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Overview of standardization of LCAs for Critical Minerals (CM) production, findings from review of Rare Earth Element (REE) & CM LCAs to define production pathways and identify data gaps and limitations.
LOC-HBu is a joint experimental program (JEEP) operating within the Nuclear Energy Agency’s (NEA’s) framework for irradiation experiments II (FIDES-II). LOC-HBu is dedicated to the understanding of light water reactor (LWR) fuel performance at high burnup under loss of coolant accident (LOCA) conditions. LOC-HBu aims to support burnup extension needs by addressing identified R&D priorities to achieve an improved understanding of fuel fragmentation, relocation, and dispersal (FFRD) of HBu fuel during LOCA events. The need for such experiments was identified in the United States by the Fuel Performance Testing Technical Expert Group (FPTTEG) under the Electric Power Research Institute (EPRI)’s Collaborative Research on Advanced Fuel Technologies (CRAFT) framework. A combined in-pile and furnace testing program plan was developed by researchers at Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) which was reviewed and approved by the FPTTEG Combined TREAT-LOC and SATS LOCA Experiment Plan. The first 4 tests identified in this plan are being put forward by the U.S. Department of Energy (U.S. DOE) and INL for inclusion in the FIDES-II second triannual work period. The data produced under this plan will be used to further validate and confirm existing models and inform future R&D and model development. The experimental program was specifically developed to address data gaps and opportunities identified via detailed review of the existing public knowledge base on LOCA FFRD, as well as reviewing specific experimental development activities regarding prototypic LOCA conditions for light-water reactor (LWR) systems. The program will systematically investigate the impacts of prototypic HBu fuel/cladding thermomechanical behaviors under postulated LWR LOCA conditions not yet fully investigated. These conditions correspond with prototypic decay-energy heatup (DEH) and stored-energy heatup (SEH) conditions.
This dataset contains continuous gap-filled precipitation, solar radiation, photosynthetically active radiation (PAR), air temperature, relative humidity, wind speed, and barometric pressure data recorded primarily at the Marshview Farm weather station within the Plum Island Long Term Ecosystems Research (PIE LTER) in Newbury Massachusetts (MA) from 2004 to 2023. We compiled the data set from published annual data packages in 15min resolution available on DataOne. Gaps were filled using different statistical techniques or available observations from the vicinity, e.g. the US-PLo and the US-PHM Ameriflux sites, also located within the PIE LTER. Flags are included in this dataset to indicate the origin of each data point. Metadata files ELMPFLOTRAN_met_dd.csv and ELMPFLOTRAN_met_flmd.csv contain more information on site locations, gap filling protocols, data variables, flags, and QA/QC methods. The data set was used in the spin up and simulations of a land surface model coupled to a biogeochemical reaction network (ELM PFLOTRAN) assessing impacts of hydrology and salinity input on methane fluxes in 2022 and 2023 (Sulman et al., 2024).
Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).
Many in the scientific community, particularly in publicly funded research, are pushing to adhere to more accessible data standards to maximize the findability, accessibility, interoperability, and reusability (FAIR) of scientific data, especially with the growing prevalence of machine learning augmented research. Online FAIR data repositories, such as the Open Science Framework (OSF), help facilitate the adoption of these standards by providing frameworks for storage, access, search, APIs, and other features that create organized hubs of scientific data. However, the wider acceptance of such repositories is hindered by the lack of support of hierarchical data formats, such as Technical Data Management Streaming (TDMS) and Hierarchical Data Format 5 (HDF5), that many researchers rely on to organize their datasets. Various tools and strategies should be used to allow hierarchical data formats, FAIR data repositories, and scientific organizations to work more seamlessly together. A pilot project at Los Alamos National Laboratory (LANL) addresses the disconnect between them by integrating the OSF FAIR data repository with hierarchical data renderers, extending support for additional file types in their framework. The multifaceted interactive renderer displays a tree of metadata alongside a table and plot of the data channels in the file. This allows users to quickly and efficiently load large and complex data files directly in the OSF webapp. Users who are browsing files can quickly and intuitively see the files in the way they or their colleagues structured the hierarchical form and immediately grasp their contents. This solution helps bridge the gap between hierarchical data storage techniques and FAIR data repositories, making both of them more viable options for scientific institutions like LANL which have been put off by the lack of integration between them.
The Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) data over Europe have been processed for 2 days of the ozone sensor intercomparison balloon campaign in June 1981 at Gap, France. The June 19 TOMS map shows an elongated ozone maximum (about 400 D.U) extending in a NW-SE direction across central Europe and an ozone minimum (300 D.U.) in the central Atlantic. This pattern produces a 12 D.U. decrease along the balloon trajectory from the fist ascent to the second descent. On June 26 a much smaller ozone maximum (400 D.U.) is centered in northern France with its main axis running in a SW-NE direction. The total ozone contours tend to lie parallel to the balloon trajectory so that the total ozone during the flight is nearly constant.
Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.
This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs. The KOMTCRE2 station is located on Mt. Crested Butte in Colorado. More information can be found at: https://www.wunderground.com/dashboard/pws/KCOMTCRE2/table/2020-12-7/2020-12-7/daily. The purpose of this dataset is to use as input to hydrological and flow-reactive transport models as meteorological forcing. One attached file includes metadata files with definitions for all variables and units. Subsequent files include the same time series dataset, but aggregated and gap-filled at different time-stamp intervals. These include 5 minute time-series, 15 minute time-series, 30 minute time-series, 1 hour time-series, daily time-series, and hourly and daily modeled evapotranspiration.
The rapid expansion of data centers, primarily driven by artificial intelligence, is outpacing the adaptability of the U.S. electric grid. This report, developed by Idaho National Laboratory (INL) , presents a gap analysis of some of the infrastructure challenges associated with large-scale data center deployment. Drawing from a national workshop hosted by INL in October of 2025, the report synthesizes stakeholder insights, survey data, and technical discussions to identify critical barriers and research needs. Key findings highlight the growing preference for behind-the-meter generation, the perceived inadequacy of legacy interconnection processes, and the urgent need for improved coordination between utilities, regulators, and data center developers. Environmental concerns such as water use and noise pollution, as well as economic constraints like equipment lead times and cost allocation, are also explored. The report outlines national lab capabilities in modeling, simulation, and technical assistance, and proposes targeted R&D priorities to support resilient, scalable, and efficient integration of data centers into the grid.
The goal of climate informatics, an emerging discipline, is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the field's remaining challenges. Given the impact of climate change, understanding the climate system is an international priority. The goal of climate informatics is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the remaining challenges.
Ca(GaP)2 crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of two Ca(GaP)2 sheets oriented in the (0, 0, 1) direction. Ca2+ is bonded to six equivalent P3- atoms to form edge-sharing CaP6 octahedra. All Ca–P bond lengths are 2.93 Å. Ga2+ is bonded in a trigonal non-coplanar geometry to three equivalent P3- atoms. All Ga–P bond lengths are 2.45 Å. P3- is bonded to three equivalent Ca2+ and three equivalent Ga2+ atoms to form a mixture of edge and corner-sharing PCa3Ga3 octahedra. The corner-sharing octahedral tilt angles are 0°.
BaGa2P2 crystallizes in the monoclinic P2_1/c space group. The structure is three-dimensional. Ba2+ is bonded to six P3- atoms to form distorted edge-sharing BaP6 pentagonal pyramids. There are a spread of Ba–P bond distances ranging from 3.22–3.37 Å. There are two inequivalent Ga2+ sites. In the first Ga2+ site, Ga2+ is bonded in a trigonal non-coplanar geometry to three P3- atoms. There are a spread of Ga–P bond distances ranging from 2.44–2.49 Å. In the second Ga2+ site, Ga2+ is bonded in a trigonal non-coplanar geometry to three P3- atoms. There are a spread of Ga–P bond distances ranging from 2.44–2.48 Å. There are two inequivalent P3- sites. In the first P3- site, P3- is bonded to three equivalent Ba2+ and three Ga2+ atoms to form a mixture of distorted edge and corner-sharing PBa3Ga3 octahedra. The corner-sharing octahedra tilt angles range from 23–100°. In the second P3- site, P3- is bonded to three equivalent Ba2+ and three Ga2+ atoms to form a mixture of distorted edge and corner-sharing PBa3Ga3 octahedra. The corner-sharing octahedra tilt angles range from 22–100°.
