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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Using Public Participation to Improve MELs Energy Data Collection

Miscellaneous and electronic loads (MELs) comprise an increasing share of building energy consumption. Large-scale data collection is needed to inform meaningful energy reduction strategies because of the diversity of MELs and our lack of understanding about how people use them. Traditional methods of data collection, however, usually incur high labor and metering equipment expenses. As an alternative, this paper investigates the feasibility of crowdsourcing data collection to satisfy at least part of the data collection needs with acceptable accuracy. We assessed the reliability and accuracy of crowd-sourced data by recruiting 18 volunteers and testing our crowdsourcing protocol. The protocol asked volunteers to perform measurement tasks for three MELs devices of increasing complexity 1) record power meter and MELs product characteristics, 2) identify and measure all power modes available, and 3) report the measured power. Volunteers performed reasonably well for devices with functionalities with which they were familiar, but many could not correctly identify all available power modes in complex devices. Accuracy may improve when participants measure the power used by familiar devices in their home, or by providing more specific instructions, e.g. videos. Furthermore, crowdsourcing data collection from individual homeowners has the potential to generate valuable information about MELs energy use in homes when integrated with existing programs such as Home Energy Saver and Building America.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

NASA MUREP-DEAP Institutes - Year 1 Updates on Capacity Building, Community Building and Research Initiatives

Through the Minority University Research and Education Project (MUREP), NASA engages underrepresented populations via various initiatives, including by means of three year competitive awards for Data Science Equity, Access, and Priority in Research and Education (DEAP) Institutes led by Historically Black Colleges and Universities (HBCUs). MUREP DEAP Institutes, multi-institutional consortia, conduct data science research and capacity building initiatives that aim to enhance the research, academic, and technological capabilities of the participating HBCUs while providing NASA-specific knowledge, skills and opportunities for students and faculty who have historically been underrepresented and underserved in the STEM workforce. Three MUREP DEAP Institutes, led by Bethune-Cookman University, North Carolina A&T University, and North Carolina Central University, are conducting data science and natural hazard related research utilizing remote sensing and earth observations, with mentorship for the Institutes provided by scientists from NASA’s Marshall Space Flight Center. Each Institute has unique capabilities, needs, and goals, but overlap in their common interest to conduct cutting edge data science and natural hazard related research and to build capacity, broaden participation, and increase retention of a diverse group of participating students and faculty​. Here, successes and challenges from the first year of activities at each MUREP DEAP institute are discussed, where activities focused primarily on the recruitment of a diverse group of students and the upskilling of students and faculty in data science and remote sensing concepts. The planned activities and goals of each DEAP Institute for the upcoming two years of activities will also be discussed, both from research and community-building perspectives.

Ronan Lucey↗
DOE OSTI2017

Enabling Diverse Software Stacks on Supercomputers using High Performance Virtual Clusters.

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed com- puting models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging soft- ware ecosystems. In this paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifi- cally, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, ef- fectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Genomes OnLine Database (GOLD) v.10: new features and updates

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Data analysis report on ATS-F COMSAT millimeter wave propagation experiment, part 1

The 13/18 GHz COMSAT Propagation Experiment (CPE) was performed to measure attenuation caused by hydrometeors along slant paths from transmitting terminals on the ground to the ATS-6 satellite. The effectiveness of site diversity in overcoming this impairment was also studied. Problems encountered in assembling a valid data base of rain induced attenuation data for statistical analysis are considered. The procedures used to obtain the various statistics are then outlined. The graphs and tables of statistical data for the 15 dual frequency (13 and 18 GHz) site diversity locations are discussed. Cumulative rain rate statistics for the Fayetteville and Boston sites based on point rainfall data collected are presented along with extrapolations of the attenuation and point rainfall data.

Hyde, G.↗
DOE OSTI2018

Skluma: An extensible metadata extraction pipeline for disorganized data

To mitigate the effects of high-velocity data expansion and to automate the organization of filesystems and data repositories, we have developed Skluma-a system that automatically processes a target filesystem or repository, extracts content-and context-based metadata, and organizes extracted metadata for subsequent use. Skluma is able to extract diverse metadata, including aggregate values derived from embedded structured data; named entities and latent topics buried within free-text documents; and content encoded in images. Skluma implements an overarching probabilistic pipeline to extract increasingly specific metadata from files. It applies machine learning methods to determine file types, dynamically prioritizes and then executes a suite of metadata extractors, and explores contextual metadata based on relationships among files. The derived metadata, represented in JSON, describes probabilistic knowledge of each file that may be subsequently used for discovery or organization. Skluma's architecture enables it to be deployed both locally and used as an on-demand, cloud-hosted service to create and execute dynamic extraction workflows on massive numbers of files. It is modular and extensible-allowing users to contribute their own specialized metadata extractors. Thus far we have tested Skluma on local filesystems, remote FTP-accessible servers, and publicly-accessible Globus endpoints. We have demonstrated its efficacy by applying it to a scientific environmental data repository of more than 500,000 files. We show that we can extract metadata from those files with modest cloud costs in a few hours.

data swamp↗
DOE OSTI2014

Ethnic Diversity in Materials Science and Engineering. A report on the workshop on ethnic diversity in materials science and engineering.

