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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Outcomes of PAX sapiens-Supported Global Wildlife Data Sharing Conferences for Enhanced One Health Security (GWDSC)

Across two consecutive Global Wildlife Data Sharing Conferences supported by PAX sapiens—Year 1 (May 2024) at Pacific Northwest National Laboratory and Year 2 (2025) in Ciudad Real, Spain—the initiative converted wildlife data sharing from aspiration into operational reality, producing measurable impacts in platform development, data mobilization, standards harmonization, and international partnership formation. The conferences addressed a critical gap in global health security: while 75% of emerging infectious diseases affect both humans and animals and over 60% originate in wildlife, wildlife health surveillance has historically lagged behind human and agricultural sectors due to fragmented databases, inconsistent terminology, uneven capacity, and limited cross-border coordination. By convening practitioners, government agencies, international organizations, academic institutions, and NGOs, the GWDSC catalyzed trust-based relationships and practical workflows that enable earlier detection, better risk assessment, and more effective prevention of threats at the wildlife–domestic animal–human–environment interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data Format and Descriptions for the Alabama Carbon Storage: Data Sharing and Engagement Project

The Alabama Carbon Storage: Data Sharing and Engagement (ACS-DSE) project seeks to develop publicly accessible geologic carbon storage models and data across the southern Gulf Coastal Plain of Alabama. The public online platform developed for this project will include geologic, geophysical, infrastructure, and other relevant datasets and geologic models of the study area. Datasets, model surfaces (e.g. structural contour maps, isolith maps, porosity maps), and infrastructure data (e.g. offshore pipelines, field boundaries) will be downloadable in commonly used file formats. The anticipated primary geologic datasets are well headers, formation tops, average reservoir properties, and core analyses; these will be available as commaseparated values (CSV) text files and MS Excel workbooks. Geophysical logs will be available in Log ASCII Standard (LAS) file format. Modeled surfaces, such as structure contour maps, will be available in ArcGIS formats and text files. Infrastructure data will be available as ArcGIS shapefiles. This document provides information on the data sources and attributes of the datasets.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Bioenergy Research Centers Data Sharing Portal

The bioenergy.org website is the end product of the Data Sharing Shared Research objective for the Bioenergy Centers. The objective is to Enhance BRC data legacy through the development and use of shared software tools to make previously published datasets more findable and accessible through the Inter-BRC Data Products Portal, and to explore the use of generative AI to assist in the exploration of published datasets. The software repository is released per the license information below.

Thrower, Nicholas↗
DOE OSTI2025

Secure and Privacy Aware Data Sharing Approach for Smart Electric Vehicles

The integration of smart electric vehicles (SEVs) into smart cities marks a significant step toward creating efficient, sustainable, and connected urban spaces. However, secure and private data sharing is a major challenge as SEVs connect with smart city systems. The interaction between SEVs and consumer electronic devices (CEDs) raises serious concerns about data security and privacy. Here, to address these challenges, this article presents how blockchain technology and federated learning (FL) can address these issues. The proposed approach provides a secure and privacy-aware framework for data exchange between SEVs and CEDs in smart cities. The experiment results demonstrate the effectiveness of the proposed framework for secure data sharing and maintaining system reliability in smart city environments. It also enables trust and promotes the widespread adoption of interconnected urban technologies.

Das, Debashis [Meharry Medical College, Nashville,↗
DOE OSTI2025

Data Sharing as a Catalyst for Expanding the Energy Frontier

As the energy landscape evolves to include technologies such as geothermal energy, comprehensive data become essential for driving innovation and scalability, particularly with the growing use of tools like machine learning and artificial intelligence. In emerging sectors, the cost of gathering high-quality data across large spatial areas can present a significant barrier. A key solution is leveraging existing data from well-established industries like oil and gas. However, the proprietary nature of data in these industries often hinders collaboration. This paper explores how cultivating a culture of data sharing can act as a catalyst for progress, fueling breakthroughs across both conventional and renewable energy sectors. Practical compromises that protect business interests while enabling data access are proposed, and real-world success stories are highlighted, demonstrating how collaboration has accelerated advancements in geothermal, carbon capture, and other innovative technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Alabama Carbon Storage: Data Sharing and Engagement (Final Report)

This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Data sharing helps avoid “smoking gun” claims of topological milestones

Manipulating the topology of electronic bands can realize new states of matter, with possible implications for information technology. A central question is how to tell whether a topological regime has been achieved. Experiments are often guided by a prediction of a distinct and self-explanatory signal called “the smoking gun.” However, in micrometer- or nanometer-scale specimens, phenomenology can mimic the anticipated behavior without containing the exotic states. We show limited data that are consistent with the presence of four topological phenomena; by considering additional data, we identified the most likely origins of the observed patterns as trivial. Finally, we argue that the reliability of smoking gun–type claims can be greatly enhanced by releasing comprehensive datasets, discussing alternative scenarios, and disclosing the total volume of study.

Frolov, S. M. [Univ. of Pittsburgh, PA (United Sta↗
DOE OSTI2025

2025 TEM Workshop

The TEM Data Management Workshop will take place on August 26 from 9 a.m. to 12 p.m. MT, and will be held virtually on TEAMS. The primary goal of this workshop is to engage NSUF users and stakeholders in discussions about the data needs for the utilization of AI and ML in the analysis of TEM data. Key topics to be covered include data storage, data sharing, data tagging, metadata inclusion, standardized data formats, data augmentation, and annotated training datasets. Additionally, the workshop will provide valuable insights into resources such as the Nuclear Research Data System (NRDS) for data storage and sharing, as well as open-source codes for data analysis.

