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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗
NASA NTRS2012

Restoration and Reexamination of Data from the Apollo 11, 12, 14, and 15 Dust, Thermal and Radiation Engineering Measurements Experiments

As part of an effort by the Lunar Data Node (LDN) we are restoring data returned by the Apollo Dust, Thermal, and Radiation Engineering Measurements (DTREM) packages emplaced on the lunar surface by the crews of Apollo 11, 12, 14, and 15. Also commonly known as the Dust Detector experiments, the DTREM packages measured the outputs of exposed solar cells and thermistors over time. They operated on the surface for up to nearly 8 years, returning data every 54 seconds. The Apollo 11 DTREM was part of the Early Apollo Surface Experiments Package (EASEP), and operated for a few months as planned following emplacement in July 1969. The Apollo 12, 14, and 15 DTREMs were mounted on the central station as part of the Apollo Lunar Surface Experiments Package (ALSEP) and operated from deployment until ALSEP shutdown in September 1977. The objective of the DTREM experiments was to determine the effects of lunar and meteoric dust, thermal stresses, and radiation exposure on solar cells. The LDN, part of the Geosciences Node of the Planetary Data System (PDS), operates out of the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. The goal of the LDN is to extract lunar data stored on older media and/or in obsolete formats, restore the data into a usable digital format, and archive the data with PDS and NSSDC. For the DTREM data we plan to recover the raw telemetry, translate the raw counts into appropriate output units, and then apply calibrations. The final archived data will include the raw, translated, and calibrated data and the associated conversion tables produced from the microfilm, as well as ancillary supporting data (metadata) packaged in PDS format.

McBride, Marie J.↗
DOE OSTI2022

Perceived Costs and Benefits of ICON Science and Foundational Documents associated with “Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles"

This data package is associated with the publication "Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles" in Earth and Space Science (Goldman et al. 2022; https://doi.org/10.1029/2021EA002099). The manuscript is an introductory article for a special collection of commentary articles across 19 geoscience disciplines that explore the challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross-system transferability of science outcomes. This data package contains data, figures, and R scripts associated with the cost/benefit analysis presented in the manuscript. The writing teams involved in the special collection placed each letter of ICON on a plot with perceived cost on one axis and perceived benefit on the other to summarize their perceptions of pursuing each principle of ICON science. These data were subsequently quantified and analyzed. Files are saved as .csv, .R, and .pdf. This data package also contains (1) the public foundational and instructional documents that enabled the crowdsourced creation of the special collection; (2) file-level metadata (flmd) that lists each file in the data package with a description; (3) data dictionary (dd) that defines column headers that appear in csv files. Files are saved as .pdf and .csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf gas exchange raw data, 2015 - 2017, at Manaus, Brazil

This data package contains raw leaf gas exchange data from the NGEE Tropics K34 tower site on a plateau near Manaus, Brazil. Data was collected using sensor and leaf sample measurements. Leaves on a target tree were chosen by accessing a branch near the tower. In some cases, the branch was naturally positioned near the tower and in other cases, a small rope was used to pull the branch closer to the tower for better access to leaves. Mature leaves were targeted in nearly all cases. The gas exchange system was hauled with a climbing rope to the level of the branch on the tower and the sensor head/leaf enclosure was installed near the branch of interest using a magic arm. We measured leaf gas exchange from 5:00 AM to 5:00 PM, randomly cycling through the approximately 20 leaves in the targeted branch(es). Data were collected over two time periods, detailed within the files. The attached data files contain raw data, sensor manuals, and PDFs with detailed methods and procedures separated into multiple folders. Data is in Excel file format. See data references for data package containing related metadata. This dataset replaces the leaf gas exchange data of two retired packages, NGT0019 and NGT0040.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hyperspectral remote sensing-based plant community map for region around NGEE-Arctic intensive research watersheds at Seward Peninsula, Alaska, 2017-2019

