Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “metadata extraction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reproductive and leaf litterfall fluxes in forest ecosystem sites globally (1950-2022)

Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Whole metagenome sequencing and 16S rRNA gene amplicon analyses reveal the complex microbiome responsible for the success of enhanced in-situ reductive dechlorination (ERD) of a tetrachloroethene-contaminated Superfund site

The North Railroad Avenue Plume (NRAP) Superfund site in New Mexico, USA exemplifies successful chlorinated solvent bioremediation. NRAP was the result of leakage from a dry-cleaning that operated for 37 years. The presence of tetrachloroethene biodegradation byproducts, organohalide respiring genera (OHRG), and reductive dehalogenase (rdh) genes detected in groundwater samples indicated that enhanced reductive dechlorination (ERD) was the remedy of choice. This was achieved through biostimulation by mixing emulsified vegetable oil into the contaminated aquifer. This report combines metagenomic techniques with site monitoring metadata to reveal new details of ERD. DNA extracts from groundwater samples collected prior to and at four, 23 and 39 months after remedy implementation were subjected to whole metagenome sequencing (WMS) and 16S rRNA gene amplicon (16S) analyses. The response of the indigenous NRAP microbiome to ERD protocols is consistent with results obtained from microcosms, dechlorinating consortia, and observations at other contaminated sites. WMS detects three times as many phyla and six times as many genera as 16S. Both techniques reveal abundance changes in Dehalococcoides and Dehalobacter that reflect organohalide form and availability. Methane was not detected before biostimulation but appeared afterwards, corresponding to an increase in methanogenic Archaea. Assembly of WMS reads produced scaffolds containing rdh genes from Dehalococcoides, Dehalobacter, Dehalogenimonas, Desulfocarbo, and Desulfobacula. Anaerobic and aerobic cometabolic organohalide degrading microbes that increase in abundance include methanogenic Archaea, methanotrophs, Dechloromonas, and Xanthobacter, some of which contain hydrolytic dehalogenase genes. Aerobic cometabolism may be supported by oxygen gradients existing in aquifer microenvironments or by microbes that produce O 2 via microbial dismutation. The NRAP model for successful ERD is consistent with the established pathway and identifies new taxa and processes that support this syntrophic process. This project explores the potential of metagenomic tools (MGT) as the next advancement in bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Leaf phenology data at The Morton Arboretum Forestry Plots 2019-2023

We are collecting long-term leaf phenology data at The Morton Arboretum to determine seasonal patterns of leaf production in trees. This data on leaf phenology will be integrated with other ongoing data streams to create a connection between above- and below-ground tree processes. This data package contains raw and smooth outputs from phenology data, as well as extracted phenophase dates (i.e., start, peak, and end of season): the raw and smooth outputs from the PhenoCam GUI can be found in the "leafRaw.csv" and "leafSmooth.csv" files, respectively, and the extracted phenophase dates can be found in the "leafPhenophaseDates2019-2023.csv" file. Extracted phenophase dates for evergreen species in 2023 are currently unavailable, and the files will be updated once they are extracted. Additional information on units and other file-level metadata can be found within each data file's respective data dictionary, and metadata for each of the 23 surveyed plots can be found within the "Location_metadata.csv" file. While the "leafRaw.csv" and the "leafSmooth.csv" files contain all data for all species, the "leafPhenophaseDates2019-2023.csv" file currently excludes the dates for evergreen species in 2023. Another version of the file will be added as those dates are extracted.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Master Metadata Repository and Metadata-Management System

A master metadata repository (MMR) software system manages the storage and searching of metadata pertaining to data from national and international satellite sources of the Global Ocean Data Assimilation Experiment (GODAE) High Resolution Sea Surface Temperature Pilot Project [GHRSSTPP]. These sources produce a total of hundreds of data files daily, each file classified as one of more than ten data products representing global sea-surface temperatures. The MMR is a relational database wherein the metadata are divided into granulelevel records [denoted file records (FRs)] for individual satellite files and collection-level records [denoted data set descriptions (DSDs)] that describe metadata common to all the files from a specific data product. FRs and DSDs adhere to the NASA Directory Interchange Format (DIF). The FRs and DSDs are contained in separate subdatabases linked by a common field. The MMR is configured in MySQL database software with custom Practical Extraction and Reporting Language (PERL) programs to validate and ingest the metadata records. The database contents are converted into the Federal Geographic Data Committee (FGDC) standard format by use of the Extensible Markup Language (XML). A Web interface enables users to search for availability of data from all sources.

