3D Covalent Organic Framework Materials database
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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"The soft costs of solar — costs not associated with hardware — remain stubbornly high. Among the biggest soft costs are those associated with inefficiencies in local permitting and inspection. A study by the National Renewable Energy Laboratory and Lawrence Berkeley National Laboratory estimates that these costs add an average of $0.22/W per residential installation. This project helps reduce non-hardware/balance of system (BOS) costs by creating and maintaining a free and available site of permitting requirements and solar system verification software that installers can use to reduce time, capital, and resource investments in tracking permitting requirements. Software tools to identify best permitting practices can enable government stakeholders to optimize their permitting process and remove superfluous costs and requirements. Like ""a Wikipedia for solar permitting"", users can add, edit, delete, and update information for a given jurisdiction. We incentivize this crowdsourcing approach by recognizing users for their contributions in the form of SEO benefits to their company or organization by linking back to users' websites."
THE NGDS IS A USER-FRIENDLY, WEB-BASED APPLICATION TO SUPPORT FINDING, VISUALIZING, MAPPING, AND ACQUISITION OF DATA BY END-USERS. THIS APPLICATION ALLOWS USERS TO DISCOVER AND ACCESS RESOURCES MADE AVAILABLE ACROSS ALL NGDS NODES, AND TO SEARCH FOR DATA ACROSS THE SYSTEM BASED ON TOPIC, LOCATION, AND OR OTHER CRITERIA. STANDARDIZED METADATA DESCRIBING EACH DATASET PROVIDES THE USER WITH THE INFORMATION NECESSARY TO DETERMINE THE UTILITY OF THAT DATASET FOR THEIR PURPOSES. GEOGRAPHIC DATASETS CAN BE VISUALIZED THROUGH A MAP INTERFACE THAT ALLOWS USERS TO INSPECT THE DETAILS OF INDIVIDUAL DATA POINTS (E.G. WELLS, TEMPERATURE MEASUREMENTS, ETC.) FROM PROPERLY FORMATTED DATASETS. IN ADDITION TO VISUALIZATION WITHIN THE APPLICATION, THE INTERFACE PROVIDES THE INFORMATION NECESSARY FOR USERS TO ACCESS THE DATA FROM OTHER APPLICATIONS.
This is a the code and data for a website created to display collision cross section data for metabolites and small molecules. This repository hosts two things. First is the source code (js) for a website created to display collision cross section data for metabolites and other small molecules. The second is the underlying CCS dataset. The growing interest in metabolomic and exposomic studies is also inciting a need for new techniques to analyze these diverse molecules. Mass spectrometry-based studies are often preferred for the confident identification of small molecules. Ion mobility spectrometry (IMS) is capable of separating molecules that have the same m/z but different conformational arrangements, which is very useful in metabolomic and exposomic analyses. As the desire for IMS measurements of metabolites continues to grow, so does the need for high quality collision cross section (CCS) values.
This software is an API for create and modify XML that are used for Plexos. DISCLAIMER: PLEXOS is a registered trademark of Energy Exemplar Pty Ltd ("Owner"). Owner has no affiliation to or participation in this software. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC ("Alliance"). The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.
A bioinformatics workflow for deduplicating, QC-ing, and annotating viral genomes recovered from metagenomic sequencing.
The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.
