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DOE OSTI2026

Data and Code for: Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits

This repository contains the simulation outputs and processing scripts associated with the study of winter wheat traits across the United States, utilizing the Ecosys agroecosystem model. The dataset includes model results for both rainfed and irrigated winter wheat systems, supporting the findings presented in the manuscript titled "Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits." Data includes the original Ecosys simulation outputs (archived in .db format within the compressed .zip files) and extracted analysis data (stored in .pkl files for efficient processing). Python code for data processing and figure generation is provided in a Jupyter notebook. External Observational Datasets should refer to the following official repositories for the input and validation data used in this study. The eddy covariance data from the AmeriFlux network (https://ameriflux.lbl.gov/). Climate-forcing data of NLDAS-2 from NASA LDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data). Soil data from the Gridded Soil Survey Geographic Database (gSSURGO), available at (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database). Crop yields, planting and harvest dates from the USDA public databases (https://quickstats.nass.usda.gov/; https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx). Satellite-derived SLOPE GPP data from ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786). Land use and crop progress information from the USDA Crop Data Layer and Crop Progress and Condition Gridded Layers (https://www.nass.usda.gov/Research_and_Science/). The Ecosys model code is available online at https://github.com/jinyun1tang/ECOSYS.

Wheat↗

NASA NTRS2021

Northern Great Plains Disasters: Using Earth Observations to Enhance Flood Monitoring on Tribal Lands in the Northern Great Plains

In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.

Anna Ballasiotes↗

DOE OSTI2026

Topsoil bulk geochemical compositions - An updated harmonized global dataset

Mineral weathering is a key biogeochemical process because of the capacity of minerals to stabilize organic matter. However, predicting soil weathering status across large spatial areas still isn’t possible due to a lack of global data and theoretical frameworks. To address this knowledge gap, multiple global datasets of bulk topsoil geochemical compositions have been harmonized using R. These datasets document topsoil bulk geochemical compositions across five continents (n = ~16,000 observations). Source data for these observations include the EuroGEOSurveys Geochemical Baseline Database (FOREGS), the US Geological Survey National Geochemical Database (NASGLP), the Geochemical Atlas of Australia (GAA), the US Geological Survey Alaska Geochemical Database (AGD84), the National Cooperative Soil Survey (NCSS), the European Geochemical Mapping of Agricultural Soil (GEMAS), Ecorespira-Amazon (ERA), the New Zealand Geochemical Baseline Survey (NZ_GBS), and the African Soil Information Service (AFSIS). Major elements observed include Aluminum (Al), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Sodium (Na), Titanium (Ti), Manganese (Mn), Phosphorus (P), Carbon (C), and Sulfur (S). This data package includes the harmonized dataset itself, and the R scripts necessary to harmonize these datasets, in addition to metadata that describes all columns, files, and databases used in this project. Methods & Sampling Step 1 – Databases of geochemical data identified This study aimed to leverage existing measurements of topsoil geochemical data. Databases were first identified and deemed appropriate for inclusion if they were measuring soils and performed these measurements on the <2mm soil fraction. Databases such as NCSS and AGD84 needed more post processing to include in the database and this was done using the NCSS_datamerge_031626 R file and Alaska_USGSmerge_031626 R file, respectively. Step 2 – Database harmonization Once appropriate databases were identified, they were harmonized for ease of analysis using the R script Database_Harmonization_031826. This included removing columns from original datasets that would not be used in analysis (removed columns are noted in the code). Then, data cleaning procedures specific to each dataset were undertaken. This includes standardizing columns to include units and adding metadata columns regarding procedures for analyzing specific elements. Functions for standardizing measurements and units are outline in R files: calculate element_mg_kg_031626, calculate_oxide_wt_perc_031626, change_oxide_caps_031626, and conv_2_numeric_031626. This also included adding a unique identifier for each sample to identify it with its respective database (see CD_ID in data dictionary). Geographic information: Data reflect a compilation of datasets collected globally. Geographic areas covered by each of the datasets include: - EuroGEOSurveys Geochemical Baseline Database (FOREGS) - European continent - North American Soil Geochemical Landscapes (NASGLP) - continental United States and limited parts of Canada (see database key for more details) - National Geochemical Survey of Australia (GAA) - Australia - Alaska geochemical database (AGDB4) - Alaska - National Cooperative Soil Survey (NCSS) - Global measurements, but concentrated in the continental United States - Geochemical data for arable land and land under permanent grass cover in continental Europe (GEMAS) - continental Europe - Ecorespira-Amazon (ERA) - Geochemical data from the Amazon basin - Geochemical baseline data for New Zealand (NZGBS) - New Zealand - Geochemical data collected across continental Africa (AfSIS) - Measurements across Africa

