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The lack of clear references to and descriptions of data sets in published literature limits the usefulness of data, as well as the reproducibility and credibility of scientific findings.
Space radiation is comprised of a large number of particle types and energies, which have differential ionization power from high energy protons to high charge and energy (HZE) particles and secondary neutrons produced by galactic cosmic rays (GCR). Ground based accelerators such as the NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory (BNL) are used to simulate space radiation for radiobiology research and dosimetry, electronics parts, and shielding testing using mono-energetic beams for single ion species. As a tool to support research on new risk assessment models, we have developed a stochastic model of heavy ion beams and space radiation effects, the GCR Event-based Risk Model computer code (GERMcode). For radiobiological research on mixed-field space radiation, a new GCR simulator at NSRL is proposed. The NSRL-GCR simulator, which implements the rapid switching mode and the higher energy beam extraction to 1.5 GeV/u, can integrate multiple ions into a single simulation to create GCR Z-spectrum in major energy bins. After considering the GCR environment and energy limitations of NSRL, a GCR reference field is proposed after extensive simulation studies using the GERMcode. The GCR reference field is shown to reproduce the Z and LET spectra of GCR behind shielding within 20% accuracy compared to simulated full GCR environments behind shielding. A major challenge for space radiobiology research is to consider chronic GCR exposure of up to 3-years in relation to simulations with cell and animal models of human risks. We discuss possible approaches to map important biological time scales in experimental models using ground-based simulation with extended exposure of up to a few weeks and fractionation approaches at a GCR simulator.
In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.
Abstract Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.
Abstract The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.
Massively Parallel, Portable, and Reproducible Tractography (MaPPeRTrac) is a brain tractography workflow for high performance computing. It incorporates novel technologies to simplify and accelerate neuroimaging research.
This report documents the work performed in characterizing the vacuum conductance of porous structures made by conventional sintering (purchased as commercially available products from Mott Corporation ranging from 20 to 100 media grade) and additive manufacturing (powder bed fusion). The additively manufactured structures described in this report were originally intended to be the first iteration of several in an effort to produce desirable conductance characteristics. While resources were not available to link the experimental results to a modeling effort to better understand why certain characteristics were observed, the author hopes that this report may provide a useful set of data for future use, especially as sintered porous structures are not uncommonly procured from Mott corporation for research and development purposes. For that reason, all of the raw data is tabulated in the Appendix: it is possible to reproduce all of the figures shown in this report independently. For a quick order-of magnitude scan of the conductances, refer to Figures 5, 6, 8 and 9.
Present applications of precise time and frequency (T/F) technology can be grouped as follows: (1) Communications systems which require T/F for time division multiplexing and for using spread spectrum techniques. (2) Navigation systems which need T/F for position fixing using a timed signal. (3) Scientific-Metrological applications which use T/F as the most precisely reproducible standard of measurement. (4) Astronomical-Space applications which cover a variety of the most demanding applications such as pulsar research, Very Long Baseline Interferometry (VLBI) and laser/radar ranging. In particular, pulsar time-of-arrival measurements require submicrosecond precision over a period of one-half year referred to an extraterrestrial inertial system, and constitute the most stringent requirements for uniform timekeeping to date.
A schematic description of the steering control process is presented. It is shown that this process can be described in terms of levels of control. Level of control will depend on driver's skill in making use of 'clever' strategies which may be related to knowledge about the path to follow (input) and/or the vehicle under control. This knowledge may be referred to as an internal model of a particular task element. Internal information, as derived from these internal models will probably be used together with proprioceptive feedback. It is hypothesized that the efficiency of the higher levels of control will be dependent on the accuracy of both the internal and proprioceptive information. Based on this research philosophy a series of experiments is carried out. Two primary experiments were done in order to analyse subjects' ability to reproduce steering-wheel positions and movements without visual feedback. Steering-wheel angle amplitude, steering force and movement frequency were involved as independent variables.