Using airborne hyperspectral remote sensing data from NASA Airborne Visible-Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) platforms in a region near NGEE-Arctic intensive watersheds at Seward peninsula of Alaska, high resolution (5m) maps of plant community distribution were developed and included in this data collected. AVIRIS-NG data collected over 2017-2019 period were used to develop deep neural networks, trained using vegetation plot observations collected at NGEE-Arctic watersheds at Kougarok, Council and Teller. A hierarchical vegetation classification scheme consisting of six classes at Level I, and 16 classes at Level II contained in two .txt files were used to developed the plant community maps for the region. Two geospatial raster data files (.tif) at both thematic levels are shared in this data collection. Data files in this collection use Alaska Albers Equal Area projection. Readme files available in three formats (*.html, *.md, *.pdf) and one *.png visualization map.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 OSTI2024

iButton snow temperature measurements at Teller 27 and Kougarok 64, Seward Peninsula, Alaska, 2019-2020

Snow temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula. Data were collected from October 1, 2021 to August 16, 2022 using iButton Link DS1926-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours beginning on October 1, 2021. Tinytag data collection began between October 9, 2021 and October 12, 2021 depending on the specific Tinytag and data were collected every 30 minutes. In total, data were collected from 236 iButtons and 30 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and four *.shp files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) 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↗
NASA NTRS2017

Launch Control System Software Development System Automation Testing

The Spaceport Command and Control System (SCCS) is the National Aeronautics and Space Administration's (NASA) launch control system for the Orion capsule and Space Launch System, the next generation manned rocket currently in development. This system requires high quality testing that will measure and test the capabilities of the system. For the past two years, the Exploration and Operations Division at Kennedy Space Center (KSC) has assigned a group including interns and full-time engineers to develop automated tests to save the project time and money. The team worked on automating the testing process for the SCCS GUI that would use streamed simulated data from the testing servers to produce data, plots, statuses, etc. to the GUI. The software used to develop automated tests included an automated testing framework and an automation library. The automated testing framework has a tabular-style syntax, which means the functionality of a line of code must have the appropriate number of tabs for the line to function as intended. The header section contains either paths to custom resources or the names of libraries being used. The automation library contains functionality to automate anything that appears on a desired screen with the use of image recognition software to detect and control GUI components. The data section contains any data values strictly created for the current testing file. The body section holds the tests that are being run. The function section can include any number of functions that may be used by the current testing file or any other file that resources it. The resources and body section are required for all test files; the data and function sections can be left empty if the data values and functions being used are from a resourced library or another file. To help equip the automation team with better tools, the Project Lead of the Automated Testing Team, Jason Kapusta, assigned the task to install and train an optical character recognition (OCR) tool to Brandon Echols, a fellow intern, and I. The purpose of the OCR tool is to analyze an image and find the coordinates of any group of text. Some issues that arose while installing the OCR tool included the absence of certain libraries needed to train the tool and an outdated software version. We eventually resolved the issues and successfully installed the OCR tool. Training the tool required many images and different fonts and sizes, but in the end the tool learned to accurately decipher the text in the images and their coordinates. The OCR tool produced a file that contained significant metadata for each section of text, but only the text and coordinates of the text was required for our purpose. The team made a script to parse the information we wanted from the OCR file to a different file that would be used by automation functions within the automated framework. Since a majority of development and testing for the automated test cases for the GUI in question has been done using live simulated data on the workstations at the Launch Control Center (LCC), a large amount of progress has been made. As of this writing, about 60% of all of automated testing has been implemented. Additionally, the OCR tool will help make our automated tests more robust due to the tool's text recognition being highly scalable to different text fonts and text sizes. Soon we will have the whole test system automated, allowing for more full-time engineers working on development projects.

Automation↗
DOE OSTI2025

Groundwater table elevation and temperature from 2015 to 2024 at the Lower Montane site in the East River Watershed, Colorado.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells with Location ID's ER-DOW (alias DO1West), ER-DOE (alias DO2East), ER-MBA1 (alias M1Bend1), ER-MBA2 (alias M1Bend2), ER-UPW (alias UP1West), ER-UPM (alias UP2), ER-UPE (alias UP3East). Another dataset contains the data from wells with Location ID's ER-CPA1 to ER-CPA6. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 8 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Groundwater table elevation and temperature from 2015 to 2024 across Meander C at the Lower Montane site in the East River Watershed, Colorado.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6. Another dataset contains the data from wells at nearby Locations. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 7 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

