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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

SPRUCE: Peat Core Sample Collection Metadata, Marcell Experimental Forest, Minnesota, August 2024

This data set contains metadata associated with peat core samples collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in August 2024. This sample metadata contains no analytical results and is a reference for analytical datasets. To ensure accessibility and discoverability, each sample was assigned an International Generic Sample Number (IGSN), a persistent identifier, using System for Earth and Extraterrestrial Sample Registration (SESAR). These samples were used for downstream analysis by multiple teams of researchers the results of which will be reported separately. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. An aliquot of most samples is stored at Oak Ridge National Laboratory and may be available for further analysis. Access this collection event on SESAR https://doi.org/10.58052/IEJ9B00VQ. To inquire about obtaining archived samples for analysis, reach out using the Contact Sample Owner form located on the bottom of the landing page in SESAR. Note: Only dried and ground material from C Cores are available for new analysis.

Birkebak, Joshua [ORNL] (ORCID:0009000955611494)↗
DOE OSTI2026

SPRUCE Peat Core Sample Collection Metadata, Marcell Experimental Forest, Minnesota, August 2025

This data set contains metadata associated with peat core samples collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in August 2025. This sample metadata contains no analytical results and is a reference for analytical datasets. To ensure accessibility and discoverability, each sample was assigned an International Generic Sample Number (IGSN), a persistent identifier, using System for Earth and Extraterrestrial Sample Registration (SESAR). These samples were used for downstream analysis by multiple teams of researchers the results of which will be reported separately. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. An aliquot of most samples is stored in the SPRUCE archive and may be available for further analysis by request. Access this collection event on SESAR https://doi.org/10.58052/IEJ9B05LW. To inquire about obtaining archived samples for analysis, reach out using the Contact Sample Owner form located on the bottom of the landing page in SESAR.

EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRI↗
DOE OSTI2025

SPRUCE: Shrub-Layer Vegetation Biomass Collection Metadata, Marcell Experimental Forest, Minnesota, August 2025

This data set contains metadata associated with shrub-layer vegetation samples collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in August 2025. This sample metadata contains no analytical results and is a reference for analytical datasets. To ensure accessibility and discoverability, each sample was assigned an International Generic Sample Number (IGSN), a persistent identifier, using System for Earth and Extraterrestrial Sample Registration (SESAR). These samples were used for downstream analysis by multiple teams of researchers the results of which will be reported separately. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. An aliquot of most samples is stored in the SPRUCE archive and may be available for further analysis by request. See below under 7 Sample Access. Access this collection event on SESAR https://doi.org/10.58052/IEJ9B069L. To inquire about obtaining archived samples for analysis, reach out using the Contact Sample Owner form located on the bottom of the landing page in SESAR.

Birkebak, Joshua [ORNL] (ORCID:0009000955611494)↗
DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PNNL Soil Microbiome SFA

PNNL's Soil Microbiome Science Focus Area (SFA) is focused on understanding the basic biology underpinning how interactions among various soil microbial community members, across trophic levels, lead to the emergence of community functions. Moisture, in particular, drives microbial interactions and influences everything from cell function to substrate fate within soils. The group predicts this results in repeatable, predictable phenotypes. The sum of these phenotypes comprises the “soil metaphenome”. Understanding how the soil metaphenome shifts in response to moisture will provide a basis for modeling and predicting these shifts in reaction network responses. PNNL’s Soil Microbiome SFA project repository on DataHub allows for exploring and downloading integrated experimental omics data, experimental metadata, pre- and post-processed data files, and other associated materials directly related to experimental project publication data. Visit the PNNL Soil Microbiome Science Focus Area Program homepage for more information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SFA-VirOmics

PNNL's Soil Microbiome Science Focus Area (SFA) is focused on understanding the basic biology underpinning how interactions among various soil microbial community members, across trophic levels, lead to the emergence of community functions. Moisture, in particular, drives microbial interactions and influences everything from cell function to substrate fate within soils. The group predicts this results in repeatable, predictable phenotypes. The sum of these phenotypes comprises the “soil metaphenome”. Understanding how the soil metaphenome shifts in response to moisture will provide a basis for modeling and predicting these shifts in reaction network responses. Visit the PNNL Soil Microbiome Science Focus Area Program homepage for more information. PNNL’s Soil Microbiome SFA Virome Dataset Annotation page is an extension to the PNNL Soil Microbiome SFA repository on DataHub allows for exploring and downloading integrated experimental omics dataset annotations, associated experimental metadata, pre- and post-processed viral data files, and other associated materials directly related to experimental viral project data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

