Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “information management and knowledge discovery”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

50 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗
DOE OSTI2021

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2012

NASA's Space Launch System: A Heavy-Lift Platform for Entirely New Missions

The National Aeronautics and Space Administration's (NASA's) Space Launch System (SLS) will contribute a new capability for human space flight and scientific missions beyond low-Earth orbit (LEO). The SLS Program, managed at NASA s Marshall Space Flight Center, will develop the heavy-lift vehicle that will launch the Orion Multi-Purpose Crew Vehicle (MPCV), equipment, supplies, and major science missions for exploration and discovery. Orion will carry crews to space, provide emergency abort capability, sustain the crew during space travel, and provide safe reentry from deep-space return velocities. Supporting Orion s first autonomous flight to lunar orbit and back in 2017 and its first crewed flight in 2021, the SLS ultimately offers a flexible platform for both human and scientific exploration. The SLS plan leverages legacy infrastructure and hardware in NASA s inventory, as well as continues with advanced technologies now in development, to deliver an initial 70 metric ton (t) lift capability in 2017, evolving to a 130-t capability, using a block upgrade approach. This paper will give an overview of the SLS design and management approach against a backdrop of the missions it will support. It will detail the plan to deliver the initial SLS capability to the launch pad in the near term, as well as summarize the innovative approaches the SLS team is applying to deliver a safe, affordable, and sustainable long-range capability for entirely new missions-opening a new realm of knowledge and a world of possibilities for multiple partners. Design reference missions that the SLS is being planned to support include Mars, Jupiter, Lagrange Points, and near-Earth asteroids (NEAs), among others. The Agency is developing its mission manifest in parallel with the development of a heavy-lift flagship that will dramatically increase total lift and volume capacity beyond current launch vehicle options, reduce trip times, and provide a robust platform for conducting new missions destined to rewrite textbooks with the information they deliver, while creating a framework for further collaboration among domestic and international partners, and potentially spurring economic expansion into new markets.

Creech, Stephen D.↗
DOE OSTI2020

DOE Office of Scientific and Technical Information (OSTI) Artificial Intelligence and Machine Learning

The Department of Energy (DOE) Office of Scientific and Technical Information (OSTI) established its artificial intelligence (AI) team in the summer of 2019. The AI Team's work and research in this space are new endeavors for OSTI; identifying the appropriate areas of research and investigation are priorities for the team and will ensure results and products that support OSTI and the collection, preservation, and dissemination of R&D results. To support OSTI’s strategic plan, the AI Team has started an assessment of the current R&D results corpus (e.g., metadata and full text) collected through ingest products such as E-Link and DOE CODE and disseminated through OSTI.GOV and other discovery applications. This presentation will present applied AI and Machine Learning (ML) approaches to assess and address data challenges and discuss how these data challenges are being evaluated to establish a comprehensive corpus of R&D results, support the reuse of R&D results and its data, and extend these findings to the broader DOE community. This presentation can be presented live or via a recorded presentation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Ensuring continued operation of INSPIRE as a cornerstone of the HEP information infrastructure

The INSPIRE platform — the most widely-used discovery service specifically tailored to the needs of researchers in High Energy Physics (HEP) — has become a central component of the information infrastructure for the discipline. Despite this, INSPIRE's continued sustainability is frequently endangered by resource constraints, recently made more acute by the loss of support from historical funders changing their research priorities. If the European particle physics community wishes to ensure INSPIRE's long-term sustainability, the community should secure international support and ensure appropriate funding.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
DOE OSTI2024

A Cast of Thousands: How the IDEAS Productivity Project Has Advanced Software Productivity and Sustainability

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy’s Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities—building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery—mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Life Sciences Data Archives (LSDA) in the Post-Shuttle Era

Now, more than ever before, NASA is realizing the value and importance of their intellectual assets. Principles of knowledge management-the systematic use and reuse of information, experience, and expertise to achieve a specific goal-are being applied throughout the agency. LSDA is also applying these solutions, which rely on a combination of content and collaboration technologies, to enable research teams to create, capture, share, and harness knowledge to do the things they do well, even better. In the early days of spaceflight, space life sciences data were collected and stored in numerous databases, formats, media-types and geographical locations. These data were largely unknown/unavailable to the research community. The Biomedical Informatics and Health Care Systems Branch of the Space Life Sciences Directorate at JSC and the Data Archive Project at ARC, with funding from the Human Research Program through the Exploration Medical Capability Element, are fulfilling these requirements through the systematic population of the Life Sciences Data Archive. This project constitutes a formal system for the acquisition, archival and distribution of data for HRP-related experiments and investigations. The general goal of the archive is to acquire, preserve, and distribute these data and be responsive to inquiries for the science communities. Information about experiments and data, as well as non-attributable human data and data from other species' are available on our public Web site http://lsda.jsc.nasa.gov. The Web site also includes a repository for biospecimens, and a utilization process. NASA has undertaken an initiative to develop a Shuttle Data Archive repository. The Shuttle program is nearing its end in 2010 and it is critical that the medical and research data related to the Shuttle program be captured, retained, and usable for research, lessons learned, and future mission planning. Communities of practice are groups of people who share a concern or a passion for something they do, and learn how to do it better as they interact regularly. LSDA works with the HRP community of practice to ensure that we are preserving the relevant research and data they need in the LSDA repository. An evidence-based approach to risk management is required in space life sciences. Evidence changes over time. LSDA has a pilot project with Collexis, a new type of Web-based search engine. Collexis differentiates itself from full-text search engines by making use of thesauri for information retrieval. The high-quality search is based on semantics that have been defined in a life sciences ontology. Additionally, Collexis' matching technology is unique, allowing discovery of partially matching dicuments. Users do not have to construct a complicated (Boolean) search query, but can simply enter a free text search without the risk of getting "no results". Collexis may address these issues by virtue of its retrieval and discovery capabilities across multiple repositories.

