschung SLF\", \"affiliation_02\": \"\", \"affiliation_03\": 
            
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\"\", \"email\": \"sundu.kavitha@slf.ch\", \"given_name\": 
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\"\", \"email\": \"sundu.kavitha@slf.ch\", \"given_name\": 
            
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\"Kavitha\", \"identifier\": \"0000-0002-0445-8591\", \"name\": 
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\"Kavitha\", \"identifier\": \"0000-0002-0445-8591\", \"name\": 
            
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\"Sundu\"}, {\"affiliation\": \"WSL Institute for Snow and Avalanche 
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\"Sundu\"}, {\"affiliation\": \"WSL Institute for Snow and Avalanche 
            
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Research SLF\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", 
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Research SLF\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", 
            
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\"email\": \"rafael.ottersberg@unibe.ch\", \"given_name\": \"Rafael\", 
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\"email\": \"rafael.ottersberg@unibe.ch\", \"given_name\": \"Rafael\", 
            
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\"identifier\": \"\", \"name\": \"Ottersberg\"}, {\"affiliation\": 
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\"identifier\": \"\", \"name\": \"Ottersberg\"}, {\"affiliation\": 
            
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\"WSL Institute for Snow and Avalanche Research SLF\", 
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\"WSL Institute for Snow and Avalanche Research SLF\", 
            
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\"affiliation_02\": \"\", \"affiliation_03\": \"\", \"email\": 
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\"affiliation_02\": \"\", \"affiliation_03\": \"\", \"email\": 
            
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\"jaggi@slf.ch\", \"given_name\": \"Matthias\", \"identifier\": \"\", 
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\"jaggi@slf.ch\", \"given_name\": \"Matthias\", \"identifier\": \"\", 
            
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\"name\": \"Jaggi\"}, {\"affiliation\": \"WSL Institute for Snow and 
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\"name\": \"Jaggi\"}, {\"affiliation\": \"WSL Institute for Snow and 
            
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Avalanche Research SLF\", \"affiliation_02\": \"\", 
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Avalanche Research SLF\", \"affiliation_02\": \"\", 
            
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\"affiliation_03\": \"\", \"email\": \"loewe@slf.ch\", \"given_name\": 
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\"affiliation_03\": \"\", \"email\": \"loewe@slf.ch\", \"given_name\": 
            
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\"Henning\", \"identifier\": \"0000-0001-7515-6809\", \"name\": 
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\"Henning\", \"identifier\": \"0000-0001-7515-6809\", \"name\": 
            
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\"L\\u00f6we\"}]",
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\"L\\u00f6we\"}]",
            
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  "author_email": null,
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  "author_email": null,
            
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  "creator_user_id": "38416a29-a1f6-4b39-831a-d40c1f536159",
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  "creator_user_id": "38416a29-a1f6-4b39-831a-d40c1f536159",
            
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  "date": "[{\"date\": \"2021-08-10\", \"date_type\": \"created\", 
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  "date": "[{\"date\": \"2021-08-10\", \"date_type\": \"created\", 
            
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\"end_date\": \"2023-01-16\"}]",
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\"end_date\": \"2023-01-16\"}]",
            
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  "doi": "10.16904/envidat.436",
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  "doi": "10.16904/envidat.436",
            
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  "funding": "[{\"grant_number\": \"200020_178831\", \"institution\": 
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  "funding": "[{\"grant_number\": \"200020_178831\", \"institution\": 
            
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\"SNSF\", \"institution_url\": 
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\"SNSF\", \"institution_url\": 
            
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\"https://data.snf.ch/grants/grant/178831\"}]",
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\"https://data.snf.ch/grants/grant/178831\"}]",
            
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  "groups": [],
            
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  "id": "2fbce191-4283-4b68-aa4c-ad90723146ed",
            
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  "isopen": true,
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  "isopen": true,
            
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  "language": "en",
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  "language": "en",
            