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Active phased array (APA) antennas can electronically form multiple beams to track several targets simultaneously. Spacecraft equipped with these antennas can achieve continuous communications through a constellation of relay satellites by forming a second beam to an upcoming relay satellite before the spacecraft moves beyond the current relay’s line of sight. These make-before-break operations have utility for streaming critical data without gaps. Using characterization data from a commercially-available APA-based satellite communications terminal, we simulate performance from low-Earth orbit to a representative geosynchronous relay satellite system at Ka-band. Average data rates of 3 Mbps are achievable with a minimum rate of 0.8 Mbps during worst-case handovers. We prototype the beam-splitting algorithm in the terminal hardware and evaluate its performance in an antenna range. In an over-the-air test emulating a handover between two relays we observe error-free data in the combined telemetry stream.
Temporal gaps in discrete sampling sequences produce spurious Fourier components at the intermodulation frequencies of an oscillatory signal and the temporal gaps, thus significantly complicating spectral analysis of such sparsely sampled data. A new fast Fourier transform (FFT)-based algorithm has been developed, suitable for spectral analysis of sparsely sampled data with a relatively small number of oscillatory components buried in background noise. The algorithm's principal idea has its origin in the so-called 'clean' algorithm used to sharpen images of scenes corrupted by atmospheric and sensor aperture effects. It identifies as the signal's 'true' frequency that oscillatory component which, when passed through the same sampling sequence as the original data, produces a Fourier image that is the best match to the original Fourier space. The algorithm has generally met with succession trials with simulated data with a low signal-to-noise ratio, including those of a type similar to hourly residuals for Earth orientation parameters extracted from VLBI data. For eight oscillatory components in the diurnal and semidiurnal bands, all components with an amplitude-noise ratio greater than 0.2 were successfully extracted for all sequences and duty cycles (greater than 0.1) tested; the amplitude-noise ratios of the extracted signals were as low as 0.05 for high duty cycles and long sampling sequences. When, in addition to these high frequencies, strong low-frequency components are present in the data, the low-frequency components are generally eliminated first, by employing a version of the algorithm that searches for non-integer multiples of the discrete FET minimum frequency.
There is a vast and ever increasing amount of data on the Earth interconnected energy and hydrological systems, available from NASA remote sensing and modeling systems, and yet, one challenge persists: increasing the usefulness of these data for, and thus their use by, the geospatial communities. The Hydrology Data and Information Services Center (HDISC), part of the Goddard Earth Sciences DISC, has continually worked to better understand the hydrological data needs of the geospatial end users, to thus better able to bridge the gap between NASA data and the geospatial communities. This paper will cover some of the hydrological data sets available from HDISC, and the various tools and services developed for data searching, data subletting ; format conversion. online visualization and analysis; interoperable access; etc.; to facilitate the integration of NASA hydrological data by end users. The NASA Goddard data analysis and visualization system, Giovanni, is described. Two case examples of user-customized data services are given, involving the EPA BASINS (Better Assessment Science Integrating point & Non-point Sources) project and the CUAHSI Hydrologic Information System, with the common requirement of on-the-fly retrieval of long duration time series for a geographical point
Provides raw neutron scattering data for all plots and analysis for single crystals of RuCl3 in the intermediate field regime. Fields are applied within honeycomb plane both parallel (IPTS 27539) and perpendicular to Ru bonds (IPTS 27353).