Armstrong, Edward↗
DOE OSTI2023

Multispectral and thermal surface imagery and surface elevation mosaics (camspec-air)

This dataset contains high resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems, which have been processed for value added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures 6 spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation3. Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the 6 spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values. 1https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multispectral and thermal surface imagery and surface elevation mosaics - SGP July 2022

This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance via custom code. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values. 1 https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2 https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3 https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

The SPASE Data Model for Heliophysics Data: Is it Working?

The Space Physics Archive Search and Extract (SPASE) Data Model was developed to provide a metadata standard for describing Heliophysics (Space and Solar Physics) data within that science discipline. The SPASE Data Model has matured over the many years of its creation and is presently represented by Version 2.2.1. Information about SPASE can be obtained from the website group.org. The Data Model defines terms and values as well as the relationships between them in order to describe the data resources in the Heliophysics data environment. This data environment is quite complex, consisting of Virtual Observatories, Resident Archives, Data Providers, Partnering Data Centers, Services, Final Archives, and a Deep Archive. SPASE is the metadata language standard intended to permeate the complexity and provide a common method of obtaining and understanding data. Is it working in this capacity? SPASE has been used to describe a wide range of data. Examples range from ground-based magnetometer data to interplanetary satellite measurements to space weather model results. Has it achieved the goal of making the data easier to find and use? To find data of interest it is necessary that all the data of importance be described using the SPASE Data Model. Within the part of the data community associated with NASA (supported through NASA funding) there are obligations to use SPASE and (0 describe the old and new data using the SPASE XML schema. Although this pan of the community is not near 100% compliance with the mandate, there is good progress being made and the goal should be reachable in the future. Outside of the NASA data community there is still work to be done to convince the international community that SPASE descriptions are w011h the cost of their generation. Some of these groups such as Cluster, HELlO, GAIA, NOAA/NGDe. CSSDP, VSTO, SuperMAG, and IUGONET have agreed to use SPASE. but there are still other groups of importance that need (0 be reached. It is also assumed that the terminology is sufficiently broad and the descriptions are sufficiently complete that researchers needing data of a specific type or from a specific period can find and acquire what they need. A valid SPASE description can be very brief or very thorough depending on the willingness of the author to spend the time necessary to make the description useful. There is evidence that users are finding what they need through the SPASE descriptions, and this standard is a big step forward in Heliophysics data location. Does SPASE make it easier to use the data once they are found,) Thorough descriptions of data using SPASE can describe the data down to the level of individual parameters and exactly how the data are organized and stored. Should the SPASE data descriptions be written in such a way that they can be automatically ingested and understood by software tools'? Heliophysics instruments are becoming morc versatile all the time and the complexity of the data makes it tedious and time consuming to write SPASE descriptions with this level of sophistication even with the improvement of the tools used to generate the descriptions. Is it better to just write human-readable descriptions of the data at the parameter level or to refer to references that provide this information? This is a debate that is presently taking place and software is being developed to test what is possible.

Thieman, James↗
NASA NTRSDate not supplied

Reanalysis of Rat Data from Spacelab Life Sciences 2 (SLS-2) to Reveal Research Gaps in Spaceflight Data

Using and analyzing the legacy data obtained in space life sciences missions has the potential to provide researchers a complete picture of the molecular changes associated with space without further experimentation. This project’s objective is to extract, filter, organize, and analyze all Rattus norvegicus data and metadata obtained from Columbia’s Spacelab Life Sciences 2 (SLS-2, STS-58) mission to explore the ways that we can compile information from model organisms, in our case rats, to create a reliable model to understand biological mechanisms in response to these space flight changes. By reusing rare space legacy data coupled with data analysis techniques, we can combine individual preexisting datasets with current ones to gain new, comprehensive insights about the effects of spaceflight on our bodies. Our methods can also lead to the creation of a standardized pipeline that could be applied to other space life science datasets for analysis. In this review, every biological experiment conducted on rats in the SLS-2 Mission was studied with our pipeline to create a new biological library and model that could be used by scientists from around the world to make novel discoveries and develop new hypotheses from this priceless information without the limitation of the costs of spaceflight experimentation.