FitCache: A Transparent Drop-In Framework for Multi-Tier Caching to Accelerate Distributed Deep Learning Workloads

Training in Deep learning (DL) remains highly compute- and data-intensive, with I/O becoming a critical bottleneck as models and datasets scale. Recent studies report that data loading can dominate training time, especially on large-scale HPC systems with shared parallel file systems (PFS). Existing caching approaches either rely on single-tier designs or require intrusive modifications to training pipelines, limiting their portability and effectiveness. In this work, we present FitCache, a transparent drop-in framework for multi-tier caching to accelerate distributed DL training by coordinating fast local memory (e.g., DRAM, Persistent Memory (PMem)) and NVMe as hierarchical caches atop PFS. Our design adapts to hardware diversity, i.e., if NVMe is missing, memory transparently acts as a caching tier, ensuring stable performance. FitCache transparently intercepts I/O requests and issues concurrent fetches across all tiers, returning data from the fastest responder without centralized metadata or static redirection paths. FitCache adapts to dynamic workloads and heterogeneous clusters while maintaining POSIX compatibility. Experiments on Frontier (2048 GPUs) and smaller research clusters show that FitCache reduces training time by up to 40% and per-batch I/O latency by up to 71.6% compared to Lustre Orion PFS, offering a drop-in solution for scalable DL training.

Hu, Guangxing [ORNL] (ORCID:0009000283203614)↗
DOE OSTI2020

WHONDRS Summer 2019 Sampling Campaign: Global River Corridor Sediment FTICR-MS, Dissolved Organic Carbon, Aerobic Respiration, Elemental Composition, Grain Size, Total Nitrogen and Organic Carbon Content, Bacterial Abundance, and Stable Isotopes (v10)

The WHONDRS Summer 2019 Sampling (S19S) Campaign collected samples in 97 globally distributed river corridor systems between July and September 2019. Surficial streambed sediments were collected at three locations within each site (upstream, midstream, and downstream). Surface water was collected at the downstream site. This dataset includes a portion of the data types produced from the sediment samples and does not include any results from the surface water. Surface water data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1603775. The S19S campaign was designed with the science community to ask questions associated with links among core/transient metabolomes, microbial metabolism, biogeochemical function, and physical properties of watershed and river corridor systems. This data package was originally published in December 2020. It was updated in April 2021 (v2; modified files), May 2022 (v3; new and modified files), November 2022 (v4; new and modified files), January 2023 (v5 and v6; new and modified files), February 2023 (v7; new and modified files), August 2023 (v8; new and modified files), July 2025 (v9; new and modified files), and June 2026 (v10; new and modified files). Version 10 includes major restructuring of the files and their formats. See the change history in the readme for details. 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 contains (1) readme, (2) data dictionary, (3) file level metadata, (4) sampling protocol, (5) a data checks report, (6) field metadata, (7) international generic sample number (IGSN) mapping file, (8) joint genome institute (JGI) mapping file, (9) a folder of site photos, and (10) a folder of sample data. The sample data folder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) and FTICR methods; (2) respiration rates and normalized respiration rates calculated from laboratory sediment incubations, (3) X-ray fluorescence (XRF) elemental composition, (4) grain size distribution from the < 2 millimeter fraction of sediment, (5) total organic carbon and total nitrogen content, (6) bacterial abundance flow cytometry data, (7) sediment stable isotopes (C and N), (8) laboratory methods, (9) water and sediment mass and volume used in incubations, (10) a folder with code, inputs, and plots for the respiration rates, (11) a folder containing XRF data, (12) a folder containing unaligned, peak-picked high resolution characterization of dissolved organic matter from sediment 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), (12) and a folder containing processed FTICR-MS data. All files are .msa, .txt, .spx, .xml, .csv, .pdf, .txt, .jpg, or .jpeg.

