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DOE OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗

NASA NTRSDate not supplied

Preservation of Provenance and Context to Ensure Future Understandability of Airborne Earth Observations and Derived Data Products

Open-source science goes beyond making data from scientific projects (e.g., on-orbit/satellite missions, airborne and field investigations, and other data producing activities) openly available after they are generated, but involves and open sharing of information throughout the project lifecycle. Preservation of the data and associated information required for understanding and reusing the data well after the scientific projects is a contributor to open-source science as well. Considering the high investment in the on-orbit/satellite missions, we had developed a document titled “NASA Earth Science Data Preservation Content Specification (PCS)” in 2011. This document has been used as a requirement for recent on-orbit/satellite missions by NASA. Recently it became clear that the specifications should be applied to other scientific projects as well. Therefore, the document was revised to cover other types of projects, and a Preservation Content Implementation Guidance (PCIG) document was also developed. The revised PCS, and the PCIG, were published in 2022. The purpose of this presentation is to highlight the contents of these documents as they apply to suborbital/airborne investigations. The PCS calls for content preservation in eight general categories - Measuring Instrument/Platform Description, Instrument and Science Data Products and Metadata, Science Raw Data, Product and Algorithm Documentation, Instrument Calibration, Science Algorithm Software, Science Data Product Algorithm Inputs, Science Data Product Validation, and Science Data Access and Analysis Tools. While all these categories apply to various types of projects, a few clarifying sentences have been added to the descriptions of contents in each of the categories to show which categories are especially important to airborne and field investigations and where some contents are not applicable (or difficult to obtain). The PCIG document provides some general guidance applicable to all types of projects and specific guidance in a separate section for airborne and field investigations. This section calls out typical artifacts produced during such investigations that can meet the spirit of the various PCS categories.

remote sensing↗

DOE OSTI2026

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗

DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗

DOE OSTI2025

Instantiation of the Damara Tern Platform for Advanced Materials and Manufacturing Technologies (AMMT) Program Collaborative Data Management

This work package focused on deploying an instance of the Damara Tern platform to support AMMT collaborative research activities. The objectives were to provide selected AMMT collaborators with access to a shared environment for capturing operations, trackables, and associated metadata, and to implement data entry functionalities that reflect site-specific procedures. Key activities included creating configurable, schema-driven entry forms and validating the data collection process. The report details the deployment process, the platform infrastructure, and the implemented data entry workflows, providing a reference for end users and establishing a foundation for future production-scale deployments.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

Life Sciences data↗

DOE OSTI2025

Best Practices for Nuclear Experiment Data Preservation at Idaho National Laboratory: A Guide for Researchers and Reactor Operators

Preserving experimental data is essential for supporting advancements in nuclear science and ensuring the longevity of Idaho National Laboratory's contributions to reactor technology and safety. This report provides a comprehensive guide to best practices for experimental data management and preservation, focusing on standardized data formats, redundancy in storage, metadata documentation, and alignment with international standards. By following these recommendations, experimentalists and reactor operators can enhance the accessibility, reproducibility, and utility of critical datasets for regulatory review, validation computational methods, and future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2023

Commercial Smallsat Data Acquisition Program: Airbus U.S. Synthetic Aperture Radar Quality Assessment Summary

