Results for “value of data” — Page 8

DOE OSTI2023

Waste Glass Property Database and Data Qualification Plan

The U.S. Department of Energy vitrification facilities currently employ glass property-composition models to ensure processability of waste streams and acceptability of the final glass products. The efficiency (e.g., process flexibility, reduced down time, and reduced cost) of waste vitrification is directly tied to the size of the waste processing envelope. To increase the size of the processing envelope, a larger database was developed. The database will improve the prediction accuracy of glass properties, leading to higher waste loadings, higher waste throughput, broader process flexibility, and reduced mission life. Some of the data in this expanded database has insufficient quality assurance (QA) rigor for use in nuclear facility operation and glass qualification. A plan was developed to qualify high-value data under the appropriate QA rigor for use at the Hanford Waste Treatment and Immobilization Plant’s High-Level Waste (HLW) Facility. This qualification plan first compares the data coverage over the composition region of interest to Hanford HLW by property. Those studies that significantly improve the data coverage in the Hanford HLW glass composition region were prioritized for qualification activities. Qualification methods including QA equivalence, peer review, data corroboration, and confirmatory testing have been assigned to each high-priority study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2018

Error Characterization and Mitigation for 16Nm MLC NAND Flash Memory Under Total Ionizing Dose Effect

A data device includes a memory having a plurality of memory cells configured to store data values in accordance with a predetermined rank modulation scheme that is optional and a memory controller that receives a current error count from an error decoder of the data device for one or more data operations of the flash memory device and selects an operating mode for data scrubbing in accordance with the received error count and a program cycles count.

Li, Yue↗

NASA NTRSDate not supplied

The Field Guide to NASA’s Life Sciences Data Repositories

For over 30 years, NASA has invested in life sciences research both in space and on the ground. Data accessibility is an important tool for researchers, and NASA has committed to preserving this vital resource for ongoing use. The Life Sciences Data Archive’s multi-center collaboration between NASA’s Johnson Space Center, Ames Research Center, and Kennedy Space Center is geared toward preserving unique and high-value data from a wide variety of disciplines, data collection methods, and species within NASA’s Life Sciences Portal (NLSP). The data generated by the Human Research Program (HRP) require a systematic approach to data preservation that accounts for diverse data sources, formats, physical storage requirements, and security and privacy protections. This poster presentation will provide a guide to the repositories where the various types of human, non-human animal, plant, and microbial data NASA generates are archived and tips for navigating these data collections. Topics will include where different types of data, metadata, and biospecimens are archived or preserved, how the federated repositories work together as a data preservation ecosystem, and how researchers can access each repository’s collections.

Robert S Beaton↗

NASA NTRSDate not supplied

A Field Guide to NASA’s Life Sciences Data Repositories

For over 30 years, NASA has invested in life sciences research both in space and on the ground. Data accessibility is an important tool for researchers, and NASA has committed to preserving this vital resource for ongoing use. The Life Sciences Data Archive’s multi-center collaboration between NASA’s Johnson Space Center, Ames Research Center, and Kennedy Space Center is geared toward preserving unique and high-value data from a wide variety of disciplines, data collection methods, and species within NASA’s Life Sciences Portal (NLSP). The data generated by the Human Research Program (HRP) require a systematic approach to data preservation that accounts for diverse data sources, formats, physical storage requirements, and security and privacy protections. This poster presentation will provide a guide to the repositories where the various types of human, non-human animal, plant, and microbial data NASA generates are archived and tips for navigating these data collections. Topics will include where different types of data, metadata, and biospecimens are archived or preserved, how the federated repositories work together as a data preservation ecosystem, and how researchers can access each repository’s collections.

LSDA↗

DOE OSTI2014

The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Automated Traffic Management System and Method

A data management system and method that enables acquisition, integration, and management of real-time data generated at different rates, by multiple heterogeneous incompatible data sources. The system achieves this functionality by using an expert system to fuse data from a variety of airline, airport operations, ramp control, and air traffic control tower sources, to establish and update reference data values for every aircraft surface operation. The system may be configured as a real-time airport surface traffic management system (TMS) that electronically interconnects air traffic control, airline data, and airport operations data to facilitate information sharing and improve taxi queuing. In the TMS operational mode, empirical data shows substantial benefits in ramp operations for airlines, reducing departure taxi times by about one minute per aircraft in operational use, translating as $12 to $15 million per year savings to airlines at the Atlanta, Georgia airport. The data management system and method may also be used for scheduling the movement of multiple vehicles in other applications, such as marine vessels in harbors and ports, trucks or railroad cars in ports or shipping yards, and railroad cars in switching yards. Finally, the data management system and method may be used for managing containers at a shipping dock, stock on a factory floor or in a warehouse, or as a training tool for improving situational awareness of FAA tower controllers, ramp and airport operators, or commercial airline personnel in airfield surface operations.

Glass, Brian J.↗

DOE OSTI2017

Statistical projections for multi-dimensional visual data exploration

When working with large, multidimensional and multivariate data, science users are frequently interested in understanding variation in data, as opposed to the actual data values. Our work focuses on exploring how a simple statistical metric, the Coefficient of Variation (or Cv), can be used in several different ways to facilitate understanding variation in large data. As a statistical measure, it offers a key advantage over more widely accepted measures like standard deviation, namely to its ability to capture local variation properties. As a multidimensional projection operator, Cv is an effective way of reducing data size while preserving the key variational signal. Visualizations produced from Cv that target conveying variation in data are highly informative, especially compared to those produced with more widely known methods. We demonstrate these ideas within the context of a two-part application case study focusing on understanding long-Term trends in the the changes in precipitation and winds in large-scale climate model ensemble output.

