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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
NASA NTRS1989

Space data management at the NSSDC (National Space Sciences Data Center): Applications for data compression

The National Space Science Data Center (NSSDC), established in 1966, is the largest archive for processed data from NASA's space and Earth science missions. The NSSDC manages over 120,000 data tapes with over 4,000 data sets. The size of the digital archive is approximately 6,000 gigabytes with all of this data in its original uncompressed form. By 1995 the NSSDC digital archive is expected to more than quadruple in size reaching over 28,000 gigabytes. The NSSDC digital archive is expected to more than quadruple in size reaching over 28,000 gigabytes. The NSSDC is beginning several thrusts allowing it to better serve the scientific community and keep up with managing the ever increasing volumes of data. These thrusts involve managing larger and larger amounts of information and data online, employing mass storage techniques, and the use of low rate communications networks to move requested data to remote sites in the United States, Europe and Canada. The success of these thrusts, combined with the tremendous volume of data expected to be archived at the NSSDC, clearly indicates that innovative storage and data management solutions must be sought and implemented. Although not presently used, data compression techniques may be a very important tool for managing a large fraction or all of the NSSDC archive in the future. Some future applications would consist of compressing online data in order to have more data readily available, compress requested data that must be moved over low rate ground networks, and compress all the digital data in the NSSDC archive for a cost effective backup that would be used only in the event of a disaster.

Green, James L.↗
NASA NTRS2017

Collaborative Data Publication Utilizing the Open Data Repository's (ODR) Data Publisher

Introduction: For small communities in diverse fields such as astrobiology, publishing and sharing data can be a difficult challenge. While large, homogenous fields often have repositories and existing data standards, small groups of independent researchers have few options for publishing standards and data that can be utilized within their community. In conjunction with teams at NASA Ames and the University of Arizona, the Open Data Repository's (ODR) Data Publisher has been conducting ongoing pilots to assess the needs of diverse research groups and to develop software to allow them to publish and share their data collaboratively. Objectives: The ODR's Data Publisher aims to provide an easy-to-use and implement software tool that will allow researchers to create and publish database templates and related data. The end product will facilitate both human-readable interfaces (web-based with embedded images, files, and charts) and machine-readable interfaces utilizing semantic standards. Characteristics: The Data Publisher software runs on the standard LAMP (Linux, Apache, MySQL, PHP) stack to provide the widest server base available. The software is based on Symfony (www.symfony.com) which provides a robust framework for creating extensible, object-oriented software in PHP. The software interface consists of a template designer where individual or master database templates can be created. A master database template can be shared by many researchers to provide a common metadata standard that will set a compatibility standard for all derivative databases. Individual researchers can then extend their instance of the template with custom fields, file storage, or visualizations that may be unique to their studies. This allows groups to create compatible databases for data discovery and sharing purposes while still providing the flexibility needed to meet the needs of scientists in rapidly evolving areas of research. Research: As part of this effort, a number of ongoing pilot and test projects are currently in progress. The Astrobiology Habitable Environments Database Working Group is developing a shared database standard using the ODR's Data Publisher and has a number of example databases where astrobiology data are shared. Soon these databases will be integrated via the template-based standard. Work with this group helps determine what data researchers in these diverse fields need to share and archive. Additionally, this pilot helps determine what standards are viable for sharing these types of data from internally developed standards to existing open standards such as the Dublin Core (http://dublincore.org) and Darwin Core (http://rs.twdg.org) metadata standards. Further studies are ongoing with the University of Arizona Department of Geosciences where a number of mineralogy databases are being constructed within the ODR Data Publisher system. Conclusions: Through the ongoing pilots and discussions with individual researchers and small research teams, a definition of the tools desired by these groups is coming into focus. As the software development moves forward, the goal is to meet the publication and collaboration needs of these scientists in an unobtrusive and functional way.

easy to use and implement software tool↗
NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗
NASA NTRS1983

Data management support for selected climate data sets using the climate data access system

The functional capabilities of the Goddard Space Flight Center (GSFC) Climate Data Access System (CDAS), an interactive data storage and retrieval system, and the archival data sets which this system manages are discussed. The CDAS manages several climate-related data sets, such as the First Global Atmospheric Research Program (GARP) Global Experiment (FGGE) Level 2-b and Level 3-a data tapes. CDAS data management support consists of three basic functions: (1) an inventory capability which allows users to search or update a disk-resident inventory describing the contents of each tape in a data set, (2) a capability to depict graphically the spatial coverage of a tape in a data set, and (3) a data set selection capability which allows users to extract portions of a data set using criteria such as time, location, and data source/parameter and output the data to tape, user terminal, or system printer. This report includes figures that illustrate menu displays and output listings for each CDAS function.