Very little is known regarding the affects of the microgravity environment of space flight upon the action of antimicrobial agents on bacterial pathogens. This study was undertaken to develop a simple method for conducting antibacterial susceptibility tests during a Space Shuttle mission. Specially prepared susceptibility test research cards (bioMerieux Vitek, Hazelwood, MO) were designed to include 6-11 serial two-fold dilutions of 14 antimicrobial agents, including penicillins, cephalosporins, a Beta-lactamase inhibitor, vancomycin, erythromycin, tetracycline, gentamicin, ciprofloxacin, and trimethoprim/sulfamethoxazole. Minimal inhibitory concentrations (MICS) of the drugs were determined by visual reading of color endpoints in the Vitek research cards made possible by incorporation of a colorimetric growth indicator (alamarBlue(Trademark), Accumed International, Westlake, OH). This study has demonstrated reproducible susceptibility results when testing isolates of Staphylococcus aurezis, Group A Streptococcus, Enterococcusfaecalis, Escherichia coli (beta-lactamase positive and negative strains), Klebsiella pneumoniae, Enterobacter cloacae, and Pseudomoiias aeruginosa. In some instances, the MICs were comparable to those determined using a standard broth microdilution method, while in some cases the unique test media and format yielded slightly different values, that were themselves reproducible. The proposed in-flight experiment will include inoculation of the Vitek cards on the ground prior to launch of the Space Shuttle, storage of inoculated cards at refrigeration temperature aboard the Space Shuttle until experiment initiation, then incubation of the cards for 18-48 h prior to visual interpretation of MICs by the mission's astronauts. Ground-based studies have shown reproducible MICs following storage of inoculated cards for 7 days at 4-8 C to accommodate the mission's time schedule and the astronauts' activities. For comparison, ground-based control (normal gravity) MIC values will be generated by simultaneous inoculation and incubation of a second set of test cards in a laboratory at the launch site. This procedure can provide a safe and compact experiment that should yield new information on the affects of microgravity on the biological activities of various classes of antibiotics.
Man-made molecules called chlorofluorcarbons (CFCs) are broken apart in the stratosphere by high energy light, and the reactive chlorine gases that come from them cause the ozone hole. Since the ozone layer stops high energy light from reaching low altitudes, CFCs must be transported to high altitudes to be broken apart. The number of molecules per volume (the density) is much smaller at high altitudes than near the surface, and CFC molecules have a very small chance of reaching that altitude in any particular year. Many tons of CFCs were put into the atmosphere during the end of the last century, and it will take many years for all of them to be destroyed. Each CFC has an atmospheric lifetime that depends on the amount of energy required to break them apart. Two of the gases that were made the most are CFC13 and CF2C12. It takes more energy to break apart CF2C12 than CFC13, and its lifetime is about 100 years, nearly twice as long as the lifetime for CFC13. It is hard to figure out the lifetimes from surface measurements because we don't know exactly how much was released into the air each year. Atmospheric models are used to predict what will happen to ozone and other gases as the CFCs decrease and other gases like C02 continue to increase during the next century. CFC lifetimes are used to predict future concentrations and all assessment models use the predicted future concentrations. The models have different circulations and the amount of CFC lost according to the model may not match the loss that is expected according to the lifetime. In models the amount destroyed per year depends on how fast the model pushes air into the stratosphere and how much goes to high altitudes each year. This paper looks at the way the model circulation changes the lifetimes, and looks at measurements that tell us which model is more realistic. Some models do a good job reproducing the age-of-air, which tells us that these models are circulating the stratospheric air at the right speed. These same models also do a good job reproducing the amount of CFCs in the lower atmosphere where they were measured by instruments on NASA's ER-2, a research plane that flies in the lower stratosphere. The lifetime for CFC13 that is calculated using the models that do the best job matching the data is about 25% longer than most people thought. This paper shows that using these measurements to decide which models are more realistic helps us understand why their predictions are different from each other and also to decide which predictions are more likely.
Modern science is increasingly dependent on computational analysis of very large data sets. Organizing, referencing, publishing those data has become a complex problem. Published research that depends on such data often fails to cite the data in sufficient detail to allow an independent scientist to reproduce the original experiments and analyses. This paper explores some of the challenges related to data identification, equivalence and reproducibility in the domain of data intensive scientific processing. It will use the example of Earth Science satellite data, but the challenges also apply to other domains.
