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On August 30, 2023 at 3:04:52 PM UTC, Administrator:

Set maintainer of Monthly topsoil and near surface microclimate temperature data for Switzerland to {"affiliation": "", "email": "florian.zellweger@wsl.ch", "given_name": "Florian", "identifier": "", "name": "Zellweger"} (previously {"email":"florian.zellweger@wsl.ch","given_name":"Florian","name":"Zellweger"})

Set author of Monthly topsoil and near surface microclimate temperature data for Switzerland to [{"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "curation", "software", "publication"], "email": "eric.sulmoni@wsl.ch", "given_name": "Eric", "identifier": "", "name": "Sulmoni"}, {"affiliation": "Forest and Nature Lab, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium", "affiliation_02": "", "affiliation_03": "", "data_credit": ["validation", "publication"], "email": "Pieter.DeFrenne@UGent.be", "given_name": "Pieter", "identifier": "", "name": "De Frenne"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["validation", "publication"], "email": "niklaus.zimmermann@wsl.ch", "given_name": "Niklaus", "identifier": "0000-0003-3099-9604", "name": "Zimmermann"}, {"affiliation": "Conservation Biology, Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "publication"], "email": "david.frey@wsl.ch", "given_name": "David Johannes", "identifier": "0000-0002-4603-0438", "name": "Frey"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["software", "publication"], "email": "dirk.karger@wsl.ch", "given_name": "Dirk", "identifier": "0000-0001-7770-6229", "name": "Karger"}, {"affiliation": "University of Northumbria", "affiliation_02": "", "affiliation_03": "", "data_credit": ["software", "publication"], "email": "johanna.malle@northumbria.ac.uk", "given_name": "Johanna", "identifier": "0000-0002-6185-6449", "name": "Malle"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["validation", "software", "publication"], "email": "clare.webster@wsl.ch", "given_name": "Clare", "identifier": "0000-0002-6386-6392", "name": "Webster"}, {"affiliation": "SLF", "affiliation_02": "", "affiliation_03": "", "data_credit": ["validation", "software", "publication"], "email": "jonas@slf.ch", "given_name": "Tobias", "identifier": "0000-0003-0386-8676", "name": "Jonas"}, {"affiliation": "Remote Sensing, Landscape Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "publication"], "email": "christian.ginzler@wsl.ch", "given_name": "Christian", "identifier": "0000-0001-6365-2151", "name": "Ginzler"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "publication"], "email": "andri.baltensweiler@wsl.ch", "given_name": "Andri", "identifier": "0000-0003-1933-6535", "name": "Baltensweiler"}, {"affiliation": "Conservation Research Institute", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "validation", "curation", "software", "publication", "supervision"], "email": "florian.zellweger@wsl.ch", "given_name": "Florian", "identifier": "", "name": "Zellweger"}] (previously [{"given_name": "Eric", "name": "Sulmoni", "email": "eric.sulmoni@wsl.ch", "data_credit": ["collection", "publication", "software", "curation"], "identifier": "", "affiliation": "WSL"}, {"given_name": "Pieter", "name": "De Frenne", "email": "Pieter.DeFrenne@UGent.be", "data_credit": ["publication", "validation"], "identifier": "", "affiliation": "Forest and Nature Lab, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium"}, {"given_name": "Niklaus", "name": "Zimmermann", "email": "niklaus.zimmermann@wsl.ch", "data_credit": ["publication", "validation"], "identifier": "0000-0003-3099-9604", "affiliation": "WSL"}, {"given_name": "David Johannes", "name": "Frey", "email": "david.frey@wsl.ch", "data_credit": ["collection", "publication"], "identifier": "0000-0002-4603-0438", "affiliation": "Conservation