rats↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
NASA NTRS2020

Recovery, Restoration and Archiving of Previously Lost Data and Metadata from the Apollo Lunar Surface Experiments Package (ALSEP)

The Apollo Lunar Surface Experiments Package (ALSEP) is the name used to collectively represent the geophysical instruments deployed on the lunar surface by the astronauts on Apollo 12, 14, 15, 16, and 17. These instruments were active from the times of their deployment (November 1969 – December 1972) to September 1977. During that time, fourteen types of experiments were conducted, and their data were transmitted to Earth. The experiment PIs processed them. At the conclusion of the experiments, some of these data were submitted to the NASA Space Science Data Coordinated Archive (NSSDCA) for archiving, while others were not. The raw instrument data received from the Moon prior to March 1976 were not archived, either. The unarchived data, resided on open-reel magnetic tapes, became lost in the decades since, along with much of the metadata (the information necessary/useful in properly processing/analyzing the data). This article retraces the history of the ALSEP data archiving efforts in the 1970s, the subsequent loss of the data tapes, and the search, recovery, and restoration of the lost data by contemporary researchers in the 21st century. In 2006, NSSDCA began reformatting some of the ALSEP data archived in the 1970s to conform with the current Planetary Data System (PDS). In 2010, 440 of the previously lost magnetic tapes containing the raw ALSEP data were recovered. From these tapes, the data were extracted, re-packaged for individual experiments, and, for those with sufficient metadata, processed into higher order data readily usable by researchers. All of these data products have been recently archived with either PDS or NSSDCA. These newly restored data fill a number of gaps in the previously existing archive of the ALSEP data. In addition, tens of thousands of pages of Apollo era documents have been optically scanned and compiled into an online searchable catalog. This article also describes the content, organization, and usage of the restored raw ALSEP data and metadata.

S Nagihara↗
NASA NTRSDate not supplied

The Radiation Biology Ontology: A New Tool Supporting FAIR Principles Across Radiation Biology Facilitating Data Discovery and Integration

Development of the Radiation Biology Ontology (RBO) was motivated by the need for a comprehensive, well-structured ontology for encoding radiation biology metadata. The primary use-cases were archiving data in the STORE database (https://www.storedb.org/), the repository for the RadoNorm Project, and in GeneLab (https://genelab.nasa.gov), NASA’s ‘omics database. The scope of radiobiology research ranges from physics to radiation oncology to socio-legal studies; no existing ontology has the necessary breadth or depth. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR radiation biology data.

ontology↗
DOE OSTI2022

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Data and scripts associated with a manuscript investigating impacts of solid phase extraction on freshwater organic matter optical signatures and mass spectrometry pairing

