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The accelerator complex now known as the Los Alamos Neutron Science Center (LANSCE) began operation in 1972 as LAMPF, the Los Alamos Meson Production Facility. In the intervening 53 years, the complex has been expanded in capability several times, both in terms of its accelerator performance and the user facilities it supports.
Fish passage facilities are used to mitigate the impacts of hydropower dams on migratory fish in rivers, but information on the location, types, and characteristics of this infrastructure is incomplete at a national scale. Researchers at Oak Ridge National Laboratory partnered with federal agency and industry stakeholders to create the first national-scale database of fish passage infrastructure at U.S. hydropower developments. This database contains information of great value to a broad range of stakeholders; it includes fish passage facility engineering characteristics, targeted fish species, operational schedules, and costs. This data resource addresses a large gap in knowledge of the deployment of fish passage technology and is freely available to members of the hydropower community, including federal and state regulators and resource agencies, non-governmental organizations, industry, and other user groups to support project planning and regulatory (re)licensing activities. This database supports the U.S. Department of Energy Water Power Technologies Office objective to develop decision support tools and data resources that improve environmental performance and ensure hydropower’s long-term value to the American public.
Fish passage facilities are used to mitigate impacts of hydropower dams to migratory fish in rivers, but information on the location, types, and characteristics of this infrastructure is incomplete at a national scale. Researchers at Oak Ridge National Laboratory (ORNL) partnered with fish passage engineers and hydropower experts from the US Fish and Wildlife Service (USFWS), the National Oceanic Atmospheric Administration’s National Marine Fisheries Service (NOAA NMFS), and the Low Impact Hydropower Institute (LIHI) to create the first national scale database of fish passage infrastructure at US hydropower developments. This database consists of the ORNL_Fish_Passage_Dataset.zip file with 13 individual .csv files that contain information on fish passage facility engineering characteristics, targeted fish species, operational schedule, and costs. which is of great value to a diverse range of stakeholders. This information was collected between December 2023 and July 2025 from project partners, other hydropower stakeholders, online datasets available for download, and a stakeholder questionnaire Information. This data resource addresses a large gap in knowledge of the deployment of fish passage technology and is freely available to members of the hydropower community, including federal and state regulators and resource agencies, non-governmental organizations (NGOs), industry, and other user groups to support project planning and regulatory (re)licensing activities. This database supports the US Department of Energy Water Power Technologies Office objective to develop decision support tools and data resources that improve environmental performance and ensure hydropower’s long-term value to the American public.
Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.
Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.
This record hosts data generated by the 1000 Soils Pilot. Data will be updated as more become available. Please see the most recent data upload for current data. A beta visualization tool is available for some data types at https://shinyproxy.emsl.pnnl.gov/app/1000soils. Please submit any suggestions or comments through the 'contact' tab. We are actively working to improve visualizations and value all feedback. Data completed include: Geochemistry, texture, respiration, and enzyme activities FTICR-MS organic matter chemistry Microbial biomass C and N TOC/TDN of water-extractable OM X-ray computed tomography (derived metrics available here, raw data available upon request) Metagenomes; a variety of data formats are available upon request Soil hydraulic properties Data in progress: LC-MS/MS in development, timeline TBD, inquire for status 1000S_processed_BGC_summary.csv contains all available biogeochemical data; microbial biomass C and N; and TOC/TDN of water-extractable OM; and 1000S_Tomography.xslx contains a summary of data generated via X-ray computed tomography. icr_v2_corems2.csv contains FTICR-MS data processed by CoreMS version 2. These data are merged by formula across instrument runs to enable cross-sample comparisons. Technical replicates are merged by retaining peaks present in 2 out of 3 replicates. 1000Soils_Metadata_Site_Mastersheet_v1.csv contains site information. Soil Hydraulics_corrected_02042025.xlsx contains soil hydraulics information. Readme File_v4.xlsx is the readme file. Please contact the MONet project (monet.emsl@pnnl.gov) or Emily Graham (emily.graham@pnnl.gov) with questions. The following file and all raw data are available upon request: icr_by_mass_for_single_sample_analysis_only.csv contains FTICR-MS data processed by CoreMS and is intended for usage in the calculation of biochemical transformations within samples only. These data are not acceptable for cross-sample comparison of masses because they are from multiple instrument runs. For more information, please see: https://www.emsl.pnnl.gov/monet and https://sc-data.emsl.pnnl.gov/monet Acknowledgment: Soil data were provided by the Molecular Observation Network (MONet) at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), a DOE Office of Science user facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. The work (proposal: 10.46936/10.25585/60008970) conducted by the U.S. Department of Energy, Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science user facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. The Molecular Observation Network (MONet) database is an open, FAIR, and publicly available compilation of the molecular and microstructural properties of soil. Data in the MONet open science database can be found at https://sc-data.emsl.pnnl.gov/.
