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At least 721 records · Page 40

Space Ground Sensorwebs for Volcano Monitoring

Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New Space ventures have produced significantly greater access to data and miniaturization of sensing has enabled cubesats and smallsats to deliver data of outstanding resolution. The advent of the internet of things has produced incredible amounts of relevant terrestrial data as well. Artificial Intelligence offers the potential to automate both data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.

Zuleta, Ignacio↗

On the use of pressure broadening data to assess the accuracy of CO-He interaction potentials

The results of SCF-CI calculations of the intermolecular potential surface for CO and He are compared with pressure broadening data. Coupled states calculations were performed for both the electron gas and the more accurate SCF-CI potentials at collision energies from 15 to 1200/cm, and Boltzmann averages over collision energy were taken to compare with experimental results at liquid nitrogen (77 K), dry ice (195 K) and room (295 K) temperatures. The cross sections from the SCF-CI potential are shown to be in slightly better agreement with experiment than those from the electron gas potential, although both are consistent with the data to within experimental uncertainties. Closer examination of the experimental results reveals that this essential equivalence is due not to any inherent insensitivity of the pressure broadening technique, but to the uncertainties in the experimental values. It is thus concluded that more accurate experimental values are required to place meaningful constraints on the intermolecular potential.

Green, S.↗

Circuit Analysis of Ionizing Surface Potential Measurements of Electrolyte Solutions

Surface potential measurement values of the gas-liquid interface can be ambiguous despite the numerous electrochemical approaches used for quantification of the reported values. Calibration and normalization methods are not standardized, which often undermines the robustness of the reported values. Surface potential instrumentation and data interpretation also varies significantly across literature. Here, we propose a circuit model for an ionizing surface potential method based on the alpha decay of a radioactive americium-241 electrode. We evaluate the robustness of the circuit model for quantifying the surface potential at the air-aqueous interface. We then show successful validation of our circuit model through determination of the surface tension of the air-electrolyte interface with comparison to respective surface tension literature values. This validation reveals the reliability of surface potential measurements using the americium-241 ionizing method. We also report the surface potential difference of the air/water interface to be –0.49 V ± 0.01 V consistent with hydrogens of water pointing toward the air phase.

47 OTHER INSTRUMENTATION↗

Action-based dynamical modeling for the Milky Way disk

We present RoadMapping, a full-likelihood dynamical modeling machinery that aims to recover the Milky Way’s (MW) gravitational potential from large samples of stars in the Galactic disk. RoadMapping models the observed positions and velocities of stars with a parameterized, three-integral distribution function (DF) in a parameterized axisymmetric potential. We investigate through differential test cases with idealized mock data how the breakdown of model assumptions and data properties affect constraints on the potential and DF. Our key results are: (i) If the MW’s true potential is not included in the assumed model potential family, we can—in the axisymmetric case—still find a robust estimate for the potential, with only ≲10% difference in surface density within |z|⩽1.1 kpc inside the observed volume. (ii) Modest systematic differences between the true and model DF are inconsequential. E.g., when binning stars to define sub-populations with simple DFs, binning errors do not affect the modeling as long as the DF parameters of neighboring bins differ by <20%. In addition, RoadMapping ensures unbiased potential estimates for either (iii) small misjudgements of the spatial selection function (i.e., ≲15% at the survey volume’s edge), (iv) if distances are known to within 10%, or (v) if proper motion uncertainties are known within 10% or are smaller than δμ≲1 mas yr{sup −1}. Challenges are the rapidly increasing computational costs for large sample sizes. Overall, RoadMapping is well suited to making precise new measurements of the MW’s potential with data from the upcoming Gaia releases.

79 ASTRONOMY AND ASTROPHYSICS↗

Development and application of a uranium mononitride (UN) potential: Thermomechanical properties and Xe diffusion

We report that atomic-scale modeling of thermophysical and defect properties of uranium mononitride (UN) plays an important role in establishing a better understanding and improved models of UN fuel performance. Having an accurate interatomic potential is crucial for generating reliable data at finite temperatures using molecular dynamic simulations. We report a new interatomic potential for UN, based on a combination of many-body and pairwise interactions, a simple form that we later show could be easily adapted to include Xe-U and Xe-N interactions, i.e., generating a U-N-Xe interatomic potential. The potential was fitted to experimental thermal expansion and single crystal elastic constants, as well as Frenkel, Schottky, anti-Schottky, and antisite pair reaction energies from density functional theory (DFT) calculations. Using the potential, we successfully reproduced experimental lattice parameters, thermal expansion, single crystal elastic constants, and temperature dependent heat capacity. The potential also performs reasonably well in reproducing the energy of the aforementioned stoichiometric defect reactions and defect migration barriers calculated using DFT. However, the potential underestimates the energy difference between the tetrahedral and dumbbell uranium interstitials, and a more complex potential form might be needed to overcome this issue. The potential was also used to predict UN single crystal elastic constants and elastic properties at different temperatures, showing that UN becomes softer and more compressible with increasing temperature. We also compare our potential against literature data from previous empirical potentials, demonstrating similar or better behavior depending on the property of interest. To enable the simulation of Xe in UN a Buckingham potential has been fitted to DFT-derived Xe incorporation energies. The potential was then used to determine the activation energy for Xe diffusion due to various Xe-containing defects, with {XeU : VU} exhibiting the lowest activation energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗

Networked-based Cyber Analysis using Deep Packet Inspection (DPI) for High-Speed Networks

Today’s networked systems utilize advanced security components such as Next Generation Firewall (NGFW), Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and methods for network traffic classification. A fundamental aspect of these security components and methods is network packet visibility and packet inspection. To achieve packet visibility, a compute mechanism used by these security components and methods is Deep Packet Inspection (DPI). DPI is used to obtain visibility into packet fields by looking deeper inside packets, beyond just IP address, port, and protocol. However, DPI is considered extremely expensive in terms of compute processing costs and very challenging to implement on high speed network systems. The fundamental scientific paradigm addressed in this research project is the application of greater network packet visibility and packet inspection at data rates greater than 40Gbps to secure computer network systems. The greater visibility and inspection will enable detection of advanced content-based threats that exploit application vulnerabilities and are designed to bypass traditional security approaches such as firewalls and antivirus scanners. Greater visibility and inspection are achieved through identification of the application protocol (e.g., HTTP, SMTP, Skype) and, in some cases, extraction and processing of the information contained in the packet payload. Analysis is then performed on the resulting DPI data to identify potentially malicious behavior. In order to obtain visibility and inspect the application protocol and contents at high speed data rates, advanced DPI technologies and implementations are developed.

97 MATHEMATICS AND COMPUTING↗

Chemical/Optical Properties of Oliktok Aerosols Field Campaign Report

The arctic environment is changing at an alarming rate. During this time of escalation, it is important to understand the impacts of aerosols, both natural and anthropogenic. To support this effort, an aerosol sampling campaign was conducted at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility third Mobile Facility (AMF3) at Oliktok Point on the North Slope of Alaska (NSA). The specific objectives of the campaign were to collect aerosol particles as a function of aerodynamic particle size in order to better understand the varying chemical and optical properties of the aerosols in the different particle size modes. A DRUM-style cascading impactor (Davis Rotating-Unit for Monitoring) was deployed at the beginning of March 2017 through the end of May 2017, collecting samples in eight size bins for each 12-hour period, thereby obtaining a diurnal signature. On March 6, a fire burned down the power generation shed at the sampling site. No samples were collected again until March 10. Due to DRUM sampler rotation issues, samples were collected intermittently until March 13. From March 14 through the end of the study, samples were collected with little interference. The size bins of the instrument range from approximately 10 to 0.09 μm; however, deployment at the Oliktok Point facility required an inlet configuration that effectively filtered out the coarsest size bin. Therefore, the reported size bins are 5.0 to 2.5, 2.5 to 1.15, 1.15 to 0.75, 0.75 to 0.56, 0.56 to 0.34, 0.34 to 0.26, and 0.26 to 0.09 μm. It is also possible that ultrafine particles were removed by electrostatic charging of the high-density polyethylene inlet tubing; however, confirmation of this would require further testing. The collected samples were returned to the University of California (UC), Davis for offline analysis. Particle mass concentrations were measured by soft-beta ray attenuation while optical attenuation was determined by broadband transmission spectrometry (10 wavelengths from 349 to 849 nm in 50 nm increments, minus 649 nm). The samples were then taken to the Advanced Light Source facility at Lawrence Berkeley National Laboratory for synchrotron-induced X-ray fluorescence spectrometry (SXRF). Thirty elements were detected, from sodium to molybdenum plus lead. The polarized nature of the synchrotron beam eliminates 97.5% of the primary Compton background, providing approximately a factor of 10 greater sensitivity than unpolarized X-ray instruments (Barberie et al. 2013). The data were reduced using custom software packages and analyzed for any sources of contamination, sample damage, or other potential causes of data invalidation. The interpretation of the final data set is ongoing and only early results will be described in this report. In spite of power and instrumental issues at the start, the field campaign was largely successful. Excluding the first stage that did not receive sample and power/rotational issues, the remaining stages were 99.7% complete (number of valid measurements/total number of measurements made). The percent of measurements below detection were < 30% for all species with the exception of sodium, magnesium, arsenic, and scandium. Sodium and magnesium were expected to be difficult to measure using SXRF due to low sensitivity at that energy range. Scandium is very rare in ambient samples unless near a very strong source. Arsenic is fairly common in urban settings but is not expected in the arctic environment.