Biology, Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland"}, {"given_name": "Dirk", "name": "Karger", "email": "dirk.karger@wsl.ch", "data_credit": ["publication", "software"], "identifier": "0000-0001-7770-6229", "affiliation": "WSL"}, {"given_name": "Johanna", "name": "Malle", "email": "johanna.malle@northumbria.ac.uk", "data_credit": ["software", "publication"], "identifier": "0000-0002-6185-6449", "affiliation": "University of Northumbria"}, {"given_name": "Clare", "name": "Webster", "email": "clare.webster@wsl.ch", "data_credit": ["validation", "publication", "software"], "identifier": "0000-0002-6386-6392", "affiliation": "WSL"}, {"given_name": "Tobias", "name": "Jonas", "email": "jonas@slf.ch", "data_credit": ["software", "validation", "publication"], "identifier": "0000-0003-0386-8676", "affiliation": "SLF"}, {"given_name": "Christian", "name": "Ginzler", "email": "christian.ginzler@wsl.ch", "data_credit": ["publication", "collection"], "identifier": "0000-0001-6365-2151", "affiliation": "Remote Sensing, Landscape Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland"}, {"given_name": "Andri", "name": "Baltensweiler", "email": "andri.baltensweiler@wsl.ch", "data_credit": ["publication", "collection"], "identifier": "0000-0003-1933-6535", "affiliation": "WSL"}, {"given_name": "Florian", "name": "Zellweger", "email": "florian.zellweger@wsl.ch", "data_credit": ["software", "supervision", "curation", "collection", "validation", "publication"]}])

Updated description of Monthly topsoil and near surface microclimate temperature data for Switzerland from
Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.
to
Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

Changed the version of Monthly topsoil and near surface microclimate temperature data for Switzerland to 1.0

Changed value of field funding to [{"grant_number": "193645", "institution": "Swiss National Science Foundation", "institution_url": "https://www.snf.ch/en"}] in Monthly topsoil and near surface microclimate temperature data for Switzerland

Changed value of field publication to {"publication_year": "2023", "publisher": "EnviDat"} in Monthly topsoil and near surface microclimate temperature data for Switzerland

Changed value of field date to [{"date": "2012-04-01", "date_type": "created", "end_date": "2021-12-31"}] in Monthly topsoil and near surface microclimate temperature data for Switzerland

              
    
          
          
        
        
            f 1 { f 1 {
            n 2   "author": "[{\"given_name\": \"Eric\", \"name\": \"Sulmoni\",  n 2   "author": "[{\"affiliation\": \"WSL\", \"affiliation_02\": \"\", 
            3 \"email\": \"eric.sulmoni@wsl.ch\", \"data_credit\": [\"collection\",  3 \"affiliation_03\": \"\", \"data_credit\": [\"collection\", 
            4 \"publication\", \"software\", \"curation\"], \"identifier\": \"\",  4 \"curation\", \"software\", \"publication\"], \"email\": 
            5 \"affiliation\": \"WSL\"}, {\"given_name\": \"Pieter\", \"name\": \"De  5 \"eric.sulmoni@wsl.ch\", \"given_name\": \"Eric\", \"identifier\": 
            6 Frenne\", \"email\": \"Pieter.DeFrenne@UGent.be\", \"data_credit\":  6 \"\", \"name\": \"Sulmoni\"}, {\"affiliation\": \"Forest and Nature 
            7 [\"publication\", \"validation\"], \"identifier\": \"\",  7 Lab, Department of Environment, Faculty of Bioscience Engineering, 
            8 \"affiliation\": \"Forest and Nature Lab, Department of Environment,  8 Ghent University, Ghent, Belgium\", \"affiliation_02\": \"\", 
            9 Faculty of Bioscience Engineering, Ghent University, Ghent,  9 \"affiliation_03\": \"\", \"data_credit\": [\"validation\", 
            10 Belgium\"}, {\"given_name\": \"Niklaus\", \"name\": \"Zimmermann\",  10 \"publication\"], \"email\": \"Pieter.DeFrenne@UGent.be\", 
            11 \"email\": \"niklaus.zimmermann@wsl.ch\", \"data_credit\":  11 \"given_name\": \"Pieter\", \"identifier\": \"\", \"name\": \"De 
            12 [\"publication\", \"validation\"], \"identifier\":  12 Frenne\"}, {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", 
            13 \"0000-0003-3099-9604\", \"affiliation\": \"WSL\"}, {\"given_name\":  13 \"affiliation_03\": \"\", \"data_credit\": [\"validation\", 
