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At least 235 records · Page 13

DOE Geothermal Data Repository - Tethering Data to Information: Preprint

Data are not inherently information. Without context, data are just numbers, figures, names, or points on a line. By assigning context to data, we can validate ideas, form opinions, and generate knowledge. This is an important distinction to information scientists, as we recognize that the context in which we keep our data plays a big part in generating its value. The mechanisms used to assign this context often include their own data, supplemental to the data being described and defining semantic relationships, commonly referred to as metadata. This paper provides the status of the DOE Geothermal Data Repository (DOE GDR), including recent efforts to tether data submissions to information, discusses the important distinction between data and information, outlines a path to generate useful knowledge from raw data, and details the steps taken in order to become a node on the National Geothermal Data System (NGDS).

15 GEOTHERMAL ENERGY↗

Data Exploration Toolkit for serial diffraction experiments

This paper describes a set of tools allowing experimentalists insight into the variation present within large serial data sets. Ultrafast diffraction at X-ray free-electron lasers (XFELs) has the potential to yield new insights into important biological systems that produce radiation-sensitive crystals. An unavoidable feature of the ‘diffraction before destruction’ nature of these experiments is that images are obtained from many distinct crystals and/or different regions of the same crystal. Combined with other sources of XFEL shot-to-shot variation, this introduces significant heterogeneity into the diffraction data, complicating processing and interpretation. To enable researchers to get the most from their collected data, a toolkit is presented that provides insights into the quality of, and the variation present in, serial crystallography data sets. These tools operate on the unmerged, partial intensity integration results from many individual crystals, and can be used on two levels: firstly to guide the experimental strategy during data collection, and secondly to help users make informed choices during data processing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Enhancing the Impact of Science Data: Toward Data Discovery and Reuse

The amount of data produced in support of scientific research continues to grow rapidly. Despite the accumulation and demand for scientific data, relatively little data are actually made available for the broader scientific community. We surmise that one root of this problem is the perceived difficulty of electronically publishing scientific data and associated metadata in a way that makes it discoverable. We propose exploiting Semantic Web technologies and best practices to make metadata both discoverable and easy to publish. We share experiences in curating metadata to illustrate the cumbersome nature of data reuse in the current research environment. We also make recommendations with a real-world example of how data publishers can provide their metadata by adding limited additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery, access, and sharing can be greatly reduced and the impact of research data greatly enhanced.

Data Curation↗

Use of genomic data in risk assessment case study: I. Evaluation of the dibutyl phthalate male reproductive development toxicity data set

A case study was conducted, using dibutyl phthalate (DBP), to explore an approach to using toxicogenomic data in risk assessment. The toxicity and toxicogenomic data sets relative to DBP-related male reproductive developmental outcomes were considered conjointly to derive information about mode and mechanism of action. In this manuscript, we describe the case study evaluation of the toxicological database for DBP, focusing on identifying the full spectrum of male reproductive developmental effects. The data were assessed to 1) evaluate low dose and low incidence findings and 2) identify male reproductive toxicity endpoints without well-established modes of action (MOAs). These efforts led to the characterization of data gaps and research needs for the toxicity and toxicogenomic studies in a risk assessment context. Further, the identification of endpoints with unexplained MOAs in the toxicity data set was useful in the subsequent evaluation of the mechanistic information that the toxicogenomic data set evaluation could provide. The extensive analysis of the toxicology data set within the MOA context provided a resource of information for DBP in attempts to hypothesize MOAs (for endpoints without a well-established MOA) and to phenotypically anchor toxicogenomic and other mechanistic data both to toxicity endpoints and to available toxicogenomic data. This case study serves as an example of the steps that can be taken to develop a toxicological data source for a risk assessment, both in general and especially for risk assessments that include toxicogenomic data.

