Datasource Groups

A list of datafiles from the EMERGE and IsoGenie projects that can be downloaded directly, organized thematically. If you use this data in your research, please cite the accompanying manuscript(s) and contact the contributing PI.

Each dataset may have multiple versions and/or quality levels available. The most easily accessible files listed on this page should be the latest versions, but older versions (if applicable) can also be downloaded under "Old version(s)".

Expand the panels below for detailed descriptions of our version and quality level systems.

How do we track file versions? (And what do the terms original, corrected, formatted, and standardized mean?)

Data versioning is distinct from quality levels; it refers to the version of a dataset. As the data files are updated and published on the Data Downloads page, the database team (Ben Bolduc & Suzanne Hodgkins) assigns version numbers as "x.y.z" (0.0.0, 1.0.1, 2.0.0, etc.), starting from 0.0.0, with the following numbering convention:

The first two numbers (x & y) indicate the version number of the contributed files submitted to the database by the contact/contributor:
- "x" denotes major revisions, e.g. reflecting different quality levels)
- "y" denotes minor revisions, e.g. reflecting minor adjustments to headers, minor error corrections, etc.
The third number (z) indicates the format-standardized versions of the contributed files, generated by the database team. These standard-format code-readable files are an essential step of loading the data into the graph database, and are also useful to project team members who code (and e.g. want metadata in a standardized format for import into R).
There are three main steps to this format-standardization. These steps are given specific names, defined below, which are used in the download links to indicate the degree and type of processing applied to the contributed (i.e. original) file:
1. corrected (if applicable): Corrections to the data itself, based on careful communication with the original data generator(s), but actually edited and saved by the database team.
2. formatted: Formatting changes to simplify the original (or corrected) files. These include things like removal of merged cells and conversion of color-coded shading to text columns.
3. standardized: Formatted text files with additional columns added (ending in __), which contain metadata in the database's standardized naming convention for Sites, Habitats, etc.

How do we track data quality?

In addition to versions, the data should also be assigned quality levels. As this requires intimate knowledge of the dataset and its respective discipline, we request that project members specify each data file’s quality level during submission to the EMERGE-DB.

In a nutshell, the quality numbers indicate the level of confidence that the data reflects "reality". Note that unlike version numbers, the quality level of a given dataset may be changed retroactively. For example, a Quality 1 dataset may be downgraded to Quality 0 if errors are discovered late in the processing pipeline, or upgraded to Quality 2 after passing peer-review. The basic definitions of the three quality levels are as follows:

Quality 0: Unprocessed, raw data.
Quality 1: An internally-consistent dataset with basic processing, such as calibrations, background corrections, and quality control.
Quality 2: An externally-consistent dataset that is highly curated, in addition to undergoing the processing necessary for Quality 1. This should include all datasets that are part of peer-reviewed publications. DOIs are assigned to datasets in the Quality 2 category. The public side of the database will house mostly Quality Level 2 data.

Use the buttons below to jump to specific dataset categories. You can also search for datasets by title and identifiers, or use the checkboxes to filter by Metadata node labels (these checkboxes use "or" logic).

Coring Sheets

Image credit: Virginia Rich

Dataset ID	Dataset Name
0000_Coring20102011	Field Sampling/Coring Sheet (2010-2011)
0001_Coring2012	Field Sampling/Coring Sheet (2012)
0046_Coring2018	Field Sampling/Coring Sheet (2018)
0047_Coring2019	Field Sampling/Coring Sheet (2019)
0036_Coring2017	Field Sampling/Coring Sheet (2017)
0037_Coring2015	Field Sampling/Coring Sheet (2015)
0004_Coring2016	Field Sampling/Coring Sheet (2016)
0002_Coring2013	Field Sampling/Coring Sheet (2013)
0003_Coring2014	Field Sampling/Coring Sheet (2014)
0137_Coring2020	Field Sampling/Coring Sheet (2020)
0139_Coring2021	Field Sampling/Coring Sheet (2021)
0152_Coring2022	Field Sampling/Coring Sheet (2022)
0160_Coring2023	Field Sampling/Coring Sheet (2023)

