Results for “Data visualization and analysis” — Page 87

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

DOE OSTI2015

Design Concepts for an Outage Control Center Information Dashboard

The nuclear industry, and the business world in general, is facing a rapidly increasing amount of data to be dealt with on a daily basis. In the last two decades, the steady improvement of data storage devices and means to create and collect data along the way influenced the manner in which we deal with information. Most data is still stored without filtering and refinement for later use. Many functions at a nuclear power plant generate vast amounts of data, with scheduled and unscheduled outages being a prime example of a source of some of the most complex data sets at the plant. To make matters worse, modern information and communications technology is making it possible to collect and store data faster than our ability to use it for making decisions. However, in most applications, especially outages, raw data has no value in itself; instead, managers, engineers and other specialists want to extract the information contained in it. The complexity and sheer volume of data could lead to information overload, resulting in getting lost in data that may be irrelevant to the task at hand, processed in an inappropriate way, or presented in an ineffective way. To prevent information overload, many data sources are ignored so production opportunities are lost because utilities lack the ability to deal with the enormous data volumes properly. Decision-makers are often confronted with large amounts of disparate, conflicting and dynamic information, which are available from multiple heterogeneous sources. Information and communication technologies alone will not solve this problem. Utilities need effective methods to exploit and use the hidden opportunities and knowledge residing in unexplored data resources. Superior performance before, during and after outages depends upon the right information being available at the right time to the right people. Acquisition of raw data is the easy part; instead, it is the ability to use advanced analytical, data processing and data visualization methods to turn the data into reliable information and comprehensible, actionable information. Techniques like data mining, filtering and analysis only work reliably for well-defined and well-understood problems. The path from data to decision is more complex. The ability to communicate knowledge during outages and emergent issues is crucial. This paper presents an approach to turn the unused data into an opportunity: applying principles from semiotics, human factors and visual analytics to transform the traditional way of processing outage data into media that will improve the collective situation awareness, knowledge, decisions, actions and overall performance of the entire outage team, and also support the reliability, quality and overall effectiveness of maintenance work. The application of the proposed visualization methods will become the medium of a semi-automated analytical process where humans and machines cooperate using their respective, distinct capabilities for the most effective results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRSDate not supplied

HelioSwarm: The Swarm is the Observatory

HelioSwarm will transform our understanding of space plasma turbulence by being the first-of-its-kind simultaneous, multiscale observatory comprising multiple spacecraft. HelioSwarm was competitively selected under the Heliophysics Explorers Program 2019 Medium-Class Explorer (MIDEX) Announcement of Opportunity. The central powered-ESPA hub spacecraft is co-orbited by eight SmallSat Node spacecraft, together moving through a High Earth Orbit to obtain data in various solar wind regimes. The mission architecture is that of hub-and-spoke, with the larger hub serving as a communications relay between the nodes and DSN. Mission operations, management, and technical oversight are provided by NASA Ames Research Center; the spacecraft are provided by Northrop Grumman and BCT. The instrument suite includes foreign-contributed instruments and U.S. ones, all under the oversight of University of New Hampshire (which is also the Principal Investigator’s home institution and Science Operations Center). The mission timeline from launch through conclusion of the one-year science mission is provided along with a summarized concept of operations, with particular emphasis on placing the nodes in their proper relative orbit loops to form the geometry needed for science collection at apogee. A brief discussion of how a combination of legacy tools and custom-created swarm analysis tools are used to design the swarm and sort and visualize the collected science data and telemetry in context is provided. Finally, an exploration of the pathfinding nature of HelioSwarm and some implications for future large scientific swarms is offered.

Heliophysics↗

NASA NTRSDate not supplied

HelioSwarm: The Swarm is the Observatory

The HelioSwarm Mission will transform our understanding of space plasma turbulence by being the first-of-its-kind simultaneous, multiscale observatory comprising multiple spacecraft. HelioSwarm was competitively selected under the Heliophysics Explorers Program 2019 Medium-Class Explorer (MIDEX) Announcement of Opportunity. The central powered-ESPA Hub spacecraft is co-orbited by eight SmallSat Node spacecraft, together moving through a High Earth Orbit to obtain data in various solar wind regimes. The mission architecture is that of hub-and-spoke, with the larger hub serving as a communications relay between the Nodes and DSN. Mission operations, management, and technical oversight are provided by NASA Ames Research Center; the spacecraft are provided by Northrop Grumman and BCT. The instrument suite includes foreign-contributed instruments and U.S. ones, all under the oversight of University of New Hampshire (which is also the Principal Investigator’s home institution and Science Operations Center). The mission timeline from launch through conclusion of the one-year science mission is provided along with a summarized concept of operations, with particular emphasis on placing the Nodes in their proper relative orbit loops to form the geometry needed for science collection at apogee. A brief discussion of how a combination of legacy tools and custom-created swarm analysis tools are used to design the swarm and sort and visualize the collected science data and telemetry in context is provided. Finally, an exploration of the pathfinding nature of HelioSwarm and some implications for future large scientific swarms is offered.