60 APPLIED LIFE SCIENCES↗

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗

AmeriFlux BASE data pipeline to support network growth and data sharing

Abstract AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Data Placement Optimization for ATLAS in a Multi-Tiered Storage System within a Data Center

Scientific experiments and computations, especially in High Energy Physics, are generating and accumulating data at an unprecedented rate. Effectively managing this vast volume of data while ensuring efficient data analysis poses a significant challenge for data centers, which must integrate various storage technologies. This paper proposes addressing this challenge by designing and developing a precise data popularity prediction model utilizing state-of-theart AI/ML techniques. This model is crafted from the analysis of ATLAS data and access patterns. It enables us to migrate infrequently accessed data to more economical storage media, such as tape drives, while storing frequently accessed data on faster yet costlier storage media like HDD or SSD. This strategic approach ensures data is placed optimally into the appropriate storage classes, thereby maximizing storage capacity while minimizing data access latency for end-users. Furthermore, the paper includes a performance evaluation of the prediction model using various key metrics such as F1 score, accuracy, precision and recall. Finally, we present a prototype use case, leveraging real-world file access data to assess the model’s impact on performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data

Purpose: Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Methods: Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Results: Distributions of the parameter data were shown to be robust and derive from a student’s t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. Conclusions: IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist’s acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

60 APPLIED LIFE SCIENCES↗

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

79 ASTRONOMY AND ASTROPHYSICS↗

The Modern Research Data Portal: A Design Pattern for Networked, Data-Intensive Science

Here we describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Systems and methods of generating a plurality of data visualizations in a data visualization region

An exemplary method of generating a plurality of data visualizations comprises displaying a graphical user interface that includes a schema information region and a data visualization region. The method further includes receiving user actions to associate first, second, and third fields included in the schema information region with a plurality of shelves of the data visualization region. The method further includes generating a plurality of data visualizations in the data visualization region in accordance with the received user actions, each data visualization includes a plurality of data marks corresponding to a respective retrieved tuple of data having the first, second, and third fields. Moreover, each of the data marks has an x-position and y-position defined according to data for the first and second fields, respectively. In addition, each of the data visualizations corresponds to a respective unique value of the third field, and is displayed in a respective pane.

97 MATHEMATICS AND COMPUTING↗

Putting Data to Work: Using Building Energy Performance Data to Expand the Market for Energy Efficiency in Buildings

An increasing number of state and local jurisdictions are implementing building performance reporting laws, which generate large quantities of useful data on the characteristics and resource consumption of the building stock. Yet, to realize the potential of these policies, the data must not only be disclosed, but put to work to drive energy savings. Under a three-year pilot, Washington DC (DC), New York City (NYC) and their partners are pioneering the use of data from building performance reporting in energy efficiency programs. To minimize the administrative burden of managing, combining, and sharing these data sets, the cities are utilizing the U.S. Department of Energy’s (DOE) open-source Standard Energy Efficiency Data (SEED) Platform. The Putting Data to Work project team is working with efficiency program administrators to develop and implement new and innovative ways in which the data collected through benchmarking, energy audits, and related policies can be used to improve energy policies and planning, unlock data directly for market use, scale-up the market for energy efficiency services, drive competition, better target utility incentive programs, and inform measurement and verification. This paper details achievements and key findings in DC and NYC to date, including the importance of high compliance, data quality, and data cleansing in using the information collected; methods that the cities are using to apply data to drive maximum energy efficiency; and the importance of inter- and intra-agency collaboration in program success. We also outline the path forward and detail expected outcomes and scalability of project activities.

Beddingfield, Erin↗

Systems and methods of generating data marks in data visualizations

An example method of displaying a data visualization includes displaying a graphical user interface that includes three distinct regions, and receiving user actions to associate two different fields with two different shelves. The method also includes generating, in accordance with the received user actions, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying a data visualization that includes the generated data marks.

Stolte, Chris↗

Systems and methods of generating data marks in data visualizations

An example method of displaying a data visualization includes displaying a plurality of selectable fields and receiving user selections of two different fields from the plurality of selectable fields. The method also includes generating, in accordance with the received user selections, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying the data visualization that includes the generated data marks.

