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This is the AmeriFlux version of the carbon flux data for the site US-Mo3 LTAR CMRB Field 3 (CMRB BAU). Site Description - Site is in a 20 ha agricultural field near Centralia, MO on a broad, slightly sloped landscape. Currently managed in a corn-soybean rotation for commercial crop production
Scientific discovery at the exascale will not be possible without significant new research in the management, storage and retrieval over the long lifespan of the extreme amounts of data that will be produced. Our thesis is that adding application level knowledge about data to guide the actions of the storage system provides substantial benefits to the organization, storage, and access to extreme scale data, resulting in improved productivity for computational science. In this project we will demonstrate novel techniques to facilitate efficient mapping of data objects, even partitioning individual variables, from the user space onto multiple storage tiers, and enable application-guided data reductions and transformations to address capacity and bandwidth bottlenecks. Our goal is to address the associated Input/ Output (I/O) and storage challenges in the context of current and emerging storage landscapes, and expedite insights into mission critical scientific processes.
The Renewable Energy Wildlife Institute (REWI) was appointed as the prime awardee of DOE award number DE-EE0007883 to lead a team of scientists, wind developers, and technology manufacturers toward the overarching goal of evaluating the effectiveness of the current DTBird system in minimizing the risk of golden eagles (Aquila chrysaetos) and other large soaring raptors from approaching the rotor-swept zone (RSZ) of operating wind turbines. As part of this goal, the team set out to 1) quantify the expected reduction in collision risk for golden eagles from operation of the detection and deterrence modules in a manner that supports the approach used by the U.S. Fish and Wildlife Service (USFWS) to assess and credit facility operators for their efforts to minimize predicted collision fatalities and 2) provide information to help improve the technology to maximize its effectiveness. DTBird is an automated detection and audio deterrent system created by the Spanish company Liquen, designed to discourage birds from entering the RSZ of spinning wind turbines. The system uses cameras to automatically detect airborne targets of interest, records each such event in an online database, and triggers a warning signal (loud sound) if the tracked object has moved close to the turbine. If the object moves even closer to the RSZ, a more aggressive dissuasion signal is broadcast. To meet our objectives, the team conducted a two-year experiment at the Goodnoe Hills wind facility in Washington state, in which 14 turbines were outfitted with DTBird units. Daily, each DTBird-equipped turbine was randomly assigned to a control or treatment group. Treatment turbines operated with DTBird running as intended—broadcasting warning or deterrent signals when DTBird detected a target within range. On control turbines, no sound signals were broadcast if a moving target triggered the DTBird system. The team also flew unmanned aerial vehicles (UAVs) designed to coarsely mimic the general size, weight, and coloration of golden eagles in programmed flight transects across DTBird detection ranges to quantify DTBird’s ability to detect intended targets and to evaluate factors that influence the probability of detection and DTBird’s response distances. Additionally, the team evaluated the behavioral responses of in situ eagles exposed to spinning turbines alone (visual and sound influences) versus spinning turbines plus broadcasted DTBird audio deterrents, to estimate the effectiveness of deterrence by the DTBird system. The data and results from these investigations were combined with those from a pilot study conducted at the Manzana Wind Power Project in California to better evaluate DTBird’s effectiveness across different landscapes.
The Human Exploration Demonstration Project (HEDP) is an ongoing task at the NASA's Ames Research Center to address the advanced technology requirements necessary to implement an integrated working and living environment for a planetary surface habitat. The integrated environment consists of life support systems, physiological monitoring of project crew, a virtual environment work station, and centralized data acquisition and habitat systems health monitoring. The HEDP is an integrated technology demonstrator, as well as an initial operational testbed. There are several robotic systems operational in a simulated planetary landscape external to the habitat environment, to provide representative work loads for the crew. This paper describes the evolution of the HEDP from initial concept to operational project; the status of the HEDP after two years; the final facilities composing the HEDP; the project's role as a NASA Ames Research Center systems technology testbed; and the interim demonstration scenarios that have been run to feature the developing technologies in 1993.
The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.