Heliophysics↗

NASA NTRS2002

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu.

Non-NASA Center↗

DOE OSTI2025

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗

NASA NTRS2009

American Meteorological Society (AMS) - The Modern Era Retrospective-Analysis for Research and Applications (MERRA) Data and Accessibility

The AM Short Course on The Modern Era Retrospective-analysis for Research and Applications (MERRA) data and accessibility will be held on January 11, 2009 preceding the 89th Annual Meeting in Phoenix, Arizona. Preliminary programs, registration, hotel, and general information will be posted on the AMS Web site in mid-September 2008. Retrospective-analyses (or reanalyses) have been established as an important tool in weather and climate research over the last decade. As computer power increases, the data assimilation and modeling systems improve and become more advanced, the input data quality increases and so reanalyses become more reliable. In 2008, NASA Global Modeling and Assimilation Office began producing a new reanalysis called the Modem Era Retrospective-analysis for Research and Applications (MERRA). The initial data from the reanalysis has been made available to the community and should be complete through 30 years (1979-present) by Fall of 2009. MERRA has taken advantage of the advancement of computing resources to provide users more data than previously available. The native spatial resolution is nominally 1/2 degrees and the surface two dimensional data are one hourly frequency. In addition to the meteorological analysis data, complete mass, energy and momentum budget data and also stratospheric data are provided. The eventual data holdings will exceed 150Tb. In order to facilitate user accessibility to the data, it will be stored in online hard drives (not tape storage) and available through several portals. Subsetting tools will also be available to allow users to tailor their data requests. The goals of this short course are to provide hands on users of reanalyses instruction on MERRA systems and also interactive experience with the online data and access tools. The course is intended for students and research scientists who will be actively interested in accessing and applying MERRA data in their weather, climate or applications work. The course has three parts. There will be an overview of the MERRA system, the validation of the system and the native data format. Second, Instructors will provide examples of weather and climate data analysis using various software packages (primarily GrADS) as well as the online access tools for subsetting and download, as well as visualization (e.g. Giovanni and Google Earth). This will also include examples on changing the data format to fit user's preferences and also to regrid the data for comparisons to other reanalyses and observational data. Lastly, there will he time set aside for participants to have hands on access to the data and software while interacting with the instructors and other developers. The course convener is Dr. Michael Bosilovich, NASA GSFC Global Modeling and Assimilation Office (GMAO). He will be joined by several GMAO, Goddard Earth Science Data and information Services Center (GES DISC) and Software Integration and Visualization Office (SIVO) staff.

daSilva, Arlindo↗

DOE OSTI2014

A simplified HTc rf SQUID to analyze the human cardiac magnetic field

We have developed a four-channel high temperature radio-frequency superconducting quantum interference device (HT{sub c} rf SQUID) in a simple magnetically shielded room (MSR) that can be used to analyze the cardiac magnetic field. It is more robust and compact than existing systems. To achieve the high-quality magnetocardiographic signal, we explored new adaptive software gradiometry technology constructed by the first-order axial gradiometer with a baseline of 80mm, which can adjust its performance timely with the surrounding conditions. The magnetic field sensitivity of each channel was less than 100fT/√Hz in the white noise region. Especially, in the analysis of MCG signal data, we proposed the total transient mapping (TTM) technique to visualize current density map (CDM), then we focused to observe the time-varying behavior of excitation propagation and estimated the underlying currents at T wave. According to the clear 3D imaging, isomagnetic field and CDM, the position and distribution of a current source in the heart can be visualized. It is believed that our four-channel HT{sub c} rf SQUID magnetometer based on biomagnetic system is available to detect MCG signals with sufficient signal-to-noise (SNR) ratio. In addition, the CDM showed the macroscopic current activation pattern, in a way, it has established strong underpinnings for researching the cardiac microscopic movement mechanism and opening the way for its use in clinical diagnosis.