97 MATHEMATICS AND COMPUTING↗

Geospatial characterization of low-temperature heating and cooling demand in residential, commercial, manufacturing, agricultural, and data center sectors for potential geothermal applications in the United States

Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. Here, this study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.

15 GEOTHERMAL ENERGY↗

A modern Monte Carlo investigation of the TG-43 dosimetry parameters for an {sup 125}I seed already having AAPM consensus data

Purpose: To investigate potential causes for differences in TG-43 brachytherapy dosimetry parameters in the existent literature for the model IAI-125A{sup 125}I seed and to propose new standard dosimetry parameters. Methods: The MCNP5 code was used for Monte Carlo (MC) simulations. Sensitivity of dose distributions, and subsequently TG-43 dosimetry parameters, was explored to reproduce historical methods upon which American Association of Physicists in Medicine (AAPM) consensus data are based. Twelve simulation conditions varying{sup 125}I coating thickness, coating mass density, photon interaction cross-section library, and photon emission spectrum were examined. Results: Varying{sup 125}I coating thickness, coating mass density, photon cross-section library, and photon emission spectrum for the model IAI-125A seed changed the dose-rate constant by up to 0.9%, about 1%, about 3%, and 3%, respectively, in comparison to the proposed standard value of 0.922 cGy h{sup −1} U{sup −1}. The dose-rate constant values by Solberg et al. [“Dosimetric parameters of three new solid core {sup 125}I brachytherapy sources,” J. Appl. Clin. Med. Phys. 3, 119–134 (2002)], Meigooni et al. [“Experimental and theoretical determination of dosimetric characteristics of IsoAid ADVANTAGE™ {sup 125}I brachytherapy source,” Med. Phys. 29, 2152–2158 (2002)], and Taylor and Rogers [“An EGSnrc Monte Carlo-calculated database of TG-43 parameters,” Med. Phys. 35, 4228–4241 (2008)] for the model IAI-125A seed and Kennedy et al. [“Experimental and Monte Carlo determination of the TG-43 dosimetric parameters for the model 9011 THINSeed™ brachytherapy source,” Med. Phys. 37, 1681–1688 (2010)] for the model 6711 seed were +4.3% (0.962 cGy h{sup −1} U{sup −1}), +6.2% (0.98 cGy h{sup −1} U{sup −1}), +0.3% (0.925 cGy h{sup −1} U{sup −1}), and −0.2% (0.921 cGy h{sup −1} U{sup −1}), respectively, in comparison to the proposed standard value. Differences in the radial dose functions between the current study and both Solberg et al. and Meigooni et al. were <10% for r ≤ 5 cm, and increased for r > 5 cm with a maximum difference of 29% at r = 9 cm. In comparison to Taylor and Rogers, these differences were lower (maximum of 2% at r = 9 cm). For the similarly designed model 6711 {sup 125}I seed, differences did not exceed 0.5% for 0.5 ≤ r ≤ 10 cm. Radial dose function values varied by 1% as coating thickness and coating density were changed. Varying the cross-section library and source spectrum altered the radial dose function by 25% and 12%, respectively, but these differences occurred at r = 10 cm where the dose rates were very low. The 2D anisotropy function results were most similar to those of Solberg et al. and most different to those of Meigooni et al. The observed order of simulation condition variables from most to least important for influencing the 2D anisotropy function was spectrum, coating thickness, coating density, and cross-section library. Conclusions: Several MC radiation transport codes are available for calculation of the TG-43 dosimetry parameters for brachytherapy seeds. The physics models in these codes and their related cross-section libraries have been updated and improved since publication of the 2007 AAPM TG-43U1S1 report. Results using modern data indicated statistically significant differences in these dosimetry parameters in comparison to data recommended in the TG-43U1S1 report. Therefore, it seems that professional societies such as the AAPM should consider reevaluating the consensus data for this and others seeds and establishing a process of regular evaluations in which consensus data are based upon methods that remain state-of-the-art.

61 RADIATION PROTECTION AND DOSIMETRY↗

Data reduction for low energy nuclear physics experiments using data frames

Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint

With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.

building energy data↗