Topics covered include: Detailed Analysis of the Intra-Ejecta Dark Plains of Caloris Basin, Mercury; The Formation and Evolution of Tessera and Insights into the Beginning of Recorded History on Venus: Geology of the Fortuna Tessera Quadrangle (V-2); Geologic Map of the Snegurochka Planitia Quadrangle (V-1): Implications for the Volcanic History of the North Polar Region of Venus; Geological Map of the Fredegonade (V-57) Quadrangle, Venus: Status Report; Geologic Mapping of V-19; Geology of the Lachesis Tessera Quadrangle (V-18), Venus; Comparison of Mapping Tessera Terrain in the Phoebe Regio (V-41) and Tellus Tessera (V-10) Quadrangles; Geologic Mapping of the Devana Chasma (V-29) Quadrangle, Venus; Geologic Mapping of the Aristarchus Plateau Region on the Moon; Geologic Mapping of the Lunar South Pole Quadrangle (LQ-30); The Pilot Lunar Geologic Mapping Project: Summary Results and Recommendations from the Copernicus Quadrangle; Geologic Mapping of the Nili Fossae Region of Mars: MTM Quadrangles 20287, 20282, 25287, 25282, 30287, and 30282; Geologic Mapping of the Mawrth Vallis Region, Mars: MTM Quadrangles 25022, 25017, 25012, 20022, 20017, and 20012; Evidence for an Ancient Buried Landscape on the NW Rim of Hellas Basin, Mars; New Geologic Map of the Argyre Region of Mars: Deciphering the Geologic History Through Mars Global Surveyor, Mars Odyssey, and Mars Express Data; Geologic Mapping in the Hesperia Planum Region of Mars; Geologic Mapping of the Meridiani Region of Mars; Geologic Mapping in Southern Margaritifer Terra; Geology of -30247, -35247, and -40247 Quadrangles, Southern Hesperia Planum, Mars; The Interaction of Impact Melt, Impact-Derived Sediment, and Volatiles at Crater Tooting, Mars; Geologic Map of the Olympia Cavi Region of Mars (MTM 85200): A Summary of Tactical Approaches; Geology of the Terra Cimmeria-Utopia Planitia Highland Lowland Transitional Zone: Final Technical Approach and Scientific Results; Geology of Libya Montes and the Interbasin Plains of Northern Tyrrhena Terra, Mars: First Year Results and Second Year Work Plan; Mars Global Geologic Mapping Progress and Suggested Geographic-Based Hierarchal Systems for Unit Grouping and Naming; Progress in the Scandia Region Geologic Map of Mars; Geomorphic Mapping of MTMS -20022 and -20017; Geologic Mapping of the Medusae Fossae Formation, Mars, and the Northern Lowland Plains, Venus; Volcanism on Io: Results from Global Geologic Mapping; Employing Geodatabases for Planetary Mapping Conduct - Requirements, Concepts and Solutions; and Planetary Geologic Mapping Handbook - 2010.
The Satellite Needs Working Group (SNWG), part of the U.S. Group on Earth Observations (USGEO), surveys U.S. Federal civil agencies every two years regarding the Earth observation data they need in order to accomplish their high-priority agency objectives. The 2020 SNWG survey marked the third assessment cycle (after 2016 and 2018). Information solicited by the survey includes the nature of the need, features and phenomena being observed, data measurement characteristics, satellites and data products the agency uses or plans to use, agency satisfaction with currently available data, and limitations to agency use of satellite data. For analysis purposes, agency needs are assigned to science Focus Areas such as atmospheric composition, carbon/ecosystems, solid Earth, water, etc. Analysis of the survey responses yields insights on the current landscape of Earth observation needs across the U.S. Government agencies. The evolution over multiple survey cycles has been analyzed by the SNWG Assessment team at the Interagency Implementation and Advanced Concepts Team (IMPACT) at Marshall Space Flight Center (MSFC). Key trends emerged in the agency-agnostic analysis that can be helpful to NASA decision-makers evaluating the ongoing, evolving, and emerging needs for NASA missions and instrument datasets expressed by participating agencies in the SNWG.
The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.
Protein engineering often targets binding pockets or active sites which are enriched in epistasis—nonadditive interactions between amino acid substitutions—and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences—a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.
Here, we present a new data set from the Marcell Experimental Forest (MEF) that compiles water isotope measurements from multiple research catchments, some of which have been studied since the 1960s. The MEF is located in northern Minnesota, USA, and is home to heavily studied and monitored forests, streams, bogs, and fens. Peat-forming systems (bogs and fens) are an important component of the MEF landscape and have a profound impact on the water cycle in these catchments. Within the last decade, analysis of stable isotopes of water (expressed as δD and δ 18 O) has been implemented to characterize the different components of the water budget, and to allow researchers to look at catchment and peatland-specific hydrologic effects in the watershed. This δD and δ 18 O data set of natural waters from MEF catchments is primarily composed of measurements from three peatlands (S1, S2, S6) during an 11-year period. More recently collection and analysis were expanded to also include samples from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project in the S1 bog, peatlands S3, S4, S5, as well as nearby lakes. We establish a local meteoric water line by analyzing the isotopic composition of precipitation, which fills a void in regional meteoric water lines for Minnesota. Furthermore, we establish baseline isotopic composition for bog outlet streams, bog porewater, aquifer groundwater, overland flow, subsurface stormflow, and snowpack, as well as runoff from the SPRUCE experimental chambers. These data are publicly available and will be expanded upon in the future.
