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DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗

DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June 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 in 2017 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) in an active meander (Meander C) of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (15-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (50-88 cm depth below surface). Sediments were homogenized from the ~10 cm cores for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0151851. 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 405 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release 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.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Integrated Medical Model Verification, Validation, and Credibility

The Integrated Medical Model (IMM) was designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic (stochastic process) model based on historical data, cohort data, and subject matter expert opinion. A probabilistic approach is taken since exact (deterministic) results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation. METHODS: In 2008, the IMM team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) was published, the IMM team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. RESULTS: IMM VVC updates are compiled recurrently and include 7009 Compliance and Credibility matrices, IMM VV Plan status, and a synopsis of any changes or updates to the IMM during the reporting period. Reporting tools have evolved over the lifetime of the IMM project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the NASA-STD-7009 Guidance Document. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including Operations, Science and Technology Planning, and Exploration Planning. CONCLUSIONS: The VVC approach established by the IMM project of combining the IMM VV Plan with 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Validation↗

DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2019)

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 in 2019 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. 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 436 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release 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.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

EXCHANGE Campaign 2 - Microbial Biomass Collapse (EC2-MBC): Carbon Availability Under Prolonged Flooding in Mid-Atlantic and Great Lakes Coastal Ecosystems

Our collective research focus is on investigating the interaction between carbon availability and microbial demands during prolonged flooding. Through laboratory experiments utilizing freshly collected soil samples from diverse transition zone environments, we aim to simulate flooding and understand the mechanisms governing carbon cycling under anoxic conditions. Our primary objective is to elucidate how carbon becomes available to microbes and how they utilize it, discerning between maintenance and investment processes. By examining soils from regions with differing native redox conditions, our aim is to gain comprehensive insights into carbon dynamics across various soil types. Future data types will be added to the ESS-DIVE data package as they are completed and will be version-controlled in the Change History section of EC2_MBC_README.pdf. This data package includes: EC2_MBC_README.pdf flmd.csv dd.csv EC2_SampleIdentificationKey.csv EC2_SoilMoistureKey.csv Processed_GHGFlux_Entisols.csv Processed_GHGFlux_Inceptisols.csv Processed_GHGFlux_Ultisols.csv Processed_Porewater_Iron_II.csv Processed_Porewater_Nutrients.csv Processed_Porewater_Sulfate.csv Processed_Probe_Measurements.csv Processed_Soil_HydrolyticEnzymes.csv Processed_Soil_MicrobialCN.csv Processed_Soil_NPOC_TN_SaltExtracted.csv Processed_Soil_NPOC_TN_WaterExtracted.csv Processed_Soil_Nutrients_OlsenPExtracted.csv Processed_Soil_Nutrients_SaltExtracted.csv Processed_Soil_Nutrients_WaterExtracted.csv Processed_DO_Probe.zip Processed_Temperature_Probe.zip

Carbon Cycling↗

DOE OSTI2025

Organic Matter Composition in June 2023 and September 2023 Across the McKenzie Sub-Basin Impacted by the 2020 Holiday Farm Fire

This dataset represents results from a field study aiming to understand the variability in post-fire responses of dissolved organic matter and determine drivers of post-fire responses. Samples were collected at 58 sites within the McKenzie River Watershed (Oregon, USA) that were upstream, within, and downstream of the Holiday Farm Fire burn perimeter. The samples were collected in June 2023 and September 2023 during storm events, approximately 3 years post-fire. Samples were characterized for benezenepolycarboxylic acids (BPCA) and ultra-high resolution mass spectrometry. Dissolved organic carbon and optics (absorbance and fluorescence) data can be found in a separate data packages (https://ir.library.oregonstate.edu/concern/datasets/zc77sz60m, https://ir.library.oregonstate.edu/concern/datasets/mc87q034m). Related data from a subset of sites from 2020-2022 can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1869708 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2478546. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset contains (1) file-level metadata; (2) data dictionary; (3) data package readme; (4) metadata; (5) methods information; (6) benzene polycarboxylic acid (BPCA) concentration data; (7) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) methods; (8) folder of high resolution characterization of organic matter via 12 Tesla FTICR-MS data generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This package contains the following file types: csv, xml, pdf.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Using Digital and Web-Based Platforms to Enhance Nuclear Power Plant Knowledge Transfer - 20203