Nguyen, H↗

DOE OSTI2017

Optimization of Error-Bounded Lossy Compression for Hard-to-Compress HPC Data

Since today’s scientific applications are producing vast amounts of data, compressing them before storage/transmission is critical. Results of existing compressors show two types of HPC data sets: highly compressible and hard to compress. In this work, we carefully design and optimize the error-bounded lossy compression for hard-tocompress scientific data. We propose an optimized algorithm that can adaptively partition the HPC data into best-fit consecutive segments each having mutually close data values, such that the compression condition can be optimized. Another significant contribution is the optimization of shifting offset such that the XOR-leading-zero length between two consecutive unpredictable data points can be maximized. We finally devise an adaptive method to select the best-fit compressor at runtime for maximizing the compression factor. We evaluate our solution using 13 benchmarks based on real-world scientific problems, and we compare it with 9 other state-of-the-art compressors. Experiments show that our compressor can always guarantee the compression errors within the user-specified error bounds. Most importantly, our optimization can improve the compression factor effectively, by up to 49% for hard-tocompress data sets with similar compression/decompression time cost.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Laying The Foundations for FAIR-ER Science: ISA And LSDA Data Submission Process in NASA’s Evolving Data Management Environment

The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.

Data submission↗

NASA NTRSDate not supplied

Laying the Foundations for FAIR-er Science: ISA and the LSDA Data Submission Process in NASA's Evolving Data Management Environment

The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.

LSDA↗

DOE OSTI2018

daft [SWR-17-53]

Daft is a Haskell library that implements the key constructs of relational algebra via static structural typing. Its DataCube typeclass supports selection, projection, aggregation, disaggregation, natural joins, semijoins, and antijoins. Instances of DataCube include both map- and function-based representations. An additional abstract data type provides Vinyl-style, keyed relational tables for which an embedded domain-specific language compactly represents relational operations. Utility types and functions support serialization to tab-separated-value data files, JSON arrays, MongoDB documents, and PostgreSQL tables, including automatic detection and verification of the schema for the input data.

Bush, BrianW.↗

NASA NTRS1985

Study of spectral/radiometric characteristics of the Thematic Mapper for land use applications

Progress during ERIM's tenth quarter of effort under the LANDSAT-4 and 5 Image Data Quality Assessment program for the Thematic Mapper is described. Coincident LANDSAT-4 and 5 fully corrected (CCT-PT) TM data are analyzed in more detail and revised band-by-band relationships between the two sensors derived. An analysis technique employing the matching of cumulative distributions is developed and used and is believed to offer advantages over the histogram matching procedure currently used to produce LANDSAT data. Multiplicative factors ranging from 0.987 to 1.145 and offsets ranging from -2.7 to -6.2 video quantum levels are required to cause LANDSAT-5 data to match LANDSAT-4 data values. Evidence of low level clipping is found in TM Bands 5 and 7 of LANDSAT-5 but not LANDSAT-4. Analysis of the information content of LANDSAT TM and MSS data is continued. Components of information loss are identified and quantified and the effects of coarsened quantization are explored.

Malila, W. A.↗

DOE OSTI2022

Nuclear Data Sheets for A=269,273,277,281,285,289, and 293

Herein, information on the production, identification, half-lives and decay modes for experimentally known nuclides with mass numbers of 269, 273, 277, 281, 285, 289, and 293 are presented along with the recommended values. Data here supersede data from the previous evaluation in this region, 2005Gu33. Since the previous evaluation many new and conclusive experiments have explored the region of superheavy nuclei, thus significantly expanding our knowledge of the nuclear chart in this area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗

NASA NTRS1981

Anthropometric data error detecting and correction with a computer

Data obtained with automated anthropometric data aquisition equipment was examined for short term errors. The least squares curve fitting technique was used to ascertain which data values were erroneous and to replace them, if possible, with corrected values. Errors were due to random reflections of light, masking of the light rays, and other types of optical and electrical interference. It was found that the signals were impossible to eliminate from the initial data produced by the television cameras, and that this was primarily a software problem requiring a digital computer to refine the data off line. The specific data of interest was related to the arm reach envelope of a human being.

Chesak, D. D.↗

DOE OSTI2025

Water quality data collected from the Muskegon River (MI, USA) using AquaBOT June-July 2024.

This dataset contains processed output from AquaBOT, which combines 30-second interval measurements of GPS location and YSI water quality parameters (temperature (degrees Celsius), dissolved oxygen (mg/l), specific conductance(microSiemens/cm at 25 degrees Celsius), and turbidity (NTU)), for 3 sections of the Muskegon River in Michigan, USA. The file aquabot2024_MuskegonRiver.csv has information on locations, dates and times, and observations. The data were processed by removing observations recorded before and after AquaBOT was in the water and observations where no data values were recorded. No other QA/QC was done. This research was performed as part of the DOE Research Development and Partnership Pilot award “Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region”, which aims to expand collaborations between researchers at Central Michigan University and U.S. Department of Energy labs and projects focused on enhancing climate resilience in Great Lakes communities and ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