Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through...
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This data set provides ITS fungal community composition via DNA sequence analysis at the time of peat coring at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the Spruce and...
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This data set provides the 16S microbial community composition via DNA sequence analysis from ingrowth peat and sand cores at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the...
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This data set provides the ITS fungal community composition via DNA sequence analysis from sand and peat ingrowth cores at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the Spruce...
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This data set provides the peat microbial biomass carbon (MBC) and nitrogen (MBN), extractable organic carbon (EOC) and extractable nitrogen (EN) at the time of peat coring for Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2014-2017 from the Spruce and Peatland Responses Under...
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This data set provides the 16S microbial community composition via DNA sequence analysis at the time of peat coring at the South End bog in 2013. These samples were collected outside the experimental enclosures and are pre-treatment with no experimental manipulation. These are part of the Spruce and...
Category
This data set provides the 16S microbial community composition of peat and sand ingrowth cores via DNA and cDNA sequence analysis before and during Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2015-2016 from the Spruce and Peatlands Under Changing Environments (SPRUCE). Samples were...
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This data set provides ITS fungal community composition via DNA and cDNA sequence analysis at the time of peat coring for Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2014-2017 from the Spruce and Peatlands Under Changing Environments (SPRUCE). Samples were extracted using a Qiagen...
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This data set provides the ITS fungal community composition via DNA and cDNA sequence analysis for peat and sand ingrowth cores collected before and during Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2015-2016 from the Spruce and Peatlands Under Changing Environments (SPRUCE)...
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This data set provides the ingrowth peat extracellular enzyme potential (EE) for before and during Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2015-2016 from the Spruce and Peatlands Under Changing Environments (SPRUCE). EE potential was quantified and calculated following a...
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This data set provides the 16S microbial community composition via DNA and cDNA sequence analyses at the time of peat coring for Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2014-2017 from the Spruce and Peatlands Under Changing Environments (SPRUCE). Samples were extracted using a...
Category
This data set provides the ingrowth peat microbial biomass carbon (MBC) and nitrogen (MBN), extractable organic carbon (EOC) and extractable nitrogen (EN) for Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2015-2016 from the Spruce and Peatland Responses Under Changing Environments...
Category
This data set provides the ingrowth peat extracellular enzyme potential (EE) for before and during Deep Peat Heating (DPH) and Whole Ecosystem Warming (WEW) for 2015-2016 from the Spruce and Peatlands Under Changing Environments (SPRUCE). EE potential was quantified and calculated following a...
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ProxyTSPRD profiles are collected using NVIDIA Nsight Systems version 2020.3.2.6-87e152c and capture computational patterns from training deep learning-based time-series proxy-applications on four different levels: models (Long short-term Memory and Convolutional Neural Network), DL frameworks...