EARTH SCIENCE > LAND SURFACE > SOILS↗

NASA NTRS1995

Toward Soil Spatial Information Systems (SSIS) for global modeling and ecosystem management

The general objective is to conduct research to contribute toward the realization of a world soils and terrain (SOTER) database, which can stand alone or be incorporated into a more complete and comprehensive natural resources digital information system. The following specific objectives are focussed on: (1) to conduct research related to (a) translation and correlation of different soil classification systems to the SOTER database legend and (b) the inferfacing of disparate data sets in support of the SOTER Project; (2) to examine the potential use of AVHRR (Advanced Very High Resolution Radiometer) data for delineating meaningful soils and terrain boundaries for small scale soil survey (range of scale: 1:250,000 to 1:1,000,000) and terrestrial ecosystem assessment and monitoring; and (3) to determine the potential use of high dimensional spectral data (220 reflectance bands with 10 m spatial resolution) for delineating meaningful soils boundaries and conditions for the purpose of detailed soil survey and land management.

Baumgardner, Marion F.↗

NASA NTRS1994

Completing below-ground carbon budgets for pastures, recovering forests, and mature forests of Amazonia

The objective of this grant was to complete below-ground carbon budgets for pastures and forest soils in the Amazon. Profiles of radon and carbon dioxide were used to estimate depth distribution of CO2 production in soil. This information is necessary for determining the importance of deep roots as sources of carbon inputs. Samples were collected for measuring root biomass from new research sites at Santana de Araguaia and Trombetas. Soil gases will be analyzed for CO2 and (14)CO2, and soil organic matter will be analyzed for C-14. Estimates of soil texture from the RADAMBRASIL database were merged with climate data to calculate soil water extraction by forest canopies during the dry season. In addition, a preliminary map of areas where deep roots are needed for deep soil water was produced. A list of manuscripts and papers prepared during the reporting periods is given.

Davidson, Eric A.↗

NASA NTRS1989

Viking Biology Experiments and the Martian soil

The Viking Biology Experiments (VBE) are the most informative database on the wet chemistry and reactivity of the Martian soil available today. The simulation and chemical interpretation of the results have given valuable hints towards the characterization of the soils' mineralogy, adsorption properties, pH and redox. The characterization of Mars' soil on the basis of ten years of labelled release (LR) and other VBE simulations are reviewed.

Banin, Amos↗

DOE OSTI2025

Metagenome-assembled genomes from Wind River Basin floodplain sediments Riverton, Wyoming site (May to September 2017)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken roughly every month in the period May 18 to September 13 in 2017 at a location (Pit2) close to DOE Legacy Management well 855 at the Riverton, Wyoming floodplain site in the Wind River Basin (WRB). The groundwater at this site exhibits persistent U, Mo, and sulfate plumes and is one of the field sites in focus for the SLAC Groundwater Quality SFA program. Cores were taken with a hand-auger and separated into 5-20 cm segments based on soil horizonation down to 150 cm depth below surface. Each segment was subsampled for microbial analyses. Corresponding 16S rRNA gene amplicon data is available at the NCBI Single Read Archive (SRA) Database BioProject ID PRJNA626616, and soil geochemistry data at doi:10.15485/1631972. 40 metagenomes were sequenced through JGI and can be found under Gold sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 6993 MAG fasta files and a csv file with quality, taxonomic classification (GTDB RS220), and metagenome accessions for MAGs generated from the Wind River Basin (WRB). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Estimates of Long Term Surface Soil Moisture in the Midwestern U.S. Derived from Satellite Microwave Observations

Soil moisture is a key component of the water and energy balances of the Earth's surface, and has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, soil moisture is often somewhat difficult to measure accurately in both space and time, especially at large spatial scales. Soil moisture is highly variable, and while point measurements are typically quite accurate, subsequent areal averaging of these measurements often leads to large errors. Since remotely sensed land surface observations are already a spatially averaged or areally integrated value, they are a logical input parameter to regional or larger scale land process models. A database of long-term soil moisture was compared to satellite microwave observations over test sites in the Midwestern United States. Ground measurements of average volumetric surface soil moisture in the top ten cm were made bimonthly at 19 locations throughout the state of Illinois. Nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and a temporal frequency over the test area of about 3 nighttime orbits per week. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Because the time of satellite coverage does not always coincide with the ground measurements of soil moisture, the existing ground data were used to calibrate a water balance for the top IO cm surface layer in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area average soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture, Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Owe, M.↗

