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All sources NASA NTRS DOE OSTI

DOE OSTI2016

MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network

Large-scale high-throughput plant phenotyping (sometimes called phenomics) is becoming increasingly important in plant biology and agriculture and is essential to cutting-edge plant breeding and management approaches needed to meet the food and fuel needs for the next century. Currently, the application of these approaches is severely limited by the availability of appropriate instrumentation and by the ability to communicate experimental protocols, results and analyses. To address these issues, we have developed a low-cost, yet sophisticated open-source scientific instrument designed to enable communities of researchers, plant breeders, educators, farmers and citizen scientists to collect high-quality field data on a large scale. The MultispeQ provides measurements in the field or laboratory of both, environmental conditions (light intensity and quality, temperature, humidity, CO 2 levels, time and location) and useful plant phenotypes, including photosynthetic parameters—photosystem II quantum yield (Φ II ), non-photochemical exciton quenching (NPQ), photosystem II photoinhibition, light-driven proton translocation and thylakoid proton motive force, regulation of the chloroplast ATP synthase and potentially many others—and leaf chlorophyll and other pigments. Plant phenotype data are transmitted from the MultispeQ to mobile devices, laptops or desktop computers together with key metadata that gets saved to the PhotosynQ platform (https://photosynq.org) and provides a suite of web-based tools for sharing, visualization, filtering, dissemination and analyses. We present validation experiments, comparing MultispeQ results with established platforms, and show that it can be usefully deployed in both laboratory and field settings. We present evidence that MultispeQ can be used by communities of researchers to rapidly measure, store and analyse multiple environmental and plant properties, allowing for deeper understanding of the complex interactions between plants and their environment.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Visualization Software for VisIT Java Client

The VisIT Java Client (JVC) library is a lightweight thin client that is designed and written purely in the native language of Java (the Python & JavaScript versions of the library use the same concept) and communicates with any new unmodified standalone version of VisIT, a high performance computing parallel visualization toolkit, over traditional or web sockets and dynamically determines capabilities of the running VisIT instance whether local or remote.

Billings, JayJay↗

DOE OSTI2018

Sandia Machine Learning Laboratory

Samlab provides a workflow and data storage for machine learning experiment inputs and outputs using mongodb, and a web service to manage, search, visualize, and analyze the results.

Shead, Timothy↗

DOE OSTI2025

TR-XPS Realtime Analysis Tool (ArroyoXPS) v0.1

The ALS has developed a Time-Resolved X-ray Photoelectron Spectroscopy (TR-XPS) technique, which involves applying a specific pattern of voltage curves to a sample while measuring XPS peaks. This pattern is repeated over multiple cycles, and changes in the material's response provide valuable scientific insights. Traditionally, file-based analysis workflows have been used: scans are run for a predetermined time, and after one or more scans are complete, calculations are made. ArroyoXPS changes this by offering in-experiment scan and analysis, allowing researchers to gain insights before a scan is finished. This enables them to adjust experimental parameters quickly, potentially saving valuable beamtime. ArroyoXPS includes tools for integrating with beamline control systems, performing analysis, and visualizing scan data in a web browser.

McReynolds, Dylan [Lawrence Berkeley National Labo↗

DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Modernizing GlideinWMS Factory Monitoring with Prometheus & Grafana

Large-scale scientific experiments like CMS and DUNE rely on the distributed workload management system GlideinWMS to efficiently utilize computing resources across heterogeneous computing environments. GlideinWMS currently records Factory statistics using Round Robin Databases (RRDBs), XML, and JSON files, and these statistics are displayed via custom monitoring Web pages, thereby limiting integration with modern observability platforms. This project investigates the use of Prometheus-based instrumentation to expose Factory metrics using OpenTelemetry principles. Factory statistics related to Glidein submission and job execution are exported as Prometheus metrics through the Prometheus Python Client Library and are served via an HTTP metrics endpoint. The collected metrics are inspected using the Prometheus web-based interface and are visualized through Grafana dashboards within the Landscape monitoring infrastructure at Fermilab. This project significantly streamlines the integration of modern monitoring technologies into GlideinWMS and establishes a framework for extending observability across additional system components.

Appiah, Gideon [Grambling State U.]↗

DOE OSTI2017

Visualization Software for VisIT Java Client

Billings, Jay Jay↗

DOE OSTI2019

From Data to Models and Analytics: An Integrated Scientific Computing Platform to Accelerate Environmental Research SBIR Phase I (Final Technical Report)

Phase I research was conducted to investigate the technical feasibility of an integrated open source platform GeoLib to provide turnkey solutions for model-data integration for environmental systems science (ESS), using modern web technologies, advances in HPC and cloud computing and high-performance visualization. A key feature of our platform is that it minimizes and automates data movement between data storage and computing systems. Another critical feature of the design is that it enables users to perform high-fidelity post-processing by performing high-performance visualization server-side and in-situ on the compute cluster close to the data. The investigation, which was performed over a 12-month period ending April 2019, centered on developing prototype software to demonstrate the key enabling technologies required for this system. This included: (i) rapid access to data from multiple remote sources, via a unified data interface; (ii) software for scalable geospatial visualization of raster, vector, and point cloud data on the web; (iii) development of an easy-to-use API for creating simulation and modeling workflows; (iv) remote data visualization and processing on middle-tier servers; and (v) remote data processing on high-performance computing systems. A critical part of this work is that all of these capabilities be accessible to end users through standard desktop tools such as Project Jupyter, which is widely adopted for ESS simulation and modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗

DOE OSTI2014

SU-D-BRD-02: A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiotherapy

Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay information to the backend to process user requests. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Development of an Open Global Oil and Gas Infrastructure Inventory and Geodatabase

This submission contains a technical report describing the development process and visual graphics for the Global Oil and Gas Infrastructure database. Access the GOGI database using the following link: https://edx.netl.doe.gov/dataset/global-oil-gas-features-database There is a web-mapping tool that allows you to visualize and interact with this data also available here: https://edxspatial.arcgis.netl.doe.gov/maps/edxspatial-gogi-index.html Acknowledgement: This work was performed under a CRADA between NETL and EDF, and was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

GOGI↗

DOE OSTI2015

IdentityMap Visualization of the Super Identity Model

The Super Identity Model is a collaboration with six United Kingdom universities to develop use cases used to piece together a person's identity across biological, cyber, psychological, and biographical domains. PNNL visualized the model in a web-based application called IdentityMap. This is the first step in a promising new field of research. Interested future collaborators are welcome to find out more by emailing superid@pnnl.gov.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI

Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. Here, we demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data

Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Open Chemistry, JupyterLab, REST, and quantum chemistry

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open-source framework that offers an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open-source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state-of-the-art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗

DOE OSTI2022

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center's (NERSC's) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. Here, by providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