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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗
NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗
NASA NTRS2004

Reconfigurable Transceiver and Software-Defined Radio Architecture and Technology Evaluated for NASA Space Communications

The NASA Glenn Research Center is investigating the development and suitability of a software-based open-architecture for space-based reconfigurable transceivers (RTs) and software-defined radios (SDRs). The main objectives of this project are to enable advanced operations and reduce mission costs. SDRs are becoming more common because of the capabilities of reconfigurable digital signal processing technologies such as field programmable gate arrays and digital signal processors, which place radio functions in firmware and software that were traditionally performed with analog hardware components. Features of interest of this communications architecture include nonproprietary open standards and application programming interfaces to enable software reuse and portability, independent hardware and software development, and hardware and software functional separation. The goals for RT and SDR technologies for NASA space missions include prelaunch and on-orbit frequency and waveform reconfigurability and programmability, high data rate capability, and overall communications and processing flexibility. These operational advances over current state-of-art transceivers will be provided to reduce the power, mass, and cost of RTs and SDRs for space communications. The open architecture for NASA communications will support existing (legacy) communications needs and capabilities while providing a path to more capable, advanced waveform development and mission concepts (e.g., ad hoc constellations with self-healing networks and high-rate science data return). A study was completed to assess the state of the art in RT architectures, implementations, and technologies. In-house researchers conducted literature searches and analysis, interviewed Government and industry contacts, and solicited information and white papers from industry on space-qualifiable RTs and SDRs and their associated technologies for space-based NASA applications. The white papers were evaluated, compiled, and used to assess RT and SDR system architectures and core technology elements to determine an appropriate investment strategy to advance these technologies to meet future mission needs. The use of these radios in the space environment represents a challenge because of the space radiation suitability of the components, which drastically reduces the processing capability. The radios available for space are considered to be RTs (as opposed to SDRs), which are digitally programmable radios with selectable changes from an architecture combining analog and digital components. The limited flexibility of this design contrasts against the desire to have a power-efficient solution and open architecture.

Reinhart, Richard C.↗
NASA NTRS2002

CAPRI: Using a Geometric Foundation for Computational Analysis and Design

CAPRI (Computational Analysis Programming Interface) is a software development tool intended to make computerized design, simulation and analysis faster and more efficient. The computational steps traditionally taken for most engineering analysis (Computational Fluid Dynamics (CFD), structural analysis, etc.) are: Surface Generation, usually by employing a Computer Aided Design (CAD) system; Grid Generation, preparing the volume for the simulation; Flow Solver, producing the results at the specified operational point; Post-processing Visualization, interactively attempting to understand the results. It should be noted that the structures problem is more tractable than CFD; there are fewer mesh topologies used and the grids are not as fine (this problem space does not have the length scaling issues of fluids). For CFD, these steps have worked well in the past for simple steady-state simulations at the expense of much user interaction. The data was transmitted between phases via files. In most cases, the output from a CAD system could go IGES files. The output from Grid Generators and Solvers do not really have standards though there are a couple of file formats that can be used for a subset of the gridding (i.e. PLOT3D) data formats and the upcoming CGNS). The user would have to patch up the data or translate from one format to another to move to the next step. Sometimes this could take days. Instead of the serial approach to analysis, CAPRI takes a geometry centric approach. CAPRI is a software building tool-kit that refers to two ideas: (1) A simplified, object-oriented, hierarchical view of a solid part integrating both geometry and topology definitions, and (2) programming access to this part or assembly and any attached data. The connection to the geometry is made through an Application Programming Interface (API) and not a file system.

Haimes, Robert↗
NASA NTRS2019

Prototyping Operational Autonomy for Space Traffic Management

Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in spacecraft population in less than 10 years, nor does it support automated manuever planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), drawing on previous work by NASA to develop an architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy is an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations. Examples of autonomy within STM include syncing multiple non-authoritative catalogs of resident space objects, or determining which spacecraft maneuvers when preventing impending conjunctions between multiple spacecraft. The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry standard Docker containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness provider/SSA, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into then, can fulfil similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace. We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a predefined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning is possible using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities.

space traffic management↗
NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗
DOE OSTI2025

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗
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 OSTI2025

A product data network to enable faster, easier, and better planning of building envelopes

