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NASA NTRSDate not supplied

The Genesis Non-flight Implant Sample Archive

Introduction: For the past 20 years the Genesis science community has been analyzing solar wind captured in sample collectors now curated at Johnson Space Center (JSC). Non-flown flight like collector materials (non-flight) considered reference material was always allocated by the JSC Genesis team for testing experimental procedures. However, to expedite Genesis science, distribution of non-flown reference materials were also provided by, D. S. Burnett (Genesis PI, Caltech) and A. J. G. Jurewicz (ASU) who were funded to aid all Genesis PIs by providing engineering test materials archived at Caltech pre-flight as well as ion implants into these materials [1]. Implanted materials (non-flight reference, flight-like semiconductor materials, and NIST SRM glass) received from the Caltech collection during a pilot project are being used to develop an accession procedure for the purpose of making these materials available to researchers in the future. These implant samples are important because they can be utilized as measurement standards [2]. This is a work in progress and this work reports progress in documentation and imaging. The pilot program received 63 implanted reference materials with known implanted ions. Genesis Non-Flight Database: The existing non-flight material database was modified to allow for the description of implant sessions and to allow correlation of the implant information to individual specimens. An implant session has three main components: a known ion, a known dose (ion/cm2), and a known energy (keV). Procedure: Laboratory Procedure. Upon receiving non-flight analytical standards (independently calibrated implants), or implant samples (nominally calibrated implants), they will be imaged in their current containers to capture any information written before being brought into the Genesis Sample Laboratory. Documentation is provided by the donor and used for correlating the data between the implant sessions and implant samples. This information is not verified by Genesis Curation. The microscope used to image samples in the laboratory has an automated stage with a stainless-steel plate to hold samples and a camera that utilizes the software Surveyor to take photomosaic images of the implant samples. The camera attached to the microscope is a QICAM High Performance IEEE 1394 FireWire Digital CCD Camera. This setup, shown in Fig. 1, allows for photomosaics of small-scale samples with a 10x objective or 25x objective. Examples of images taken with this microscope setup are shown in Fig.2 and Fig. 3. After imaging, samples get stored in a clean fluoroware container or polypropylene vial depending on sample size for long term storage. Database Procedure. The Genesis Non-Flight Database currently allows for the addition of implant sessions. The required fields for adding an implant session are the implant session ion, the implant session dose, and the implant session energy. There are also fields for the date and vendor of the sessions as well as a field for if the session was calibrated or not. Once an implant session has been inserted into the database, additional documentation from the session can be attached. The database will automatically assign an Implant Session Number. After inserting an implant session, the implanted samples can be inserted. The fields required for an implant sample are the material of the sample and the size of the sample. The Generic name for the implant sample with be automatically generated by the database once it has been inserted. Implant sample numbers begin with 3X to distinguish from flown Genesis sample numbers. There are also fields for processors to add the location (room, cabinet, tray) and container type (fluoroware, polypropylene vial). Once an implant session and an associated sample(s) have been inserted into the database the sample can be then tied to all associated implant sessions. An implant sample can be tied to multiple implant sessions. Future work: For curation, future work consists of creating an online catalogue for implant samples as well as updating the database with more samples as we receive them. For the scientific community these implant samples have many potential uses. For Genesis research, they are directly applicable for testing and, in some cases, as analytical standards. For non-Genesis research, implants (especially calibrated implants) could be used for calibrating other, unique planetary materials [1].

C D Calva↗
DOE OSTI2025

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Missing microbial eukaryotes and misleading meta-omic conclusions

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Proteome-scale Structure Prediction Data - Pseudodesulfovibrio mercurii

