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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.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

q-nonabelianization for line defects

We consider the q-nonabelianization map, which maps links L in a 3-manifold M to combinations of links $\tilde{L}$ in a branched N -fold cover $\tilde{M}$. In quantum field theory terms, q-nonabelianization is the UV-IR map relating two different sorts of defect: in the UV we have the six-dimensional (2, 0) superconformal field theory of type gl(N ) on M × R 2,1 , and we consider surface defects placed on L × {x 4 = x 5 = 0}; in the IR we have the (2, 0) theory of type gl (1) on $\tilde{M}$ × R 2,1 , and put the defects on $\tilde{L}$ × {x 4 = x 5 = 0}. In the case M = R 3 , q-nonabelianization computes the Jones polynomial of a link, or its analogue associated to the group U(N ). In the case M = C × R, when the projection of L to C is a simple non-contractible loop, q-nonabelianization computes the protected spin character for framed BPS states in 4d N = 2 theories of class S. In the case N = 2 and M = C × R, we give a concrete construction of the q-nonabelianization map. The construction uses the data of the WKB foliations associated to a holomorphic covering $\tilde{C}$ →C.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

A large interlaboratory electron diffraction study of monolayer graphene

Standardisation of data collection and analysis is essential to enable commercialisation of 2D materials in a wide range of technologies. Selected area electron diffraction (SAED) in the transmission electron microscope (TEM) is one of the key methods for distinguishing monolayer from bilayer and few-layer graphene by comparing the 1st and 2nd order diffraction spot intensities. Yet there are many factors that can affect the reliability of data collection and interpretation, causing the measurement of monolayer samples to deviate from the literature boundary condition of $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1 for monolayer graphene (1LG). Here we present the results of a large interlaboratory SAED comparison study, where 15 international laboratories measured and analysed nominally identical samples of chemical vapour deposited graphene. Large variations were observed in the measured ratios of diffraction spot intensities, with the largest variance associated with poor quality SAED data resulting from inadequate specimen handling and storage. To inform the reliable determination of monolayer thickness from SAED patterns we provide a description of best practice for specimen handling, TEM operation, data collection and analysis. This work was undertaken within VAMAS Technical Working Area 41: Graphene and related 2D materials—Project 9, the results of which have been directly incorporated into ISO/TS 21356–2 for the characterisation of graphene sheets. We find that when this methodology is followed, 1LG can be distinguished from bilayer or thicker material with high confidence where analysis of a single SAED pattern gives $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1.2, even in the absence of precise specimen tilting.

2D materials↗
DOE OSTI2026

Water Observations of Flow/No-Flow for the East-Taylor Watershed, Colorado (June-July 2025 and 2026)

This dataset provides multi-year, ground-truth visual observations of surface water flow/no-flow conditions within the East-Taylor Watershed, Colorado, collected during June and July of 2025 and 2026. In June and July 2025, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function Scientific Focus Area (SFA) and Rocky Mountain Biological Laboratory (RMBL) Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (further details are provided within the CHESS Project Description). We obtained 377 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. These ground-truth observations were collected to validate classification maps from remote sensing data and model results within the East-Taylor Watershed. In 2025, flow/no-flow measurements were collected using a field-based app for the CHESS Campaign (Zerion iForm). Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included information about visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, and beaver activity. For 2025 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2025_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2025_Water_Observations_Locations.kmz); (3) photos (.jpg and .jpeg) of the water observation points, organized by location, contained within 2025_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2025_Water_Observation_Protocols.pdf). In June and July 2026, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function SFA project. We obtained 365 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. The 2026 observations focused on collecting repeat measurements at the 2025 flow/no-flow observation locations conducted as part of the CHESS campaign. These ground-truth observations were collected to understand differences in flow/no-flow in 2026, given the unprecedented 2026 drought in Colorado. In 2026, flow/no-flow measurements were collected using ArcGIS (Geographic Information System) Survey123. Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included repeat information from the 2025 water observation effort, including visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, beaver activity, and a new metadata component of estimated stream depth (for select locations). For 2026 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2026_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2026_Water_Observations_Locations.kmz); (3) photos (.jpg) of the water observation points, organized by location, contained within 2026_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2026_Water_Observation_Protocols.pdf). For 2025 and 2026 water observations, this dataset contains: (1) a location metadata file (locations.csv); (6) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (7) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. 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. 2026-09-02: This dataset was updated to include 2026 water observation measurements. The 2025 observation files were also updated to ensure a consistent file naming convention across water observation years.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2022