            14 \"David Johannes\", \"name\": \"Frey\", \"email\":  14 \"publication\"], \"email\": \"niklaus.zimmermann@wsl.ch\", 
            15 \"david.frey@wsl.ch\", \"data_credit\": [\"collection\",  15 \"given_name\": \"Niklaus\", \"identifier\": \"0000-0003-3099-9604\", 
            16 \"publication\"], \"identifier\": \"0000-0002-4603-0438\",  16 \"name\": \"Zimmermann\"}, {\"affiliation\": \"Conservation Biology, 
            17 \"affiliation\": \"Conservation Biology, Biodiversity and Conservation  17 Biodiversity and Conservation Biology, Swiss Federal Institute for 
            18 Biology, Swiss Federal Institute for Forest, Snow and Landscape  18 Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, 
            19 Research WSL, 8903 Birmensdorf, Switzerland\"}, {\"given_name\":  19 Switzerland\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", 
            20 \"Dirk\", \"name\": \"Karger\", \"email\": \"dirk.karger@wsl.ch\",  
            21 \"data_credit\": [\"publication\", \"software\"], \"identifier\":  
            22 \"0000-0001-7770-6229\", \"affiliation\": \"WSL\"}, {\"given_name\":  
            23 \"Johanna\", \"name\": \"Malle\", \"email\":  
            24 \"johanna.malle@northumbria.ac.uk\", \"data_credit\": [\"software\",  
            25 \"publication\"], \"identifier\": \"0000-0002-6185-6449\",  
            26 \"affiliation\": \"University of Northumbria\"}, {\"given_name\":  
            27 \"Clare\", \"name\": \"Webster\", \"email\": \"clare.webster@wsl.ch\",  
            28 \"data_credit\": [\"validation\", \"publication\", \"software\"],  20 \"data_credit\": [\"collection\", \"publication\"], \"email\": 
            21 \"david.frey@wsl.ch\", \"given_name\": \"David Johannes\", 
            22 \"identifier\": \"0000-0002-4603-0438\", \"name\": \"Frey\"}, 
            23 {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", 
            24 \"affiliation_03\": \"\", \"data_credit\": [\"software\", 
            25 \"publication\"], \"email\": \"dirk.karger@wsl.ch\", \"given_name\": 
            26 \"Dirk\", \"identifier\": \"0000-0001-7770-6229\", \"name\": 
            27 \"Karger\"}, {\"affiliation\": \"University of Northumbria\", 
            28 \"affiliation_02\": \"\", \"affiliation_03\": \"\", \"data_credit\": 
            29 [\"software\", \"publication\"], \"email\": 
            30 \"johanna.malle@northumbria.ac.uk\", \"given_name\": \"Johanna\", 
            29 \"identifier\": \"0000-0002-6386-6392\", \"affiliation\": \"WSL\"},  31 \"identifier\": \"0000-0002-6185-6449\", \"name\": \"Malle\"}, 
            30 {\"given_name\": \"Tobias\", \"name\": \"Jonas\", \"email\":  32 {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", 
            31 \"jonas@slf.ch\", \"data_credit\": [\"software\", \"validation\",  33 \"affiliation_03\": \"\", \"data_credit\": [\"validation\", 
            34 \"software\", \"publication\"], \"email\": \"clare.webster@wsl.ch\", 
            35 \"given_name\": \"Clare\", \"identifier\": \"0000-0002-6386-6392\", 
            36 \"name\": \"Webster\"}, {\"affiliation\": \"SLF\", \"affiliation_02\": 
            37 \"\", \"affiliation_03\": \"\", \"data_credit\": [\"validation\", 
            38 \"software\", \"publication\"], \"email\": \"jonas@slf.ch\", 
            32 \"publication\"], \"identifier\": \"0000-0003-0386-8676\",  39 \"given_name\": \"Tobias\", \"identifier\": \"0000-0003-0386-8676\", 
            33 \"affiliation\": \"SLF\"}, {\"given_name\": \"Christian\", \"name\":  40 \"name\": \"Jonas\"}, {\"affiliation\": \"Remote Sensing, Landscape 
            34 \"Ginzler\", \"email\": \"christian.ginzler@wsl.ch\", \"data_credit\":  
            35 [\"publication\", \"collection\"], \"identifier\":  
            36 \"0000-0001-6365-2151\", \"affiliation\": \"Remote Sensing, Landscape  
            37 Change Science, Swiss Federal Institute for Forest, Snow and Landscape  41 Change Science, Swiss Federal Institute for Forest, Snow and Landscape 