This data package is associated with the publication “Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high-resolution mass spectrometry data in a freshwater system” submitted to “Limnology and Oceanography: Methods.” This data is an extension of the River Corridor and Watershed Biogeochemistry SFA’s Spatial Study 2021 (https://doi.org/10.15485/1898914). Other associated data and field metadata can be found at the link provided. The goal of this manuscript is to assess the impact of solid phase extraction (SPE) on the ability to pair ultra-high resolution mass spectrometry data collected from SPE extracts with optical properties collected on ambient stream samples. Forty-seven samples collected from within the Yakima River Basin, Washington were analyzed dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), absorbance, and fluorescence. Samples were subsequently concentrated with SPE and reanalyzed for each measurement. The extraction efficiency for the DOC and common optical indices were calculated. In addition, SPE samples were subject to ultra-high resolution mass spectrometry and compared with the ambient and SPE generated optical data. Finally, in addition to this cross-platform inter-comparison, we further performed and intra-comparison among the high-resolution mass spectrometry data to determine the impact of sample preparation on the interpretability of results. Here, the SPE samples were prepared at 40 milligrams per liter (mg/L) based on the known DOC extraction efficiency of the samples (ranging from ~30 to ~75%) compared to the common practice of assuming the DOC extraction efficiency of freshwater samples at 60%. This data package folder consists of one main data folder with one subfolder (Data_Input). The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) final data summary output from processing script; and (5) the processing script. The R-markdown processing script (SPE_Manuscript_Rmarkdown_Data_Package.rmd) contains all code needed to reproduce manuscript statistics and figures (with the exception of that stated below). The Data_Input folder has two subfolders: (1) FTICR and (2) Optics. Additionally, the Data_Input folder contains dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data (SPS_NPOC_Summary.csv) and relevant supporting Solid Phase Extraction Volume information (SPS_SPE_Volumes.csv). Methods information for the optical and FTICR data is embedded in the header rows of SPS_EEMs_Methods.csv and SPS_FTICR_Methods.csv, respectively. In addition, the data dictionary (SPS_SPE_dd.csv), file level metadata (SPS_SPE_flmd.csv), and methods codes (SPS_SPE_Methods_codes.csv) are provided. The FTICR subfolder contains all raw FTICR data as well as instructions for processing. In addition, post processed FTICR molecular information (Processed_FTICRMS_Mol.csv) and sample data (Processed_FTICRMS_Data.csv) is provided that can be directly read into R with the associated R-markdown file. The Optics subfolder contains all Absorbance and Fluorescence Spectra. Fluorescence spectra have been blank corrected, inner filter corrected, and undergone scatter removal. In addition, this folder contains Matlab code used to make a portion of Figure 1 within the manuscript, derive various spectral parameters used within the manuscript, and used for parallel factor analysis (PARAFAC) modeling. Spectral indices (SPS_SpectralIndices.csv) and PARAFAC outputs (SPS_PARAFAC_Model_Loadings.csv and SPS_PARAFAC_Sample_Scores.csv) are directly read into the associated R-markdown file. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRS2010

SPASE, Metadata, and the Heliophysics Virtual Observatories

To provide data search and access capability in the field of Heliophysics (the study of the Sun and its effects on the Solar System, especially the Earth) a number of Virtual Observatories (VO) have been established both via direct funding from the U.S. National Aeronautics and Space Administration (NASA) and through other funding agencies in the U.S. and worldwide. At least 15 systems can be labeled as Virtual Observatories in the Heliophysics community, 9 of them funded by NASA. The problem is that different metadata and data search approaches are used by these VO's and a search for data relevant to a particular research question can involve consulting with multiple VO's - needing to learn a different approach for finding and acquiring data for each. The Space Physics Archive Search and Extract (SPASE) project is intended to provide a common data model for Heliophysics data and therefore a common set of metadata for searches of the VO's. The SPASE Data Model has been developed through the common efforts of the Heliophysics Data and Model Consortium (HDMC) representatives over a number of years. We currently have released Version 2.1 of the Data Model. The advantages and disadvantages of the Data Model will be discussed along with the plans for the future. Recent changes requested by new members of the SPASE community indicate some of the directions for further development.

Thieman, James↗
DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) 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 provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. 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-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

The SPASE Data Model: A Metadata Standard for Registering, Finding, Accessing, and Using Heliophysics Data Obtained from Observations and Modeling

The Space Physics Archive Search and Extract Consortium has developed and implemented the SPASE Data Model that provides a common language for registering a wide range of Heliophysics data and other products. The Data Model enables discovery and access tools such that any researcher can obtain data easily, thereby facilitating research, including on space weather. The Data Model includes descriptions of Simulation Models and Numerical Output, pioneered by the Integrated Medium for Planetary Exploration (IMPEx) group in Europe, and subsequently adopted by the Community Coordinated Modeling Center (CCMC). The SPASE group intends to register all relevant Heliophysics data resources, including space-, ground-, and model-based. Substantial progress has been made, especially for space-based observational data and associated observatories, instruments, and display data. Legacy product registrations and access go back more than 50 years. Real-time data will be included. The National Aeronautics and Space Administration (NASA) portion of the SPASE group has funding that assures continuity in the upkeep of the Data Model and aids with adding new products. Tools are being developed for making and editing data descriptions. Digital Object Identifiers (DOIs) for Data Products can now be included in the descriptions. The data access that SPASE facilitates is becoming more uniform, and work is progressing on Web Service access via a standard Application Programming Interface. The SPASE Data Model is stable; changes over the past 9 years were additions of terms and capabilities that are backward compatible. This paper provides a summary of the history, structure, use, and future of the SPASE Data Model.

Roberts, D. Aaron↗