Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “system metadata”

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

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

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

A terminology for scientific workflow systems

The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. Furthermore, this terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.

Community-based terminology↗
DOE OSTI2022

Orthoimagery and Shapefiles Documenting Pre- and Post-August 2019 Slope Disturbances, Teller Road Site, Seward Peninsula, Alaska, 2018-2019

This dataset was derived from UAS aerial photos and dGPS data collected at the Teller mile marker 47 site in 2018 and 2019 and used to support research quantifying the timing and rate of surface movements and analyze soil transport process in Arctic landscapes. In July 2018, a Phantom uncrewed aerial system (UAS) collected >6800 high resolution aerial photos of the lower portion of the Teller 47 watershed (see for raw photos; pending archive NGA281). During the UAS survey, 25 x 25 cm tile ground control points (GCPs) were laid out and secured with one rebar rod in the center. The four corners and rebar tops were surveyed with differential GPS, and these coordinates were used to construct and validate an orthomosaic of the site. These points were resurveyed in August 2019 and used to track annual movement. Changes in position between the two surveys are reported in (Lathrop et al. 2022; NGA254). Agisoft Metashape photogrammetry software was used to construct a georeferenced orthomosaic image (*.tif file) of a portion (0.68 km2) of the watershed with a final resolution of ~1 cm. Manual delineation of the perimeters of failures visible in the UAS imagery (*.tif files) was conducted to create shapefile polygons (two *.zip files). The failures were identified by the exposure of bare mineral soils, which were made visible by disruption of the overlying tundra vegetation. The shapefiles were then used to analyse the topographic distribution and sizes of the failures. In August 2019 an additional ~300 georeferenced UAS images of slope instability features were collected (data pending submission) and compared with the 2018 orthomosaic. Overview maps of the failure locations included as a *.pdf.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 OSTI2021

November 2021 Operational Highlights

Completed: 1. the initial tests of document workflows for Titan on the Red, which included standing up a software encryption capability to test data ingestion; 2. a software encryption capability in order to ingest the first data source by the end of the month; 3. a stand-alone computer for classified ontology data entry. Titan on the Red is an artificial intelligence/machine learning system to make digitizing, cataloging, and searching NSRC collections easier and more efficient. Created Online Vault backups, with one copy stored at LANL and one shipped to Lawrence Livermore National Laboratory. The Online Vault is a classified, searchable library of LANL’s nuclear weapons design and test history. Imported new Laboratory Directed Research and Development (LDRD) documents based on revised access categories. Bulk ingested nearly 10,000 documents via java-based PowerLoader. This application ingests metadata and content into the Online Vault to meet the requirements for the NSRC collections. Rehoused 300 linear feet of weapons physics documents in archival, acid-free storage folders and boxes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.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

Snow Camera Photos at the Teller 27 Field Site from 2022-2023, Seward Peninsula, Alaska

Snow distribution in the Arctic is highly variable and driven by high winds and microtopography, which result in deep snow drifts and shallow scoured areas. To better understand snow drifting and scouring, 4 Reconyx HyperFire 2 game cameras were installed at the Teller 27 Watershed Field Site on the Seward Peninsula, Alaska from September 2022 to September 2023. Game cameras were installed at 4 locations across the watershed to capture pictures of snow in drift and scour areas. Three-meter tall, red PVC poles were used as snow-stakes to measure snow depth from these pictures. One photo was taken at each camera once per hour during daylight. Distances between cameras and snow stakes were measured so that snow depth can later be calculated from the pictures. This dataset contains four folders of *.jpg files from each camera and one *.kml file of camera locations.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↗
DOE OSTI2022

LungMAP Portal Ecosystem: Systems-level Exploration of the Lung

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). Finally, as this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

East River Pore Water FTICR-MS Data associated with "Geomorphic and Hydrogeochemical Controls on Fine-Scale Hyporheic Microbiome Assembly and Function"

Pore water samples were collected from distributed locations around Meander A in East River (Crested Butte, CO, USA) during the summer of 2018. This dataset consists of the characterization of dissolved organic matter using 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS; in .xml format) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory).This study aimed to understand organic matter assemblages differ within a streambed with differing geomorphology and the interplay between streambed organic matter and microbiome function. Any published work that utilizes data presented in this dataset should cite the dataset using the DOI number. If using the dataset, please acknowledge the U.S. Department of Energy (DOE) Office of Science (BER) Subsurface Biogeochemical Research (SBR) program, the Environmental Molecular Sciences Laboratory (EMSL), and Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS). The publication DOI will be added to this abstract when available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tides on South Mill Apartment Complex

This dataset includes data from the building management system for a large multi-family rental property located in Tempe, Arizona. The name of this rental property is “Tides on South Mill” and it is owned by the FCP which is a real-estate investment company based in Maryland with a large portfolio of rental properties that spreads out throughout the U.S. FCP’s portfolio includes large multi-family housing and commercial buildings. The property is split into four quadrants. Each quadrant has roughly 130 apartments. The total floor area of each Quadrant is about 94,000 sq. ft. The campus of Solara spreads over 27 acres. The mechanical system consists of a 2-pipe system that runs chilled or hot water thru individual Fan Coil Units depending upon the season. The gas-fired boiler will supply hot water when seasonal change-over will take place. This dataset spans October to December 2021 and includes time-series measurements corresponding electricity meter data (Cooling Tower Fan, Pumps, Chillers,) with 1-minute resolution, chilled water temperature data and outside air data with 5-minute resolution etc. as CSV files. In addition to the measurements, a metadata .json file, and a .pdf dataset description file are also included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Data and scripts associated with a manuscript on a meta-analysis synthesizing stream biogeochemical response to wildfires across space and time (v2)

