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At least 19 records

Estimating pediatric entrance skin dose from digital radiography examination using DICOM metadata: A quality assurance tool

Purpose: To develop an automated methodology to estimate patient examination dose in digital radiography (DR) imaging using DICOM metadata as a quality assurance (QA) tool. Methods: Patient examination and demographical information were gathered from metadata analysis of DICOM header data. The x-ray system radiation output (i.e., air KERMA) was characterized for all filter combinations used for patient examinations. Average patient thicknesses were measured for head, chest, abdomen, knees, and hands using volumetric images from CT. Backscatter factors (BSFs) were calculated from examination kVp. Patient entrance skin air KERMA (ESAK) was calculated by (1) looking up examination technique factors taken from DICOM header metadata (i.e., kVp and mA s) to derive an air KERMA (k{sub air}) value based on an x-ray characteristic radiation output curve; (2) scaling k{sub air} with a BSF value; and (3) correcting k{sub air} for patient thickness. Finally, patient entrance skin dose (ESD) was calculated by multiplying a mass–energy attenuation coefficient ratio by ESAK. Patient ESD calculations were computed for common DR examinations at our institution: dual view chest, anteroposterior (AP) abdomen, lateral (LAT) skull, dual view knee, and bone age (left hand only) examinations. Results: ESD was calculated for a total of 3794 patients; mean age was 11 ± 8 yr (range: 2 months to 55 yr). The mean ESD range was 0.19–0.42 mGy for dual view chest, 0.28–1.2 mGy for AP abdomen, 0.18–0.65 mGy for LAT view skull, 0.15–0.63 mGy for dual view knee, and 0.10–0.12 mGy for bone age (left hand) examinations. Conclusions: A methodology combining DICOM header metadata and basic x-ray tube characterization curves was demonstrated. In a regulatory era where patient dose reporting has become increasingly in demand, this methodology will allow a knowledgeable user the means to establish an automatable dose reporting program for DR and perform patient dose related QA testing for digital x-ray imaging.

60 APPLIED LIFE SCIENCES↗

The health care and life sciences community profile for dataset descriptions

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

Science & Technology - Other Topics↗

Challenges in Long-Term Records Preservation and Access - 19549

The US Department of Energy Office of Legacy Management (LM) mission includes preserving records and information about the nation's Cold War-era weapons projects and making that information available for years to come. As LM assumes responsibility for each project, records documenting operations and cleanup activities are absorbed into a central recordkeeping system. Each record category is governed by a federal retention requirement. Routine administrative records are kept just a few years, while the most important records have 'permanent' retention requirements and are transferred to the National Archives typically within 25 years of creation. It is relatively easy for LM to meet retention requirements for these routine and permanent records. LM's big preservation challenge is with records with tremendously long retention requirements. Records with 75-year retention requirements are common, and a small subset of LM records pertaining to waste disposal requires 150-year retention periods. LM must consider the best format for maintaining each of its records collections. Should physical records be boxed up for long-term storage, or should they be digitized? Each option has associated benefits, costs, and risks. Keeping records in paper format avoids digitizing expense and preserves the original media, but the paper must be maintained in a certified storage facility. A vermin infestation or sprinkler malfunction at a certified facility may be rare but could easily destroy records. Keeping the paper and making reference or backup electronic copies reduces the risk of loss but requires diligence and resources to keep the physical records and electronic versions synchronized. This becomes an overwhelming task in a database with millions of records. Entirely replacing paper records with digitized versions as electronic records introduces the risk that records could be lost due to a cybersecurity incident or made inaccessible by evolving technology. LM's early electronic records management efforts were limited to documents saved to PDF format. More recently, LM has embraced the benefits of maintaining records in native formats. (A spreadsheet includes formulas and other details lost when converted to a PDF file.) But whether the file is saved as a Microsoft Excel spreadsheet or PDF document, how do we ensure the file is readable in 75 or 150 years when these records meet their retention requirement? We have no way of knowing for sure from our 2019 vantage point-no more than we would expect to get good technology advice from someone 150 years ago. We cannot predict the technological future, but we can make sure our current electronic records environment is healthy and future ready. This includes making sure the electronic recordkeeping system is built on a modern platform and ensuring that each record in the system is saved in a current format. Quality standards are critical for electronic records to be future ready. Implementing quality involves an organization-wide approach to having consistently formatted file names and complete and descriptive metadata. Quality PDF documents are scanned to settings that ensure characters are machine-readable (with optical character recognition, or OCR). LM is currently replacing its electronic recordkeeping system and simultaneously modernizing its organizational approach to data management; it is replacing a traditional electronic records repository with a system that also manages records stored anywhere across the computer network. This new records management approach will require new thinking in how LM manages a dramatically increased volume of information across the network while not losing focus on its responsibility for long-term records preservation and access. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗

National Geothermal Data System: Interactive Assessment of Geothermal Energy Potential in the U.S.