Competition of magnetocrystalline anisotropy of uranium layers and zigzag chains in UNi 0.34 Ge 2 single crystals

Structural and thermodynamic properties of single-crystalline UNi 1-x Ge 2 with x = 0.66 have been investigated by measuring magnetization, specific heat, and thermal expansion over a wide range of temperatures and magnetic fields. The measurements revealed the emergence of a long-range antiferromagnetic ordering of uranium magnetic moments below the Néel temperature T N = 45.5 (1) K and the existence of two easy axes in the studied compound, namely b and c, which correspond to the planes of the uranium zig-zag chains. A magnetic field applied along these two crystallographic directions induces in the system a first-order metamagnetic phase transition (from antiferromagnetic to field-polarized paramagnetism), and the width of the magnetic hysteresis associated with that transition reaches as much as 40 kOe at the lowest temperatures. A magnetic phase diagram developed from the experimental data showed that the metastable region associated with that magnetic hysteresis forms a funnel that narrows toward the N´eel point in a zero magnetic field. The four-layer Ising model has successfully predicted the colinear antiferromagnetic structure in UNi 0.34 Ge 2 (known from earlier reports), its magnetic phase diagram, and temperature and field variations of its magnetization. Moreover, it suggests that the first-order phase transition extends down to zero magnetic field, although it is barely detectable in the experiments performed in low magnetic fields. According to this model, the second-order phase transition occurs in the compound only in zero field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Evaluation and Intercomparison of Small Uncrewed Aircraft Systems Used for Atmospheric Research

Abstract Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Large magnetoresistance and first-order phase transition in antiferromagnetic single-crystalline EuAg 4 Sb 2

Here, we present the results of a thorough investigation of the physical properties of EuAg 4 Sb 2 single crystals using magnetization, heat capacity, and electrical resistivity measurements. High-quality single crystals, which crystallize in a trigonal structure with space group 𝑅⁢$\bar{3}$𝑚, were grown using a conventional flux method. Temperature-dependent magnetization measurements along different crystallographic orientations confirm two antiferromagnetic phase transitions around 𝑇 𝑁⁢1 = 10.5K and 𝑇 𝑁⁢2 = 7.5K. Isothermal magnetization data exhibit several metamagnetic transitions below these transition temperatures. Antiferromagnetic phase transitions in EuAg 4 Sb 2 are further confirmed by two sharp peaks in the temperature-dependent heat capacity data at 𝑇 𝑁⁢1 and 𝑇 𝑁⁢2 , which shift to lower temperature in the presence of an external magnetic field. Our systematic heat capacity measurements utilizing a long-pulse and single-slope analysis technique allow us to detect a first-order phase transition in EuAg 4 Sb 2 at 7.5 K. The temperature-dependent electrical resistivity data also manifest two features associated with magnetic order. The magnetoresistance exhibits a broad hump due to a field-induced metamagnetic transition. Remarkably, the magnetoresistance keeps increasing without showing any tendency to saturate as the applied magnetic field increases, and it reaches ∼20 000% at 1.6 K and 60 T. At high magnetic fields, several magnetic quantum oscillations are observed, indicating a complex Fermi surface. A large negative magnetoresistance of about −55% is also observed near 𝑇 𝑁⁢1 . Moreover, the 𝐻−𝑇 phase diagram constructed using magnetization, heat capacity, and magnetotransport data indicates complex magnetic behavior in EuAg 4 Sb 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows

Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps. Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment. In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows. Science Capsule captures, manages and represents both computational and human elements of a workflow. It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts. Science Capsule also allows users to create `workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users. Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.

Ghoshal, Devarshi↗
DOE OSTI2019

Design and implementation of a real-time underwater acoustic telemetry system for fish behavior study and environmental sensing

Underwater acoustic telemetry is an important tool for studying the behavior of aquatic animals and assessing the environmental impact of man-made structures such as water power turbines. However, current design of the acoustic telemetry system lacks the ability to transmit collected data continuously in real-time and requires manual retrieval of the recorded measurements, leading to a long delay of data retrieval, high operational cost associated with manpower as well as safety risk for the operators. In addition, there is no efficient way to monitor the status of the acoustic telemetry system, including acoustic transmitters and receivers. For example, if multiple receivers in an array fail, the detection efficiency could be significantly reduced. Herein, we developed a cloud-based, real-time underwater acoustic telemetry system that can be used to estimate behavior or survival of tagged aquatic animals and monitor environmental parameters in near-real-time (< 2 hours). The system incorporates microcontrollers and integrated circuits to create real-time data communication and processing units with a cloud-based service that processes the transmitted data stream to derive behavior and survival information of the tagged animals.