As the workforce ages, the loss of experienced personnel can result in a loss of important knowledge. Updating Electric Power Research Institute (EPRI) research and guidance with the latest technology developments and operating experience is an important method to transfer knowledge between the retiring and incoming workforce. Studies suggest that the incoming workforce is accustomed to using digital and/or internet-based platforms to find information and may have a higher comfort with these types of platforms than they have with static, written reports. Additionally, the ease and speed of updating and delivering content through digital formats may offer an effective method for knowledge transfer and combining information from multiple reports into a single access point. EPRI has recently produced an online Decommissioning Hub available to members and is working on replicating the effort in the area of radioactive waste management called the Radwaste Web References. EPRI has extensive guidance in both disciplines. The Decommissioning Hub uses a wiki format to provide a platform that allows for both the updating of that guidance resulting from new research and new operating experiences, and combining related topical information from multiple reports. It also allows users to access the information based on key words and topical searches, and can be a tool for users to input experience to share with others. The Decommissioning Hub is accessed via a web-based portal and includes topical pages; experience summaries; a question and answer capability; and a smart search function of all EPRI decommissioning reports. Use of a wiki format allows users to input new experiences or modify existing experience summaries to add additional detail. EPRI moderates all content. The experience data base has been initially populated with information available in published EPRI reports and unpublished information currently available within EPRI. Moving forward, population of the database will continue with information available within the open literature, new information developed from current decommissioning projects, and user inputs. The web site became active in late 2017, and a major update will be issued in early 2020. Larger scale updates are planned biannually, and smaller-scale updates will occur continuously. Capturing and applying lessons learned from industry experiences is a hallmark of the global nuclear industry. This is even more important in the decommissioning technology area since very few plant staff have relevant experience and thus there is a steep learning curve. Moreover, decommissioning involves a number of complex, non-routine and specialized tasks. The web-accessible and searchable experience database provides an easy-to-use resource for capturing and disseminating practical experiences from completed and ongoing decommissioning projects. The EPRI Radwaste Web References will build upon the philosophy and objectives of the Radwaste Desk References that were published in the 1990's. The purpose of the original Radwaste Desk References was to provide nuclear power plant professionals with a how-to manual for managing radioactive waste. The Radwaste Web References will adopt this original purpose and will be a wiki type web site that provide fundamental practical and theoretical information, operating experience (OE), and information about advanced technologies and methods. As much as practicable, features that allow for interaction between users to share OE, best practices, and benchmarking will be added. Where they exist and are appropriate, the various EPRI Guidelines documents on radwaste topics will be updated and incorporated into the Radwaste Web References. The first Radwaste Web Reference will be on Low Level Waste Characterization and will build upon the EPRI Low Level Waste Characterization Guidelines (EPRI Report TR-107201, 1996) and will be available in 2020. The next Radwaste Web Reference will be on the topic of liquid radioactive waste processing and management, tentatively set for unveiling in 2021. This paper summarizes the EPRI's previous experiences with web-based platforms and future plans in this area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Pinus ponderosa physiology data from the 2023 girdling manipulation field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains physiology data on Pinus ponderosa saplings, including: leaf water potential (MPa, megapascals), leaf osmotic potential (MPa, megapascals), leaf pressure potential (MPa, megapascals), leaf relative water content (% saturated mass), as well as soluble sugars (% dry mass),starch (% dry mass), and total nonstructural carbohydrates (% dry mass) found in branch phloem and xylem. Data files and data dictionary(ies) are uploaded as .csv files and .xlsx files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Spatial Study 2021: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected during the same two-week period at 47 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Related sensor data are published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054. This data package was originally published September 2022. It was updated May 2023 (modified files) and November 2024 (new and modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), ions, and benzene polycarboxylic acid (BPCA) concentration and stable isotope data; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol (8) readme; (9) methods codes; (10) international generic-sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We thank the United States Forest Service, Washington Department of Natural Resources, Washington Department of Fish and Wildlife, Washington State Parks, Confederated Tribes and Bands of the Yakama Nation, and Cowiche Canyon Conservancy for access to field locations where these samples were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data and Scripts associated with “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling.”

This data package is associated with the publication “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling” submitted to Geoscientific Model Development (Muller et al., 2024). In this manuscript, organic matter chemistry and thermodynamics are directly connected to reactive transport simulators through the newly developed Lambda-PFLOTRAN (Parallel Reactive Flow and Transport model) workflow tool that succinctly incorporates organic matter chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate aerobic respiration of the organic matter and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow, executed through a Jupyter Notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow, and transport code PFLOTRAN. This data package contains Jupyter Notebook based workflows for two test cases for running biogeochemical simulations of organic matter oxidation identified by FTICR-MS. It contains four primary folders (workflow, data, src, and analysis), a file-level metadata file (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_flmd.csv) that lists all the files contained in this data package with a short description of each, and a data dictionary (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_dd.csv) file that describes the tabular column headers. The ‘workflow’ folder contains the Jupyter Notebook based workflows for running the lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘data’ folder contains the FTICR-MS data, initial conditions, and incubation data for test cases 1 and 2 in folders titled ‘WHONDRS’ and ‘Colloids’, respectively. The data folder also has a ‘Database’ folder containing a reaction network for bulk organic matter (assumed to be CH2O) and a general database for PFLOTRAN (hanford_rxn_network). The CH2O reaction network defines bulk organic matter oxidation. Biogeochemical simulations are completed for both the lambda binned organic matter and bulk organic matter reaction networks. The ‘hanford_rxn_network’ database includes information required for PFLTORAN simulations including ion size, molar mass, and charge of the aqueous species, gases, and minerals phases. The ‘src’ folder contains python source codes for performing lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘analysis’ folder contains outputs from the test cases 1 and 2 including lambda analysis, PFLOTRAN runs and the calibration results.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