Geothermal-relevant geosciences data from all 50 states (www.stategeothermaldata.org), federal agencies, national labs, and academic centers are being digitized and linked in a distributed online network via the U.S. Department of Energy-funded National Geothermal Data System (NGDS) to foster geothermal energy exploration and development through use of interactive online ‘mashups,’data integration, and applications. Emphasis is first to make as much information as possible accessible online, with a long range goal to make data interoperable through standardized services and interchange formats. An initial set of thirty geoscience data content models is in use or under development to define a standardized interchange format: aqueous chemistry, borehole temperature data, direct use feature, drill stem test, earthquake hypocenter, fault feature, geologic contact feature, geologic unit feature, thermal/hot spring description, metadata, quaternary fault, volcanic vent description, well header feature, borehole lithology log, crustal stress, gravity, heat flow/temperature gradient, permeability, and feature descriptions data like developed geothermal systems, geologic unit geothermal properties, permeability, production data, rock alteration description, rock chemistry, and thermal conductivity. Map services are also being developed for isopach maps, aquifer temperature maps, and several states are working on geothermal resource overview maps. Content models are developed preferentially from existing community use in order to encourage widespread adoption and promulgate minimum metadata quality standards. Geoscience data and maps from other NGDS participating institutions, or “nodes” (USGS, Southern Methodist University, Boise State University Geothermal Data Coalition) are being supplemented with extensive land management and land use resources from the Western Regional Partnership (15 federal agencies and 5 Western states) to provide access to a comprehensive, holistic set of data critical to geothermal energy development. As of September 2011, we have over 34,000 records registered in the system catalog, and 234,942 data resources online, along with scores of Web services to deliver integrated data to the desktop for free downloading or online use. The data exchange mechanism is built on the U.S. Geoscience Information Network (USGIN, http://usgin.org and http://lab.usgin.org) protocols and standards developed as a partnership of the Association of American State Geologists (AASG) and U.S. Geological Survey.

15 GEOTHERMAL ENERGY↗

C-HER Metadata Overview: Approach, Standards, and Rigor for the Centralized Health and Exposomic Resource

The Centralized Health and Exposomic Resource (C-HER) unifies environmental, demographic, geographic, and health-related data for exposomic research. The source data differ in format, geographic coverage, time period, resolution, terminology, and documentation. We use a common metadata framework to describe those differences and to record how each data resource has been processed, documented, and ingested. This document relates only to the C-HER metadata framework. It explains the information that is recorded for each resource, the standards used to organize that information, the conditions for metadata completeness, and the relationship between metadata and quality review. It is intended for those who need to understand what C-HER metadata communicates and how it supports appropriate use of the data. It is not an implementation specification or procedure. It does not document the database schema, source code, deployment configuration, transformation algorithms, or dataset-specific QA/QC thresholds. Those materials are maintained separately.

MacFarland, Midgie [ORNL] (ORCID:0009000807354078)↗

The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

The Genomes OnLine Database (GOLD; http://www.genomesonline.org) is a comprehensive online resource to catalog and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Within this paper, we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19 200 studies, 56 000 Biosamples, 56 000 sequencing projects and 39 400 analysis projects. More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. Lastly, GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards.

59 BASIC BIOLOGICAL SCIENCES↗

ARM Data File Standards Version 1.2

The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility performs routine in situ and remote-sensing observations to provide a detailed and accurate description of the Earth atmosphere in diverse climate regimes. The result is a huge archive of diverse data sets containing observational and derived data, currently accumulating at a rate of 30 terabytes (TB) of data and 150,000 different files per month (http://www.archive.arm.gov/stats/). Continuing the current processing while scaling this to even larger sizes is extremely important to the ARM Facility and requires consistent metadata and data standards. The standards described in this document will enable development of automated analysis and discovery tools for the ever growing data volumes. It will enable consistent analysis of the multiyear data, allow for development of automated monitoring and data health status tools, and allow future capabilities of delivering data on demand that can be tailored explicitly for the user needs. This analysis ability will only be possible if the data follows a minimum set of standards. This document proposes a hierarchy of required and recommended standards.

