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NASA NTRS2001

Syntactic and Semantic Validation without a Metadata Management System

The ability to maintain quality information is essential to securing the confidence in any system for which the information serves as a data source. NASA's Global Change Master Directory (GCMD), an online Earth science data locator, holds over 9000 data set descriptions and is in a constant state of flux as metadata are created and updated on a daily basis. In such a system, the importance of maintaining the consistency and integrity of these-metadata is crucial. The GCMD has developed a metadata management system utilizing XML, controlled vocabulary, and Java technologies to ensure the metadata not only adhere to valid syntax, but also exhibit proper semantics.

Pollack, Janine↗

DOE OSTI2025

Data about data – when, why and how metadata can support the digital plant

A structured approach for recording data quality and contextual information about how and why a signal exists – i.e. metadata – is central to interpret and use sensor data correctly. This is becoming increasingly important with the global trend with data-driven applications such as digital twins and AI-models. But a structured metadata collection and organization of sensor data is not routine in most plants, which can result in lost information and missed opportunities to make use of the investments made in the data collection. Therefore, the IWA task group on Metadata Collection and Organization in wastewater resource recovery systems (MetaCO) was initiated in 2020 and recently delivered the IWA scientific and technical report number 31. The report gives and in-depth description about metadata in water resources recovery facilities (WRRFs) and is available as open access at IWA publishing. The report is the outcome of the collaboration between more than 80 water professionals with the intention to serve WRRF data users with a guide on how to structure and make use of metadata throughout the data pipeline in order to maximize the value of sensor data.

Alferes, Janelcy [VITO, Belgium]↗

DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Practical procedures for sensor quality assessment

Sensors are increasingly deployed for process monitoring and control. These produce on-line measurements at a high frequency, in parallel with low-frequency laboratory measurements. Compared to laboratory practices, sensor data quality assessment and control practices are far less structured at most utilities. This leads to inaccurate sensor data with unknown uncertainty factors.This chapter shows how to establish standard operating procedures (SOPs) to support sensor data quality assessment and control and subsequent maintenance actions by producing relevant sensor metadata. Furthermore, SOPs are provided for the most commonly used wastewater quality sensors, inspired by utility and academic best practices. This chapter builds on definitions provided in Chapter 3 and provides additional definitions specifically related to sensors maintenance. Chapter 6 complements the methods in this chapter, which are based on reference measurements, with data-analytical techniques.

Alferes, Janelcy↗

NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗

NASA NTRS2016

MoonDB: Restoration and Synthesis of Lunar Petrological and Geochemical Data

About 2,200 samples were collected from the Moon during the Apollo missions, forming a unique and irreplaceable legacy of the Apollo program. These samples, obtained at tremendous cost and great risk, are the only samples that have ever been returned by astronauts from the surface of another planetary body. These lunar samples have been curated at NASA Johnson Space Center and made available to the global research community. Over more than 45 years, a vast body of petrological, geochemical, and geochronological studies of these samples have been amassed, which helped to expand our understanding of the history and evolution of the Moon, the Earth itself, and the history of our entire solar system. Unfortunately, data from these studies are dispersed in the literature, often only available in analog format in older publications, and/or lacking sample metadata and analytical metadata (e.g., information about analytical procedure and data quality), which greatly limits their usage for new scientific endeavors. Even worse is that much lunar data have never been published, simply because no forum existed at the time (e.g., electronic supplements). Thousands of valuable analyses remain inaccessible, often preserved only in personal records, and are in danger of being lost forever, when investigators retire or pass away. Making these data and metadata publicly accessible in a digital format would dramatically help guide current and future research and eliminate duplicated analyses of precious lunar samples.

Lehnert, Kerstin A.↗

DOE OSTI2024

fluxfinder: An R Package for Reproducible Calculation and Initial Processing of Greenhouse Gas Fluxes From Static Chamber Measurements

Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.

Wilson, Stephanie J.↗

NASA NTRS2018

ISO 191* Revisions and Amendments

ISO standards have changed recently in response to new requirements from introduced by NASA, NOAA, and other ESIP members. These changes address important uses cases related to identification, parameter metadata, and platform/instrument/sensor metadata. Each change will be described along with discussion of how they may be used.

data quality↗

DOE OSTI2026

Surface Water Quality Data from Beaver-Impacted Streams; Trail Creek and East River, Colorado 2025