Cloud liquid water path, albedo, and cloud fraction susceptibilities for marine warm boundary-layer clouds are quantified using the Meteosat retrieved cloud microphysical properties at the ARM Eastern North Atlantic (ENA) observatory in the Azores. The Meteosat cloud retrievals are based on the methods applied by the Clouds and the Earth’s Radiant Energy System (CERES) project, which are specifically tailored to support the ARM user facility over the ARM ground-based observation sites (enavisstpx2dm11minnisX1.c1). Cloud susceptibility is quantified as the slope between cloud properties and Nd within a 1-degree-by-1-degree grid box at each satellite time step using a least-square regression. To reduce uncertainties associated with the adiabatic assumption in the Nd retrieval, for each cloud object, we exclude cloudy pixels at the cloud edge, defined as those adjacent to cloud-free pixels. To minimize influence of cloud heterogeneity and co-variability on the retrieved cloud susceptibility, cloud properties are averaged to 0.25-degree resolution using the 3-km pixel-level retrieval, and the 1-degree cloud susceptibilities are estimated using the smoothed data. Cloud type classification is applied to distinct cloud object, labeling all contiguous cloudy pixels. Marine warm boundary-layer clouds are defined as clouds with 90% of cloud tops below 3km, and Meteosat retrieved cloud phase as liquid.
The Biological and Environmental Research (BER) program advances fundamental research and scientific user facilities to support Department of Energy (DOE) missions in scientific discovery and innovation, energy security, and environmental responsibility. BER seeks to understand the biological, biogeochemical, and physical principles needed to predict a continuum of processes occurring across scales, from molecular and genomics-controlled mechanisms at the smallest scales to environmental and Earth system change at the largest scales.
A fire hazard analysis, required for many U.S. Department of Energy (DOE) facilities, is a complex, cumbersome, and costly process. Fire hazard analyses may be viewed as a checkbox, but ideally and in spirit with the DOE-STD-1066, the fire hazard analysis (FHA) should be a part of the workflow and used to help in modifications, maintenance, and improving operational safety. With current FHA development processes, it is both time and cost prohibitive for true integration. A tool called Fire Risk Investigation in 3D or FRI3D was developed under the DOE Light Water Reactor Sustainability program to simplify and automate many aspects of a fire probabilistic risk analysis for existing nuclear power plants. The FRI3D tool automates fire scenarios by combining approved fire simulation codes, U.S. Nuclear Regulatory Commission fire calculations methods, 3D modeling and visualization, and probabilistic risk analysis models into a single workflow supported with a user interface. FRI3D was initially designed for used in combination with a PRA, this case study, evaluated using FRI3D for a plant modification, determined the benefits that detailed fire modeling can have for U.S. Department of Energy facilities with or without a PRA model. It also looked at what tasks from DOE requirements could be reduced using the tool and what is needed to integrate fire hazard analysis into site workflow.
A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.
There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.
Irrigation systems are some of the oldest existing infrastructure in the United States. Increasing demands on water resources in combination with the age of these systems has resulted in a need to invest in the modernization of irrigation systems. Modernization activities are difficult to quantify, requiring expertise across multiple different domain areas. Traditionally, the scoping of a modernization project takes several years and requires significant financial investment before the potential of the project can be determined. The Irrigation Modernization Decision Support Engine allows a user to build out different scenarios. These scenarios allow for a course estimation of a project’s potential and the associated costs and benefits of pursuing the scenario. The design of the Decision Support Engine also allows for the easy sharing between different stakeholders, allowing for increased communication and understanding.