54 ENVIRONMENTAL SCIENCES↗

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

59 BASIC BIOLOGICAL SCIENCES↗

Data Citation Explorer (DCE) v1.0

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data whether or not provenance was clearly indicated. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

Parker, Charles↗

PVInsight (Final Technical Report)

Data generated from real-world photovoltaic (PV) systems represent significant opportunities for the industry–from digital operations and maintenance to real-time planning and forecasting. However, these data also come with substantial, unique challenges. A particular challenge is the analysis of unlabeled PV performance data, which we define as time-series measurements of real power production (or sometimes current or voltage) that do not have corresponding meteorological measurements (irradiance, temperature, etc.) or system configuration information (sometimes called system metadata). These challenges are amplified in the distributed rooftop sector, in which data quality, completeness, and metadata can be very poor.

14 SOLAR ENERGY↗

GRinding Automated Classification Engine

This work is an ML-driven framework for automated surface analysis of microscopy images. We create a training dataset by imaging stainless steel samples to benchmark four developed deep neural network architectures. These models, based on a YOLOv8n-cls backend, integrate image features and process metadata using various fusion methods to distinguish between acceptable and unacceptable surface finishes. This code is associated with publication "Classifying Alloy Surface Preparation Quality with Metadata-Infused Machine Learning for Rapid Alloy Discovery" for project APEX LDRD-ER (25-ERD-039)

Gongora, AldairE [Lawrence Livermore National Labo↗

Metadata for a systematic description of signal data

This chapter aims to provide a comprehensive overview of metadata types that may be useful during system design, optimization, and automation. Metadata are grouped into three main categories: (a) metadata describing signal generation, (b) metadata describing signal quality, and (c) contextual information in the form of annotations. Each of these categories is introduced and explained in three separate sections. Importantly, this chapter mainly answers what is considered metadata. To a lesser degree, recommendations are made regarding the selection of metadata for long-term storage. Chapter 4 will explain where and how to store metadata. Chapters 5 and 6 explain how to collect certain metadata through dedicated sensor validation tests (Chapter 5) or algorithmic analysis (Chapter 6).

Alferes, Janelcy↗

Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations

This work is one of a series of articles by the Nanomaterial Data Curation Initiative. Other articles in this series discuss data curation workflows, data completeness and quality, curator responsibilities, and metadata. Many groups within the broad nanotechnology field are already developing data repositories and tools driven by their individual organizational goals. Integrating these data across disciplines, and with other non-nanotechnology resources, can support multiple objectives by reusing the same information, and can serve as the impetus for novel scientific discoveries through deeper data analyses. Based on the results of a community-based survey of organizations that maintain nanomaterial repositories, this article discusses current data integration practices in nanoinformatics and mature fields such as genomics, as well as nanotechnology-specific challenges impacting data integration. Recommendations for achieving integration of existing operational nanotechnology resources, as based on results from a community-wide survey, are presented herein. Nanotechnology-specific data integration challenges, if effectively resolved, can foster the application and validation of nanotechnology within and across disciplines.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Open Data for Nuclear Explosion Monitoring (NEM) [Slides]

The data sources tend to have the highest quality data and metadata, particularly for more recent data sets. Early data from sources such as IRIS tend to have some metadata issues.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

RC-SFA Data Management Templates and Guidance for Standardized, Reusable AI-Ready Data Packages

This data package provides templates and supporting documentation developed by the River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) to communicate its approach to managing and publishing AI-ready data. The package is intended to help data users and data producers understand the structures, metadata practices, and quality-control approaches that support consistent, reusable, and machine-actionable data products across RC-SFA studies. Rather than focusing on a single experimental dataset, this package documents the data management framework used to make RC-SFA data easier to find, ingest, navigate, and interpret. The materials in this package reflect RC-SFA practices for standardized data package organization, including the use of a human- and machine-readable README, file-level metadata, data dictionaries, descriptive file naming, method identifiers, and automated and review-based quality assurance procedures. Together, these components illustrate how RC-SFA extends FAIR data principles toward AI-readiness by prioritizing deep metadata, consistency across data packages, and support for informed downstream reuse by both humans and computational tools. This dataset is comprised of (1) readme; (2) presentation slides with an overview of RC-SFA approach and guidance; (3) document of RC-SFA best practices; (4) data dictionary (dd); (5) file level metadata (flmd); and a subfolder containing templates for dd and flmd. All files are .csv and .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.

AI-readiness↗

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