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  "license_id": "odc-odbl",
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  "license_id": "odc-odbl",
            
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  "license_title": "ODbL with Database Contents License (DbCL)",
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  "license_title": "ODbL with Database Contents License (DbCL)",
            
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  "license_url": "https://opendefinition.org/licenses/odc-odbl",
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  "license_url": "https://opendefinition.org/licenses/odc-odbl",
            
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  "maintainer": "{\"affiliation\": \"WSL Institute for Snow and 
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  "maintainer": "{\"affiliation\": \"WSL Institute for Snow and 
            
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Avalanche Research SLF\", \"email\": \"loewe@slf.ch\", \"given_name\": 
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Avalanche Research SLF\", \"email\": \"loewe@slf.ch\", \"given_name\": 
            
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\"Henning\", \"identifier\": \"0000-0001-7515-6809\", \"name\": 
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\"Henning\", \"identifier\": \"0000-0001-7515-6809\", \"name\": 
            
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\"L\u00f6we\"}",
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\"L\u00f6we\"}",
            
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  "maintainer_email": null,
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  "maintainer_email": null,
            
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  "metadata_created": "2023-10-10T08:45:00.817542",
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  "metadata_created": "2023-10-10T08:45:00.817542",
            
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  "metadata_modified": "2023-10-10T09:13:54.541717",
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  "metadata_modified": "2023-10-10T11:30:03.292598",
            
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  "name": "slow-snow-compression",
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  "name": "slow-snow-compression",
            
t
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  "notes": "# A grain-size driven transition in the deformation 
t
41
  "notes": "We conducted consecutive loading-relaxation experiments at 
            
42
mechanism in slow snow compression\r\n\r\n\r\nWe conducted consecutive 
42
low strain rates to study the viscoplastic behavior of the intact ice 
            
43
loading-relaxation experiments at low strain rates to study the 
43
matrix in snow. The experiments were conducted using a 
            
44
viscoplastic behavior of the intact ice matrix in snow. The 
44
micro-compression stage within the X-ray tomography scanner in the SLF 
            
45
experiments were conducted using a micro-compression stage within the 
45
cold laboratory. \r\n\r\nNext, to evaluate the experiments, a novel, 
            
46
X-ray tomography scanner in the SLF cold laboratory. \r\n\r\nNext, to 
46
implicit solution of a transient scalar model was developed to 
            
47
evaluate the experiments, a novel, implicit solution of a transient 
47
estimate the stress exponent and time scales in the effective creep 
            
48
scalar model was developed to estimate the stress exponent and time 
48
relation (Glen's law). The result reveals that, for the first time, a 
            
49
scales in the effective creep relation (Glen's law). The result 
49
transition in the exponent in Glen's law depends on geometrical grain 
            
50
reveals that, for the first time, a transition in the exponent in 
50
size. A cross-over of stress exponent $n=1.9$ for fine grains to 
            
51
Glen's law depends on geometrical grain size. A cross-over of stress 
51
$n=4.4$ for coarse grains is interpreted as a transition from grain 
            
52
exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is 
52
boundary sliding to dislocation creep. \r\n\r\n\r\nThe dataset 
            
53
interpreted as a transition from grain boundary sliding to dislocation 
53
includes compression force data from 11 experiments and corresponding 
            
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creep. \r\n\r\n\r\nThe dataset includes compression force data from 11 
54
3D image data from tomography scans.\r\n",
            
55
experiments and corresponding 3D image data from tomography 

            
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scans.\r\n",

            
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  "num_resources": 7,
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  "num_resources": 7,
            
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  "num_tags": 5,
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  "num_tags": 5,
            
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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": "2018-11-15T15:27:32.106204",
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    "created": "2018-11-15T15:27:32.106204",
            
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    "description": "The core topic of the team \"Snow Physics\" is 
60
    "description": "The core topic of the team \"Snow Physics\" is 
            
63
structure and property of snow and firn at different scales. Our most 
61
structure and property of snow and firn at different scales. Our most 
            