Quality assessment of the Airbus X-band Synthetic Aperture Radar (SAR) satellite products was conducted by the Commercial Smallsat Data Acquisition (CSDA) program’s radar subject matter experts, following the Joint NASA/ESA (European Space Agency) assessment draft guidelines. All three Airbus SAR spacecraft (TerraSAR-X, TanDEM-X, and PAZ) are based on the TerraSAR-X platform, and each have an active phased array antenna that is 4.8 x 0.7 m in the along-track and cross-track dimensions, respectively. TerraSAR-X and TanDEM-X are in a helical orbit, creating a bistatic imaging geometry, in addition to being capable of independent monostatic observations. The PAZ mission follows TerraSAR-X and TanDEM-X in the same 11-day orbit with a 5.5-day lag. TerraSAR-X and TanDEM-X are designed, developed, and operated through a Public-Private Partnership, while PAZ is a dual-use mission (civil and defense agencies), funded and owned by the Spanish Ministry of Defense and managed by Hisdesat (Hisdesat Servicios Estratégicos, S.A.), a Spanish private communications company. The assessment presented in this document is divided into two main parts: documentation review and the assessment of test datasets. The documentation review in sections 2.1 through 2.4 includes the assessment of the Airbus documentation provided to the CSDA evaluation team. The grading of these documents is given in columns 1-4 of the maturity matrix shown in section 1.1. Section 2.5 summarizes the evaluation performed by NASA using the data purchased through the CSDA program. The grading for this is given in the last column of the maturity matrix. Section 3 provides more detailed explanations on the methods and the results of the data analysis performed by NASA. Only the documents provided by Airbus for the evaluation were considered for the review. Additional documentation with more detailed description of the calibration and validation procedures may be available online but were not considered for this evaluation. The product information provided in the available documentation (RD-1, RD-2) and the product metadata together provided adequate information to work with the data. The product details in the metadata included the required information to work with the data in the common XML file format. Metrological traceability documentation was not provided to CSDA. All relevant characterization of the SAR system and data were provided, and the metadata include all relevant ancillary information. Documentation provided to CSDA included limited pre-flight and post-launch calibration information.

Batuhan Osmanoglu↗

NASA NTRS2013

Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In

Sally Ride EarthKAM is an educational program funded by NASA that aims to provide the public the ability to picture Earth from the perspective of the International Space Station (ISS). A computer-controlled camera is mounted on the ISS in a nadir-pointing window; however, timing limitations in the system cause inaccurate positional metadata. Manually correcting images within an orbit allows the positional metadata to be improved using mathematical regressions. The manual correction process is time-consuming and thus, unfeasible for a large number of images. The standard Google Earth program allows for the importing of KML (keyhole markup language) files that previously were created. These KML file-based overlays could then be manually manipulated as image overlays, saved, and then uploaded to the project server where they are parsed and the metadata in the database is updated. The new interface eliminates the need to save, download, open, re-save, and upload the KML files. Everything is processed on the Web, and all manipulations go directly into the database. Administrators also have the control to discard any single correction that was made and validate a correction. This program streamlines a process that previously required several critical steps and was probably too complex for the average user to complete successfully. The new process is theoretically simple enough for members of the public to make use of and contribute to the success of the Sally Ride EarthKAM project. Using the Google Earth Web plug-in, EarthKAM images, and associated metadata, this software allows users to interactively manipulate an EarthKAM image overlay, and update and improve the associated metadata. The Web interface uses the Google Earth JavaScript API along with PHP-PostgreSQL to present the user the same interface capabilities without leaving the Web. The simpler graphical user interface will allow the public to participate directly and meaningfully with EarthKAM. The use of similar techniques is being investigated to place ground-based observations in a Google Mars environment, allowing the MSL (Mars Science Laboratory) Science Team a means to visualize the rover and its environment.

Andres, Paul M.↗

DOE OSTI2025

A machine learning method of modern urban building energy modeling: A case study of Chicago

