SHARI – An Integration of Tools to Visualize the Story of the Day
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Links to web resources frequently break, and linked content can change at unpredictable rates. These dynamics of the Web are detrimental when references to web resources provide evidence or supporting information. In this paper, we highlight the significance of reference rot, provide an overview of existing techniques and their characteristics to address it, and introduce our Robust Links approach, including its web service and underlying API. Robustifying links offers a proactive, uniform, and machine-actionable way to combat reference rot. In addition, we discuss our reasoning and approach aimed at keeping the approach functional for the long term. To showcase our approach, we have robustified all links in this article.
We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.
As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.
The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders of magnitude the search sensitivity reached so far.Mu2e’s Trigger and Data Acquisition System (TDAQ) uses {\it otsdaq} solution. Developed at Fermilab, {\it otsdaq} uses the {\it artdaq} DAQ framework and {\it art} analysis framework, for event transfer, filtering, and processing.{\it otsdaq} is an online DAQ software suite with a focus on flexibility and scalability, and provides a multi-user interface accessible through a web browser.A Detector Control System (DCS) for monitoring, controlling, alarming, and archiving has been developed using the Experimental Physics and Industrial Control System (EPICS) open source Platform. The DCS System has also been integrated into {\it otsdaq}, providing a GUI multi-user, web-based control, and monitoring dashboard.
The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.
The purpose of the Biofuels Information Center (BIC) task is to provide relevant data, information, reports, and web-based tools to all bioenergy stakeholders. The BIC task began in FY08 to meet the requirement under Title II, Sec. 229 of the Energy Independence and Security Act of 2007 (EISA) requires DOE to develop a "Biofuels and Biorefinery Information Center". The BIC task supports biofuels pages content on the EERE's most visited website - the Alternative Fuels Data Center (AFDC http://www.afdc.energy.gov) and the Bioenergy Atlas tools (currently archived) (previous address https://maps.nrel.gov). This task results in more than 1.7 million web pageviews (an instance of an internet user visiting a webpage) per year. In FY22, the task completed the final year of the 5 year USDA Biofuels Infrastructure Partnership (BIP). The USDA BIP expanded infrastructure for E15 and/or E85 to approximately 850 stations and NREL received and reviewed data for quality analyzed all infrastructure and sales data collected by USDA. Stations are privately held and previously it was difficult to ascertain infrastructure and sales data. This unique dataset allows insight into infrastructure data (number of pumps and tanks, costs to install new equipment) and sales data (price and volume for E10, E15, E85, and diesel by month). The 2021 USDA BIP National Summary Report is with DOE for review prior to publication. Future work will include the biannual Bioenergy Industry Status Report (4 previous versions have been published) . The task also supports the principal investigator's time to engage stakeholders on infrastructure and deployment of biofuels. This includes leading, membership, and participation in the following roles: member Board of Advisors at the Fuels Institute, voting member for multiple UL standards committees, Co-Chair of the Infrastructure team at Agriculture/Auto/Ethanol, Member of Coordination Research Council's ULSD Corrosion Committee. The Principal investigator routinely responds to industry inquires to assist in deployment of biofuels regularly.
GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous and unreliable resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.
This data package contains data and information about the operation of the Niwot Ridge Subalpine Forest AmeriFlux site (US-NR1) between Nov 1998 to the present (2020). This data archive supplements the primary 30-min data storage for the US-NR1 data (i.e., https://doi.org/10.17190/AMF/1246088) by providing the following: (i) five-minute statistics (means, variances, covariances) of all data measured by the data system between Nov 1998 and September 2020 in netCDF format, (ii) CSV data files saved within the memory of the CR23X data loggers (as well as an archive of the data logger programs), (iii) an archive of previous 30-min ASCII data versions of the US-NR1 AmeriFlux data and information related to each data release (a replica of what can be found at http://urquell.colorado.edu/data_ameriflux/), (iv) a web calendar (in HTML format) documenting activity at the site (a replica of http://urquell.colorado.edu/calendar/), (v) photos (over 15,000) and video taken at the site between years 2001 and present day (2020), and (vi) several auxiliary datasets, primary related to trees near the site, soil moisture and soil temperature, and subcanopy radiation data. The data package is setup so that the web calendar, photos, and electronic logbook can be easily accessed on a local computer using a web browser. The provided data files are in either netCDF, CSV, ASCII, or MATLAB format. To obtain a better understanding about the archive, please start by reading the PDF: README_ESS_DIVE_USNR1_readme_first.pdf.
Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.
The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.
DEPRECATED This repository was archived by the owner on Nov 18, 2022. It is now read-only. The software is a web app to create semantic metadata models, namely, Haystack and Brick. The purpose of Hasty is to assist in the auditing of building mechanical systems and controls. The software is designed to capture mechanical system topology and important information regarding real world controls implementations. The tool is intended to output Brick / Haystack / ASHRAE 223 compliant models.
Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.
Abstract Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open‐access global archive housing three‐dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB‐designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction). The RCSB PDB research‐focused web portal ( RCSB.org ) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal ( PDB101.RCSB.org ) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB‐designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org ; and (v) supports training, outreach, and education via PDB101.RCSB.org . New tools and features at RCSB.org are presented using examples drawn from high‐resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.
This data package contains data and information about Global Navigation Satellite System (GNSS)-based Vegetation Optical Depth (VOD), tree sway motion, and eddy-covariance evapotranspiration (ET) data collected at the Niwot Ridge Subalpine Forest AmeriFlux site (US-NR1). The raw GNSS data were collected between May 2022 and August 2023. Other processed datasets such as tree sway motion and ET data are also included. The goal was to study the water content within a subalpine forest and, more specifically, examine the canopy evaporation process. This data archive includes all data that were used within the following Biogeosciences discussion paper that further summarizes the research objectives and conclusions:Burns, S.P., V. Humphrey, E.D. Gutmann, M.S. Raleigh, D.R. Bowling, and P.D. Blanken, 2025: Using GNSS-based vegetation optical depth, tree sway motion, and eddy-covariance to examine evaporation of canopy-intercepted rainfall in a subalpine forest. EGUsphere [preprint],https://doi.org/10.5194/egusphere-2025-1755This data archive also supplements the 30-min Lawrence Berkeley National Laboratory (LBNL) AmeriFlux dataset for US-NR1 (i.e., https://doi.org/10.17190/AMF/1246088) and updates what was in the 2020 ESS-DIVE US-NR1 archive (https://doi.org/10.15485/1671825) to include data from the years 2020-2025. More specifically, the following updates are provided: (i) five-minute statistics (means, variances, covariances) of all data measured by the US-NR1 data system between Sep 2020 and Jun 2025 in netCDF format, (ii) the electronic logbook of US-NR1 site visits, (iii) a web calendar (in HTML format) documenting activity at the site (a replica of https://urquell.colorado.edu/calendar/), (iv) photos taken at the site between years 2020 and present day (Aug 2025), and (v) several auxiliary datasets, primary related to trees near the site, soil properties, soil moisture and soil temperature, and subcanopy radiation data. The data package is setup so that the web calendar, photos, and electronic logbook can be easily accessed on a local computer using a web browser. The provided data files are in either BINEX or SBF format (for the raw GNSS data), netCDF, CSV, ASCII, or MATLAB format. To obtain a better understanding about the archive, please start by reading the following PDF which is included within the data archive:README_ESS_DIVE_USNR1_2025_readme_first.pdf.