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Big Data in nuclear comes in the form of many structures, systems, components, data streams, data types, and more.
The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.
We extend and correct a recently proposed maximum-likelihood halo-independent method to analyze unbinned direct dark matter detection data. Instead of the recoil energy as independent variable we use the minimum speed a dark matter particle must have to impart a given recoil energy to a nucleus. This has the advantage of allowing us to apply the method to any type of target composition and interaction, e.g. with general momentum and velocity dependence, and with elastic or inelastic scattering. We prove the method and provide a rigorous statistical interpretation of the results. As first applications, we find that for dark matter particles with elastic spin-independent interactions and neutron to proton coupling ratio f{sub n}/f{sub p}=−0.7, the WIMP interpretation of the signal observed by CDMS-II-Si is compatible with the constraints imposed by all other experiments with null results. We also find a similar compatibility for exothermic inelastic spin-independent interactions with f{sub n}/f{sub p}=−0.8.
Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.
Using the science verification data of the Dark Energy Survey for a new sample of 106 X-ray selected clusters and groups, we study the stellar mass growth of bright central galaxies (BCGs) since redshift z ~ 1.2. Compared with the expectation in a semi-analytical model applied to the Millennium Simulation, the observed BCGs become under-massive/under-luminous with decreasing redshift. Here, we incorporate the uncertainties associated with cluster mass, redshift, and BCG stellar mass measurements into an analysis of a redshift-dependent BCG-cluster mass relation, $m_*$ $∝ \huge{(}$$\frac{M_{200}}{1.5x10^{14}M_⊙}\huge{)}$$^{0.24±0.08}$ $(1 + z)$$^{-0.19±0.34}$, and compare the observed relation to the model prediction. We estimate the average growth rate since z = 1.0 for BCGs hosted by clusters of M 200,z = 10 13.8 M ⊙ ; at z = 1.0: m * , BCG appears to have grown by 0.13 ± 0.11 dex, in tension at the ~2.5σ significance level with the 0.40 dex growth rate expected from the semi-analytic model. We show that the build-up of extended intracluster light after z = 1.0 may alleviate this tension in BCG growth rates.
Replacing fossil fuels with renewable fuels derived from lignocellulosic biomass can contribute to the mitigation of global warming and the economic development of rural communities. This will require lignocellulosic biofuels to become price competitive with fossil fuels. Techno-economic analyses can provide insights into which parts of the biofuel production process need to be optimized to reduce cost or energy use. Here, we used data obtained from a pilot biorefinery to model a commercial-scale biorefinery that processes lignocellulosic biomass to ethanol, with a focus on the minimum ethanol selling price (MESP). The process utilizes a phosphoric acid-catalyzed pre-treatment of sweet sorghum bagasse followed by liquefaction and simultaneous saccharification and co-fermentation (L+SScF) of hexose and pentose sugars by an engineered Escherichia coli strain. After validating a techno-economic model developed with the SuperPro Designer software for the conversion of sugarcane bagasse to ethanol by comparing it to a published Aspen Plus model, six different scenarios were modeled for sweet sorghum bagasse Under the most optimistic scenario, the ethanol can be produced at a cost close to the energy-equivalent price of gasoline. Aside from an increase in the price of gasoline, the gap between ethanol and gasoline prices could also be bridged by either a decrease in the cost of cellulolytic enzymes or development of value-added products from lignin.
Abstract Not Provided
This data package contains data from terrestrial laser scanning (TLS) at the Pasoh Forest Reserve, Malaysia. The Pasoh Forest Reserve is a facility of the Forest Research Institute Malaysia, and contains evergreen lowland dipterocarp forest. The Next-Generation Ecosystem Experiments Tropics (NGEE-Tropics) study areas at Pasoh were established to study how different species respond to climatic variation and soil water availability. Two study areas were chosen representing different topography and species. The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree-level characterization of woody structure and leaf area for 12 focal trees with FloraPulse and sap flux sensors, facilitating estimation of woody biomass and leaf area to allow upscaling of water content and transpiration data to the tree-level. Scan positions were not selected to provide consistent data for non-focal trees with the study areas. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.
This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.
LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem(BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications. Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem(BSVE).While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accessible. We conducted a survey of the national notifiable disease reporting systems notating how the data is reported in two important dimensions: date standards and case definitions. The development of software to regularly ingests notifiable disease data frand makes this data available involved four main steps scraping, extracting, parsing and persisting. For scraping: we would examine website designs and determine reporting mechanisms for each country/website as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of report pdf files, for each country. We stored report pdfs along with appropriate metadata for extracting and parsing. For extracting: we developed software that can extract notifiable disease data presented in tabular form from a pdf file. We combined the methodology of figure placement detection with the in-house developed table extraction and annotation heuristics. For parsing: we determined what to extract from each pdf dataset from the survey conducted. We then parsed the extracted data into uniform data structures correctly accommodating the dimensions surveyed and the various human languages. This task involved ingesting notifiable disease data in many disparate formats extracted from pdf files and coalescing the data into a standardized format. For persisting: We then store the data in the Epi Archive PostgreSQL database and make it available through the BSVE. The EpiArchive tool currently contains subnational notifiable disease data from 10 nations. When a user accesses the EpiArchive site, they are prompted with four fields: country, region, disease, and date duration. These fields allow the user to specify the location(down to the state level), the disease of interest, and the duration of interest. Upon form submission, a time series is generated from the users’ specifications. The generated time series can then be downloaded into a csv file if a user is interested in performing personal analysis. Additionally, the data from EpiArchive can be reached through an API. LANL as part of a currently funded DTRA effort so that it will automatically and continuously collect global notifiable disease data—particularly data stored in pdf files—and make it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will provide data to analytics and users that will improve the prediction and early warning of disease events and other applications.
Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.
The 2006 evaluation of A=140 was updated using experimental results published before November 2018 from the various reaction and decay studies leading to the nuclei {sup 140}Sb, {sup 140}Te, {sup 140}I, {sup 140}Xe, {sup 140}Cs, {sup 140}Ba, {sup 140}La, {sup 140}Ce, {sup 140}Pr, {sup 140}Nd, {sup 140}Pm, {sup 140}Sm, {sup 140}Eu, {sup 140}Gd, {sup 140}Tb, {sup 140}Dy, {sup 140}Ho. These data are summarized and presented, together with adopted level schemes and properties.
Background In 2022, the Office of Science and Technology Policy (OSTP) issued a memo that significantly reshaped the landscape of access to federally funded research. The memo mandated that all taxpayer-funded research be made available to the public without delay upon publication, without an embargo period, superseding the 2013 OSTP public access policy. This public access policy promotes transparency and the democratization of knowledge, ensuring that the fruits of scientific endeavors funded by federal agencies could be immediately accessed and built upon by scientists, educators, students, and the public at large. To implement the requirements of the OSTP guidance and DOE Public Access Plan, the Office of Nuclear Energy (NE) has implemented public access plan guidance and has identified several areas where better data management practices would further expand public access to important nuclear energy related scientific data, reports, and other technical products. Significant NE supported efforts are already underway for data management and public access to important nuclear energy related data.1 2 To address gaps in data management practices, and improve retention and accessibility of data, NE is actively exploring enhanced data management options utilizing its high-performance computing resources administered by its Nuclear Scientific User Facility Program. A newly piloted system, the Nuclear Research Data System (NRDS) acts as a portal for data collection and dissemination. Nuclear Energy University Program Research and Development Portfolio According to Web of Science, NEUP has produced 2,345 journal publication that have been cited more than 61,000 times3 and countless conference proceedings. These publications are publicly available through OSTI.gov and in the open literature. Additional scientific and technical products including project milestones that are not publications and NEUP project final reports are vetted through OSTI.gov and released once reviewed and approved by DOE. Since 2009, NEUP has awarded close to 1,000 different R&D projects in technical areas across the NE research programs. As of June 2023, 512 NEUP reports are publicly available on OSTI. The underlying data for projects is still held at universities, and data transfer, co-location, and dissemination has not occurred in a systematic way. NEUP data is currently accessible through myriad university-based data repositories, or through direct requests to PIs. The program identified this patchwork of repositories, or often lack of publicly available data, as a significant barrier to an organized, accessible, and comprehensive solution to sharing data with the larger nuclear energy community. Approach The goal of this pilot project is to establish a pathway to a consolidated long-term repository for NEUP project data. To accomplish this goal, the pilot strives to accomplish the following objectives: Establish data collection standards, including a standard set of required supplementary information to contextualize and support raw data files. Work with the HPC group collect and upload information and to modify the NRDS system, as needed, to support a standardized approach. Resolve potential barriers to successful roll out of an expanded data collection strategy, including modifying data management plan guidelines and establishing a document and data release process that accounts for potential intellectual property and/or export control concerns. Results Overall, the pilot was successful in collecting 8,982 raw and processes data files, 220 reports, 56 calibration files, and 5,931 other supplementary documents. Supplementary documents included experimental plans, methods, journal publications and conference proceedings, milestone reports, and final reports. Figure 2 shows the number of data sets and supplementary project information provided by each project. Projects has significantly different input, depending on experimental data produced and completeness of the datasets provided.
Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.
A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. As a result, we have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.
Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.