Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Guidelines”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Redox Potential of Intermittently Wet Soil, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2023-05-10 to 2023-12-15

This dataset contains collected reduction-oxidation (redox) potential measurements of the underlying soil at various depths within a wetland, referred to as The Cove, at Old Woman Creek Estuarine Research Reserve in Huron, OH. Redox potential was measured in the sediments of a coastal wetland to assess how redox potential varies over time with changes in hydrological events. Measurements were collected by Campbell Scientific CR1000X dataloggers paired with a PaleoTerra redox probe and reference electrodes. Measurements were collected at 3 different locations within The Cove at multiple depths into the underlying soil of the wetland and were collected every 10 minutes. The Redox_Datafile.csv contains the recorded measurements of the redox probes, and the RefElecDataFile.csv contains the background-noise measurements collected by the reference electrodes. Redox_InstallMethods describes the installation methods of the datalogger and associated probes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Porewater and Surface Water Chemistry of Wetland, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2022-07-06 to 2023-12-15

This dataset contains the chemistry data of surface water and porewater samples collected from a wetland, referred to as The Cove, at Old Woman Creek Estuarine Research Reserve in Huron, OH. Surface and pore water samples were taken to analyze what nutrient and/or metal constituents were present at different depths in the wetland at different times and to compare to redox potential values. Porewater was collected by constructed sippers and deployed into the underlying soil of the wetland, and they were samples approximately every 2 weeks (when groundwater was present) in 2023. Surface water was collected approximately 1 inch below the surface of the water column (if available) via a syringe. Unfiltered and 0.45 micron filtered samples were collected. Samples were analyzed for various chemistry and is reported in the SWChem (surface water chemistry) and PWChem (porewater chemistry) .csv datafiles. Collection information about the samples can be found within the PWChem_SWChem_SampleMetdata.csv file. All files associated with this dataset are listed in the PWChem_SWChem_FLMD.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PreSens Dissolved Oxygen and Temperature Data, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2022-07-06 to 2023-12-15

This dataset contains collected dissolved oxygen (DO) and temperature measurements of the air, surface water, and underlying soil of a wetland at Old Woman Creek Estuarine Research Reserve in Huron, OH. Dissolved oxygen and temperature measurements were made in the first few centimeters of wetland sediment to assess how oxygen profiles changed over time with hydrological events. Measurements taken outside of the sediment were taken as comparison. Measurements were taken by hand with a PreSens Fibox 4 transmitter, paired with an oxygen dipping probe and temperature sensor, at each field outing. Measurements were taken approximately every 2 weeks around 10 AM EST. The PreSens_DO_Temp_Datafile.csv contains the recorded measurements, and the PreSens_InstallationMethods.csv describes the deployment of the sensor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mineralogy of floodplain sediments from Meanders C, O, and Z in the East River Watershed, CO, USA

This dataset includes bulk X-ray diffraction data from floodplain sediments collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of biogeochemical cycling and other river corridor processes on riverine export of solutes. Sediment cores were collected from Meander C, Meander O, and Meander Z in July 2016 to September 2017 to depths of approximately 40-95 cm. Sample metadata including locations, depths, and sample dates are included in a csv file ("sample_list_and_locations.csv"). The file "diffraction_data.csv" contains raw diffraction data, and mineral quantification is in the file "mineral_abundance.csv". 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↗
DOE OSTI2025

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from 7 Perennial and 7 Intermittent Streams across San Antonio, Texas (v3)

This dataset supports a broader study examining the effects of intermittency on sediment respiration. The dataset provides sediment and surface water geochemistry and in situ sensor data from 7 perennial and 7 intermittent streams in San Antonio, Texas. Each stream/site was visited both in summer during base flow (July-September 2023) and winter during peak flow (January-February 2024). Related data were collected and will be published separately in collaboration with A. Veach. The data package was originally published in April 2025. It was updated in June 2025 (v2; modified and new files) and September 2025 (v3; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocol; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) sediment grain size data; (4) sediment iron (II) data and averages; (5) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment percent carbon and nitrogen; (11) sediment X-ray diffraction (XRD) data; (12) gravimetric moisture and averages; (13) a subfolder with sediment incubation respiration data, scripts, and plots; (14) surface water and sediment FTICR methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: The data processing methods for FTICR described in “v3_WHONDRS_AV1_Methods_Codes.csv” mistakenly indicate that users should process the data in Formultitude. The corrected description should read: “Both unprocessed and processed data are provided to allow users flexibility in data processing. Instructions and scripts for processing the data using CoreMS are included.” CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NGEE Arctic Phase 4 Plant Functional Type Framework for Pan-Arctic Vegetation