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subsidence Measurements at the Teller 27 Field Site, Seward Peninsula, Alaska, 2021

In September 2021, differential GPS (dGPS) points were collected in areas of visual subsidence in the Teller watershed near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway on the Seward Peninsula of Alaska. This study aimed to understand how structural permafrost loss is affecting meter-scale ground elevation. As ice thaws, heaving and slumping of the landscape creates new microtopographical features on the landscape that may alter surface hydrology and soil moisture and thus plant community composition and biogeochemical cycling. The Teller 27 field site is underlain with discontinuous permafrost, so the landscape features a variety of permafrost features. We collected ground elevations from eight subsidence areas in two forms: 1) Transects were sampled laterally through areas of ground subsidence to capture the high edges and low spots where the earth slumped. 2) The circumference of the subsidence area was sampled in order to measure the ground area affected by subsidence. These data were collected with an Emlid Reach RS2 dGPS and a base station. This dataset includes one *.csv of dGPS locations of the eight subsidence locations and one *.kml of measurement locations.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↗
DOE OSTI2022

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

SeeQ: A Programming Model for Portable Data-Driven Building Applications

This paper introduces SeeQ, a programming model and an abstraction framework that facilitates the development of portable data- driven building applications. Data-driven approaches can provide insights into building operations and guide decision-making to achieve operational objectives. Yet the configuration of such applications per building requires extensive effort and tacit knowledge. In SeeQ, we propose a portable programming model and build a software system that enables self-configuration and execution across diverse buildings. The configuration of each building is captured in a unified data model - in this paper, we work with the Brick ontology without loss of generality. SeeQ focuses on the distinction between the application logic and the configuration of an application against building-specific data inputs and systems. We test the proposed approach by configuring and deploying a diverse range of applications across five heterogeneous real-world buildings. The analysis shows the potential of SeeQ to significantly reduce the efforts associated with the delivery of building analytics.

analytics↗
DOE OSTI2024

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico

Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. 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 OSTI2021

NGEE Arctic, Canopy Spectral Reflectance, Utqiagvik (Barrow), Alaska, 2014-2016

Measurements of full-spectrum (i.e. 350-2500 nm) canopy spectral reflectance of Arctic plant species within the BEO, Barrow, Alaska. Spectra were collected using Spectra Vista Corporation (SVC) HR-2014i and Spectral Evolution (SE) PSR+ instruments mounted on a tripod or monopod together with a Spectralon white plate to calibrate each measurement under variable illumination conditions. Data were collected in Utqiagvik (Barrow), Alaska during the 2014 to 2016 period. Data package includes processed spectra (*.csv) files and original instrument output and processing files (*.zip).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) 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

Canopy spectral reflectance along the NGEE-Arctic tram transect, Utqiagvik (Barrow), Alaska, 2015-2017

Full-spectrum (350-2500 nm) canopy spectral reflectance measured in July 2015, 2016 and 2017 along the NGEE-Arctic tram located on the Barrow Environmental Observatory (BEO), Barrow, Alaska. Measurements were made using a SVC HR1024i spectroradiometer mounted on a manually operated instrument cart. Spectra were collected at 137 stop points at 0.5 m intervals along the tram transect, with each measurement calibrated to variable illumination conditions using a Spectralon white plate. Data were collected in July, 2015-2016, and in June, July and September in 2017. Processed spectral data are presented in .csv and .xlsx files for each year and the complete SVC instrument output (.sig) are also included in .zip files. The data package includes some photographs showing the tram transect and spectroradiometer setup and a written protocol with details of equipment used and methods.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 OSTI2022

Near-Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub-Arctic Watershed: Modeling Archive

This Modeling Archive is in support of a NGEE-Arctic publication: Shirley et al. (2022) “Near-Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub-Arctic Watershed". [DOI].The dataset contains outputs from the global sensitivity analysis (GSA) of the “ecosys” model as reported in Shirley et al. (2022). The study showed that discontinuous permafrost environments are characterized by complex feedback loops and strong spatial heterogeneity which is created by variability in near-surface hydrology and soil properties. Additionally, the study demonstrated that missing representation of sub-grid heterogeneity in terrestrial ecosystem models can lead to biased estimates of the high-latitude carbon budget. Included in this dataset are the factor values for each run in the GSA and the model outputs used in this study. Included are two *.csv data files and one *.pdf.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

Maps of plant functional type (PFT), PFT fractional cover, and uncertainty derived from AVIRIS-NG data, 2019, Seward Peninsula

Remote sensing maps of plant functional type (PFT), PFT fractional cover (fCover), and fCover uncertainty maps derived from NASA’s Airborne Visible / Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380 ~ 2510 nm) was collected as a part of the collaboration between NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE’s Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps for flights over Teller, Kougarok, and Council watersheds created using eight AVIRIS-NG imagery collected in July, 2019. The map data and metadata are provided as image (ENVI, *.png) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as GIS *.kml files. Detailed description of the methods for each map are provided in Yang et al., (2023). These datasets are provided in support of Yang et al., (In revision) “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.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↗