Yang, Yang↗
DOE OSTI2024

Airborne LiDAR to Improve Canopy Fuels Mapping for Wildfire Modeling

Increasing conflict between wildfire and the built environment has increased the need for more up-to-date and finer resolution canopy fuels data to improve wildfire modeling and associated risk forecasts. The US Forest Service and US Department of the Interior’s LANDFIRE product, which provides 30-m resolution canopy fuels data for the entire US, is one of the most widely used sources of fuels data. However, the last complete mapping effort for LANDFIRE is based on 2016 conditions, and subsequent updates reflect disturbances 1-2 years behind the release year. Airborne systems equipped with Light Detection and Ranging (LiDAR) sensors can be deployed to actively sense canopy structure and estimate canopy fuels data (cover, height, base height, bulk density) at finer resolutions. Canopy base height (CBH) and canopy bulk density (CBD) are difficult to measure both in the field and in LiDAR point clouds. Still, they are important for accurately modeling crown fires, which are often intense and difficult to contain. Additionally, point cloud datasets are large, and calculations require efficient utilization of computational resources. To address these challenges, we are working on an approach that uses openly available National Ecological Observatory Network (NEON) airborne LiDAR data, with calculations processed in the R programming language and parallelized through the lidR package. CBH and CBD are often derived from tree height, diameter at breast height, and species-specific allometries using the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). We aim to test if airborne LiDAR can estimate CBH and CBD without the use of empirical equations. Reliable estimates of canopy fuels data directly from airborne LiDAR could streamline quick, fine-resolution updates for use in wildfire behavior models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cost of Fish Exclusion Technologies for Hydropower

This dataset compiles data on the costs of environmental mitigations related to fish exclusion for 35 hydropower projects based on documents obtained from the U.S Federal Energy Regulatory Commission (FERC). Data for all measures and their associated project-specific information, including installed and hydraulic capacities, were manually extracted from environmental impact statements and/or environmental assessment (EIS/EA) documents. All data were consolidated in a comma-separated (.csv) file, referred to as the Cost of Fish Exclusion Technologies for Hydropower Database.

13 HYDRO ENERGY↗
DOE OSTI2021

Thermodynamic modeling of calcium carbonate scale precipitation: aqueous Na + -Ca 2+ -Cl – -HCO 3 – -CO 3 2– -CO 2 system

To allow for accurate calculations of calcium carbonate scaling in highly saline produced waters, we present a comprehensive thermodynamic model based on the electrolyte nonrandom two-liquid (eNRTL) activity coefficient equation for the aqueous Na ⁺ -Ca ²⁺ -Cl – -HCO 3 – -CO 3 ²– -CO 2 system. The eNRTL binary interaction parameters for the H 2 O:(Na ⁺ -CO 3 2– ) pair, the H 2 O:(Na ⁺ -HCO 3 – ) pair, the (Na⁺-Cl–):(Na ⁺ -CO 3 2– ) pair, and the (Na ⁺ -Cl – ):(Na ⁺ -HCO 3 – ) pair are identified in this work via the regression of thermodynamic, calorimetric, and phase equilibria experimental data. The binary interaction parameters associated with the H 2 O:(Na ⁺ -Cl – ) pair, the CO 2 :(Na ⁺ -Cl – ) pair, the H 2 O:(Ca ²⁺ -Cl – ) pair, and the (Na ⁺ -Cl – ):(Ca ²⁺ -Cl – ) pair are retrieved from the literature. The remaining binary interaction parameters are retrieved from Aspen Plus or set to zero. In addition, the solubility product constants are identified for Na 2 CO 3 ·10H 2 O (s) , Na 2 CO 3 · 7H 2 O (s) , Na 2 CO 3 ·H 2 O (s) , Na 2 CO 3 ·NaHCO 3 2H 2 O (s) , Na 2 CO 3 ·3NaHCO 3 (s) , and CaCO 3(s) via regression of solubility data. Here, the model is capable of accurately calculating all phase equilibria and calorimetric properties at temperatures up to 473.15 K and salt concentrations up to saturation.