The immediate goal of the workshop was to elevate and identify issues and challenges that have impeded participation of diverse individuals in MSE. The longerterm goals are to continue forward by gathering and disseminating data, launching and tracking initiatives to mitigate the impediments, and increase the number of diverse individuals pursuing degrees and careers in MSE. The larger goal, however, is to create over time an ever-increasing number of role models in science fields who will, in turn, draw others in to contribute to the workforce of the future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Brick : Metadata schema for portable smart building applications

Buildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. Here, this paper describes Brick, a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

"Giovanni at 20: Consistency and Persistency in Making Earth Remote Sensing Data Available (and Useful) to the Earth Science Community"

Since its creation in the year 2000, the NASA Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) has been an exemplar of how to provide remotely-sensed satellite data and related Earth science datasets to a broad and globally diverse researcher community. One of the hallmarks of Giovanni system usage is that it is common to see practitioners of many branches of science – particularly those in fields not traditionally associated with remote-sensing data, such as animal behavior and paleooceanography – both accessing and employing datasets which the system provides. This usage pattern is attested to by the diversity of published science citing Giovanni. Giovanni has evolved through several versions, each of which increased analytical and visualization options while also enhancing ease-of-use. Information resources supporting Giovanni from the Goddard Earth Sciences Data and Information Services Center (GES DISC), which hosts the system, start with basic mapping and plotting functions and extend to “How-to” recipes demonstrating interusability with other data analysis systems and software. Giovanni is now being developed for use in a cloud environment, potentially expanding datasets the system can be applied to, and also improving performance for data having high spatial and temporal resolution. This presentation covers the system’s historical success with interesting examples of Giovanni-citing research, and will apply its current capabilities to a multi-dataset examination of derecho events associated with mesoscale convective phenomena in the summer of 2020, indicating how Giovanni is prepared for ongoing support of Earth science in a changing world.

Giovanni Derecho visualization↗
DOE OSTI2015

Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons

Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli , Phaeobacter inhibens , Pseudomonas stutzeri , Shewanella amazonensis , and Shewanella oneidensis . To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens , we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative D-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

Fuel Injector Patternation Evaluation in Advanced Liquid-Fueled, High Pressure, Gas Turbine Combustors, Using Nonintrusive Optical Diagnostic Techniques

Planar laser-induced fluorescence (PLIF) imaging and planar Mie scattering are used to examine the fuel distribution pattern (patternation) for advanced fuel injector concepts in kerosene burning, high pressure gas turbine combustors. Three diverse fuel injector concepts for aerospace applications were investigated under a broad range of operating conditions. Fuel PLIF patternation results are contrasted with those obtained by planar Mie scattering. Further comparison is also made for one injector with data obtained through phase Doppler measurements. Differences in spray patterns for diverse conditions and fuel injector configurations are readily discernible. An examination of the data has shown that a direct determination of the fuel spray angle at realistic conditions is also possible. The results obtained in this study demonstrate the applicability and usefulness of these nonintrusive optical techniques for investigating fuel spray patternation under actual combustor conditions.

Locke, R. J.↗
DOE OSTI2015

Scanning the landscape of genome architecture of non-O1 and non-O139 Vibrio cholerae by whole genome mapping reveals extensive population genetic diversity