Dear JLF community, I cannot believe I am now entering my third year as JLF director — time definitely flies when you are having fun! FY25 was another pivotal year for the Jupiter Laser Facility, marked by both scientific achievement and growing visibility for our community. Building on the successful reopening and refurbishment of the facility, we continued to demonstrate how JLF drives innovation in high energy density and fusion energy science, laser technology, and workforce development. Across Janus, Titan, and COMET, users executed a diverse portfolio of experiments, from dynamic compression and opacity measurements to laser plasma interactions, laboratory astrophysics, and advanced diagnostics. These efforts are highlighted in this report, including the development of new probes that capture the time evolution of plasmas on a single shot, and diagnostics and platforms that are already impacting experiments at NIF and other large facilities. JLF continues to serve as both a testbed for new ideas and a bridge to larger scale campaigns. FY25 also showcased the broader role of JLF within the Laboratory and the national HED science ecosystem. The NIF JLF User Groups Meeting in February brought nearly 180 participants to Livermore and highlighted the scientific progress made during JLF’s first full year of renewed operations. JLF research and users were recognized with Director’s Institutional Awards and Early and Mid Career awards, underscoring the quality and impact of the work performed here. Our team also contributed prominently to national conversations about laser safety, plasma physics, and inertial fusion energy through invited talks, conferences, and professional society leadership. JLF’s integration with LaserNetUS deepened this year as well. We launched a new technical exchange program across LaserNetUS facilities and kicked it off with a JLF team visit to the BELLA Center at Lawrence Berkeley National Laboratory. These exchanges are strengthening operations, sharing best practices, and improving the user experience across the network. Filming for the LaserNetUS “Behind the Scenes” series and participation in the annual LaserNetUS meeting further increased the visibility of our facility and our users. At the same time, JLF continues to play a central role in ambitious new programs, such as the Big Aperture Thulium laser effort funded through one of the DOE Office of Science Microelectronics Science Research Centers, which will use JLF infrastructure to explore next generation high rep rate lasers for EUV and x-ray source development. A core part of our mission remains training the next generation of scientists. In FY25, we welcomed another cohort of summer students, who joined experimental teams on Titan and presented their research at LLNL’s student poster symposium and national inertial fusion energy meetings. JLF users and early career scientists showcased their work at conferences across the country, highlighting experiments performed at the facility. These hands on experiences, and the mentoring provided by our staff and user teams, are central to JLF’s identity as a true user facility. Finally, FY25 reinforced JLF’s role as a focal point for partnerships and outreach. We hosted visits from international collaborators, science leaders, and we shared the story of the facility through venues such as the Big Ideas Lab podcast. These interactions help connect our work to a broader scientific and policy audience and open new pathways for collaboration. As we look ahead, the combination of refurbished hardware, new capabilities like STILETTO and enhanced short pulse performance on Titan, strong partnerships across LLNL and LaserNetUS, and a growing user community positions JLF for an even more ambitious program in the coming years. I am deeply grateful to our technical and operations staff for their dedication, to our LLNL partners for their continued support, and to our users for bringing bold, creative ideas to the facility. I look forward to more experiments, capabilities, partnerships, and groundbreaking science in the years to come! With brightest regards, Félicie Albert, JLF Director.
This report documents Oak Ridge National Laboratory’s (ORNL’s) FY 2025 progress toward establishing readiness to use the Battelle Energy Alliance (BEA) Research Reactor (BRR) shipping cask in support of the Nuclear Science User Facilities (NSUF). The BRR cask will provide a new shared infrastructure capability for transporting irradiated fuels and materials between ORNL and Idaho National Laboratory (INL), thereby supporting NSUF’s mission of enabling user access to advanced nuclear research facilities. In FY 2025, ORNL advanced the regulatory, contractual, safety review, and planning activities necessary to qualify its facilities and staff for BRR cask handling. Although loaded shipments were delayed due to fabrication lead times for Orano Federal Services LLC’s internal basket hardware, the program progressed to the point where an empty cask dry run is scheduled for October 2025. This dry run represents a critical step in demonstrating ORNL’s ability to receive, handle, and return the BRR cask.