42 ENGINEERING↗
DOE OSTI2025

Teaching sterile neutrinos in an undergrad laboratory: a sterile-neutrino search using data from the MicroBooNE liquid-argon time projection chamber performed in an undergraduate teaching laboratory

Dataset associated with "A sterile-neutrino search using data from the MicroBooNE liquid-argon time projection chamber performed in an undergraduate teaching laboratory" an educational paper to teach data analisys within particle physics for 3-4 yr physics major undergrads. Data format are csv, h5 and pkl

Gramellini, Elena [Manchester U.] (ORCID:000000031↗
DOE OSTI2024

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The SDSS-Gaia View of the Color–Magnitude Relation for Blue Horizontal-branch Stars

We present an updated sample of blue horizontal-branch (BHB) stars selected from the photometric and spectroscopic data from Sloan Digital Sky Survey and its associated project Sloan Extension for Galactic Understanding and Exploration (SEGUE). With these data, we selected candidates for A-type stars in the color–color space and then a mixture modeling technique was implemented in order to distinguish between BHB and main-sequence/blue-straggler stars based on their surface gravity values (log g) estimated by the SEGUE Stellar Parameter Pipeline. Our robust approach allows us to attribute individual probabilities of each star truly being in the BHB stage. Hence, our method is advantageous in comparison to previous SEGUE BHB selections that adopted simple log g cuts. We also revisit the color–magnitude relation for these stars and propose two calibrations, based on updated distances for Galactic globular clusters, to estimate absolute magnitudes with (g – r) 0 and (u – r) 0 colors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

25 ENERGY STORAGE↗
DOE OSTI2026

Data from: Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery

The repository folder contains spreadsheets and script for soil greenhouse gas (GHG) fluxes, soil moisture, soil temperature, air temperature, and precipitation measurements collected from the Tropical Responses to Altered Climate Experiment (TRACE) at the Sabana Research Field Station, El Yunque National Forest (USDA Forest Service; 18°19′28.74″ N, 65°43′50.09″ W) — an open-air field warming experiment located in a lowland tropical forest in Puerto Rico within the Luquillo Experimental Forest (LEF) — six to seven years after Hurricanes Irma and Maria (2017). All spreadsheets for soil and air microclimate data, as well as soil greenhouse gas data, are included as csv files. Air temperature data are also included as Excel spreadsheets (.xlsx). The script is built in R Studio, which is the only software required to run data analysis. This dataset is associated with the manuscript “Larocca Conte G ; Zuvela L ; Cruz-Pérez R ; Barreto-Vélez T ; Becerra-Santillan N ; Campbell S ; Chu H ; Dam T ; Grullón-Penkova I ; Kleit M ; Ortiz-Iglesias D ; Rubio-Lebrón L ; Cavaleri M ; Reed S ; Sihi D ; Wood T ; O'Connell C., 2026. Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery. Agricultural and Forest Meteorology. In review". The dataset was used to test the effect of warming on soil CH4 dynamics following long-term legacy effects of hurricane disturbance. The dataset includes: - An overall README file in word and pdf format describing methodology and spreadsheets’ structure. - Continuous measurements of soil temperature and moisture from January 2023 to July 2024 measured with Campbell CS655 probes (“TRACE_soil_temperature_and_moisture_2023_cleaned(in).csv” and “TRACE_soil_temperature_and_moisture_2024_cleaned. csv”). - Air temperature data measured with a HOBO MX23O1A data logger (“Hobo air temperature 2023 Sep 2024” and “Hobo air temperature 2023 Sep 2024” – “CSV FILES folders”). - Precipitation data from a nearby weather tower downloaded from González et al. (2025; “sabana_2020-2025.csv”). - Soil CH4 and CO2 effluxes measured intermittently in two summer campaigns (June – August 2023 and June – July 2024) with a LI-COR 8200-01S Portable Smart Chamber coupled with a LI-COR LI-7810 CH4/ CO2/H2O Trace Gas Analyzer (“23_24COMBO2.0.csv”). - R markdown script for data analysis (“Trace new_PLOTS.Rmd”).

54 ENVIRONMENTAL SCIENCES↗