Terrestrial Biogeochemistry

Image credit: Claire Wilson

Dataset ID	Dataset Name
0007_GeochemPoregas20102012	Pore gas geochemistry 2010-2012
0008_ErrorsGeochemSolid20102012	Standard errors: Peat geochemistry 2010-2012
0009_ErrorsGeochemPorewater20102012	Standard errors: Pore water geochemistry 2010-2012
0010_ErrorsGeochemPoregas20102012	Standard errors: Pore gas geochemistry 2010-2012
0005_GeochemSolid20102012	Peat geochemistry 2010-2012
0006_GeochemPorewater20102012	Pore water geochemistry 2010-2012
0067_CH4Oxidation_ChemicalConcentrations_transects	Methane oxidation data from Malhotra and Roulet (2015) transects, including incubation-derived oxidation rates, and in situ pH, Eh, and concentrations of dissolved CH4 and electron acceptors (O2, SO4, NO3, Mn, & Fe)
0068_CH4Oxidation_AdditionalExperiments	Methane oxidation data from Malhotra and Roulet (2015) transects, additional experiments
0070_210Pb_14C_2011-2014_2016	210Pb, 14C, and bulk density for cores and porewater collected in 2011-2014 and 2016
0071_IncubationJuly2014peat	Incubation CH4 and CO2 production and δ13C for peat collected in July 2014
0087_incubations-vs-field_geochem	Comparison of microbial processes in anaerobic incubations and field samples: Geochemical data, including greenhouse gases (CO2/CH4 production and δ13C) and organic matter chemistry (FTIR, FT-ICR MS, UV/Vis, and EEMS)
0140_Wilson-etal-2022-STOTEN_FTIR-peat-plants	Area-normalized FT-IR absorbances for the peat and plant species across the three main habitat types (palsa, bog, fen)
0142_Wilson-etal-2022-STOTEN_ICR-peat-porewater	Log-transformed FT-ICR MS peak intensities for the dissolved organic matter and the extracts of solid peat across the three main habitat types (palsa, bog, fen)
0144_Wilson-etal-2022-STOTEN_DOC-TON	Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in the porewater from the fen (E) and bog (S) sites
0148_MainAutochamber_2016_FTICRMS	EMERGE 2016 Autochamber Sites FT-ICRMS
0149_MainAutochamber_2016_EnzymeAssays	EMERGE 2016 Autochamber Sites Enzyme Assays
0150_MainAutochamber_2016_NMR	EMERGE 2016 Autochamber Sites NMR
0151_MainAutochamber_2016_LCMS	EMERGE 2016 Autochamber Sites LC-MS
0157_FTICRMS_GCMS_June2012	June 2012 FT-ICR MS and GC-MS data from: "Controls on soil organic matter degradation and subsequent greenhouse gas emissions across a permafrost thaw gradient in Northern Sweden"

Terrestrial Gas Fluxes

Image credit: Patrick Crill

Dataset ID	Dataset Name
0033_Flux20022007	Autochamber Fluxes 2002-2007
0073_ThawPondBubbles20122015	Thaw pond bubble fluxes (2012-2015)
0074_ThawPondTemps20132018	Thaw pond temperatures (2013-2018)
0138_DailyFlux_20122018	Daily CH4 and CO2 fluxes (from LGR at autochamber sites) and peat temperatures (from InterAct Fen tower), 2012-2018
0072_IsoFluxQCL_public	Autochamber CH4 Fluxes and δ13C Values from QCL (public release)

Environmental Data from the Mire

Image credit: ICOS Sweden (http://www.icos-sweden.se/station_stordalen.html)

Dataset ID	Dataset Name
0062_Manual_ALD_WTD	Manual ALD and WTD measurements from autochamber sites (2003-2017 mostly)