Fitts, Mary A.↗
DOE OSTI2023

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Improving Discovery, Sharing, and Use of Water Data: Initial Findings and Suggested Future Work

Collaborative management of water resources requires a broad suite of “water data” that extends beyond basic information about water quantity and quality to other related topics such as water infrastructure, aquatic ecosystem health, socioeconomic factors, and power generation. Water data are disparate in nature because they are collected and provided by many entities, and in some cases, remain challenging to access and use. The U.S. Department of Energy’s Water Power Technologies Office initiated a project to characterize relevant categories of water data; describe the current state of accessing, using, and visualizing water data; and outline investigatory pathways for future efforts aimed at improving the discovery, sharing, and use of water data. Input on these topics was solicited from a small but diverse cross section of members of the water resources community. Fourteen broad categories of water data were identified: dams; ecology; flood control; hydroclimatology; hydrography; hydrology; hydropower; management landscape; migratory barriers; recreation and aesthetic importance; socioeconomic; water quality; water availability and use; and weather. Stakeholder perspectives on the accessibility and usability of water data indicate these aspects are affected by a complex set of technical and social factors. However, stakeholders generally agreed that better access to water data can provide a range of benefits to water management, and they stressed the need to generate broad support from water data users and producers. Two investigatory pathways were outlined that, taken together, provide a logical progression toward the goals of the project. The first pathway emphasizes further investigation to better define target audiences and data needs, identify opportunities for collaboration between related efforts, and conduct value demonstration activities to generate further support for improving discovery and access of water data. The second pathway focuses on creating a comprehensive vision for potential solutions that improve the discovery of water data. Several activities that align with the first pathway are suggested for the next phase of the project.

13 HYDRO ENERGY↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2017

Program Options to Explore Ocean Worlds

Including Earth, roughly a dozen water ocean worlds exist in the solar system: the relict worlds Ceres and Mars, vast oceans inside most of the large Jovian and Saturnian icy moons, and Kuiper Belt Objects like Triton, Charon, and Pluto whose geologies are dominated by water and ammonia. Key pieces of the ocean-world science puzzle – which when completed may reveal whether life is widespread in the cosmos, why it exists where it does, and how it originates – are distributed among them. The eventual exploration of all these worlds will yield humanity’s total tangible knowledge about life in the universe, essentially forever. Thus, their exploration has existential significance for humanity’s self-regard, and indeed perhaps of our place in the natural scheme. The matter of planning how to pursue such a difficult and unprecedented exploration opportunity is therefore historic. The technical challenges are formidable, far harder than at Mars: missions to the Jovian and Saturnian ocean worlds are severely power-limited; trip times can be as much as a half decade and decade, respectively. And the science targets are global-scale oceans beneath kilometers of cryogenic ice. Reaching and exploring them would be a multi-generational undertaking, so again it is essential to plan and prepare. Today, we lack the instrumentation, subsystems, and remote operational-intelligence technologies needed to build and use exploration avatars as good as what we can envision needing. Each ocean world holds a piece of the puzzle, but the three priority targets are Europa at Jupiter, and Enceladus and Titan at Saturn. As with the systematic exploration of Mars, exploring these diverse worlds poses a complex technical and programmatic challenge – a strategic challenge – that needs to be designed and managed if each generation is to see its work bear fruit, and if the space science community is to make most effective use of the public money devoted to the quest. Strategic programs benefit from coherence. In only 15 years, the Mars Exploration Program (MEP) has transformed humanity’s view of Mars as a once and future habitable place, a world quite possibly holding relict evidence of life. Finding such evidence, we would study it to know if that life shared an origin common with Earth life. However, life in the ocean worlds could not have shared our origin, so exploring them opens another level in our quest to understand life in the universe: not only to places with vast salt-water seas known to contain organics and hydrothermal seafloors active today, but to places where anything alive cannot be related to us. MEP’s success – from its presence in the public consciousness to its rewriting of planetary habitability – make it an obvious template and source of lessons learned for a viable ocean worlds exploration program (OWEP). Six attributes of the MEP are analyzed for application to a potential OWEP. From this, five hypothetical programmatic scenarios are compared to the default case, and conclusions drawn. A coherent OWEP should have several parts: first, dedicated continuous investment in enabling technologies; and second, two directed-purpose, medium-class (~$1B) missions per decade that conduct pivotal investigations on a documented roadmap. Science could start in 2035, informing development of decadal flagship missions after Europa Clipper, to the places revealed to hold the most promise. The fastest pace of scientific discoveries would require access to high-performance propulsion infrastructure, e.g., the Space Launch System, Falcon Heavy, and high-power in-space solar electric propulsion, all capable of halving trip time. Not including these boosts, such a program would cost about a half-billion dollars more per year than NASA’s existing mission portfolio; the program architecture funded today cannot deliver a strategic OWEP while also sustaining balance among other solar system exploration priorities and opportunities.

Naderi, F.↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