Quantifying the impact of workshops promoting microbiome data standards and data stewardship
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59 BASIC BIOLOGICAL SCIENCES↗
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Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository
The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.
accessibility↗
Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository
The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.
15 GEOTHERMAL ENERGY↗
Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository: Preprint
The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.
accessibility↗
Laser Disdrometer / Standardized Data
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Lidar / Standardized Data
This dataset contains standardized data from the 10-minute averaged STA files from NREL's profiling lidar (Windcube v2.1) at the WFIP3 BARG site. Note: these data have NOT been corrected for the motion of the barge.
17 WIND ENERGY↗
The Vertebrate Breed Ontology: Toward Effective Breed Data Standardization
Abstract Background Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Hypothesis/Objectives A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals No live animals were used. Methods The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence–readiness. Results VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and Clinical Importance The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.
Veterinary Sciences↗
WFIP3 / WHOI ASIT Lidar / Standardized Data
This dataset contains standardized raw data from the WHOI ASIT deployed for WFIP-3. A ZX 300M is currently installed at the tower and has been deployed there since September 2021.
17 WIND ENERGY↗
Buoy 130 / Standardized Data
These are the standardized buoy data collected during the WFIP3 project period, initially deployed near the Martha's Vineyard region for validation and later deployed at the WFIP3 location. The NetCDF files contain the data for all of the *.csv files for a given day.
17 WIND ENERGY↗
Eddy Covariance Flux / Standardized Data
This dataset contains standardized Eddy Covariance Flux data.
17 WIND ENERGY↗
Natural environment and physical standard data for Mars, Venus and interplanetary space through the asteroid belt
Natural environments and physical standard data for Mars, Venus and interplanatary space through asteroid belt
Liwshitz, M.↗
Why data system standards?
Data systems coordinating standards developed from experience with STADAN, discussing operating environment and administrative decisions influence on implementation
Coates, R. J.↗
Standard data systems architecture for the Space Station
Attention is given to an end-to-end Space Station Data System (SSDS) architecture which is based on internationally-recommended standards developed by the Consultative Committee for Space Data Systems (CCSDS). The proposed system uses simple modular building blocks that are recursively replicated and linked to construct essentially any desired data system configuration. The SSDS concept provides for a user-transparent data transport system which is entirely independent of the characteristics of the user data being transported, and in addition, has the flexibility to accommodate mission-induced changes in data traffic. SSDS physical elements include the following: (1) on-orbit local area networks, (2) space-to-ground, ground-to-space, and space-to-space data links, and (3) ground mission support facilities containing telemetry and telecommand data handling termini and preprocessing services.
Greenberg, E.↗
Specifications for a Federal Information Processing Standard Data Dictionary System
The development of a software specification that Federal agencies may use in evaluating and selecting data dictionary systems (DDS) is discussed. To supply the flexibility needed by widely different applications and environments in the Federal Government, the Federal Information Processing Standard (FIPS) specifies a core DDS together with an optimal set of modules. The focus and status of the development project are described. Functional specifications for the FIPS DDS are examined for the dictionary, the dictionary schema, and the dictionary processing system. The DDS user interfaces and DDS software interfaces are discussed as well as dictionary administration.
Goldfine, A.↗
Data Standards for Machine Learned Interatomic Potentials of Plasma Facing Materials
Explore the source record for details and available documents.
Wood, Mitchell Anthony [Sandia National Laboratori↗
Laser Disdrometer / Standardized Data
This dataset contains raw data from the laser disdrometer at Nantucket. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
TTP - WREF LLNL Streamline Scanning Lidar / Standardized Data
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
17 WIND ENERGY↗