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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2017 Puget Sound Regional Travel Study

# 2017 Puget Sound Regional Travel Study The 2017 Puget Sound Regional Travel Study collected household- and person-level activity and travel pattern information from residents throughout the Puget Sound Regional Council's four-county region in Washington State. It followed the [2014-2015 Puget Sound Regional Travel Study](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-puget-sound-travel-study), starting a planned six-year data collection that includes 2019 and 2021. The multiyear program's goal is to maintain an updated source of household travel behavior data that: - Supports modeling and planning needs - Facilitates trend analysis over time - Allows for regular study design updates to integrate evolving data collection methods and emerging travel behaviors and transportation issues. ## Data Collection Agency The Puget Sound Regional Council conducted the study. ## Methodology The 2017 study featured both the design and administration of a one-day household travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone global positioning system (GPS) diary (approximately 20% of households before data cleaning). It combined data collection methods, including smartphone, online, and telephone. The survey design included several stages to recruit and collect data about households, their members, and their travel behaviors during the assigned travel period. ## Survey Records Survey records include a total of 6,254 participants. ## More Information For more information, see the [survey documentation](https://www.nrel.gov/media/docs/libraries/tsdc/zip/tsdc-2017-puget-sound-travel-study-documentation.zip?sfvrsn=45991f2f_1). ## Transportation Data The data set contains a demographic and socioeconomic composition of 6,254 people from 3,285 households in the Puget Sound regional area, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged over 508 thousand vehicle miles of travel by participants during 52,492 trips. Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Tool: Input Data Processing Layer for Bulk Power System

This paper introduces an advanced input data processing layer for EMT simulations of large-scale bulk power systems. The paper proposes two versions of the RE-INTEGRATE EMT simulation tool, RE-INTEGRATE Gen-0 and RE-INTEGRATE Gen-1, which are developed to enhance simulation generalizability, scalability, and accuracy. The framework leverages a generic class design for components to incorporate linear equations, which are generated by discretizing the Differential-Algebraic Equations (DAEs) that represent the dynamics of the components. In addition, the framework employs a parsing algorithm that parses a power system’s raw and dyr files to generate a connectivity graph which is then traversed to form the overall system’s dynamics. The proposed input data processing layer is used to simulate the IEEE 39-bus test system. The obtained results demonstrate the framework’s capability to achieve simulation scalability and accuracy. Further, the results indicate that EMT simulations performed using the proposed automations can effectively handle complex grid configurations.

Mishra, Rahul [ORNL] (ORCID:0000000328205932)↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2025

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY↗
DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Enabling pan-repository reanalysis for big data science of public metabolomics data

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

El Abiead, Yasin↗
DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗
DOE OSTI2025

A platform to measure isentropes from proton-heated warm dense matter on short pulse laser facilities

We describe the development of an experimental platform that measures the release isentrope of materials heated isochorically to temperatures of a few electron volts, using short-pulse laser-produced protons to heat the sample and long-pulse laser-produced x rays to perform streaked x-ray radiography. The density profiles derived from the radiography data are integrated to generate pressure–density isentropes, independent of prior knowledge of the equation of state of the sample material. In order to understand the sensitivities of isentrope extraction from radiography data, we analyze synthetic radiographs generated by a radiation hydrodynamics code. Noise reduction and high spatial resolution are critical for isentrope reconstruction, as demonstrated by the analysis of a proof-of-principle shot day on the OMEGA-EP facility. In conclusion, the data demonstrate the feasibility of the platform for characterizing isentropes, and we discuss the necessary improvements to enhance precision in differentiating between equation-of-state models.

Equations of state↗
DOE OSTI2026

Challenges and Opportunities for Electric Utility Modeling and Asset Valuation Frameworks: Case Study on Valuing New Pumped Storage Hydropower

Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.

13 HYDRO ENERGY↗
DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗
DOE OSTI2026

An expanded registry of candidate cis -regulatory elements

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, the ENCODE consortium mapped biochemical signals across hundreds of cell types and tissues and integrated these data to develop a registry containing 0.9 million human and 300,000 mouse candidate cis-regulatory elements (cCREs) annotated with potential functions. Here we have expanded the registry to include 2.37 million human and 967,000 mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays such as STARR-seq, massively parallel reporter assay, CRISPR perturbation and transgenic mouse assays have profiled more than 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer and silencer roles in different cellular contexts. Integrating the registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by the identification of KLF1 as a novel causal gene for red blood cell traits. This expanded registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

Moore, Jill E. [Univ. of Massachusetts, Worchester↗
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↗