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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Maps of the Magellanic clouds from combined South Pole Telescope and Planck data

Here, we present maps of the Large and Small Magellanic Clouds from combined South Pole Telescope (SPT) and Planck data. Both instruments are designed to make measurements of the cosmic microwave background but are sensitive to any source of millimeter-wave (mm-wave) emission. The Planck satellite observes in nine mm-wave bands, while the SPT data used in this work were taken with the three-band SPT-SZ camera. The SPT-SZ bands correspond closely to three of the nine Planck bands, namely those centered at 1.4, 2.1, and 3.0 mm. The angular resolution of the Planck data in these bands ranges from 5 to 10 arcmin, while the SPT resolution in these bands ranges from 1.0 to 1.7 arcmin. The combined maps take advantage of the high resolution of the SPT data and the long-timescale stability of the space-based Planck observations to deliver high signal-to-noise and robust brightness measurements on scales from the size of the maps down to ~1 arcmin. In each of the three bands, we first calibrate and color-correct the SPT data to match the Planck data, then we use noise estimates from each instrument and knowledge of each instrument's beam, or point-spread function, to make the inverse-variance-weighted combination of the two instruments' data as a function of angular scale. Furthermore, we create maps assuming a range of underlying emission spectra (for the color correction) and at a range of final resolutions. We perform several consistency tests on the combined maps and estimate the expected noise in measurements of features in the maps. Finally, we compare the maps of the Large Magellanic Cloud (LMC) from this work to maps from the Herschel HERITAGE survey, finding general consistency between the datasets. Furthermore, the broad wavelength coverage provides evidence of different emission mechanisms at work in different environments in the LMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Expediting Scientific Data Analysis with Reorganization of Data

Data producers typically optimize the layout of data files to minimize the write time. In most cases, data analysis tasks read these files in access patterns different from the write patterns causing poor read performance. In this paper, we introduce Scientific Data Services (SDS), a framework for bridging the performance gap between writing and reading scientific data. SDS reorganizes data to match the read patterns of analysis tasks and enables transparent data reads from the reorganized data. We implemented a HDF5 Virtual Object Layer (VOL) plugin to redirect the HDF5 dataset read calls to the reorganized data. To demonstrate the effectiveness of SDS, we applied two parallel data organization techniques: a sort-based organization on a plasma physics data and a transpose-based organization on mass spectrometry imaging data. We also extended the HDF5 data access API to allow selection of data based on their values through a query interface, called SDS Query. We evaluated the execution time in accessing various subsets of data through existing HDF5 Read API and SDS Query. We showed that reading the reorganized data using SDS is up to 55X faster than reading the original data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The NeXus data format

NeXus is an effort by an international group of scientists to define a common data exchange and archival format for neutron, X-ray and muon experiments. NeXus is built on top of the scientific data format HDF5 and adds domain-specific rules for organizing data within HDF5 files, in addition to a dictionary of well defined domain-specific field names. The NeXus data format has two purposes. First, it defines a format that can serve as a container for all relevant data associated with a beamline. This is a very important use case. Second, it defines standards in the form of application definitions for the exchange of data between applications. NeXus provides structures for raw experimental data as well as for processed data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Establishing a Commercial Buildings Energy Data Framework for India: A Comprehensive Look at Data Collection Approaches, Use Cases and Institutions