36 MATERIALS SCIENCE↗

DOE OSTI2026

EMPHATIC Silicon Strip Detector Efficiencies

EMPHATIC is an experiment at Fermilab which aims to reduce current neutrino flux uncertainties. This report discusses the limitations current neutrino flux uncertainties places on large scale neutrino experiments, provides background on the EMPHATIC experiment, and details the project of determining the efficiency of the Silicon Strip Detectors (SSDs) used in EMPHATIC. As part of the data analysis process and in order to increase the accuracy of EMPHATIC’s simulations a representation of efficiency of each SSD is required. To achieve this a data-driven analysis was performed on EMPHATIC's collected data using the Root and Art frameworks. Visual and numerical representations of efficiency were determined. The average efficiency over all SSDs is 98.58\%, however this number deflated as it includes known bad channels.

Olson, Virginia [Illinois U., Urbana (main)]↗

NASA NTRS1971

MITNYS - A hybrid program for on-line analysis of nystagmus

Hybrid computer program for data reduction or on-line analysis of nystagmus during closed loop experiment involving visual and/or vestibular function

Tole, J. R.↗

NASA NTRS1989

Three-dimensional visualization and display technologies; Proceedings of the Meeting, Los Angeles, CA, Jan. 18-20, 1989

Special attention was given to problems of stereoscopic display devices, such as CAD for enhancement of the design process in visual arts, stereo-TV improvement of remote manipulator performance, a voice-controlled stereographic video camera system, and head-mounted displays and their low-cost design alternatives. Also discussed was a novel approach to chromostereoscopic microscopy, computer-generated barrier-strip autostereography and lenticular stereograms, and parallax barrier three-dimensional TV. Additional topics include processing and user interface isssues and visualization applications, including automated analysis and fliud flow topology, optical tomographic measusrements of mixing fluids, visualization of complex data, visualization environments, and visualization management systems.

Robbins, Woodrow E.↗

NASA NTRS1990

Expert systems and imaging - NASA's start-up work in intelligent image management

The problem of image management for NASA's Space Shuttle program and the future role of expert systems are considered. The difficulties involved in the storage and retrieval of the estimated 100,000 color photographs used for verification of essential installation, test, and repair procedures for Space Shuttle prelaunch are discussed. It is noted that the collection, classification, storage, and retrieval of high-quality pictoral images is possible through the use of pixels and an expert systems-assisted relational data base management system. The development of a query by pictorial example process is discussed. The NASA Intelligent Interactive Visual Data Base Management Systems project is detailed. It aims to conduct a systems analysis of a large-scale traditional image processing operation, collect data on critical human performance factors in the areas of image creation, processing, classification, storage, and retreival, and validate the attributes identified through the creation and testing of prototype models.

Ragusa, James M.↗

NASA NTRS1992

Initial results from the Magellan stereo experiment

The second global radar image coverage of Venus by Magellan included a 1 day experiment to produce a set of eight images along orbits that combined with the original nominal imagery into stereo image pairs suited for visual stereo interpretation and mapping of topographic elevations. Initial analysis shows excellent stereo radar data with good vertical exaggeration of six and more, parallax accuracies of +/- 0.6 pixels or +/- 45 m and a surface definition with +/- 30 m. The topographic relief can be very accentuated, with local elevation differences of 2.5 km and terrain slopes often in excess of 25 deg. This data set has proven the usefulness of global stereo coverage and marks the beginning of significant planetary stereo radargrammetric developments. Extensive mapping with this same stereo geometry is being carried out in cycle 3, and preparations are underway to develop a detailed stereo radargrammetric topographic model of the planet.

Leberl, Franz W.↗

NASA NTRS1993

A photogrammetric solution to a particular problem

A closed-form mathematical solution to the classical photogrammetric problem is presented. Although quite general, the solution is more applicable to problems in which the image-space conjugates are very difficult to match but one of the elements of the pair is not. Additionally, observations are made that should make the solution to the general problem of automatic matching less computationally intensive. This approach was used to analyze flow visualization data for the F-18 High Alpha Research Vehicle. The conditions for this analysis were less than ideal for image-to-object-space transformation.

Hedgley, David R., Jr.↗

NASA NTRS1992

Processing infrared images of aircraft lapjoints

Techniques for processing IR images of aging aircraft lapjoint data are discussed. Attention is given to a technique for detecting disbonds in aircraft lapjoints which clearly delineates the disbonded region from the bonded regions. The technique is weak on unpainted aircraft skin surfaces, but can be overridden by using a self-adhering contact sheet. Neural network analysis on raw temperature data has been shown to be an effective tool for visualization of images. Numerical simulation results show the above processing technique to be an effective tool in delineating the disbonds.

Syed, Hazari↗

NASA NTRS1998

Visual Methods for Model and Grid Validation

This joint research interchange proposal allowed us to contribute in two directions that are of interest to NASA. These are: (a) data level comparative visualization of experimental and computational fluid flow, and (b) visualization tools for analysis of adaptively refined Cartesian grids.

Pang, Alex↗