This data package contains surface water chemistry measurements collected in 2025 to evaluate how beaver damming and low-tech process-based stream restoration influence water quality and metal mobility in mountainous headwater systems of the Upper Colorado River Basin. Sampling was conducted at Trail Creek (Taylor Park watershed, Colorado), a tributary undergoing restoration through installation of low-tech process-based structures (i.e., beaver dam analogs), and at off-channel beaver ponds within the East River floodplain (East River watershed, Colorado). Samples were collected along longitudinal transects spanning upstream control reaches, beaver-influenced ponded reaches, and downstream segments. Additional samples were collected from near-surface pore waters within a beaver dam seepage face. The dataset includes concentrations of major and trace elements measured by inductively coupled plasma–mass spectrometry (ICP-MS) and inductively coupled plasma–optical emission spectrometry (ICP-OES), major anions measured by ion chromatography (IC), and dissolved organic carbon (DOC; reported as non-purgeable organic carbon, NPOC). Samples were size-fractionated at 0.45 micrometers (µm), 0.22 µm, and 0.02 µm to distinguish particulate (>0.45 µm), colloidal (0.22–0.02 µm), and dissolved (<0.02 µm) fractions. The data package consists of comma-separated value (.csv) files containing tabulated chemical concentration data, sample metadata (site identifiers, geographic coordinates, sampling dates, fraction type), and quality control flags. All files are provided in open, non-proprietary formats that can be accessed using standard data analysis software such as Microsoft Excel, R, Python, MATLAB, or other programs capable of reading .csv files. Units, detection limits, and analytical methods are documented in accompanying metadata files. The dataset is designed to support analyses of (1) how beaver impoundment and restoration structures alter elemental partitioning and transport, (2) the role of iron and organic carbon in mediating trace metal mobility, and (3) reach-scale changes in water quality across restoration gradients. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Anions↗

NASA NTRS2022

Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations

Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high cadence, high-resolution multiwavelength observations, growing amounts of data from realistic modeling, and operational needs for uninterrupted science-quality data coverage generate the demand for a solar metadata standardization and overall healthy data infrastructure. This white paper is prepared as an effort of the working group “Uniform Semantics and Syntax of Solar Observations and Events” created within the “Towards Integration of Heliophysics Data, Modeling, and Analysis Tools” EarthCube Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss current advances and identify future needs for the solar research cyberinfrastructure. The white paper summarizes presentations and discussions held during the special working group session at the EarthCube Annual Meeting on June 19th, 2020, as well as community contribution gathered during a series of preceding workshops and subsequent RCN working group sessions. The authors provide examples of the current standing of the solar research cyberinfrastructure, and describe the problems related to current data handling approaches. The list of the top-level recommendations agreed by the authors of the current white paper is presented at the beginning of the paper.

SMD↗

NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from spaceflight biological and health studies are increasingly being made findable, accessible, interoperable, and reusable for the scientific public. These data, as well as space science-relevant biospecimens, are available through NASA’s Open Science Data Repository (OSDR), which is the new umbrella grouping of NASA GeneLab, the Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection (NBISC). The quality of data is underpinned by datasets having rich metadata (determined through Analysis Working Group members), processing pipelines to enable data reuse standards, and ontologies specifying terminology semantics (e.g., the Radiation Biology Ontology).

space biology↗

NASA NTRS2015

Magnetospheric Multiscale Instrument Suite Operations and Data System

The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of approximately 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SOC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept.

Baker, D. N.↗

DOE OSTI2025

NEPATEC2.0: NEPA Text Corpus v2.0

The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.

environmental review↗

DOE OSTI2025

NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Montane Conifer, Aspen, Meadow, and Sagebrush Metagenome Resolved Genomes and Traits in East River Watershed, Colorado, USA

Climate change is driving vegetation shifts in mountain watersheds, with unknown impacts on biogeochemical cycles. We hypothesize that these shifts will reshape soil microbiomes and associated biogeochemical processes. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus Area (SFA), we assessed microbiome and microbial functional trait differences between soils under conifer, aspen, forby meadows, and sagebrush across the East River Watershed, CO, controlling for elevation and aspect.Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from soils 0-20cm in depth across three locations in the watershed—Headwaters, Upper Reaches, and Lower Reaches from August 3-11th 2016. Each location was further subdivided into two blocks, with one block on a west facing aspect, and two on the east aspect of the valley. Within blocks, two samples per vegetation type were taken (one at each depth). This resulted in 66 samples, which were sequenced at JGI and can be found under the Joint Genome Institute (JGI) Genomes Online Database (GOLD) sequencing project Gs0118068. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>75%) and contamination (<25%), and dereplicated at 95% ANI using drep. The dataset includes a zip file of 687 genomes (Vegtype_MAGS.zip), the accession numbers for the underlying metagenomes, a csv file with MAG quality metrics and taxonomy from Genome Taxonomy Database (GTDB) and National Center for Biotechnology Information (NCBI) taxonomic representative genome proteins (EastRiver_Vegtype_drep_genome_info.csv), and a file containing MAG quality metrics and taxonomy (gtdb_drep_bin_taxonomy.csv). The dataset additionally includes a sample metadata file (EastRiver_Vegtype_sample_metadata.csv), a metadata file used to register associated samples with IGSNs (International Generic Sample Numbers) (samples.csv), a Google KML file for the sampled locations (sample_collection_sites.kml), a location metadata file (locations.csv), a file-level metadata file (flmd.csv), and a data dictionary (dd.csv) file.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

terrain↗