A national survey of policies on disclosure of conflicts of interest in biomedical research

BACKGROUND: Conflicts of interest pose a threat to the integrity of scientific research. The current regulations of the U.S. Public Health Service and the National Science Foundation require that medical schools and other research institutions report the existence of conflicts of interest to the funding agency but allow the institutions to manage conflicts internally. The regulations do not specify how to do so. METHODS: We surveyed all medical schools (127) and other research institutions (170) that received more than $5 million in total grants annually from the National Institutes of Health or the National Science Foundation; 48 journals in basic science and clinical medicine; and 17 federal agencies in order to analyze their policies on conflicts of interest. RESULTS: Of the 297 institutions, 250 (84 percent) responded by March 2000, as did 47 of the 48 journals and 16 of the 17 federal agencies. Fifteen of the 250 institutions (6 percent)--5 medical schools and 10 other research institutions--reported that they had no policy on conflicts of interest. Among the institutions that had policies, there was marked variation in the definition and management of conflicts. Ninety-one percent had policies that adhered to the federal threshold for disclosure ($10,000 in annual income or equity in a relevant company or 5 percent ownership), and 9 percent had policies that exceeded the federal guidelines. Only 8 percent had policies requiring disclosure to funding agencies, only 7 percent had such policies regarding journals, and only 1 percent had policies requiring the disclosure of information to the relevant institutional review boards or to research subjects. Twenty journals (43 percent) reported that they had policies requiring disclosure of conflicts of interest. Only four federal agencies had policies that explicitly addressed conflicts of interest in extramural research, and all but one of the agencies relied primarily on institutional discretion. CONCLUSIONS: There is substantial variation among policies on conflicts of interest at medical schools and other research institutions. This variation, combined with the fact that many scientific journals and funding agencies do not require disclosure of conflicts of interest, suggests that the current standards may not be adequate to maintain a high level of scientific integrity.

Human↗

DOE OSTI2024

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023

This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method.The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground.Update on 2024-10-18: Updates were made to the 2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv and dd.csv files to correct units (ppb instead of ppm).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Post-fire time series photos from five sites across the Oak Creek watershed, Washington

This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2, 2024, affecting all five sites. This dataset provides time series game camera photos, while the broader study includes continuous water quality monitoring, biogeochemical sampling of water and soils, precipitation data, and organic matter analysis. The other data types and additional metadata (include site environmental information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. Because this study is ongoing, this data package will be updated regularly to include newly collected photos. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and (6) folders of game camera photos. The game camera photos are organized by site with subfolders by month of collection. The field metadata contains a subset of the information collected that is most relevant to photo-processing. The full set of field metadata can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. All files are .csv, .pdf, or .jpg.

Burn severity↗

DOE OSTI2025

Data for Wilson and Megonigal (2025), "Nitrate reduction across soils transitioning from coastal forest to wetland are hotspots for denitrification"

Sea level rise drives spatial migration of coastal ecosystems and can lead to the accelerated replacement of coastal forests with tidal wetlands. Soil biogeochemical cycles in steady-state upland and wetland ecosystems are well studied, but pathways and rates in rapidly changing ecosystems are largely unconstrained. Wilson and Megonigal (2025) performed a one-time sampling and a subsequent incubation experiment, and characterized the reduction of reactive nitrogen (N) via denitrification and dissimilatory nitrate reduction to ammonia. Sampling was done at four sites where coastal deciduous forest is undergoing ecosystem state change and becoming wetland throughout the Chesapeake Bay, USA. The COMPASS-FME project (http://compass.pnnl.gov) established the sites sampled in this study in 2022–2023.This dataset consists of:* Isotope-labeled incubation results comparing nitrate reduction rates across transects spanning upland, transition, and wetland; and* Ancillary porewater chemistry data.All files in this dataset are plain text, comma-separated value (CSV), and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

HRP's Healthcare Spin-Offs Through Computational Modeling and Simulation Practice Methodologies