Lake Data

Image credit: Ruth Varner

Dataset ID	Dataset Name
0024_LakeTemps111020120529	Lake water temperatures (5- to 60-minute resolution): Oct 2011 - May 2012
0023_LakeTemps110611111020	Lake water temperatures (5- to 60-minute resolution): June 2011 - Oct 2011
0022_LakeTemps100928110610	Lake water temperatures (5- to 60-minute resolution): Sept 2010 - June 2011
0020_LakeTemps100721100828	Lake water temperatures (5- to 60-minute resolution): July 2010 - Aug 2010
0021_LakeTemps100829100927	Lake water temperatures (5- to 60-minute resolution): Aug 2010 - Sept 2010
0019_LakeTemps100609100720	Lake water temperatures (5- to 60-minute resolution): June 2010 - July 2010
0016_LakeTemps090611090828	Lake water temperatures (5- to 60-minute resolution): June 2009 - Aug 2009
0017_LakeTemps090829091012	Lake water temperatures (5- to 60-minute resolution): Aug 2009 - Oct 2009
0018_LakeTemps091015100602	Lake water temperatures (5- to 60-minute resolution): Oct 2009 - June 2010
0039_LakeTemps151001160607	Lake water temperatures (5- to 60-minute resolution): Oct 2015 - June 2016
0040_LakeTemps160607161016	Lake water temperatures (5- to 60-minute resolution): June 2016 - Oct 2016
0041_LakeTemps161016170316	Lake water temperatures (5- to 60-minute resolution): Oct 2016 - Mar 2017
0042_LakeTemps170610170929	Lake water temperatures (5- to 60-minute resolution): June 2017 - Sept 2017
0043_LakeTemps170930180615	Lake water temperatures (5- to 60-minute resolution): Sept 2017 - June 2018
0044_LakeTemps180615181016	Lake water temperatures (5- to 60-minute resolution): June 2018 - Oct 2018
0045_LakeTemps181018190606	Lake water temperatures (5- to 60-minute resolution): Oct 2018 - June 2019
0025_LakeTemps120530120928	Lake water temperatures (5- to 60-minute resolution): May 2012 - Sept 2012
0026_LakeTemps120928130627	Lake water temperatures (5- to 60-minute resolution): Sept 2012 - June 2013
0027_LakeTemps130620130923	Lake water temperatures (5- to 60-minute resolution): June 2013 - Sept 2013
0028_LakeTemps130923140609	Lake water temperatures (5- to 60-minute resolution): Sept 2013 - June 2014
0029_LakeTemps140609140829	Lake water temperatures (5- to 60-minute resolution): June 2014 - Aug 2014
0030_LakeTemps140829141003	Lake water temperatures (5- to 60-minute resolution): Aug 2014 - Oct 2014
0031_LakeTemps141003150620	Lake water temperatures (5- to 60-minute resolution): Oct 2014 - June 2014
0032_LakeTemps150620151001	Lake water temperatures (5- to 60-minute resolution): June 2015 - Oct 2015
0094_Lake_sediment_microbiota_16S-reads	Lake sediment microbiota: Raw 16S rRNA sequencing reads
0095_Lake_sediment_microbiota_metaG-reads	Lake sediment microbiota: Metagenomic sequencing reads
0096_Lake_sediment_microbiota_assemblies	Lake sediment microbiota: Assemblies

Microbial Sequencing

Image credit: stock image (public domain)

Dataset ID	Dataset Name
0059_IsoGenie_sequence_samplecodes	Mapping of sequencing sample names to standardized sample metadata from the DB
0060_IsoGenieDB-Sequencing_Imports	Microbial and viral sequencing metadata
0075_M-stor	Candidatus 'Methanoflorens stordalenmirensis' draft genome (built from bog metagenomes in BioProject PRJNA215012)
0076_iTags_20102011	iTags (2010-2011) (BioProject PRJNA215012)
0077_iTags_2011	iTags (2011) (BioProject PRJNA235935)
0078_metaGs_20102012	Metagenomes (2010-2012) (BioProject PRJNA386568)
0079_MAGs_20102012	Metagenome-assembled genomes (MAGs) (2010-2012) (BioProject PRJNA386568)
0080_metaTs_20102012	Metatranscriptomes (2010-2012) (BioProject PRJNA386568)
0081_CaldisericaCryosericum_MAGs	Caldiserica/Cryosericum MAGs (BioProject PRJNA482249)
0082_CaldisericaCryosericum_iTags	iTags associated with Caldiserica/Cryosericum MAGs (BioProject PRJNA482249)
0083_metaPs_fen2010	Fen metaproteomes (2010) (PXD000410)
0084_metaPs_2012	Metaproteomes (2012) (PXD009096)
0085_incubations-vs-field_sequencing-reads	Comparison of microbial processes in anaerobic incubations and field samples: iTag and metagenome raw reads
0086_incubations-vs-field_PICRUSt	Comparison of microbial processes in anaerobic incubations and field samples: PICRUSt analysis code and input data (processed iTags)
0099_MicrobiomeMetadataSheet_public	Sample Metadata Sheet for Samples with Microbiomes: Detailed field and geochemical (meta)data for samples with linked metaGs (formerly "Microbiome Metadata Sheet") - Public edition
0153_MAGs_pre-release	Metagenome-assembled genomes (MAGs) from Stordalen Mire, Sweden

Viral Sequencing

Image credit: Pagaling and Haigh et al., 2007 (DOI:10.1186/1471-2164-8-410)