The NGEE-Arctic research team identified a common set of hierarchical plant functional types (PFTs) for pan-arctic vegetation that we will use across our research activities. Interdisciplinary work within a large team requires agreement regarding levels of functional organization so that knowledge, data, and technologies can be shared and combined effectively. The team has identified plant functional types as a crucial area where such interoperability is needed. PFTs are used to represent plant pools and fluxes within models, summarize observational data, and map vegetation across the landscape. Within each of these applications, varying levels of PFT specificity are needed according to the specific scientific research goal, computational limitations, and data availability. By agreeing on a specific hierarchical framework for grouping variables in our vegetation data, we ensure the resulting research products will be robust, flexible, and scalable. In this document, we lay out the agreed upon PFT framework with definitions and references to existing literature. Table 1 included in the "NGA700_Phase4PFTFramework_about*" file outlines the relationship between NGEE-Arctic Phase 4, Tier 1 PFTs and the PFTs used within prominent arctic literature as well as publications by the NGEE-Arctic team during phases 1-3.This dataset consists of a table detailing a hierarchical PFT framework that spans 4 tiers with the most granular PFTs listed in tier 1 and the most general PFTs in tier 4. The PFTs within each tier has a single column in the dataset where the PFTs are named and a separate column where the characteristics used to define that PFT are listed. Grey fill of the cells is used to indicate where a given PFT starts to “lose” tier 1 details as you look from left to right. Note the excel file has merged cells to indicate grouping of PFTs across the Tiers- it will not translate into a delimited filetype (.csv, .txt, etc) without modification thus the hierarchical PFT framework table is available in three different file formats: 1) NGA700_Phase4PTS.xlsx – maintains the merged cells and grey fill; 2) NGA700_Phase4PTS.csv – merged cells are split, and grey fill is removed; 3) NGA700_Phase4PTS.pdf – image of the table with merged cells and grey fill. Metadata document included as a *.pdf and file-level metadata and data dictionary as *.csv files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

miniDOT Logger Dissolved Oxygen and Temperature Data of Wetland Surface Water, Old Woman Creek NERR, Huron, OH, 2022-06-15 to 2023-12-15

This dataset contains timeseries data of dissolved oxygen (DO) and temperature measurements of the surface water in a wetland at Old Woman Creek Estuarine Research Reserve in Huron, OH. Dissolved oxygen and temperature measurements were made in the overlying water column of wetland to assess how oxygen changed over time with hydrological events. Measurements were collected by a miniDOT Logger. Data was collected over 1.5 years (June 2022 to December 2023). The miniDOT_DO_Temp_DataFile.csv contains the DO and temperature measurements that were collected every 10 minutes. The water levels of the site varied over-time as the wetland flooded and dried. So, sometimes the miniDOT logger would be out of the water column, resulting in high oxygen levels. Depth of the water column was recorded at every in-person site visit. The miniDOT_Depth_DataFile.csv contains the hand-measured surface water depth measurements for comparison to the logger-collected data. Information on the deployment and measurement methods can be found in the miniDOT_InstallationMethods.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil Core Chemistry of Wetland, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2023-10-24 to 2024-05-20

This dataset contains the chemistry data of soil samples collected from a wetland, referred to as The Cove, at Old Woman Creek Estuarine Research Reserve in Huron, OH. Soil core extractions were performed to analyze what nutrient and/or metal constituents were present at different depths and what biogeochemical activity this could indicate. Three soil cores were collected at three locations within The Cove. The soil cores were removed from their core tubing and were cut into 4 segments down the length (or depth) of the core: top to 1-inch deep, from 1 inch to 5 inches, 5 inches to 7 inches, and 7 inches to the bottom of the core (approximately 10 inches). These soils segments were each homogenized and sub-sampled for various chemical analyses. Soil chemistry measurements are reported in the SoilChem_DataTable.csv file. Collection information about the samples can be found within the SoilChem_SampleMetdata.csv file. All files associated with this dataset are listed in the SoilChem_FLMD.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity (v1.0.0)