The number of proteins predicted for Pseudodesulfovibrio mercurii is 3,446, each of which have five predicted structures from an AlphaFold run, as well as structural alignment results using the TMscore-based structural alignment method within the APoc program. Specifically, AlphaFold outputs the atoms and coordinates of the protein model in human-readable PDB files and quantitative prediction metrics in Python PICKLE files. The 5 models have been ranked based on the predicted TM-score (pTMS), a quantitative confidence metric output by AlphaFold that reports on protein model quality. The top ranked model has undergone an energy minimization calculation to relax and remove any potential clashes in the atomic coordinates. Structural alignment results are stored in two files for each protein; the top ranked model (as discussed above) is used for all alignment analyses. Both are compressed gzip files that, once unpacked, are human readable. The first file is the TMalign score results and contains the quantitative metrics for the top alignments between the predicted structure and experimental structures from the PDB70, a curated non-redundant database of about 80,000 experimental structures developed by the Soding lab. Each data point in this file is directly associated with one experimental structure; PDB ID and brief meta-data about the protein taken from the PDB70 file are reported alongside the quantitative metrics. The second results file contains the raw results associated with each alignment reported in the score results file. Specifically, the translation and rotation arrays for each alignment are provided so that the structural alignment can be recreated. Additionally, residue-level scores are reported to quantify the closeness of the aligned residues between the predicted and experimental models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

$\mathrm{COBRA}$ improves the completeness and contiguity of viral genomes assembled from metagenomes

Viruses are often studied using metagenome-assembled sequences, but genome incompleteness hampers comprehensive and accurate analyses. Contig Overlap Based Re-Assembly (COBRA) resolves assembly breakpoints based on the de Bruijn graph and joins contigs. Here we benchmarked COBRA using ocean and soil viral datasets. COBRA accurately joined the assembled sequences and achieved notably higher genome accuracy than binning tools. From 231 published freshwater metagenomes, we obtained 7,334 bacteriophage clusters, ~83% of which represent new phage species. Notably, ~70% of these were circular, compared with 34% before COBRA analyses. We expanded sampling of huge phages (≥200 kbp), the largest of which was curated to completion (717 kbp). Improved phage genomes from Rotsee Lake provided context for metatranscriptomic data and indicated the in situ activity of huge phages, whiB-encoding phages and cysC- and cysH-encoding phages. COBRA improves viral genome assembly contiguity and completeness, thus the accuracy and reliability of analyses of gene content, diversity and evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Case Study: NREL Campus Chilled Water Storage Potential: Benchmark Datasets Development and Applications, Task 4 - Use Case Demonstration

The Benchmark Datasets Development and Applications project is a three-year collaboration between the National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Lawrence Berkeley National Laboratory. The project seeks to collect and curate high-resolution, well-calibrated time series of building operational and indoor/outdoor environmental data, which are crucial to understanding and optimizing building energy efficiency performance and demand flexibility capabilities as well as benchmarking energy algorithms. Project outcomes include approximately twelve high-fidelity building datasets, enhanced data representation tools, and four case studies to illustrate example applications. The goal of these case studies is to define and execute analyses that demonstrate how one or more datasets collected through this project can address a data gap or challenge historically faced by building stakeholders. This technical paper summarizes the findings of one of these case studies, in which we studied the operational efficiencies of the central cooling system at NREL. We looked at three years of data from the three chillers in the Field Test Laboratory Building (FTLB), from 2019 to 2021, to compare equipment operation and demand throughout the time period. Our analysis indicates that all three chillers are operating at or below the optimal loading conditions for most of the operation time, and thus there was no efficiency drop due to loading of the chillers at full capacity. Our recommendation is that no chiller capacity increase is needed; instead, the central plant could benefit from adopting advanced control logics for optimal sequencing of chillers during part load operations. Analysis of adding chilled water thermal storage to the central plant indicated 34% savings in demand cost and 24.5% savings in total cost (energy consumption and demand charge cost). The payback period is estimated to be 11-22 years with an assumed TES cost of $\$$100-$200 per ton. This case study shows how a selected dataset is used to solve a practical building problem - learning the operational status of its components, analyzing the effectiveness of a proposed new technique, and aiding decision-making for the building operations and maintenance team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2000

International Agreement on Planetary Protection

The maintenance of a NASA policy, is consistent with international agreements. The planetary protection policy management in OSS, with Field Center support. The advice from internal and external advisory groups (NRC, NAC/Planetary Protection Task Force). The technology research and standards development in bioload characterization. The technology research and development in bioload reduction/sterilization. This presentation focuses on: forward contamination - research on the potential for Earth life to exist on other bodies, improved strategies for planetary navigation and collision avoidance, and improved procedures for sterile spacecraft assembly, cleaning and/or sterilization; and backward contamination - development of sample transfer and container sealing technologies for Earth return, improvement in sample return landing target assessment and navigation strategy, planning for sample hazard determination requirements and procedures, safety certification, (liaison to NEO Program Office for compositional data on small bodies), facility planning for sample recovery system, quarantine, and long-term curation of 4 returned samples.