Reph, M. G.↗
NASA NTRSDate not supplied

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗
DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

DOE↗
DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

access↗
DOE OSTI2022

Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Why We Do What We Do: Data Reuse, Open Access and Privacy in Data Management at the Life Sciences Data Archive

As custodians of the unique and irreplaceable collections of human subject research data generated by the Human Research Program and its predecessors throughout the agency’s history, the Life Sciences Data Archive (LSDA) is charged with protecting participants’ privacy and implementing their consent decisions as it provides retrospective data for use in new studies. This active, stewardship-focused approach to data management and preservation shapes the products that LSDA provides to researchers and the responsibilities of researchers in using the data and publishing their results. This presentation reviews how federal and agency mandates shape LSDA’s data management procedures and expectations for researchers. Topics covered will include LSDA’s movement towards implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and the archive’s evolving data management practices; collaboration between LSDA and the Lifetime Surveillance of Astronaut Health (LSAH) project (the repository of astronaut medical data); LSDA’s response to the challenges of performing its stewardship role and maintaining trust given the public profiles of the subjects whose data it preserves; and the ever-increasing challenges to expectations of subject privacy stemming from the growing power and ubiquity of data analysis and aggregation tools.

data management↗
NASA NTRS2018

The Land Surface Data Toolkit (LDT v7.2) - A Data Fusion Environment for Land Data Assimilation Systems

The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

droughts and floods↗
NASA NTRS2025

Why We Do What We Do: Data Reuse, Open Access, and Privacy in Data Management at the Life Sciences Data Archive

As custodian of the unique and irreplaceable collections of human subject research data generated by the Human Research Program and its predecessors throughout the agency’s history, the Life Sciences Data Archive (LSDA) is charged with protecting participants’ privacy and implementing their consent decisions as it provides retrospective data for use in new studies. This active, stewardship-focused approach to data management and preservation shapes the products that LSDA provides to researchers and the responsibilities of researchers in using the data and publishing their results. This presentation reviews how federal and agency mandates shape LSDA’s data management procedures and expectations for researchers. Topics covered will include LSDA’s movement towards implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and how the archive’s evolving data management practices support FAIR-ness; collaboration between LSDA and the Lifetime Surveillance of Astronaut Health (LSAH) project (the repository of astronaut medical data); LSDA’s response to the challenges of performing its stewardship role and maintaining trust given the public profiles of the subjects whose data it preserves; and the ever-increasing challenges to expectations of subject privacy stemming from the growing power and ubiquity of of data analysis and aggregation tools.

Data↗
DOE OSTI2022

Data for: Patel et al. Carbon flux estimates are sensitive to data source: A comparison of field and lab temperature sensitivity data

This dataset contains data and code used for the paper "Carbon flux estimates are sensitive to data source: A comparison of field and lab temperature sensitivity data" [DOI COMING SOON]A large literature exists on mechanisms driving soil production of the greenhouse gases CO2 and CH4. Measurements of these gases’ fluxes are often performed using closed-chamber incubations in the laboratory or in situ, i.e., in the field. Although it is common knowledge that measurements obtained through field studies vs. laboratory incubations can diverge because of the vastly different conditions of these environments, few studies have systematically examined these patterns. It is crucial to understand the magnitude and reasons for any differences, as these data are used to parametrize and benchmark ecosystem- to global-scale models, which are then susceptible to the biases of the source data. Here, we specifically examine how greenhouse gas measurements may be influenced by whether the measurement/incubation was conducted in the field vs. laboratory, focusing on CO2 and CH4 measurements. We use Q10 of greenhouse gas flux (temperature sensitivity) for our analyses, because of the ubiquity of this metric in biological and Earth system sciences and its importance to many modeling frameworks. We predicted that laboratory measurements would be less variable, but also less representative of true field conditions. However, there was greater variability in the Q10 values calculated from lab-based measurements of CO2 fluxes, because lab experiments explore extremes rarely seen in situ, and reflect the physical and chemical disturbances occurring during sampling, transport, and incubation. Overall, respiration Q10 values were significantly greater in laboratory incubations (mean = 4.19) than field measurements (mean = 3.05), with strong influences of incubation temperature and climate region/biome. However, this was in part because field measurements typically represent total respiration (Rs), whereas lab incubations typically represent heterotrophic respiration (Rh), making direct comparisons difficult to interpret. Focusing only on Rh-derived Q10, these values showed almost identical distributions across laboratory (n = 1110) and field (n = 581) experiments, providing strong support for using the former as an experimental proxy for the latter, although we caution that geographic biases in the extant data make this conclusion tentative. Due to a smaller sample size of CH4 Q10 data, we were unable to perform a comparable robust analysis, but we expect similar interactions with soil temperature, moisture, and environmental/climatic variables. Our results here suggest the need for more concerted efforts to document and standardize these data, including sample and site metadata. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/kaizadp/field_lab_q10 (DOI: 10.5281/zenodo.7106554).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Salvaging Data Records with Missing Data: Data Imputation using the Multivariate t Distribution