54 ENVIRONMENTAL SCIENCES↗

Rooftop Energy Potential of Low Income Communities in America REPLICA

The Rooftop Energy Potential of Low Income Communities in America REPLICA data set provides estimates of residential rooftop solar technical potential at the tract-level with emphasis on estimates for Low and Moderate Income LMI populations. In addition to technical potential REPLICA is comprised of 10 additional datasets at the tract-level to provide socio-demographic and market context. The model year vintage of REPLICA is 2015. The LMI solar potential estimates are made at the tract level grouped by Area Median Income AMI income tenure and building type. These estimates are based off of LiDAR data of 128 metropolitan areas statistical modeling and ACS 2011-2015 demographic data. The remaining datasets are supplemental datasets that can be used in conjunction with the technical potential data for general LMI solar analysis planning and policy making. The core dataset is a wide-format CSV file seeds_ii_replica.csv that can be tagged to a tract geometry using the GEOID or GISJOIN fields. In addition users can download geographic shapefiles for the main or supplemental datasets. This dataset was generated as part of the larger NREL-led SEEDSII Solar Energy Evolution and Diffusion Studies project and specifically for the NREL technical report titled Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States by Sigrin and Mooney 2018. This dataset is intended to give researchers planners advocates and policy-makers access to credible data to analyze low-income solar issues and potentially perform cost-benefit analysis for program design. To explore the data in an interactive web mapping environment use the NREL SolarForAll app.

14 SOLAR ENERGY↗

Synchronized Waveforms – A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

17 WIND ENERGY↗

ISS Plasma Interaction: Measurements and Modeling

Ionospheric plasma interaction effects on the International Space Station are discussed in the following paper. The large structure and high voltage arrays of the ISS represent a complex system interacting with LEO plasma. Discharge current measurements made by the Plasma Contactor Units and potential measurements made by the Floating Potential Probe delineate charging and magnetic induction effects on the ISS. Based on theoretical and physical understanding of the interaction phenomena, a model of ISS plasma interaction has been developed. The model includes magnetic induction effects, interaction of the high voltage solar arrays with ionospheric plasma, and accounts for other conductive areas on the ISS. Based on these phenomena, the Plasma Interaction Model has been developed. Limited verification of the model has been performed by comparison of Floating Potential Probe measurement data to simulations. The ISS plasma interaction model will be further tested and verified as measurements from the Floating Potential Measurement Unit become available, and construction of the ISS continues.

Barsamian, H.↗

Taxonomic Re-Evaluation and Genomic Comparison of Novel Extracellular Electron Uptake-Capable Rhodovulum visakhapatnamense and Rhodovulum sulfidophilum Isolates

Rhodovulum spp. are anoxygenic phototrophic purple bacteria with versatile metabolisms, including the ability to obtain electrons from minerals in their environment to drive photosynthesis, a relatively novel process called phototrophic extracellular electron uptake (pEEU). A total of 15 strains of Rhodovulum sulfidophilum were isolated from a marine estuary to observe these metabolisms in marine phototrophs. One representative strain, Rhodovulum sulfidophilum strain AB26, can perform phototrophic iron oxidation (photoferrotrophy) and couples carbon dioxide fixation to pEEU. Here, we reclassify two R. sulfidophilum isolates, strainAB26 and strain AB19, as Rhodovulum visakhapatnamense using taxonomic re-evaluation based on 16S and pufM phylogenetic analyses. The strain AB26 genome consists of 4,380,746 base-pairs, including two plasmids, and encodes 4296 predicted protein-coding genes. Strain AB26 contains 22 histidine kinases, 20 response regulators, and dedicates ~16% of its genome to transport. Transcriptomic data under aerobic, photoheterotrophy, photoautotrophy, and pEEU reveals how gene expression varies between metabolisms in a novel R. visakhapatnamense strain. Genome comparison led by transcriptomic data under pEEU reveals potential pEEU-relevant genes both unique to R. visakhapatnamense strains and shared within the R. sulfidophilum genomes. With these data we identify potential pEEU-important transcripts and how speciation may affect molecular mechanisms of pEEU in Rhodovulum species from the same environment.

59 BASIC BIOLOGICAL SCIENCES↗

The reduction, verification and interpretation of Magsat magnetic data over Canada

The primary concern of this investigation is to detect and study variations in the magnetic field originating in the solid Earth, as measured by Magsat. Most of this field originates in the core, but an important part of the field is of lithospheric origin. Magnetic anomalies of lithospheric origin are weak at Magsat altitudes (20 to 30 nT at most), and they can easily be masked by much larger effects caused by field aligned and other currents at high latitudes. Most of Canada lies under the influence of ionospheric currents in the auroral zone and polar cap. Therefore, before Magsat data had become available, but after the October 30, 1979 launch, criteria were developed for selecting times when subsets of potentially usable Magsat data could be expected. Subsequently, as Magsat data became available, these critieria were applied.

Coles, R. L.↗

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2). Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t/ha). This result highlights the synergy between 3D and multispectral data in AGB modelling. Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.

space-borne imagery↗

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗

Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India

Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.

Data↗

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