64
important tools in the cold laboratory are micro-computed tomography 
62
important tools in the cold laboratory are micro-computed tomography 
            
65
(micro-CT), nature-identical snow production, and our in-house 
63
(micro-CT), nature-identical snow production, and our in-house 
            
66
designed snow-breeders.\r\n\r\nBased on the three-dimensional 
64
designed snow-breeders.\r\n\r\nBased on the three-dimensional 
            
67
representation of snow, we can now calculate fundamental structural 
65
representation of snow, we can now calculate fundamental structural 
            
68
parameters as density variations at a spatial resolution of a few 
66
parameters as density variations at a spatial resolution of a few 
            
69
millimeters, e.g. revealing the finely layered structure of weak 
67
millimeters, e.g. revealing the finely layered structure of weak 
            
70
layers or of polar firn. In addition, the readily available data are 
68
layers or of polar firn. In addition, the readily available data are 
            
71
used to calculate the correlation function in all 3D, which enables a 
69
used to calculate the correlation function in all 3D, which enables a 
            
72
deeper understanding of the interactions between structure and 
70
deeper understanding of the interactions between structure and 
            
73
functional properties, e.g. for microwaves.\r\n\r\nIn addition, we are 
71
functional properties, e.g. for microwaves.\r\n\r\nIn addition, we are 
            
74
able to use the exact microstructure of snow for numerical 
72
able to use the exact microstructure of snow for numerical 
            
75
simulations. Our own or adapted codes allow to calculate thermal 
73
simulations. Our own or adapted codes allow to calculate thermal 
            
76
conductivity, mechanical properties, and optical properties. Direct 
74
conductivity, mechanical properties, and optical properties. Direct 
            
77
numerical simulation proves to be a highly valuable tool to understand 
75
numerical simulation proves to be a highly valuable tool to understand 
            
78
the complexity of snow.\r\n\r\nOur micro-CT is equipped with the 
76
the complexity of snow.\r\n\r\nOur micro-CT is equipped with the 
            
79
ability to perform time-lapse tomography using so called 
77
ability to perform time-lapse tomography using so called 
            
80
snow-breeders. The snow breeder made the first in-situ time-lapse 
78
snow-breeders. The snow breeder made the first in-situ time-lapse 
            
81
movie of metamorphosing snow under a temperature gradient 
79
movie of metamorphosing snow under a temperature gradient 
            
82
possible.\r\n\r\nOur developments don't stop at the microstructure. 
80
possible.\r\n\r\nOur developments don't stop at the microstructure. 
            
83
The quantification of snow properties at the larger scale of a snow 
81
The quantification of snow properties at the larger scale of a snow 
            
84
profile or on a field requires new techniques to link the micro- to 
82
profile or on a field requires new techniques to link the micro- to 
            
85
the macro-scale. For this purpose we developed the SnowMicroPen, a 
83
the macro-scale. For this purpose we developed the SnowMicroPen, a 
            
86
high-resolution penetrometer, which is able to discern different snow 
84
high-resolution penetrometer, which is able to discern different snow 
            
87
types using signal processing. Near-infrared photography has become a 
85
types using signal processing. Near-infrared photography has become a 
            
88
standard tool to quantify spatial variation of the specific surface 
86
standard tool to quantify spatial variation of the specific surface 
            
89
area, and, concurrently, the equivalent optical grain size. Currently, 
87
area, and, concurrently, the equivalent optical grain size. Currently, 
            
90
we are developing new optical techniques which try to measure density 
88
we are developing new optical techniques which try to measure density 
            
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and specific surface area at the same time.",
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and specific surface area at the same time.",
            
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    "image_url": 
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    "image_url": 
            
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    "is_organization": true,
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    "state": "active",
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    "state": "active",
            
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    "title": "Snow Physics",
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    "title": "Snow Physics",
            
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  "publication": "{\"publication_year\": \"2023\", \"publisher\": 
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