Source record↗
DOE OSTI2021

Smart Semi-Supervised Accumulation of Large Repositories for Industrial Control Systems Device Information

Industrial Control Systems device manufacturers frequently add new features to improve their product performance. Oftentimes, these changes are mainly vendor-driven initiatives, and customers may not be aware of the full impact of these new capabilities on their cybersecurity posture. In the energy sector, this can lead to considerable dissonance between vendor-provided cybersecurity claims and a customer’s responsibility for Operation Technology cybersecurity compliance. Thus, the resulting dynamic verification burden is shifted towards the customer and may pose a significant cybersecurity risk to the energy sector landscape. We found that there is very limited research into cybersecurity auditing for Operational Technology. However, a solution is needed for vetting the vendor-supplied feature claims and their adherence to cybersecurity requirements and standards. We are presently engaged in an effort to develop such a system. This paper demonstrates one vital aspect of this effort in proposing an end-to-end framework to accumulate a large repository of ICS device information for this vetting system, curate the dataset, and conduct extensive processing. This framework is designed to use web scraping, data analytics and Natural Language Processing (NLP) techniques to identify vendor websites, automate the collection of website-accessible documents and automatically derive metadata from them for identification of product documents relevant to the repository. We have found that this automated approach to vendor identification, document extraction into a product repository, and NLP pre-processing is unique and has not been previously presented in the literature. The preliminary work shows that this is feasible and can produce reliable results with minimum supervision. Future work will be built upon this foundation in order to achieve semi-supervised vetting of device technical information – a vital capability for ensuring that vendor-claimed device cybersecurity capabilities match industry requirements.

Ameri, Kimia↗
DOE OSTI2026

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipelines ability to ingest several modalities of information. We present (1) a curated dataset of 93 questions designed to evaluate a pipeline's ability to ingest textual data, tables, images, multimodal data, and cross-document multimodal data; (2) a phrase-level recall metric for correctness; (3) a nearest-neighbor embedding classifier in an attempt to classify pipeline hallucinations; (4) a comparative evaluation of 2 pipelines built with open-source retrieval mechanisms and 4 closed-source foundational models; and (5) a third-party human evaluation of the alignment of our correctness and hallucination metrics. We find that closed-source pipelines significantly outperform open-source pipelines in both the correctness and halucination metrics, with a wider performance gap in questions relying on multimodal and cross-document information. We also find after a human evaluation of our correctness and hallucination metric compared with our questions and pipeline responses, average agreement was 4.62 for correctness 4.53 for hallucination detection on a 1-5 Likert scale with 5 being strongly agree with our determination.

Hildebrand, Samuel [ORNL] (ORCID:0009000465963104)↗
DOE OSTI2025

Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs

The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Reference Site Conditions for Floating Wind Arrays in the United States

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.

17 WIND ENERGY↗
NASA NTRS2017

The NASA Ames Research Center Institutional Scientific Collection: History, Best Practices and Scientific Opportunities

The NASA Ames Life Sciences Institutional Scientific Collection (ISC), which is composed of the Ames Life Sciences Data Archive (ALSDA) and the Biospecimen Storage Facility (BSF), is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and animal biospecimens collected from life science spaceflight experiments and matching ground control experiments. Both fixed and frozen spaceflight and ground tissues are stored in the BSF within the ISC. The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). As part of our best practices, a viability testing plan has been developed for the ISC, which will assess the quality of archived samples. We expect that results from the viability testing will catalyze sample use, enable broader science community interest, and improve operational efficiency of the ISC. The current viability test plan focuses on generating disposition recommendations and is based on using ribonucleic acid (RNA) integrity number (RIN) scores as a criteria for measurement of biospecimen viablity for downstream functional analysis. The plan includes (1) sorting and identification of candidate samples, (2) conducting a statiscally-based power analysis to generate representaive cohorts from the population of stored biospecimens, (3) completion of RIN analysis on select samples, and (4) development of disposition recommendations based on the RIN scores. Results of this work will also support NASA open science initiatives and guides development of the NASA Scientific Collections Directive (a policy on best practices for curation of biological collections). Our RIN-based methodology for characterizing the quality of tissues stored in the ISC since the 1980s also creates unique scientific opportunities for temporal assessment across historical missions. Support from the NASA Space Biology Program and the NASA Human Research Program is gratefully acknowledged.