Historically, cholera outbreaks have been linked to V. cholerae O1 serogroup strains or its derivatives of the O37 and O139 serogroups. A genomic study on the 2010 Haiti cholera outbreak strains highlighted the putative role of non O1/non-O139 V. cholerae in causing cholera and the lack of genomic sequences of such strains from around the world. Here we address these gaps by scanning a global collection of V. cholerae strains as a first step towards understanding the population genetic diversity and epidemic potential of non O1/non-O139 strains. Whole Genome Mapping (Optical Mapping) based bar coding produces a high resolution, ordered restriction map, depicting a complete view of the unique chromosomal architecture of an organism. To assess the genomic diversity of non-O1/non-O139 V. cholerae , we applied a Whole Genome Mapping strategy on a well-defined and geographically and temporally diverse strain collection, the Sakazaki serogroup type strains. Whole Genome Map data on 91 of the 206 serogroup type strains support the hypothesis that V. cholerae has an unprecedented genetic and genomic structural diversity. Interestingly, we discovered chromosomal fusions in two unusual strains that possess a single chromosome instead of the two chromosomes usually found in V. cholerae . We also found pervasive chromosomal rearrangements such as duplications and indels in many strains. The majority of Vibrio genome sequences currently in public databases are unfinished draft sequences. The Whole Genome Mapping approach presented here enables rapid screening of large strain collections to capture genomic complexities that would not have been otherwise revealed by unfinished draft genome sequencing and thus aids in assembling and finishing draft sequences of complex genomes. Furthermore, Whole Genome Mapping allows for prediction of novel V. cholerae non-O1/non-O139 strains that may have the potential to cause future cholera outbreaks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗
DOE OSTI2018

Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products

USAID Bangladesh and the U.S. Department of Energy’s National Renewable Energy Laboratory partnered with the Government of Bangladesh to develop a national wind resource assessment. The assessment used sophisticated resource modeling that was validated by a ground measurement campaign. Results from the project included a long-term, correlated wind data set; validated high-resolution wind resource maps; and publicly available data accessible through the RE Data Explorer (https://www.re-explorer.org/). The Renewable Energy (RE) Data Explorer allows users to access and download the Bangladesh wind resource assessment data and related geographic information system (GIS) data sets and perform customized technical potential analyses. Figure ES-1 is an example wind resource map for Bangladesh that can be created with the RE Data Explorer. This project improved upon existing global data sets by using best-in-class modeling techniques and analysis. Global models are good first steps in predicting wind resources in various parts of the world, but they do not generate the accuracy needed to reduce project risk and stimulate renewable investment. This project improved the quality of modeled wind resource data for Bangladesh. In addition to the modeling effort, the project team used a multi-year, local data-collection campaign to validate the model and further improve the accuracy of the data sets. The measurement campaign consisted of nine meteorological sites representing all geographical regions of the country. Seven meteorological towers and one remote-sensing, sonic detection and ranging unit (deployed at two sites) collected the data. Site-selection criteria focused on geographic diversity and proximity to existing transmission lines. Figure ES-1 shows measurement locations. The measurement campaign spanned June 2014 through December 2017. The results of the Bangladesh wind resource assessment will help Bangladesh overcome significant energy challenges. Its energy sector suffers from power shortages, increasing demand, decreasing domestic natural gas reserves, and inadequate transmission infrastructure. As part of a comprehensive plan to overcome these challenges, Bangladesh has committed that 10% of the total generation capacity will be renewable energy by 2021 (Power Division 2016). High-quality renewable energy resource data and other GIS data, such as those developed in this assessment, are important if Bangladesh wishes to reach its 10% renewable energy capacity target. These data support informed decision making, ranging from policy and investment decisions to reliable power sector planning. Specifically, the Bangladesh wind resource assessment will help reduce technical risk and encourage private-sector interest in the nascent wind market in Bangladesh. In addition, the Government of Bangladesh may use the results of the wind resource assessment to develop well-designed policies that could encourage investment in wind energy. This report provides a comprehensive description of the Bangladesh wind resource assessment, including details on the modeling approach and methods, instrumentation, data quality-control techniques, and resulting data sets.

17 WIND ENERGY↗
NASA NTRS2019

The SkyView Data Model: Handling Heterogeneous Data Using Dynamic Software

The SkyView Virtual Telescope allows users to query over 75 distinct datasets of astronomical imagery including more than 190 distinct surveys. These holdings are extraordinarily diverse ranging over more than 15 orders of magnitude in frequency and5 orders of magnitude in resolution. To accommodate supporting these data, SkyView has developed a simple data model for surveys. Nor surprisingly, given the diverse origins of the datasets included, some surveys do not easily fit within this model. The conventional approach to dealing with such outliers is to build a layer on top of the actual data which more faithfully adheres to the data model. With SkyView we have used a somewhat alternative approach. We allow the data model to define new behaviors in the SkyView code itself either by customizing the use of existing capabilities or pointing to new classes that have non-standard behavior. This approach is feasible since in modern computer languages the code that is run can be selected dynamically with new classes completely overriding traditional behavior. This paper describes this approach and considers how it may become more useful as our software structures become increasingly dynamically linked.

Thomas McGlynn↗