The Global Spectra-Trait Initiative (GSTI) aims to generate generalizable spectra trait models using reflectance data to predict leaf traits associated with the photosynthesis capacity of leaves. It comprises a synthesized dataset of leaf trait data, input datasets and code. Leaf traits include the maximum carboxylation rate of rubisco (Vcmax), the maximum electron transport rate (Jmax), the dark respiration, as well as the prediction of leaf nitrogen, leaf mass per area (LMA), and leaf water content (LWC). The dataset comprises >7500 paired observations from around 400 species from a broad range of biomes. This dataset comprises a zip file of the GSTI GitHub repository (https://github.com/plantphys/gsti), the synthesized database (.csv) and database metadata files. This dataset was updated on 2025-12-12 with minor edits to mirror the accepted manuscript version and GitHub release (Version 1.0.0 (ESSD accepted version)). Edits included minor changes to the project documentation on GitHub and removal of 12 duplicate entries from the database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ground surface temperature derived Snow Cover Properties, Seward Peninsula, Alaska, 2019-2023

Snow-ground interface temperatures have been collected at the Teller mile marker 27 and Kougarok mile marker 64 field sites on the Seward Peninsula, Alaska from 2019 through 2023 (with data missing from Fall 2020 through Summer 2021 due to COVID). Temperatures were measured using iButton Link DS1921G-F5# Thermochron miniature temperature sensors and Tinytag TGP-4017 internal sensors deployed across the Kougarok 64 and Teller 27 field sites. These sensors are a cost-efficient way to collect snow-ground interface temperatures at a high spatial resolution, and when paired with air temperature data these measurements can provide insight into fine-scale variability in snowpack characteristics across the study sites. From this data, snow process metrics were calculated at each sensor location based on the methods outlined in Staub and Delaloye, 2017. Metrics are calculated daily for each sensor as well as over the entire season. These metrics include ground surface temperature (°C), the number of days under snow cover (number of days), the insulation effect of snow (unitless), the length of the transitional snow periods (number of days), as well as intermediaries such as temperature variability. Calculating these snow processes relies on the assumption that when snow covers a temperature sensor, it is buffered from diurnal fluctuations in air temperature by the insulating snow layer. More information on the calculated metrics can be found in the User Guide of this dataset, as well as in Staub and Delaloye’s 2017 publication Using Near-Surface Ground Temperature Data to Derive Snow Insulation and Melt Indices for Mountain Permafrost Applications. This dataset includes one daily and one seasonal *.csv file of metrics for every year of data, a daily and a seasonal *.csv data dictionary, and one User Guide document (*.pdf) describing data collection and processing.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Levoglucosan data from five coastal streams impacted by the 2020 CZU Lightning Complex Fires, California, United States

This dataset includes levoglucosan data for five coastal California (United States) streams impacted by the 2020 CZU Lightning Complex Fires which burned from August 16th through September 22nd. Levoglucosan is a highly soluble and biolabile fraction of pyrogenic carbon. The five watersheds (San Lorenzo River, Pescadero Creek, Majors Creek, Laguna Creek, and Scott Creek) were impacted by the fires with watersheds experiencing a range of burn severity and extents. Grab samples were collected from each stream between October 2020 and May 2021, targeting both baseflow and event flow hydrologic conditions. Additional biogeochemistry data (i.e., organic and black carbon concentrations) can be found in a separate data package (https://doi.org/10.4211/hs.421c0226bb38460c8393d67fe0c4f802). This data package consists of one main data folder that contains (1) readme; (2) file-level metadata; (3) data dictionary; (4) field metadata with international generic sample numbers (IGSN); (5) methods codes; and (6) levoglucosan data. All files are .csv or .pdf.

2020 CZU Lightning Complex Fires↗
DOE OSTI2025

Walker Branch Watershed: Daily Stream Metabolism and Organic Carbon Spiraling Metrics in the West Fork of Walker Branch, Tennessee, USA, 2004-2010

This dataset contains daily metabolism estimates of gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP), in addition to organic carbon spiraling length (SOC) and mineralization velocity (VfOC) estimates at the West Fork of Walker Branch, a small headwater stream, in the Walker Branch Watershed, Tennessee, USA. Observations were made from 2004-2010 (2004-01-01 to 2010-12-31). These data were generated to assess seasonal and interannual variability in metabolism and organic carbon spiraling and to explore potential driver variables, as analyses of intra- and interannual variability in metabolism and organic carbon spiraling are currently limited, leaving knowledge gaps in the driving mechanisms of and future changes to stream metabolism and carbon processing under climate change. Additionally, measurements of discharge (Q), stream width, stream- and canopy-level photosynthetically active radiation (PAR), water temperature, and precipitation from this time frame are included. This dataset contains one data file in comma separated (*.csv) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with: “Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization”