The building envelopes contributes significantly to the energy-efficiency of the building. Building performance simulation has made it possible to compare façade technologies regarding energy demand, daylighting, thermal and visual comfort in detail. Planners, such as architects and engineers, need experience to find product data with the right quality and level of detail, and to process the data to fit the calculation and the application. In the available time, planners can compare only a limited number of products, which means that better solutions could go unnoticed. This paper presents a new concept for making product data easily accessible for building façade planning. The concept consists of a network of databases for the efficient exchange and use of optical and calorimetric data of glazing units, shading devices, and combinations of both. The paper presents the research questions, an analysis of the current challenges, six design goals for the product data network and its implementation together with a discussion. Many product data sources can be connected to many planning software applications via the specified application programming interface. When planning software connects to the product data network, the planning of building envelopes can be much faster because planners do not need to spend so much time to search and process product data manually. The planning of building envelopes can also become much easier, especially for planners with limited experience. They do not need to understand all the details about which data fits which calculation if the software company implements this. The planning of building envelopes can become much more reliable when software companies validate their use of the product data network, because the current manual process is prone to errors. The planning of building envelopes can also improve because more products can be compared in the available time, allowing better solutions to be found.

Maurer, Christoph↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗
DOE OSTI2024

Forte: A suite of advanced multireference quantum chemistry methods

Software development plays a critical role in advancing quantum chemistry, enabling the exploration of new fundamental theoretical ideas and modeling systems of ever-increasing complexity. In the past decade, the availability of quantum chemistry packages that use modular designs and provide application programming interfaces (APIs) has enabled the creation of specialized software plugins, enhancing the capabilities of the original codes. Here, the availability of well-documented APIs is particularly beneficial in the context of academic scientific software development because it reduces the entry barrier for new developers and shields them from the complexities of large software projects.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE

Summary Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, “Artificial Intelligence Driven protein Estimation (AIDE)”, enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API), i.e. drop-in compatible with scikit-learn transformers and pipelines. Availability and implementation AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MTUQ: a framework for estimating moment tensors, point forces, and their uncertainties

SUMMARY We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1-D and 3-D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface, accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: (1) a 2009 intraslab earthquake near Anchorage, Alaska; (2) an episode of the 2021 Barry Arm landslide in Alaska; and (3) the 2017 Democratic People’s Republic of Korea underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multimodal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

58 GEOSCIENCES↗
DOE OSTI2024

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC’s data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others’) through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

VirJenDB: a FAIR (meta)data and bioinformatics platform for all viruses

High-throughput sequencing has generated an unprecedented volume of data. However, researcher-submitted data in repositories requires extensive curation and quality control for reuse. These tasks are hindered by the multiplicity of repositories, the sheer volume of the data, and the complexity of virus (meta)data curation. To address these challenges, VirJenDB offers a user-friendly platform to facilitate versioned, community-driven curation, and ontology development. Virus sequences were ingested from 16 sources, including ~200 fields of metadata or standards, covering taxonomy, sample, and host information. Up to 85 metadata fields have undergone at least one round of curation, and are linked to 15.4 million virus sequences, with 88 % from those infecting eukaryotes and the remaining infecting prokaryotes. Subsets were created, including a novel collection of 0.91 million viral operational taxonomic unit (vOTU) sequences across all viruses, while keeping the original sequences from each vOTU to facilitate downstream analyses, e.g. sequence variation. The VirJenDB web portal (https://www.virjendb.org) provides HTTPS and Application Programming Interface (API) access to the sequence datasets and metadata, offering a search engine, filtering, download, visualizations, and documentation. VirJenDB aims to connect the phage and eukaryotic virus research communities by supporting webtool integration, meta-analyses, and metadata schema extensions.

Saghaei, Shahram↗
DOE OSTI2025

BioPortal: an open community resource for sharing, searching, and utilizing biomedical ontologies

Abstract BioPortal (https://bioportal.bioontology.org) is the world’s most comprehensive repository of biomedical ontologies. It provides infrastructure for finding, sharing, searching, and utilizing biomedical ontologies. Launched in 2005, BioPortal now includes 1549 ontologies (1182 of them public). Its open, freely accessible website enables anyone (i) to browse the ontology library, (ii) to search for terms across ontologies, (iii) to browse mappings between terms, (iv) to see popularity ratings and recommendations on which ontologies are most relevant to their use cases, (v) to annotate text with ontology terms, (vi) to submit an ontology, and (vii) to request ontology changes. The library of ontologies can be accessed programmatically via a REST application programming interface (API). Recent enhancements include a BioPortal knowledge graph that integrates knowledge from multiple ontologies; a unified data model for interoperability with other knowledge sources; ontology popularity ratings and recommendations for relevant ontologies; and the ability to request ontology changes via a simple user interface that automatically converts user change requests to GitHub Pull Requests that specify the edits that will be made to the ontology upon approval.

Vendetti, Jennifer↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