Dataset ID	Dataset Name
0088_QuantAmp_dsDNA_Viromes_2014	Quantitatively-amplified dsDNA viromes, collected 2014
0089_ViralPopulationContigs	Viral population contigs (1907 total), based on: 178 bulk soil metagenomes (sampled in 2010-2012; Woodcroft & Singleton et al. (2018)) 12 size-fractionated metagenomes (sampled in 2014) 7 viromes (sampled in 2014; Trubl et al. (2016, 2018))
0090_TrublSoilViromes	Trubl Soil Viromes (QC reads)
0091_QuantAmp_ssDNA_dsDNA_Viromes_20162017	Quantitatively-amplified ssDNA and dsDNA viromes, Nextera XT and Accel-NGS 1S Plus libraries, collected 2016-2017
0155_VirusGenomes_vOTUs	Virus operational taxonomic units (vOTUs) from Stordalen Mire, Sweden

Plant-Associated Data

Image credit: Nicole Raab

Dataset ID	Dataset Name
0092_iTags_rhizosphere_phyllosphere_processed	iTag-based microbial abundances from the rhizosphere and phyllosphere, and comparison with bulk peat (2016): Processed data
0093_iTags_rhizosphere_phyllosphere_raw	iTag-based microbial abundances from the rhizosphere and phyllosphere, and comparison with bulk peat (2016): Raw data
0141_Wilson-etal-2022-STOTEN_ICR-plants	Log-transformed FT-ICR MS peak intensities for plant samples across the three main habitat types (palsa, bog, fen)
0143_Wilson-etal-2022-STOTEN_plant-tissue-proportions	Biomass coverage of plant species across the three main habitat types (palsa, bog, fen)

Remote Sensing

Image credit: Nicole Raab

Dataset ID	Dataset Name
0145_Wv2-2014_GroundCoverClassifications	Stordalen Mire ground cover classifications, based on 2014 WorldView-2 satellite imagery
0146_Wv2-2014_GroundCoverProbabilities	For the Stordalen Mire ground cover classifications, probability of the neural network assigning each cover class (see publication for details): H2O, HM, OT, RK, SW, TG, TS, WT
0147_Drone-2014	Drone imagery (2014) over Stordalen Mire
0147_Drone-2015	Drone imagery (2015) over Stordalen Mire
0147_Drone-2016	Drone imagery (2016) over Stordalen Mire
0147_Drone-2017	Drone imagery (2017) over Stordalen Mire

Modeling

Image credit: Virginia Rich

Dataset ID	Dataset Name
0098_Ecosys_2011	Ecosys model products for Stordalen, 2011
0097_Ecosys_19012010	Ecosys model products for Stordalen, 1901-2010
0156_IncubationSimulation	Data from: "Soil incubation methods lead to large differences in inferred methane production temperature sensitivity"

A2A Historical Datasets: APEX (Alaska)

Dataset ID	Dataset Name
0505_ApexAlphaMeteorology11	APEX Alpha Meteorology (2011)
0504_ApexAlphaMeteorology10	APEX Alpha Meteorology (2010)
0503_ApexAlphaMeteorology09	APEX Alpha Meteorology (2009)
0502_ApexAlphaMeteorology08	APEX Alpha Meteorology (2008)
0501_ApexAlphaMeteorology07	APEX Alpha Meteorology (2007)

A2A Summary Files

Dataset ID	Dataset Name
0154_A2A_Summary_Files	Data summaries from the five peatland complexes sampled by the A2A project (APEX, Lutose, Smith Creek, Kaamanen, Stordalen Mire). Data includes: CH4 fluxes and δ13C values, porewater CH4 and CO2 concentrations and δ13C and δD values, vegetation cover (by species and functional type), environmental data (pH, water table depth, and soil temperatures), and BAWLD landcover classifications (Olefeldt et al. 2021 ESSD) for each sampled site.

Dataset ID

Dataset Name

0154_A2A_Summary_Files

Data summaries from the five peatland complexes sampled by the A2A project (APEX, Lutose, Smith Creek, Kaamanen, Stordalen Mire). Data includes: CH4 fluxes and δ13C values, porewater CH4 and CO2 concentrations and δ13C and δD values, vegetation cover (by species and functional type), environmental data (pH, water table depth, and soil temperatures), and BAWLD landcover classifications (Olefeldt et al. 2021 ESSD) for each sampled site.