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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...

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...

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...

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...

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...

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...

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...

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...

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...

The EyeSea underwater video dataset was assembled for developing algorithms for detecting fish in real world underwater video data. The data were recorded as part of environmental monitoring efforts at three different water power sites. The Ocean Renewable Power Company (ORPC) data were recorded in...

"Moisture modulates soil reservoirs of active DNA and RNA viruses" Soil is known to harbor viruses, but the majority are uncharacterized and their responses to environmental changes are unknown. Here, we used a multi-omics approach (metagenomics, metatranscriptomics and metaproteomics) to detect...

Two factors that are well-known to influence soil microbiomes are the depth of the soil as well as the level of moisture. Previous works have demonstrated that climate change will increase the incidence of drought in soils, but it is unknown how fluctuations in moisture availability affect soil...

The Human Islet Research Network (HIRN) is a large consortia with many research projects focused on understanding how beta cells are lost in type 1 diabetics (T1D) with a goal of finding how to protect against or replace the loss of functional beta cells. The consortia has multiple branches of...

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The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems...

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David Degnan is a biological data scientist who develops bioinformatic and statistical pipelines for multi-omics data, specifically the fields of proteomics, metabolomics, and multi-omics (phenotypic) data integration. He has experience with top-down & bottom-up proteomics analysis, genomics &...