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Background: High birth weight (BW), 4000 g or larger, is an established risk factor for childhood leukemia. However, its association with central nervous system (CNS) tumor risk is yet unclear. The present study examined it, analyzing data obtained from a case-control study conducted among three states from the US. The association with childhood leukemia risk was also further examined. Methods: In this study, a data set provided by the Comprehensive Epidemiologic Data Resource was analyzed with an official permission. The original case-control study was conducted to examine the association between paternal preconception exposure to ionizing radiation and childhood cancer risk. Cases with childhood cancer were mainly ascertained from local hospitals, and controls were selected, matched with birth year (1-year category), county of residence, sex, ethnicity and maternal age (+/-2 years). Since the ID numbers were unavailable, conventional logistic analyses were conducted adjusting for those matching variables except for the county of residence. In addition to those variables, gestational age, age at diagnosis and study sites as covariables were included in the logistic models. Results: Analyzed subjects were 72 CNS tumor cases, 124 leukemia cases and 822 controls born from 1945 to 1989. The odds ratios (ORs) of CNS tumor risk for children with low BWs (<2500 g) and high BWs (>4000 g) were 2.0 (95% confidence interval [CI]) = 0.7, 5.9) and 2.5 (95%CI = 1.2, 5.2)], respectively. When high-BW children were restricted to those who were large for gestational age (LGA), the OR for high-BW children remained similar (OR = 2.7; 95%CI = 1.1, 6.2). On the other hand, the ORs of leukemia risk for children with low and high BWs were 0.8 (95%CI = 0.2, 3.0) and 1.4 (95%CI = 0.7, 2.6), respectively. In the normal range of BW (2500–4000 g), higher BW was positively associated with CNS tumor risk (beta = 0.0011, p for trend = 0.012). However, the association with leukemia risk was not significant (beta = -0.0002, p for trend = 0.475). Conclusion: High-BW and LGA children had an elevated childhood CNS tumor risk. In the normal BW range, the BW itself was positively related to CNS tumor risk. No significant association between BW and childhood leukemia risk was observed in this study.
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This data provides the underlying project-level analysis and data sources complied in response to the DOE request to determine the amount of geothermal capacity that could be available to meet the President's request to double renewable energy capacity by 2020. The enclosed data contains compiled data on individual project names and locations (by geothermal area and region), ownership, estimated nameplate capacity, and project status, and also contains inferred data on the barriers and viability of the project to meet a 2020 development timeline. The analysis of this data is discussed in the attached NREL report.
Well data were mined from Geothermal Prospector (GTP), Southern Methodist University (SMU), the Colorado Oil and Gas Conservation Commission (COGCC), and the Colorado Geological Survey (CGS). The well data gathered was then used to assess sedimentary geothermal feasibility in the Denver Basin, the Piceance Basin, and the Raton Basin.
With the rapid increase in vehicle electrification, there is a need for up-to-date, publicly available national data to understand end user charging and driving patterns, as well as vehicle and infrastructure performance, to inform research planning. Energetics worked with various partners to collect and analyze plug-in electric vehicle (PEV) and electric vehicle supply equipment (EVSE) data from 2019 to 2022. All sensitive attributes have been removed from this publicly available dataset. Researchers from one of the partner national labs under non-disclosure agreement (NDA) can request access to additional attributes by reaching out to evwattsdata@energetics.com.
In support of the U.S. Department of Energy’s Energy Storage Grand Challenge, Pacific Northwest National Laboratory is applying its rich history of battery research and development to provide the U.S. Department of Energy (DOE) and industry with a guide to current energy storage costs and performance metrics for various technologies. Cost and performance metrics for individual technologies track the following to provide an overall cost of ownership for each technology: cost to procure, install, and connect an energy storage system; associated operational and maintenance costs; and end-of-life costs. These metrics are intended to support DOE and industry stakeholders in making sound decisions about future R&D directions and priorities that move the United States closer to its goal of energy independence. The technologies currently being evaluated are lithium-ion [lithium iron phosphate (LFP) and nickel manganese cobalt (NMC)] batteries, vanadium redox flow batteries, lead-acid batteries, zinc-based batteries, hydrogen energy storage, pumped storage hydropower, gravitational energy storage, compressed air energy storage, and thermal energy storage.
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This dataset contains ab-initio calculation results for the temperature-dependent anomalous Hall conductivity, the anomalous Nernst effect, and the Seebeck coefficient. All calculations are based on ab-inito Quantum Espresso (PWSCF v.6.3) + Wannier90 (v.3.0.0). The dependence on carrier doping is also calculated. For all calculations a ferromagnetic order has been assumed, which might not correspond to the true ground state of the system. Tabulated values for the magnetic moments and essential input files for Quantum Espresso are available for download as attachments. This project has been supported by the RIKEN Transformative Research Innovation Platform (TRIP), Use Case: Many-body Electron Systems.
Project objective was to understand water-related constraints affecting conventional powerplant operations, specifically coal-fired power plants. Of concern were threats related to drought and flood affecting both water supply and ability of plant to discharge wastewater. Also of interest were adaptive measures taken to mitigate identified threats. This spreadsheet contains the responses provided by the power plant operators and owners. Data have been "sanitized" (e.g., random plant ID, binned location and generation capacity information) to ensure anonymity of individual power plants.