NASA NTRS1986

International Symposium on Remote Sensing of Environment, 19th, Ann Arbor, MI, October 21-25, 1985, Proceedings. Volumes 1 & 2

The technology and applications of terrestrial remote sensing (RS) are discussed in reviews and reports. Topics examined include the future of the NASA earth-sciences program, NOAA plans for earth observations in the 1990s, space RS in France, international coordination of RS satellite programs, and applications of geocoded imagery. Consideration is given to spatial and tabular databases for order-three soil surveys, an AVHRR and Landsat regional inventory of irrigated agriculture, classification of wetlands, microwave radiometry of ocean surface winds and sea ice, and floodplain land-cover mapping with Thematic-Mapper data.

Source record↗

NASA NTRS1985

The 19th International Symposium on Remote Sensing of Environment. Proceedings, Volume 1

Source record↗

NASA NTRS2005

The Effect of Soil Hydraulic Properties vs. Soil Texture in Land Surface Models

This study focuses on the effect of Soil Hydraulic Property (SHP) selection on modeled surface fluxes following a rain storm in a semi-arid environment. SHPs are often defined based on a Soil Texture Class (STC). To examine the effectiveness of this approach, the Noah land surface model was run with each of 1306 soils in a large SHP database. Within most STCs, the outputs have a range of 350 W/m2 for latent and sensible heat fluxes, and 8K for surface temperature. The average difference between STC median values is only 100 W/m2 for latent and sensible heat. It is concluded that STC explains 5-15% of the variance in model outputs and should not be used to determine SHPs.

Gutmann, E. D.↗

DOE OSTI2025

Warming is Associated With More Encoded Antimicrobial Resistance Genes and Transcriptions Within Five Drug Classes in Soil Bacteria: A Case Study and Synthesis

ABSTRACT The effect of warming on anti‐microbial resistance (AMR) genes in the environment has critical implications for public health but is little studied. We collected published soil bacterial genomes from the BV‐BRC database and tested the correlation between reported optimal growth temperature and the number of encoded AMR genes. Furthermore, we tested the relationship between temperature and AMR gene transcription in a natural ecosystem by analysing soil transcriptomes from a warming manipulation experiment in an Alaskan boreal forest. We hypothesised that there is a positive relationship between warming and AMR prevalence in gene content in bacterial genomes and transcriptomic sequences, and that this effect would vary by drug class. Regarding the bacterial genomes, we found a positive relationship between the fraction of encoded AMR genes and the reported optimal temperature of soil bacteria. The drug classes tetracycline and lincosamide/macrolide/streptogramin had the strongest positive relationship with reported optimal temperature. For the case study in a natural ecosystem, we found 61 significantly upregulated AMR gene‐associated transcripts spanning eight drug classes in warmed plots. In the Alaskan soil samples, we found that warming elicited the strongest positive effect on transcripts targeting lincosamide/streptogramin, beta‐lactam and phenicol/quinolone antibiotics. Overall, higher temperatures were linked to AMR gene prevalence.

Hacopian, Melanie T. [Department of Ecology and Ev↗

NASA NTRS1989

Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and synergistically-related infectious diseases.

Rochon, Gilbert L.↗

NASA NTRS2011

Deriving Soil Moisture with the Combined L-Band Radar and Radiometer Measurements

In this study, we develop a combined active/passive technique to estimate surface soil moisture with the focus on the short vegetated surfaces. We first simulated a database for both active and passive signals under SMAP s sensor configurations using the radiative transfer model with a wide range of conditions for surface soil moisture, roughness and vegetation properties that we considered as the random orientated disks and cylinders. Using this database, we developed 1) the techniques to estimate surface backscattering and emission components and 2) the technique to estimate soil moisture with the estimated surface backscattering and emission components. We will demonstrate these techniques with the model simulated data and its validation with the airborne PALS image data from the soil moisture SGP 99 and SMEX 02 experiments.