RhizoGrid Indexed Sorghum Rhizosphere Multi-Omics

PerCon SFA project data dentification of spatially resolved biomarkers of drought in Sorghum bicolor rhizosphere molecular-microbe interactions using a novel root cartography "RhizoGrid" system for sampling plants under drought and control conditions across 10 equally sized root zone environments (4 quadrants each). Each quadrant was sampled and processed for 16S amplicon, metabolomics, and X-ray computed tomography (XCT). Data download includes experimental metadata and results files for 16S rRNA sequence analysis of microbial community assembly (processed data files), liquid chromatography mass spectrometry (LC-MS) metabolomics analysis of microbial community root exudates (processed data files), X-ray computed tomography (XCT) spatial gradient analysis (raw and processed data files) of microbial community composition, and related computational modeling outputs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗
DOE OSTI2020

Introduction to Metadata and Ontologies: Everything You Always Wanted to Know About Metadata and Ontologies (But Were Afraid to Ask)

Metadata are contextual data about your experimental data. Metadata are the who, what, when, where, and why of these data. Metadata puts these data into context. In microbiome research, metadata includes information about the sample: when it was collected, where it was collected from, what kind of sample it is, and what were the properties of the environment or experimental condition from which the sample was taken. Information about sample processing is also metadata: methods used to extract and purify molecules (e.g., DNA) from the sample, type of DNA sequencing or other ’omics analyses done, and where the raw experimental data are located. This documentation includes information on the following topics: What are metadata? What kinds of metadata are there? Why are metadata important? Why should metadata be standardized? Why should metadata be machine readable? What is an ontology? Why are ontologies important? What are some examples of ontologies? The documentation also includes further resources on metadata guides and tools and tips for formatting metadata for maximum utility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The Materials Provenance Store

Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

imaging↗
DOE OSTI2025

PPI DataHub Project Data Package: S. elongatus PCC 7942 Limited Proteolysis and Thermal Proteome Profiling Structural Proteomics (JM-PB-DP3)

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques using limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and redox enrichment (Redox-MS) in evaluating alterations solvent accessibility and structural stability caused by light perturbation at the molecular level. Experimentally processed sample data for LiP and TPP proteomic datasets were derived from the same cell culture stock, prepared simultaneously in parallel, and acquired by mass spectrometry. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental samples processed for LiP-MS label-free quantification (LFQ) or TPP-MS tandem mass tag (TMT) 10-plex were acquired using a Q-Exactive HF-X mass spectrometer and processed/compiled using either MSGF+ (v2024.03.26) or ​​​​PlexedPiper for proteome evaluation. Additional software supporting downstream proteomic analysis include FragPipe (v.4.0), MSFragger (v.22.1), and an adapted Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and processed protein annotated abundance files.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting

Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

14 SOLAR ENERGY↗
DOE OSTI2021

Research Software Engineering Efforts for DataFlow: FY2021 Developments

DataFlow is a web application that helps scientific data to flow from one source location to another destination location. DataFlow helps scientists easily capture scientific metadata associated with an experiment and transmit both metadata and experimental data to a designated, centralized data storage resource. This report describes the software engineering efforts and architecture of the project for the fiscal year 2021 developments. We hope it effectively communicates findings from our work, challenges we have overcome, and how we will continue our development of DataFlow in the future.

42 ENGINEERING↗
DOE OSTI2025

Rhodotorula toruloides Nitrogen Limitation PTM Profiling Multi-Omics (TZ-DP1)

The purpose of this experiment was to evaluate the regulatory stress response of Oleaginous yeast species Rhodotorula toruloides NBRC 0880 (JGI strain IFO0880 v4.0) under nitrogen-rich and nitrogen-limited conditions over time. Time course experimental samples (0, 24, 48, and 72 hours after inoculation) were prepared using a semi-automated multi-PTM proteomic approach, using tandem mass tag 18-plex (TMT18), and lipidome remodeling for downstream multi-omics analysis. Processed datasets are openly accessible from PNNL DataHub and contain secondary processed proteomic (redox, phospho, and global TMT) and lipidomic (positive and negative ion mode) results files and experimental design metadata.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Omics-Lethal Human Viruses, Influenza A Experiment ICL105

The purpose of this experiment was to evaluate the host epigenetic response to Influenza A (H5N1) viral infection from human lung adenocarcinoma cells (Calu-3) high throughput sequencing (ChIP-Seq) sample data. Host-associated viral downloads contain one or more primary host-pathogen experimental study metadata design workbooks (supporting metadata only).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Omics-Lethal Human Viruses, Influenza A Experiment ICL106

The purpose of this experiment was to evaluate the human host epigenetic response to Influenza A H5N1 virus and MERS-CoV viral infection from human lung adenocarcinoma cell (Calu-3) high throughput sequencing (MeDIP-Seq) sample data. Host-associated viral downloads contain one or more primary host-pathogen experimental study metadata design workbooks (supporting metadata only).

59 BASIC BIOLOGICAL SCIENCES↗