Enhancing energy efficiency of the commercial building stock is an important aspect of any national energy policy. Understanding how buildings use energy is critical to formulating any new policy that may impact energy use, underscoring the importance of credible data. Data enables informed decision making and good quality data is essential for policy makers to prioritize energy saving strategies and track implementation. Given the uniqueness of the buildings sector and challenges to collecting relevant energy data, this study characterizes various elements involved in pertinent data collection and management, with the specific focus on well-defined data requirements, appropriate methodologies and processes, feasible data collection mechanisms, and approaches to institutionalizing the collection process. This report starts with a comprehensive review of available examples of energy data collection frameworks for buildings across different countries. The review covers the U.S. experience in the commercial buildings sector, the European experience in the buildings sector and other data collection initiatives in Singapore and China to capture the more systematic efforts in Asia in the commercial sector. To provide context, the review includes a summary and status of disparate efforts in India to collect and use commercial building energy data. Using this review as a key input, the study developed a data collection framework for India with specific consideration to relevant use cases. Continuing with the framework for data collection, this study outlines the key performance indicators applicable to the use cases and their collection feasibility, as well as immediate priorities of the participating stakeholders. It also discusses potential considerations for data collection and the possible approaches for survey design. With the specific purpose of laying out the possible ways to structure and organize data collection institutionally, the study collates existing mechanisms to analyze building energy performance in India and opportunities for standardizing data collection. This report describes the existing capacities and resources for establishing an institutional framework for data collection, the legislation and mandates that support such activity, and identifies roles and responsibilities of the relevant ministries and organizations. Finally, the study presents conclusions and identifies two major data collection strategies within the existing legal framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

THE DATA REDUCTION PIPELINE FOR THE SDSS-IV MaNGA IFU GALAXY SURVEY

Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is an optical fiber-bundle integral-field unit (IFU) spectroscopic survey that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). With a spectral coverage of 3622–10354 Å and an average footprint of ∼500 arcsec{sup 2} per IFU the scientific data products derived from MaNGA will permit exploration of the internal structure of a statistically large sample of 10,000 low-redshift galaxies in unprecedented detail. Comprising 174 individually pluggable science and calibration IFUs with a near-constant data stream, MaNGA is expected to obtain ∼100 million raw-frame spectra and ∼10 million reduced galaxy spectra over the six-year lifetime of the survey. In this contribution, we describe the MaNGA Data Reduction Pipeline algorithms and centralized metadata framework that produce sky-subtracted spectrophotometrically calibrated spectra and rectified three-dimensional data cubes that combine individual dithered observations. For the 1390 galaxy data cubes released in Summer 2016 as part of SDSS-IV Data Release 13, we demonstrate that the MaNGA data have nearly Poisson-limited sky subtraction shortward of ∼8500 Å and reach a typical 10 σ limiting continuum surface brightness μ = 23.5 AB arcsec{sup −2} in a five-arcsecond-diameter aperture in the g -band. The wavelength calibration of the MaNGA data is accurate to 5 km s{sup −1} rms, with a median spatial resolution of 2.54 arcsec FWHM (1.8 kpc at the median redshift of 0.037) and a median spectral resolution of σ = 72 km s{sup −1}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The accurate assessment of small-angle X-ray scattering data

Small-angle X-ray scattering (SAXS) has grown in popularity in recent times with the advent of bright synchrotron X-ray sources, powerful computational resources and algorithms enabling the calculation of increasingly complex models. However, the lack of standardized data-quality metrics presents difficulties for the growing user community in accurately assessing the quality of experimental SAXS data. Here, a series of metrics to quantitatively describe SAXS data in an objective manner using statistical evaluations are defined. These metrics are applied to identify the effects of radiation damage, concentration dependence and interparticle interactions on SAXS data from a set of 27 previously described targets for which high-resolution structures have been determined via X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. Studies show that these metrics are sufficient to characterize SAXS data quality on a small sample set with statistical rigor and sensitivity similar to or better than manual analysis. The development of data-quality analysis strategies such as these initial efforts is needed to enable the accurate and unbiased assessment of SAXS data quality.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?

Data quality plays a crucial role in successful applications of synchrophasor data in power system operation and control. This paper presents the results of a data quality analysis of a multi-year field-recorded synchrophasor dataset. The analysis has identified several typical data quality issues encountered in the field data. An examination of the PMU status words included with the dataset has revealed several inconsistent implementations and the lack of correlation between the PMU data quality and the status word, which impacts the usefulness of such information. Our investigation has concluded that the status word alone as found in the recorded field dataset could not be used as a reliable indicator of data quality for field-recorded data. Several recommendations are proposed to improve the usefulness of the PMU status word.