ALSDA↗
NASA NTRS2019

NASA GeneLab Platform Utilized for Space Radiation Dosimetry Biological Response Compared to Radiation Ground Studies

Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The NASA GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) includes datasets from both spaceflight and ground-based studies, a majority of which involved exposure to ionizing radiation. Recently GeneLab has also assessed radiation dosimetry data with omics datasets associated with samples flown to the International Space Station (ISS). The combination of the detailed information on radiation exposure for ground-based studies and curated dosimetry information for spaceflight experiments allows GeneLab to be the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. We demonstrate the usefulness of these datasets by analyzing multiple GeneLab datasets associated with both radiation ground-based studies and spaceflight studies. The radiation ground based studies we analyzed includes both in vivo and in vitro work with a range ions from protons to iron particles with doses from 0.1Gy to 2Gy. These datasets were compared to both in vivo and in vitro datasets from samples flown to the ISS and on shorter shuttle missions with total doses of 0.1 mGy to 30 mGys. From this analysis we were able to associate distinct biological signatures associating specific ions to specific biological response to radiation exposure in space. For example, we discovered radiation biological response related to cardiovascular effects from proton ground studies are the dominating response for samples related to cardiovascular effects on the ISS. With this work we will provide a summary of how different ions will impact different biological response in space and how this can be used in future studies to assess optimal ground experiments to simulate space radiation.

Beheshti, Afshin↗
NASA NTRS2016

Biospecimen Culling: Temporal RNA Integrity Analysis Across Spaceflight Missions Dating from 1985 to 2011

The Ames Life Science Data Archive (ALSDA) at NASA Ames Research Center is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and biospecimens collected from life science spaceflight experiments and matching ground control experiments. They are stored in the Ames biobank, which is located in the Biospecimen Storage Facility (BSF). The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). The BSF maintains both fixed and frozen spaceflight and ground tissues, collected from recent and past spaceflight missions. Due to the ever increasing demand for space to preserve current and future flight biospecimens, the ALSDA has initiated the development of a culling plan for biospecimens currently stored in the BSF. Culling enables the ALSDA to assess the quality of archived samples, and supports the development of standardized culling procedures that improve the operational efficiency of the BSF. The culling plan focuses on generating disposition recommendations for samples in the BSF, and currently is based on measuring ribonucleic acid (RNA) integrity number (RIN). The culling process includes (1) sorting and identification of candidate samples for RIN analysis, (2) completion of RIN analysis on select samples, and (3) development of disposition recommendations for specimens based on the RIN values. Furthermore, our approach allows for unique scientific opportunities, including development of a RIN-based methodology for culling, and temporal assessment of the quality of the tissues that have been stored in BSF since the 1980s. Results of this work will also support NASA open science initiatives.

biospecimen↗
DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗
NASA NTRS2018

Sentinel-5P/TROPOMI and S-NPP/OMPS Data Support at GES DISC

The TROPspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (Sentinel-5P) is the first of the Atmospheric Composition Sentinels by the European Space Agency (ESA) that provides measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The early afternoon orbit of Sentinel-5P mission provides a strong synergy with the U.S. Suomi National Polar-orbiting Partnership (S-NPP) satellite, especially in that the S-NPP Ozone Monitoring and Profiling Suite (OMPS) facilitates high vertically resolved stratospheric and lower mesospheric ozone profiles. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) supports over a thousand data collections in the Focus Areas of Atmospheric Composition, Water & Energy Cycles, and Climate Variability and it is the Distributed Active Archive Center (DAAC) that is curating both offline Sentinel-5P TROPOMI and S-NPP OMPS Level-1B (L1B) and Level-2 (L2) products. Through its convenient and enhanced tools/services such as OPeNDAP and L2 Subsetting, GES DISC offers air quality remote sensing user communities facile solutions for complex Earth science data and applications. This presentation will demonstrate TROPOMI and OMPS products including earthview radiance, solar irradiance, and currently available L2 datasets, as well as easy ways to access, visualize and subset data. The implementation of the End User License Agreement (EULA) between NASA GES DISC and all data users accessing data at GES DISC will be emphasized as well.

TROPOMI↗