Science, technology, and planetary mission communities have a growing interest in components and systems that are capable of working in extreme (high) temperature and pressure conditions. Terrestrial applications range from scientific research, aerospace, defense, automotive systems, energy storage and power distribution, deep mining and others. As the target environments get increasingly extreme, capabilities to develop and test the sensors and systems designed to operate in such environments will be required. An application of particular importance to the planetary science community is the ability for a robotic lander to survive on the Venus surface where pressures are nearly 100 times that of Earth and temperatures approach 500C. The scientific importance and relevance of Venus missions are stated in the current Planetary Decadal Survey. Further, several missions to Venus were proposed in the most recent Discovery call. Despite this interest, the ability to accurately simulate Venus conditions at a scale that can test and validate instruments and spacecraft systems and accurately simulate the Venus atmosphere has been lacking. This paper discusses and compares the capabilities that are known to exist within and outside the United States to simulate the extreme environmental conditions found in terrestrial or planetary surfaces including the Venus atmosphere and surface. The paper then focuses on discussing the recent additional capability found in the NASA Glenn Extreme Environment Rig (GEER). The GEER, located at the NASA Glenn Research Center in Cleveland, Ohio, is designed to simulate not only the temperature and pressure extremes described, but can also accurately reproduce the atmospheric compositions of bodies in the solar system including those with acidic and hazardous elements. GEER capabilities and characteristics are described along with operational considerations relevant to potential users. The paper presents initial operating results and concludes with a sampling of investigations or tests that have been requested or expected.
Venus is Earth’s closest planetary neighbor, and it is often considered as Earth’s twin for its size and density. However, the extreme Venus surface conditions (temperatures of ~ 460 o C, pressure of ~90 bars, and reactive atmospheric chemical species such as supercritical CO 2 , sulfuric acid, and other toxic elements) results in short duration of Venus lander missions to date to survive for only 127 minutes. It is essential to enable multiple longer-lived Venus surface missions to understand the planet’s origin, history, climate, and interior. A systematic approach for future Venus lander material selection is to characterize and study the reactivity of high potential materials by exposing materials to a simulated Venus surface conditions. Glenn Extreme Environments Rig (GEER) at NASA’s Glenn Research Center (GRC) has been the leading facility simulating the high-temperature and pressure extremes of Venus as well as reproducing conditions of the gas mixture expected at the Venus surface since 2015. Here, we report on GEER’s recent accomplishments in assessing risks for future potential Venus missions and ongoing testing process and capability enhancement in supporting other planetary science missions.
The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.
Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.
Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.
This report documents the experimental critical configurations and measurements for IER-297, Thermal/Epithermal eXperiments (TEX) using highly enriched uranium (HEU) fuel, moderated and reflected by polyethylene. These experiments establish the baseline TEX-HEU configurations spanning thermal, intermediate, and fast fission energy regimes. The experiment campaign was completed in FY2020 at the National Critical Experiments Research Center at the Nevada National Security Site. The campaign produced six documented critical configurations (0”, ¹/₈” A, ¹/₈” B, ¹/₄”, ¹/₂”, and 1¹/₂”) and one reproducibility measurement (¹/₈” R). Table 1 summarizes these seven experiments along with their period and calculated reactivity, in order of execution. The Comet General Purpose Critical Assembly Machine was used to conduct the TEX-HEU experiments. The HEU fuel consists of U(93+) plates, collectively known as the “Jemima” plates. Each experiment consisted of an alternating stack of these HEU plates with or without polyethylene moderator plates. By varying the thicknesses of the polyethylene plates, the neutron spectrum of the experiment could be fine-tuned to a specific fission energy regime. The experiments were reflected by 1” of polyethylene consisting of a bottom and top plate and outer reflector rings to surround the HEU and polyethylene plates. The fine reactivity control of the system was provided by the thickness of the top reflector plate, which could be varied in increments of ¹/₃₂”.