            n 38 Research WSL, 8903 Birmensdorf, Switzerland\"}, {\"given_name\":  n 42 Research WSL, 8903 Birmensdorf, Switzerland\", \"affiliation_02\": 
            39 \"Andri\", \"name\": \"Baltensweiler\", \"email\":  43 \"\", \"affiliation_03\": \"\", \"data_credit\": [\"collection\", 
            40 \"andri.baltensweiler@wsl.ch\", \"data_credit\": [\"publication\",  44 \"publication\"], \"email\": \"christian.ginzler@wsl.ch\", 
            41 \"collection\"], \"identifier\": \"0000-0003-1933-6535\",  45 \"given_name\": \"Christian\", \"identifier\": 
            42 \"affiliation\": \"WSL\"}, {\"given_name\": \"Florian\", \"name\":  46 \"0000-0001-6365-2151\", \"name\": \"Ginzler\"}, {\"affiliation\": 
            43 \"Zellweger\", \"email\": \"florian.zellweger@wsl.ch\",  47 \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", 
            44 \"data_credit\": [\"software\", \"supervision\", \"curation\",  48 \"data_credit\": [\"collection\", \"publication\"], \"email\": 
            49 \"andri.baltensweiler@wsl.ch\", \"given_name\": \"Andri\", 
            50 \"identifier\": \"0000-0003-1933-6535\", \"name\": \"Baltensweiler\"}, 
            51 {\"affiliation\": \"Conservation Research Institute\", 
            52 \"affiliation_02\": \"\", \"affiliation_03\": \"\", \"data_credit\": 
            45 \"collection\", \"validation\", \"publication\"]}]", 53 [\"collection\", \"validation\", \"curation\", \"software\", 
            54 \"publication\", \"supervision\"], \"email\": 
            55 \"florian.zellweger@wsl.ch\", \"given_name\": \"Florian\", 
            56 \"identifier\": \"\", \"name\": \"Zellweger\"}]",
            46   "author_email": null, 57   "author_email": null,
            47   "creator_user_id": "1dd92ceb-e53e-4f6a-9e04-b6c97115f1fe", 58   "creator_user_id": "1dd92ceb-e53e-4f6a-9e04-b6c97115f1fe",
            n 48   "date":  n 59   "date": "[{\"date\": \"2012-04-01\", \"date_type\": \"created\", 
            49 \":\"created\",\"date\":\"2012-04-01\",\"end_date\":\"2021-12-31\"}]", 60 \"end_date\": \"2021-12-31\"}]",
            50   "doi": "10.16904/envidat.431", 61   "doi": "10.16904/envidat.431",
            n 51   "funding": "[{\"institution\":\"Swiss National Science  n 62   "funding": "[{\"grant_number\": \"193645\", \"institution\": \"Swiss 
            52 _number\":\"193645\",\"institution_url\":\"https://www.snf.ch/en\"}]", 63 National Science Foundation\", \"institution_url\": 
            64 \"https://www.snf.ch/en\"}]",
            53   "groups": [], 65   "groups": [],
            54   "id": "a336dd92-b141-4657-a1bc-c1e36f5e9762", 66   "id": "a336dd92-b141-4657-a1bc-c1e36f5e9762",
            55   "isopen": true, 67   "isopen": true,
            n n 68   "language": "en",
            56   "license_id": "cc-by-sa", 69   "license_id": "cc-by-sa",
            57   "license_title": "Creative Commons Attribution Share-Alike  70   "license_title": "Creative Commons Attribution Share-Alike 
            58 (CC-BY-SA)", 71 (CC-BY-SA)",
            59   "license_url": "https://creativecommons.org/licenses/by-sa/4.0/", 72   "license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
            n 60   "maintainer":  n 73   "maintainer": "{\"affiliation\": \"\", \"email\": 
            61 ellweger@wsl.ch\",\"given_name\":\"Florian\",\"name\":\"Zellweger\"}", 74 \"florian.zellweger@wsl.ch\", \"given_name\": \"Florian\", 
            75 \"identifier\": \"\", \"name\": \"Zellweger\"}",
            62   "maintainer_email": null, 76   "maintainer_email": null,
            63   "metadata_created": "2023-08-17T10:48:10.331509", 77   "metadata_created": "2023-08-17T10:48:10.331509",
            n 64   "metadata_modified": "2023-08-21T09:06:34.209760", n 78   "metadata_modified": "2023-08-30T15:04:52.619185",
            65   "name":  79   "name": 
            66 psoil-and-near-surface-microclimate-temperature-data-for-switzerland", 80 psoil-and-near-surface-microclimate-temperature-data-for-switzerland",