Bachhav, Mukesh↗
DOE OSTI2026

Data Interfaces for Automated Vehicle Services - A Municipality Perspective

As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

National Solar Thermal Test Facility: Operations & Maintenance Report

The NSTTF O&M Project continues the operation and maintenance activities of the existing critical capabilities and infrastructure at the NSTTF. This project is to support and assure the success of Solar Heat for Industrial Process in the United States and the larger global community by ensuring the NSTTF is a safe and operational facility. The primary goal of this project will be to maintain the solar tower and heliostat field while also allowing NSTTF staff to improve processes for operations and maintenance. This includes expanding our preventative maintenance program, inventory systems, and our data sharing capabilities. Additionally, this will support an outreach program with regular seminars, sharing of data, and the release of open-source software to support heliostat metrology

14 SOLAR ENERGY↗
DOE OSTI2025

A cost and community perspective on the barriers to microbiome data reuse

Microbiome research is becoming a mature field with a wealth of data amassed from diverse ecosystems, yet the ability to fully leverage multi-omics data for reuse remains challenging. To provide a view into researchers’ behavior and attitudes towards data reuse, we surveyed over 700 microbiome researchers to evaluate data sharing and reuse challenges. We found that many researchers are impeded by difficulties with metadata records, challenges with processing and bioinformatics, and problems with data repository submissions. We also explored the cost constraints of data reuse at each step of the data reuse process to better understand “pain points” and to provide a more quantitative perspective from sixteen active researchers. The bioinformatics and data processing step was estimated to be the most time consuming, which aligns with some of the most frequently reported challenges from the community survey. From these two approaches, we present evidence-based recommendations for how to address data sharing and reuse challenges with concrete actions for future work.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MRCI Task 3: Facilitating Data Collection, Sharing, and Analysis Final Technical Summary Report

The Midwest Regional Carbon Initiative (MRCI) Task 3.0 was defined to facilitate development of carbon capture, utilization, and storage (CCUS) in the region by collection and sharing of existing and new technical data from CCUS projects and research. The task also included support for further analysis and assessment of tools by the project team and by researchers working on programs such as National Risk Assessment Partnership (NRAP), machine learning (ML) techniques, and assessment and improvement of CCUS site assessment, operations, and monitoring aspects. Work under Task 3.0 addressed key issues related to CCUS deployment and provided foundational research and datasets to help establish CCUS projects in the MRCI. Report Authors and Principal Technical Contributors: Joel Sminchak, Laura Keister, Mackenzie Scharenberg, Priya Ravi-Ganesh, Autumn Haagsma, Srikanta Mishra, Jared Hawkins, Jared Schuetter, Amy Lang, Jaelen Lewis, Derrick James, Jorge Barrios, Stuart Skopec, and Sanjay Mawalkar (Battelle). Chris Korose, Carl Carmen, Nate Grigsby, Nathan Webb (Illinois State Geological Survey). Principal Investigators: Dr Neeraj Gupta, Dr. Chris Korose.

MRCI,NRAP,data collection,data compilation,legacy ↗
DOE OSTI2025

District Geothermal Heating + Cooling Deployment in a CT Environmental Justice Community

The report marks the team’s completion of all required tasks and milestones. Work completed for Task 1 (Technical and Economic Feasibility Assessment & Procurement Drafting) included development of analysis and design model; completion of technical, economic, and environmental assessments; and technical outreach and coalition design. Components for Task 2 (Outreach & Community Engagement) involved broad outreach and community-engagement efforts (including stakeholder meetings and a webinar as well as development of a formal engagement plan) and development of a web page and a case study. For Task 3 (Workforce Transition, Development, & Training Plan), the team undertook a formal statewide geothermal workforce needs assessment, developed corresponding recommendations for both the state as a whole and the Wallingford project, and held several workshops. For Task 4 (Project Management & Data Sharing), the team drafted a data-sharing plan.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART): Data Format and Preparation Guidance (V.5.0)

The Joint Office of Energy and Transportation maintains the Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART), which provides a centralized hub for submitting electric vehicle (EV) charging infrastructure data directed by the Federal Highway Administration (23 CFR 680.112(a)-(c)). EV-ChART provides a streamlined data submission process and an integrated set of analytic tools, connects to other data sources, and empowers data sharing and access across stakeholders, including the public. Any data shared publicly will be aggregated and anonymized to stay in accordance with 23 CFR 680. This EV-ChART Data Format and Preparation Guidance provides a comprehensive overview of the data reporting requirements as authorized under 23 CFR 680.112(a)-(c)). The guidance is intended to be used alongside the EV-ChART Data Input Template, which defines the tabular data structure that these data submissions must follow. Per 23 CFR 680.112(a)-(c), the annual and quarterly data submissions are required of all National Electric Vehicle Infrastructure (NEVI) Formula Program projects, as well as projects for the construction of publicly accessible EV chargers that are funded with funds made available under Title 23, United States Code, including any EV charging infrastructure project funded with federal funds that is treated as a project on a federal-aid highway. One-time data submissions are required of both the NEVI Formula Program projects and grants awarded under 23 U.S.C. 151(f) for projects that are for EV charging stations located along and designed to serve the users of designated Alternative Fuel Corridors (AFCs). Other information and data required in 23 CFR 680, such as 23 CFR 680.112(d), 23 CFR 680.116(c), and 23 CFR 680.106(a), are not discussed in this guidance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