Spaceflight missions expose astronauts to novel operational and environmental conditions that pose health risks that are currently not well understood, and perhaps unanticipated. Furthermore, given the limited number of humans that have flown in long duration missions and beyond low Earth-orbit, the amount of research and clinical data necessary to predict and mitigate these health and performance risks are limited. Consequently, NASA's Human Research Program (HRP) conducts research and develops advanced methods and tools to predict, assess, and mitigate potential hazards to the health of astronauts. In this light, NASA has explored the possibility of leveraging computational modeling since the 1970s as a means to elucidate the physiologic risks of spaceflight and develop countermeasures. Since that time, substantial progress has been realized in this arena through a number of HRP funded activates such as the Digital Astronaut Project (DAP) and the Integrated Medical Model (IMM). Much of this success can be attributed to HRP's endeavor to establish rigorous verification, validation, and credibility (VV&C) processes that ensure computational models and simulations (M&S) are sufficiently credible to address issues within their intended scope. This presentation summarizes HRP's activities in credibility of modeling and simulation, in particular through its outreach to the community of modeling and simulation practitioners. METHODS: The HRP requires all M&S that can have moderate to high impact on crew health or mission success must be vetted in accordance to NASA Standard for Models and Simulations, NASA-STD-7009 (7009) [5]. As this standard mostly focuses on engineering systems, the IMM and DAP have invested substantial efforts to adapt the processes established in this standard for their application to biological M&S, which is more prevalent in human health and performance (HHP) and space biomedical research and operations [6,7]. These methods have also generated substantial interest by the broader medical community though institutions like the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) to develop similar standards and guidelines applicable to the larger medical operations and research community. DISCUSSION: Similar to NASA, many leading government agencies, health institutions and medical product developers around the world are recognizing the potential of computational M&S to support clinical research and decision making. In this light, substantial investments are being made in computational medicine and notable discoveries are being realized [8]. However, there is a lack of broadly applicable practice guidance for the development and implementation of M&S in clinical care and research in a manner that instills confidence among medical practitioners and biological researchers [9,10]. In this presentation, we will give an overview on how HRP is working with the NIH's Interagency Modeling and Analysis Group (IMAG), the FDA and the American Society of Mechanical Engineers (ASME) to leverage NASA's biomedical VV&C processes to establish a new regulatory standard for Verification and Validation in Computational Modeling of Medical Devices, and Guidelines for Credible Practice of Computational Modeling and Simulation in Healthcare.

Mulugeta, Lealem↗

DOE OSTI2022

Temporal Study 2021-2022: Sensor-Based Time Series of Surface Water Temperature, Specific Conductance, Total Dissolved Solids, Turbidity, pH, and Dissolved Oxygen from across Multiple Watersheds in the Yakima River Basin in Washington, USA (v2)

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides periodic (weekly or biweekly) in situ hydrological and water chemistry sensor data, handheld sensor water chemistry data, general environmental context photos, and field metadata collected at six sites within multiple rivers in the Yakima River Basin in Washington, USA. In addition to the sensor data, there are plots of continuous in situ sensor data and R scripts used to generate the plots. Related sample-based water chemistry data are published separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912.The data package was originally published in September 2022. It was updated in June 2025 (v2; 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 one main data folder containing two sensor-specific subfolders, and one photographs folder. The main data folder includes file-level metadata (flmd), data dictionary (dd), installation methods, field metadata, handheld sensor data, field data collection protocols, international generic sample number (IGSN) mapping file, and a readme file. Each sensor subfolder (BarotrollAtm and MantaRiverData) contains a subfolder containing sensor timeseries data and plots. The BarotrollAtm Data subfolder contains In Situ Rugged BaroTROLL sensor pressure and air temperature data. The MantaRiverData subfolder contains Eureka Manta+ 35B multisonde temperature, specific conductance, and turbidity. The FieldPhotos folder contains environmental context photographs and videos. All files are .csv, .pdf, .R, .jpg, .jpeg, .heic, .mov, or .mp4.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Assessment of Natural and Anthropogenic Aerosol Air Pollution In the Middle East Using MERRA-2, CAMS Data Assimilation Products, and High-Resolution WRF-Chem Model Simulations

Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO2 emissions in WRF-Chem are based on the novel OMI-HTAP SO2 emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMSOA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R = 0.8), followed by MERRA-2 (R = 0.66), CAMS-OA (R = 0.65), and WRF-Chem (R = 0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO2 emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈ 75%–95%) compared to other aerosol types. Near and downwind from the SO2 emission sources, non-dust aerosols (primarily sulfate) contribute up to 30% to PM(sub 2.5). The contribution of sea salt to PM in coastal regions can reach 5%. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM(sub 10) and PM(sub 2.5) (particles with diameters less than 10 and 2.5 μm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the non-dust component to PM(sub 2.5) is < 25%, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Alexander Ukhov↗