Cheng, Zheyuan↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2019

Nuclear Data and Benchmarking Program: Nuclear Data Performance Assessment for Advanced Reactors

Over the last several Evaluated Nuclear Data File (ENDF)/B releases, many updates have been introduced to the nuclear data libraries that can significantly change computational results. These changes can be particularly important for advanced reactor concepts, which are not as thoroughly investigated as those used in light water reactor (LWR) systems. Therefore, a performance assessment of the ENDF/B libraries using the SCALE 6.2.3 code package was conducted under the auspices of the U.S., Department of Energy Office of Nuclear Energy (DOE-NE). The following systems relevant to the advanced reactor community were chosen as subject for investigation in this report: sodium cooled fast reactor systems, graphite moderated high temperature gas-cooled reactors, and several molten salt reactor models. At first, the similarity of the integral performance of the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries caused by compensating errors in important differential data was demonstrated. In calculations for a shipping container of high-assay low enriched uranium, the data of individual isotopes were systematically swapped between the two ENDF/B libraries. An eigenvalue difference of up to 450 pcm was found due to the use of 235 U and 238 U from one library and 1 H and 16 O from the other library. This clearly demonstrates a cross-correlation between reaction data sets of different isotopes within a library that should be reported in the evaluations. For sodium-cooled fast reactor (SFR) fuel assemblies, significant eigenvalue (k inf ) differences (200– 450 pcm) were found between calculations using the 2011 ENDF/B-VII.1 data and 2018 ENDF/B-VIII.0 data. These differences were mainly caused by updates of 238 U and 239 Pu neutron cross sections. Results from the multigroup (MG) calculations further revealed that the group structure of the applied MG library strongly influences the MG bias due to the importance of an appropriate energy resolution of the resonances in higher energy ranges. The application of previously used MG libraries requires new verification for advanced reactor simulation in comparisons with reference continuous-energy calculations. Nuclear data uncertainty analyses of these SFR systems resulted in eigenvalue uncertainties between 1,400 and 1,800 pcm, which are three to four times higher than corresponding uncertainties in light water reactor (LWR) systems. The main contributor to this uncertainty was found to be inelastic scattering on 238 U, which shows an uncertainty of up to 50% in the fast energy range. Other important contributors are the scattering reactions of 56 Fe and 23 Na which have so far not appeared in LWR analysis. Since the largest contribution to the eigenvalue uncertainty of SFR systems is coming from scattering reactions, it is expected that uncertainties in the angular scattering distributions also have a significant impact. Those uncertainties are, however, not available for the majority of nuclides, and the capabilities to determine sensitivities to this data are currently underdeveloped. For two graphite moderated high temperature gas-cooled reactor (HTGR) benchmarks, only the ENDF/BVII. 1 calculations resulted in consistent eigenvalues (k eff ) with the corresponding experimental measurements. Eigenvalue differences of about 1,000 pcm were found between the 2006 ENDF/B-VII.0 data and the 2011 ENDF/B-VII.1 data, primarily due to an updated carbon capture cross section in the thermal energy range in the later ENDF/B release. The ENDF/B-VIII.0 eigenvalue was larger than the ENDF/B-VII.1 eigenvalue by about 300 pcm mainly due to updates in multiple 235 U neutron cross sections, with offsetting updates in the 238 U cross sections. With ENDF/B-VIII.0, graphite can be modeled as perfect crystal or with two different porosities. The choice of the graphite evaluation can have a significant influence of the eigenvalue. For a HTGR benchmark, maximum eigenvalue differences as high as 650 pcm due to different porosities were observed. More detailed studies of the impact of the porosity are necessary while considering that the graphite porosity can vary between the used materials and changes as a function of neutron fluence. xii The uncertainty of the HTGR eigenvalues due to nuclear data uncertainties was found between 500 and 600 pcm, with the top contributor being the neutron multiplicity of 235 U. A gap in the form of missing uncertainties in graphite thermal scattering data, that might have significant impact on HTGR reactors, was identified. This gap is being investigated by the DOE-NE Nuclear Data and Benchmarking Program, as documented in a separate report The fast spectrum molten salt reactor calculations revealed similar comparisons as for the SFR assemblies. The eigenvalue uncertainties were significantly influenced by inelastic scattering on 238 U, while the impact of angular scattering distributions is unknown. For the graphite and zirconium hydride moderated system, eigenvalue uncertainties of up to 700 pcm were observed, requiring in depth analyses of the cross sections and corresponding uncertainties of the included materials. In general, it was observed that the flux spectra of the various molten salt reactor systems show significant differences, even between a fresh state and a depleted state. The choice of the energy group structure of MG calculations is therefore highly relevant; the applicability of previously used MG libraries needs to be verified. Due the unavailability of pin power measurements of advanced reactor systems, pin power calculations with both ENDF/B-VII.1 and VIII.0 data were performed for a light water reactor and compared to corresponding measurements. Both calculations show good agreement with the measurements. A comparison between the ENDF/B-VII.1 and VIII.0 results revealed small differences, mostly in the range of about 0.2%.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