Urban-scale building energy modeling is vital for urban planning. However, it can be challenging to assimilate reliable non-geometry building data for urban-scale modeling without extensive investment. Here, this study introduces a novel approach to developing modern urban-scale building energy stock data using geographic information systems and machine learning algorithms without necessarily requiring pre-supplied non-geometric metadata. The proposed framework integrates building footprint and height data to estimate gross floor areas, and matches each building to a pool of candidate records from ComStock or ResStock—filtered to the same county and ranked by geometric similarity—demonstrate a proof-of-concept case study in Chicago for predicting energy use intensity (EUI) using scalable datasets. The model achieved a mean bias error (MBE) of 0.08 kWh/m² and root mean square error (RMSE) of 14.84 kWh/m² under full metadata input for EUI prediction. With only location inputs, the model captured 69.2 % of EUI within predicted ranges. These results demonstrate the model’s potential to support early-stage urban planning, identify candidates for energy-efficient retrofits. By removing the dependency on detailed pre-surveys or extensive building metadata, the approach overcomes a key barrier in traditional urban-scale building energy modeling, illustrating a pathway toward broader and more cost-effective application, though further multi-city validation and improved treatment of pre-1925 buildings are needed.

Energy Use Intensity↗

DOE OSTI2026

QPatLib v1.0 — Measurement-based quantum simulation Pauli string unitary pattern collections

This Zenodo record accompanies the paper “Scalable Measurement-Based Quantum Simulation Patterns for Benchmarking” arXiv.2605.12502 and provides QPatLib v1.0 measurement-pattern datasets in human-readable JSONL together with a ZIP archive of OpenQASM 3.0 circuits used for validation and reproducibility. The patterns and circuits implement Pauli string unitaries for benchmark cases. Cases include all possible string combinations for less than 6 qubits and strings used in Hamiltonians for certain diatomic molecules for 6 or more qubits. Format: Each pattern_*.jsonl file is containins measurement patterns for all subsets for a given model/instance and subset strategy: it begins with a preamble containing model metadata, subset definitions, provenance, and (when feasible) full-pattern test results, followed by one pattern entry per subset. Each subset entry includes a required pattern_ascii field storing the measurement pattern in the measurement-calculus/Graphix standard with signal shifting, written left-to-right in the canonical order nodes → edges → measurements (with signal dependencies) → byproduct corrections (X/Z). The circuits are included as circuit_files.zip. Patterns in this record were validated against the corresponding circuits and checked for causal flow. Codes for generating these patterns can be found at QPatLib repository on Github

Graphix↗

DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗

DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2006

Data Model Management for Space Information Systems

The Reference Architecture for Space Information Management (RASIM) suggests the separation of the data model from software components to promote the development of flexible information management systems. RASIM allows the data model to evolve independently from the software components and results in a robust implementation that remains viable as the domain changes. However, the development and management of data models within RASIM are difficult and time consuming tasks involving the choice of a notation, the capture of the model, its validation for consistency, and the export of the model for implementation. Current limitations to this approach include the lack of ability to capture comprehensive domain knowledge, the loss of significant modeling information during implementation, the lack of model visualization and documentation capabilities, and exports being limited to one or two schema types. The advent of the Semantic Web and its demand for sophisticated data models has addressed this situation by providing a new level of data model management in the form of ontology tools. In this paper we describe the use of a representative ontology tool to capture and manage a data model for a space information system. The resulting ontology is implementation independent. Novel on-line visualization and documentation capabilities are available automatically, and the ability to export to various schemas can be added through tool plug-ins. In addition, the ingestion of data instances into the ontology allows validation of the ontology and results in a domain knowledge base. Semantic browsers are easily configured for the knowledge base. For example the export of the knowledge base to RDF/XML and RDFS/XML and the use of open source metadata browsers provide ready-made user interfaces that support both text- and facet-based search. This paper will present the Planetary Data System (PDS) data model as a use case and describe the import of the data model into an ontology tool. We will also describe the current effort to provide interoperability with the European Space Agency (ESA)/Planetary Science Archive (PSA) which is critically dependent on a common data model.

RDF↗

NASA NTRS2003

Enabling knowledge discovery: taxonomy development for NASA

This white paper provides the background for why it is important to take the next steps with the NASA taxonomy including test and validation, XML schema development, integration with the FirstGov federal search engine, the OneNASA portal and its supporting web content management system.

taxonomy metadata classification schema NASA porta↗

NASA NTRS2016

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