            67   "notes": "Climate data matching the scales at which organisms  81   "notes": "Climate data matching the scales at which organisms 
            68 experience climatic conditions are often missing. Yet, such data on  82 experience climatic conditions are often missing. Yet, such data on 
            69 microclimatic conditions are required to better understand  83 microclimatic conditions are required to better understand 
            70 climate-change impacts on biodiversity and ecosystem functioning. Here  84 climate-change impacts on biodiversity and ecosystem functioning. Here 
            71 we combine a national network of microclimate temperature measurements  85 we combine a national network of microclimate temperature measurements 
            72 with a novel radiative transfer model to map monthly minimum, mean and  86 with a novel radiative transfer model to map monthly minimum, mean and 
            73 maximum temperatures during the vegetation period at a 10 meter  87 maximum temperatures during the vegetation period at a 10 meter 
            74 spatial resolution across Switzerland. The temperature measurements  88 spatial resolution across Switzerland. The temperature measurements 
            75 took place in 107 sampling plots distributed across different habitat  89 took place in 107 sampling plots distributed across different habitat 
            76 types, with 62 plots in forests, 22 below trees outside forests, and  90 types, with 62 plots in forests, 22 below trees outside forests, and 
            77 23 in open grasslands. In each plot we measured temperature in the  91 23 in open grasslands. In each plot we measured temperature in the 
            78 topsoil (-5cm), as well as in the air at 5cm and 100cm height above  92 topsoil (-5cm), as well as in the air at 5cm and 100cm height above 
            79 ground. Spatial interpolation was achieved by using a hybrid approach  93 ground. Spatial interpolation was achieved by using a hybrid approach 
            80 based on linear mixed effects models with input from detailed  94 based on linear mixed effects models with input from detailed 
            81 radiation estimates that account for topographic and vegetation  95 radiation estimates that account for topographic and vegetation 
            82 shading, as well as other predictor variables related to the  96 shading, as well as other predictor variables related to the 
            83 macroclimate, topography and vegetation height. Our data reveals  97 macroclimate, topography and vegetation height. Our data reveals 
            84 strong horizontal and vertical variability in microclimate  98 strong horizontal and vertical variability in microclimate 
            85 temperature, particularly for maximum temperatures at 5 cm above the  99 temperature, particularly for maximum temperatures at 5 cm above the 
            86 ground and within the topsoil. Compared to macroclimate conditions as  100 ground and within the topsoil. Compared to macroclimate conditions as 
            87 measured by weather stations outside forests, diurnal air and topsoil  101 measured by weather stations outside forests, diurnal air and topsoil 
            88 temperature ranges inside forests were reduced by up to 3.0 and 7.8  102 temperature ranges inside forests were reduced by up to 3.0 and 7.8 
            89 \u00b0C, respectively, while below trees outside forests, e.g. in  103 \u00b0C, respectively, while below trees outside forests, e.g. in 
            90 hedges and below solitary trees, this buffering effect was 1.8 and 7.2  104 hedges and below solitary trees, this buffering effect was 1.8 and 7.2 
            91 \u00b0C. We also found that in open grasslands, maximum temperatures  105 \u00b0C. We also found that in open grasslands, maximum temperatures 
            92 at 5 cm above ground are on average 3.4 \u00b0C warmer than that of  106 at 5 cm above ground are on average 3.4 \u00b0C warmer than that of 