When doing multivariate data analysis, one commonobstacle is the presence of incomplete observations, i.e., observationsfor which one or more key fields are blank. Missing datais often countered by deleting entire observations that containmissing data. The negative effects of deleting entire observationsare multiple: deleting observations reduces sample size andcan also result in biased inferences even if data is missing atrandom. In addition, knowledge contained within incompleteobservations is knowledge lost when they are deleted– and theeffort spent collecting that knowledge is effort wasted. Data imputationmethods, or methods of statistically “filling-in” missingdata, can help combat small sample sizes by using the existinginformation in partially complete observations with the end goalof producing less biased and higher confidence inferences. Whena sample from a multivariate normal population is only partiallycomplete, and the missing data meets appropriate assumptions(missing at random), robust data imputation of the missing datacan be implemented with monotone data augmentation (MDA)using the multivariate t distribution.Missing data imputation is applied to data from the NASA InstrumentCost Model (NICM) using the MDA algorithm underthe assumption of having a multivariate t distribution with fixeddegrees of freedom. A sensitivity analysis to the degrees offreedom parameter is presented to demonstrate robustness ofthe multivariate t distribution when dealing with small samplesas compared to the multivariate normal distribution.

DiNicola, Michael↗
DOE OSTI2025

Data-Conforming Data-Driven Control: Avoiding Premature Generalizations Beyond Data

Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This article seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slowing down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude this article with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Respiration data, microbial community assembly data, and FTICR-MS data associated with: “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks. Sengupta et al., 2021, Biogeosciences”

This data package is associated with the manuscript “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks, in revision in Biogeosciences (Sengupta et al. and 2021;https://bg.copernicus.org/preprints/bg-2021-51/). The study used hyporheic zone sediments as a model system to provide an integrated view of how disturbance modulates linkages among microbial ecology, biogeochemistry, and organic matter thermodynamics. Laboratory experiments exposed hyporheic sediment to varying wetting/drying dynamics. Data types include dissolved oxygen rates used to derive respiration rates, Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data used to derive thermodynamic properties of organic matter, and microbial community assembly metrics derived from amplicon-sequence data of putatively active (cDNA) and whole community (gDNA). The outcomes of the study are condensed into a broadly applicable conceptual model linking external forcing, internal dynamics, and history. This data package is comprised of a file-level metadata (FLMD) csv, metadata csv, and seven folders that contain csv files, R scripts, xml files, and associated documentation: (1) Rates, (2) bNTI, and (3) FTICR, (4) Statistics_Analyses, (5) Raw OTU Beta dispersion Analysis, (6) bMNTD Randomizations, and (7) Data Dictionaries. The FLMD file has a description of each file included in the data package.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Solar-Geophysical Data Number 496, February 1986. Part 1: (Prompt reports). Data for January 1986,December 1985 and late data

Solar-Geophysical Data Number 498, February 1986, Part 1 (Prompt Reports), Data for January 1986, December 1985 and Late Data contains the following:detailed index for 1985-86; data for January 1986--(IUWDS alert periods (advanced and worldwide), Solar activity indices, Solar flares, Solar radio emission, Vostok inferred interplanetary magnetic field polarity, Stanford mean solar magnetic field); data for December 1985--(Solar active regions, Sudden ionospheric disturbances, Solar radio spectral observations, Cosmic ray measurements by neutron monitor, Geomagnetic indices, Radio propagation indices); late data--(Solar radio emission Nancay interferometric chart December 1985, Solar radio spectral observations Culgoora May 1985, Geomagnetic indices sudden commencements November 1985, Calcium plage data).

Coffey, H. E.↗