13C↗
DOE OSTI2024

MAPSTER: Automated Geospatial Data Sharing – Version 1.4.0

The US Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) developed MAPSTER which is a geospatial data management tool that aggregates, organizes, and shares data from dispersed sources such as unmanned aerial systems (UAS). Built specifically for use in environments where communications may be limited, MAPSTER utilizes two key technologies to effectively manage data in the field and enable easy data sharing with authorized partners: Observer and Checkpoint. Observer is a lightweight software package on an edge device, such as a laptop, that automatically detects newly processed UAS data and sends to a central server called Checkpoint. Checkpoint is a centralized server at ORNL that receives and manages data from all Observer instances. Even in a very low bandwidth environment, Observer can still send information about the UAS data product almost instantly as it generates its own metadata package on the size of KB (kilobytes). MAPSTER is not only for UAS data but for any geospatial data collected at the austere edge and dispersed sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature: Supporting Data

This dataset was used to assess the potential of a Soil Deformation and Temperature Monitoring System developed and presented in the article named "Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature" and published in Sensors. There are 21 comma-delimited data files (.csv). 14 files contain current measurements performed in a lab setting, enabling the electrical evaluation of the sensor probe. These files are generated by the Keithley DMM6500 multimeter, and list the measured supply current (first column) as a function of time (3rd column). Another set of 6 files is also acquired in lab experiments, but contain soil temperature an deformation measurements, enabling an assessment of the developed device's accuracy. In these files, the first column contains the time (UTC), followed by the sensor's battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The measurements were acquired every 5 seconds. Another .csv file contains field data in a similar format (time, temperature, acceleration) collected at the Teller road (mile 27) site near Nome, Alaska with one probe. 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↗
NASA NTRS2012

Long-Lasting Science Returns from the Apollo Heat Flow Experiments

The Apollo astronauts deployed geothermal heat flow instruments at landing sites 15 and 17 as part of the Apollo Lunar Surface Experiments Packages (ALSEP) in July 1971 and December 1972, respectively. These instruments continuously transmitted data to the Earth until September 1977. Four decades later, the data from the two Apollo sites remain the only set of in-situ heat flow measurements obtained on an extra-terrestrial body. Researchers continue to extract additional knowledge from this dataset by utilizing new analytical techniques and by synthesizing it with data from more recent lunar orbital missions such as the Lunar Reconnaissance Orbiter. In addition, lessons learned from the Apollo experiments help contemporary researchers in designing heat flow instruments for future missions to the Moon and other planetary bodies. For example, the data from both Apollo sites showed gradual warming trends in the subsurface from 1971 to 1977. The cause of this warming has been debated in recent years. It may have resulted from fluctuation in insolation associated with the 18.6-year-cycle precession of the Moon, or sudden changes in surface thermal environment/properties resulting from the installation of the instruments and the astronauts' activities. These types of reanalyses of the Apollo data have lead a panel of scientists to recommend that a heat flow probe carried on a future lunar mission reach 3 m into the subsurface, approx 0.6 m deeper than the depths reached by the Apollo 17 experiment. This presentation describes the authors current efforts for (1) restoring a part of the Apollo heat flow data that were left unprocessed by the original investigators and (2) designing a compact heat flow instrument for future robotic missions to the Moon. First, at the conclusion of the ALSEP program in 1977, heat flow data obtained at the two Apollo sites after December 1974 were left unprocessed and not properly archived through NASA. In the following decades, heat flow data from January 1975 through February 1976, as well as the metadata necessary for processing the data (the data reduction algorithm, instrument calibration data, etc.), were somehow lost. In 2010, we located 450 original master archival tapes of unprocessed data from all the ALSEP instruments for a period of April through June 1975 at the Washington National Records Center. We are currently extracting the heat flow data packets from these tapes and processing them. Second, on future lunar missions, heat flow probes will likely be deployed by a network of small robotic landers, as recommended by the latest Decadal Survey of the National Academy of Science. In such a scenario, the heat flow probe must be a compact system, and that precludes use of heavy excavation equipment such as a rotary drill for reaching the 3-m target depth. The new heat flow system under development uses a pneumatically driven penetrator. It utilizes a stem that winds out of a reel and pushes its conical tip into the regolith. Simultaneously, gas jets, emitted from the cone tip, loosen and blow away the soil. Lab experiments have demonstrated its effectiveness in lunar vacuum.