Wearable robotic systems including robotic limbs for supporting a load while a user moves through an environment and their methods of use are described. In one embodiment, a robotic system includes robotic limbs with first and second robotic limb segments that are movable between different configurations to support a load while a user is standing or crawling. In another embodiment, a robotic system includes first and second robotic limbs that are substantially located within a plane parallel to a frontal plane of a user when the robotic system is worn. In another embodiment, a wearable robotic system includes first and second robotic limbs and an associated base that is attachable to a user's torso. The first and second robotic limbs may include a plurality of actuators and associated robotic limb segments to couple the robotic legs to the base and control their movement.
Safety and security are priorities of U.S. Department of Energy (DOE) radioactive materials shipping campaigns. In the more than 70-year history of domestic transport of spent nuclear fuel (SNF), there has never been a transportation-related radiological injury. To support transportation planning, among the tools that DOE uses is the Stakeholder Tool for Assessing Radioactive Transportation (START). START contains geospatial data and transportation route analyses capabilities designed to support a range of DOE transportation planning initiatives. One of those functions is the capability to support emergency response planning and training for State and Tribal jurisdictions located on routes used for DOE shipments of radioactive materials. As part of the Department's commitment to public safety, DOE provides federally-funded radiological response training to emergency responders along DOE radioactive materials transportation corridors through its Transportation Emergency Preparedness Program (TEPP). START contains spatial data representing the locations and emergency-response capabilities of fire departments, police, hospitals, State emergency response centers, and where TEPP-trained personnel are based. The START tool supports State and Tribal users' ability to evaluate emergency-response coverage on active and potential DOE radioactive materials transport routes through their jurisdictions, provide expected response times to reach the scene of an incident, identify equipment available to support a response, and identify the number of response personnel and their respective training levels. In addition, START can be used to identify gaps in coverage along a transportation corridor where additional radiological emergency response training may be needed. This paper describes the data, features, and functionality DOE uses to provide a resource for emergency response training needs assessments for States and Tribes along active and potential routes for transporting radioactive materials, and illustrates its use. It also discusses future plans to integrate TEPP and Federal Emergency Management Agency (FEMA) radiological training data to provide a more comprehensive source of geospatial information on personnel who have received equivalent radiological response training. (authors)
Collaborative management of water resources requires a broad suite of “water data” that extends beyond basic information about water quantity and quality to other related topics such as water infrastructure, aquatic ecosystem health, socioeconomic factors, and power generation. Water data are disparate in nature because they are collected and provided by many entities, and in some cases, remain challenging to access and use. The U.S. Department of Energy’s Water Power Technologies Office initiated a project to characterize relevant categories of water data; describe the current state of accessing, using, and visualizing water data; and outline investigatory pathways for future efforts aimed at improving the discovery, sharing, and use of water data. Input on these topics was solicited from a small but diverse cross section of members of the water resources community. Fourteen broad categories of water data were identified: dams; ecology; flood control; hydroclimatology; hydrography; hydrology; hydropower; management landscape; migratory barriers; recreation and aesthetic importance; socioeconomic; water quality; water availability and use; and weather. Stakeholder perspectives on the accessibility and usability of water data indicate these aspects are affected by a complex set of technical and social factors. However, stakeholders generally agreed that better access to water data can provide a range of benefits to water management, and they stressed the need to generate broad support from water data users and producers. Two investigatory pathways were outlined that, taken together, provide a logical progression toward the goals of the project. The first pathway emphasizes further investigation to better define target audiences and data needs, identify opportunities for collaboration between related efforts, and conduct value demonstration activities to generate further support for improving discovery and access of water data. The second pathway focuses on creating a comprehensive vision for potential solutions that improve the discovery of water data. Several activities that align with the first pathway are suggested for the next phase of the project.
Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.