            93 macroclimate, suggesting that in such habitats heat exposure close to  107 macroclimate, suggesting that in such habitats heat exposure close to 
            94 the ground is often underestimated when using macroclimatic data.  108 the ground is often underestimated when using macroclimatic data. 
            95 After accounting for macroclimate effects, microclimate patterns were  109 After accounting for macroclimate effects, microclimate patterns were 
            96 primarily driven by radiation, with particularly strong effects on  110 primarily driven by radiation, with particularly strong effects on 
            97 maximum temperatures. Results from spatial block cross-validation  111 maximum temperatures. Results from spatial block cross-validation 
            98 revealed predictive accuracies as measured by RSME\u2019s ranging from  112 revealed predictive accuracies as measured by RSME\u2019s ranging from 
            99 1.18 to 3.43 \u00b0C, with minimum temperatures generally being  113 1.18 to 3.43 \u00b0C, with minimum temperatures generally being 
            100 predicted more accurately than maximum temperatures. The microclimate  114 predicted more accurately than maximum temperatures. The microclimate 
            101 maps presented here enable a more biologically relevant perspective  115 maps presented here enable a more biologically relevant perspective 
            102 when analysing climate-species interactions, which is expected to lead  116 when analysing climate-species interactions, which is expected to lead 
            103 to a better understanding of biotic and ecosystem responses to climate  117 to a better understanding of biotic and ecosystem responses to climate 
            n 104 and land use change.\n", n 118 and land use change.\r\n",
            105   "num_resources": 3, 119   "num_resources": 3,
            106   "num_tags": 7, 120   "num_tags": 7,
            107   "organization": { 121   "organization": {
            108     "approval_status": "approved", 122     "approval_status": "approved",
            109     "created": "2016-08-08T20:30:16.444200", 123     "created": "2016-08-08T20:30:16.444200",
            110     "description": "The availability of consistent and comprehensible  124     "description": "The availability of consistent and comprehensible 
            111 spatial data is fundamental for scientific research, in particular in  125 spatial data is fundamental for scientific research, in particular in 
            112 the context of long term observation series. An adequate documentation  126 the context of long term observation series. An adequate documentation 
            113 of the data quality and its history finally enables users to work  127 of the data quality and its history finally enables users to work 
            114 responsibly with these data.\r\n\r\nAs the research community gets  128 responsibly with these data.\r\n\r\nAs the research community gets 
            115 closer and the sharing of data becomes more common and important a  129 closer and the sharing of data becomes more common and important a 
            116 simple and intuitive access to these environmental data is a key  130 simple and intuitive access to these environmental data is a key 
            117 factor in large projects. Methods of spatial information analysis  131 factor in large projects. Methods of spatial information analysis 
            118 allow to build and link complex process models of different scientific  132 allow to build and link complex process models of different scientific 
            119 fields.\r\n\r\n###Tasks and main research\r\n\r\n* Developing  133 fields.\r\n\r\n###Tasks and main research\r\n\r\n* Developing 
            120 innovative methods to store the history, quality and accuracy of  134 innovative methods to store the history, quality and accuracy of 
            121 spatial & temporal data (event oriented spatial databases), developing  135 spatial & temporal data (event oriented spatial databases), developing 