The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO 2 streams. Optimization methods identify trade-offs and design strategies leading to systems with attractive environmental and economic performance. Strategic and operational decisions depend on underlying spatial features and are sensitive to biofuel demand and CCS incentives. US CCS incentives neglect to motivate greenhouse gas mitigation from all supply chain emission sources, which leverage spatial interactions between CCS, electricity prices and the biomass landscape.
The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.
This is the AmeriFlux version of the carbon flux data for the site US-Mo1 LTAR CMRB Field 1 (CMRB ASP). Site Description - Site is in a 36 ha agricultural research field near Centralia, MO on a broad, slightly sloped landscape. Currently farmed in a corn-soybean-wheat-hay rotation. Used for research since 1991; prior to that was managed for commercial crop production.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ABe Archbold Ungrazed Wetland. This is the FLUXNET version of the carbon flux data for the site US-ABe Archbold Ungrazed Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The studied wetland is embedded within an intensively managed pasture, a common landscape in Florida, with the C4 bahiagrass (Paspalum notatum Flueggé) as forage grass. The wetland is an isolated, seasonal freshwater wetland. Soil was Samsula muck of Samsula Series (hyperthermic Terric Haplosaprists)
An examination is made of temperature and dewpoint soundings obtained by an airborne prototype of the High-resolution Interferometer Sounder (HIS) on two flight days, to ascertain their error characteristics and their utility in mesoscale analyses. Crude estimates of Bowen ratio were obtained from HIS data using a mixing-line approach; the HIS retrievals indicated that areas of thunderstorm formation were the regions of greatest instability. HIS soundings were also able to detect some of the landscape variability and temperature and humidity fluctuations present.
Unquestionably, urbanization causes tremendous changes in land cover and land use, as well as impacting a host of environmental characteristics. For example, unlike natural surfaces, urban surfaces have very different thermal energy properties whereby they store solar energy throughout the day and continue to release it as heat well after sunset. This effect, known as the 'Urban Heat Island', serves as a catalyst for chemical reactions from vehicular exhaust and industrial activities leading to the deterioration in air quality, especially exacerbating the production of ground level ozone. 'Cool Community' strategies that utilize remote sensing data, are now being implemented as a way to reduce the impacts of the urban heat island and its subsequent environmental impacts. This presentation focuses on how remote sensing data have been used to provide descriptive and quantitative data for characterizing the Atlanta, Georgia metropolitan area - particularly for measuring surface energy fluxes, such as the thermal or "heat" energy that emanates from different land cover types across the Atlanta urban landscape. In turn, this information is useful for developing a better understanding of how the thermal characteristics of the city surface affect the urban heat island phenomena and, ultimately, air quality and other environmental parameters over the Atlanta metropolitan region. Additionally, this paper also provides insight on how remote sensing, with its synoptic approach, can be used to provide urban planners, local, state, and federal government officials, and other decision-makers, as well as the general public, with information to better manage urban areas as sustainable environments.
The risk of severe wildfires in Texas has been related to weather phenomena such as climate change and recent urban expansion into wild land areas. During recent years, Texas wild land areas have experienced sequences of wet and dry years that have contributed to increased wildfire risk and frequency. To prevent and contain wildfires, the Texas Forest Service (TFS) is tasked with evaluating and reducing potential fire risk to better manage and distribute resources. This task is made more difficult due to the vast and varied landscape of Texas. The TFS assesses fire risk by understanding vegetative fuel types and fuel loads. To better assist the TFS, NASA Earth observations, including Landsat and Moderate Resolution Imaging Specrtoradiometer (MODIS) data, were analyzed to produce maps of vegetation type and specific vegetation phenology as it related to potential wildfire fuel loads. Fuel maps from 2010-2011 and 2014-2015 fire seasons, created by the Texas Disasters I project, were used and provided alternating, complementary map indicators of wildfire risk in Texas. The TFS will utilize the end products and capabilities to evaluate and better understand wildfire risk across Texas.