Shi, Jiancheng↗

NASA NTRS2015

AgMIP Training in Multiple Crop Models and Tools

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

farm crops↗

NASA NTRS2008

Phoenix Lander's Thermal Evolved Gas Analyzer: Differential Scanning Calorimeter and Mass Spectrometer Database Development

The Mars Scout Phoenix lander will land in the north polar region of Mars in May, 2008. One objective of the Phoenix lander is to search for evidence of past life in the form of molecular organics that may be preserved in the subsurface soil. The Thermal Evolved Gas Analyzer (TEGA) was developed to detect these organics by coupling a simultaneous differential thermal analyzer (SDTA) with a mass spectrometer. Martian soil will be heated to approx.1000 C and potential organic decomposition products such as CO2, CH4 etc. will be examined for with the MS. TEGA s SDTA will also assess the presence of endothermic and exothermic reactions that are characteristic of soil organics and minerals as the soil is heated. The MS in addition to detecting organic decompositon products, will also assess the levels of soil inorganic volatiles such as H2O, SO2, and CO2. Organic detection has a high priority for this mission; however, TEGA has the ability to provide valuable insight into the mineralogical composition of the soil. The overall goal of this work is to develop a TEGA database of minerals that will serve as a reference for the interpretation of Phoenix-TEGA. Previous databases for the ill-fated Mars Polar Lander (MPL)-TEGA instrument only went to 725 C. Furthermore, the MPL-TEGA could only detect CO2 and H2O while the Phoenix-TEGA MS can examine up to 144 atomic mass units. The higher temperature Phoenix-TEGA SDTA coupled with the more capable MS indicates that a higher temperature database is required for TEGA interpretation. The overall goal of this work is to develop a differential scanning calorimeter (DSC) database of minerals along with corresponding MS data of evolved gases that can used to interpret TEGA data during and after mission operations. While SDTA and DSC measurement techniques are slightly different (SDTA does not use a reference pan), the results are fundamentally similar and thus DSC is a useful technique in providing comparative data for the TEGA database. The objectives of this work is to conduct DSC and MS analysis up to 1000 C of select minerals that may be found in the martian soil.

Sutter, B.↗

DOE OSTI2025

Data from TropiRoot 1.0 database: tropical root characteristics across environments

TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

1000 Soils Pilot Dataset, version 8, May 2025

This record hosts data generated by the 1000 Soils Pilot. Data will be updated as more become available. Please see the most recent data upload for current data. A beta visualization tool is available for some data types at https://shinyproxy.emsl.pnnl.gov/app/1000soils. Please submit any suggestions or comments through the 'contact' tab. We are actively working to improve visualizations and value all feedback. Data completed include: Geochemistry, texture, respiration, and enzyme activities FTICR-MS organic matter chemistry Microbial biomass C and N TOC/TDN of water-extractable OM X-ray computed tomography (derived metrics available here, raw data available upon request) Metagenomes; a variety of data formats are available upon request Soil hydraulic properties Data in progress: LC-MS/MS in development, timeline TBD, inquire for status 1000S_processed_BGC_summary.csv contains all available biogeochemical data; microbial biomass C and N; and TOC/TDN of water-extractable OM; and 1000S_Tomography.xslx contains a summary of data generated via X-ray computed tomography. icr_v2_corems2.csv contains FTICR-MS data processed by CoreMS version 2. These data are merged by formula across instrument runs to enable cross-sample comparisons. Technical replicates are merged by retaining peaks present in 2 out of 3 replicates. 1000Soils_Metadata_Site_Mastersheet_v1.csv contains site information. Soil Hydraulics_corrected_02042025.xlsx contains soil hydraulics information. Readme File_v4.xlsx is the readme file. Please contact the MONet project (monet.emsl@pnnl.gov) or Emily Graham (emily.graham@pnnl.gov) with questions. The following file and all raw data are available upon request: icr_by_mass_for_single_sample_analysis_only.csv contains FTICR-MS data processed by CoreMS and is intended for usage in the calculation of biochemical transformations within samples only. These data are not acceptable for cross-sample comparison of masses because they are from multiple instrument runs. For more information, please see: https://www.emsl.pnnl.gov/monet and https://sc-data.emsl.pnnl.gov/monet Acknowledgment: Soil data were provided by the Molecular Observation Network (MONet) at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), a DOE Office of Science user facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. The work (proposal: 10.46936/10.25585/60008970) conducted by the U.S. Department of Energy, Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science user facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. The Molecular Observation Network (MONet) database is an open, FAIR, and publicly available compilation of the molecular and microstructural properties of soil. Data in the MONet open science database can be found at https://sc-data.emsl.pnnl.gov/.

biogeochemistry↗