This data package is associated with the publication “Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization” published in European Geophysical Union - Biogeosciences (Barnes et al. 2025). This study investigates how phosphorus (P) biogeochemistry is altered by burn severity in contrasting types of vegetation chars. This data package documents the workflow used to process and generate the main figures and statistics in the manuscript. The R scripts reference minimally processed P nuclear magnetic resonance (P-NMR) and X-ray absorption near edge structure (P-XANES) data, as well as fully processed data including total elemental composition of the solid chars, total elemental composition of the char leachates (particulate and aqueous phases), and leachate aqueous phase molybdate reactive P concentration. These source data and associated metadata can be found on ESS-DIVE at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022; v3). Files and scripts included in this data package finish the processing workflow for P-NMR and P-XANES data. These data can be used to gain a better understanding of bulk chemical changes in chars and their leachates, as well as detailed molecular changes to P. This data package is associated with the GitHub repository found at https://github.com/river-corridors-sfa/rcsfa-RC3-BSLE_P. This data package is comprised of a “data” folder and a series of data processing and analysis scripts. Details on how to recreate the workflow can be found in the Critical Details section of the readme and the “workflow_readme.md” file. The file-level metadata file (file ending in “flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (file ending in “dd.csv”) describes all tabular data columns and their respective definitions and units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Files for Runoff Evaluation in an Earth System Land Model for Permafrost Regions

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce.This data set includes all files that were produced and applied in the paper Runoff Evaluation in an Earth System Land Model for Permafrost Regions [Xiang et al. in review]. The paper is in review as of July 1 2025 in Geoscientific Model Development (GMD). In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-rich integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM’s parameterized representation of total runoff. This dataset contains 2 figure image files (*.png, *jpg) that describe the study site and methods, as well as folders (Figure*.zip) that contain the associated data files (*.csv, *.dat) and python code notebooks (*.ipynb) for figures 3-7 in the paper. Jupyter notebook (*.ipynb) files that produce the figure files using the associated data files will run within a python environment configured with Jupyter Lab or Notebook packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

2019 Meander C and Meander Z floodplain groundwater chemistry from the East River Watershed, CO, USA

This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Data includes samples from two intra-meander zones: Meander C, in the Pumphouse vicinity, and Meander Z, just upstream of the confluence with Brush Creek. Floodplain piezometers installed along two transects across Meander C (MCP and MCB wells) and Meander Z (MZA and MZB wells) were sampled on daily to weekly time scales during summer-fall 2019. Some river water grab samples are also included. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], and groundwater level) along with laboratory measurements (dissolved inorganic carbon [DIC], dissolved organic carbon [DOC], metals and major cations, anions [chloride, sulfate, nitrate], and dissolved ammonium). Files are included in this dataset include: sample locations and depths in both a kmz file which can be opened in Google Earth and a csv file, aqueous geochemistry data in a csv files for Meander C and Meander Z, and analytical detection limits in a csv file. 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↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temporal Study 2022-2024: Sample-Based Surface Water Dissolved Inorganic Carbon, Dissolved Organic Carbon, Total Nitrogen, Stable Isotopes, and Total Suspended Solids from across Multiple Watersheds in the Yakima River Basin, Washington, USA

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry data generated from samples collected at bi-weekly or monthly intervals at six sites across the Yakima River Basin in Washington, USA. Sample and sensor data from previous years (2021-2022) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054, respectively. Related sensor data from 2022-2024 will be published separately. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved inorganic carbon (DIC) and averages; (6) dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC) and averages; (7) total dissolved nitrogen (TN) and averages; (8) total suspended solids (TSS); (9) stable isotopes; (10) surface water sampling protocol; (11) sensor protocol; (12) methods codes; and (13) international generic sample number (IGSN) mapping file. All files are .csv or .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. For data and scripts associated with "Shifts in rain-snow partitioning drive faster water transit times in the US Pacific Northwest" (Butler et al., 2026), go to https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3025481

18-O↗
DOE OSTI2025

Metagenome-assembled genomes from Wind River Basin floodplain sediments Riverton, Wyoming site (August 2015)

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 August 29, 2015 at a location (KB1) 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. Sediment samples from a deep soil pit were collected from 0 to 234 cm depth below surface at discrete depths every ~10-20 cm for microbial analyses. 13 metagenomes were sequenced through JGI and can be found under Gold sequencing project: Gs0131241. 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 2216 MAG fasta files and a csv file with quality, taxonomic classification (GTDB RS220), and metagenome accessions for MAGs. 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↗