The Open-source Data Inventory for Anthropogenic CO 2 , version 2016 (ODIAC2016): a global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions

The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This work describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO 2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (https://doi.org/10.17595/20170411.001).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

NATIONAL GEOTHERMAL DATA SYSTEM: AN EXEMPLAR OF OPEN ACCESS TO DATA

The formal launch of National Geothermal Data System (NGDS – www.geothermaldata.org) in 2014 will provide open access to technical geothermal-relevant data from all of the Department of Energy- sponsored geothermal development and research projects and geologic data from all 50 states. By making data easily discoverable and accessible this system will open new exploration opportunities and shorten project development. The prototype data system currently includes multiple data nodes, and nationwide data online and available to the public, indexed through a single catalog under construction at http://search.geothermaldata.org. Data from state geological surveys and partners includes more than 5 million records online, including 1.48 million well headers (oil and gas, water, geothermal), 732,000 well logs, and 314,000 borehole temperatures and is growing rapidly. There are over 250 Web services and another 138 WMS (Web Map Services) registered in the system as of August, 2013. Additional data record is being added by companion projects run by Boise State University, Southern Methodist University, and the USGS. The National Renewable Energy Laboratory is managing the Geothermal Data Repository, an NGDS node that will be a clearinghouse for data from hundreds of DOE-funded geothermal projects. NGDS is built on the US Geoscience Information Network (USGIN) data integration framework, which is a joint undertaking of the USGS and the Association of American State Geologists (AASG). NGDS is fully compliant with the White House Executive Order of May 2013, requiring all federal agencies to make their data holdings publicly accessible online in open source, interoperable formats with common core and extensible metadata. The National Geothermal Data System is being designed, built, deployed, and populated primarily with grants from the US Department of Energy, Geothermal Technologies Office. To keep this operational system sustainable after the original implementation will require four core elements: continued serving of data and applications by providers; maintenance of system operations; a governance structure; and an effective business model. Each of these presents a number of challenges currently under consideration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

XRootd, disk-based, caching proxy for optimization of data access, data placement and data replication

Following the success of the XRootd-based US CMS data federation, the AAA project investigated extensions of the federation architecture by developing two sample implementations of an XRootd, disk-based, caching proxy. The first one simply starts fetching a whole file as soon as a file open request is received and is suitable when completely random file access is expected or it is already known that a whole file be read. The second implementation supports on-demand downloading of partial files. Extensions to the Hadoop Distributed File System have been developed to allow for an immediate fallback to network access when local HDFS storage fails to provide the requested block. Both cache implementations are in pre-production testing at UCSD.

Bauerdick, L. A.T.↗