Nagihara, S.↗
DOE OSTI2021

Respiration data, microbial community assembly data, and FTICR-MS data associated with: “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks. Sengupta et al., 2021, Biogeosciences”

This data package is associated with the manuscript “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks, in revision in Biogeosciences (Sengupta et al. and 2021;https://bg.copernicus.org/preprints/bg-2021-51/). The study used hyporheic zone sediments as a model system to provide an integrated view of how disturbance modulates linkages among microbial ecology, biogeochemistry, and organic matter thermodynamics. Laboratory experiments exposed hyporheic sediment to varying wetting/drying dynamics. Data types include dissolved oxygen rates used to derive respiration rates, Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data used to derive thermodynamic properties of organic matter, and microbial community assembly metrics derived from amplicon-sequence data of putatively active (cDNA) and whole community (gDNA). The outcomes of the study are condensed into a broadly applicable conceptual model linking external forcing, internal dynamics, and history. This data package is comprised of a file-level metadata (FLMD) csv, metadata csv, and seven folders that contain csv files, R scripts, xml files, and associated documentation: (1) Rates, (2) bNTI, and (3) FTICR, (4) Statistics_Analyses, (5) Raw OTU Beta dispersion Analysis, (6) bMNTD Randomizations, and (7) Data Dictionaries. The FLMD file has a description of each file included in the data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

End-of-Winter Snow Depth, Temperature, Density, and SWE Measurements at Teller Road Site, Seward Peninsula, Alaska, 2023

Measurements of end-of-winter snow properties were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) from March 31 to April 3, 2023. This dataset contains one *.pdf user guide and four *.csv data files of spatially distributed values of snow depth, snow water equivalent (SWE), snow temperature, and snow density. Snow temperature and snow density were collected throughout the snowpack at different depths. Data were collected toward the end of the winter season from late March to early April, when the snowpack is typically near its maximum. A Snow-Hydro MagnaProbe (http://www.snowhydro.com/products/column2.html) was used to improve collection efficiency and enhance spatial coverage. This dataset is a continuation of the previous end-of-winter snow surveys conducted at the Teller Road Site in 2016-2018 (Wilson et al., 2020; https://doi.org/10.5440/1592103), 2019 (Bennett et al., 2021; https://doi.org/10.5440/1798170), and 2022 (Bennett et al., 2022; https://doi.org/10.5440/1887250).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) 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↗
NASA NTRS1998

Locating Biodiversity Data Through The Global Change Master Directory

The Global Change Master Directory (GCMD) presently holds descriptions for almost 7000 data sets held worldwide. The directory's primary purpose is for data discovery. The information provided through the GCMD's Directory Interchange Format (DIF) is the set of information that a researcher would need to determine if a particular data set could be of value. By offering data set descriptions worldwide in many scientific disciplines - including meteorology, oceanography, ecology, geology, hydrology, geophysics, remote sensing, paleoclimate, solar-terrestrial physics, and human dimensions of climate change - the GCMD simplifies the discovery of data sources. Direct linkages to many of the data sets are also provided. In addition, several data set registration tools are offered for populating the directory. To search the directory, one may choose the Guided Search or Free-Text Search. Two experimental interfaces were also made available with the latest software release - one based on a keyword search and another based on a graphical interface. The graphical interface was designed in collaboration with the Human Computer Interaction Laboratory at the University of Maryland. The latest version of the software, Version 6, was released in April, 1998. It features the implementation of a scheme to handle hierarchical data set collections (parent-child relationships); a hierarchical geospatial location search scheme; a Java-based geographic map for conducting geospatial searches; a Related-URL field for project-related data set collections, metadata extensions (such as more detailed inventory information), etc.; a new implementation of the Isite software; a new dataset language field; hyperlinked email addresses, and more. The key to the continued evolution of the GCMD is in the flexibility of the GCMD database, allowing modifications and additions to made relatively easily to maintain currency, thus providing the ability to capitalize on current technology while importing all existing records. Changes are discussed and approved through an online "interoperability" forum. The next major release of the GCMD is scheduled for early 1999 and will include the incorporation of a new matrix-based interface, a rapid valids-based query system; improvement in the operations facility - important for future distributed options; new streamlined code for greater performance and maintainability; improvements in the handling of seven current fields proposed through the interoperability forum (at no expense to the data providers); and the release of DOCmorph, a more robust version of DIFmorph to translate many 'standards' multi-directionally. Issues and actions will also be addressed.

Olsen, Lola M.↗