            122 and implementing a method database, particularly in the context of the  136 and implementing a method database, particularly in the context of the 
            123 projects National Forest Inventory NFI and Datacenter Nature and  137 projects National Forest Inventory NFI and Datacenter Nature and 
            124 Landscape DNL\r\n* Analysing spatial and temporal data and modelling  138 Landscape DNL\r\n* Analysing spatial and temporal data and modelling 
            125 natural phenomena and processes\r\n* Developing algorithms for open  139 natural phenomena and processes\r\n* Developing algorithms for open 
            126 and intuitive search in geo-databases (e.g. ontologies)\r\n*  140 and intuitive search in geo-databases (e.g. ontologies)\r\n* 
            127 Developing and implementing concepts to provide spatially distributed  141 Developing and implementing concepts to provide spatially distributed 
            128 geographic information (virtual database)\r\n* Developing methods to  142 geographic information (virtual database)\r\n* Developing methods to 
            129 handle the uncertainties and inaccuracies (due to measurement and  143 handle the uncertainties and inaccuracies (due to measurement and 
            130 spatial resolution) of spatial data when used in models\r\n*  144 spatial resolution) of spatial data when used in models\r\n* 
            131 Maintaining and advancing the NFI-database and the Datacenter Nature  145 Maintaining and advancing the NFI-database and the Datacenter Nature 
            132 and Landscape (DNL), developing software for the NFI and DNL\r\n*  146 and Landscape (DNL), developing software for the NFI and DNL\r\n* 
            133 Maintaining and advancing the Geographical Information System of the  147 Maintaining and advancing the Geographical Information System of the 
            134 WSL\r\n* Knowledge transfer at university level, mainly through  148 WSL\r\n* Knowledge transfer at university level, mainly through 
            135 teaching positions, support and supervision of Master- and PhD-theses  149 teaching positions, support and supervision of Master- and PhD-theses 
            136 in the main topics of the GIS-Group\r\n\r\n__Further information__:  150 in the main topics of the GIS-Group\r\n\r\n__Further information__: 
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            143     "title": "GIS", 157     "title": "GIS",
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            150   "publication_state": "published", 164   "publication_state": "published",
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            193 temperatures during each month from April (04) to October (10) over  209 temperatures during each month from April (04) to October (10) over 
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            292       "state": "active", 309       "state": "active",
            293       "vocabulary_id": null 310       "vocabulary_id": null
            294     }, 311     },
            295     { 312     {
            296       "display_name": "STATISTICAL INTERPOLATION", 313       "display_name": "STATISTICAL INTERPOLATION",
            297       "id": "c8bba65c-d9dc-4a6a-8fc8-571d39afca4a", 314       "id": "c8bba65c-d9dc-4a6a-8fc8-571d39afca4a",
            298       "name": "STATISTICAL INTERPOLATION", 315       "name": "STATISTICAL INTERPOLATION",
            299       "state": "active", 316       "state": "active",
            300       "vocabulary_id": null 317       "vocabulary_id": null
            301     } 318     }
            302   ], 319   ],
            303   "title": "Monthly topsoil and near surface microclimate temperature  320   "title": "Monthly topsoil and near surface microclimate temperature 
            304 data for Switzerland", 321 data for Switzerland",
            305   "type": "dataset", 322   "type": "dataset",
            t 306   "url": null t 323   "url": null,
            324   "version": "1.0"
            307 } 325 }