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At least 415 records · Page 23

A large dataset of detection and submeter-accurate 3-D trajectories of juvenile Chinook salmon

Abstract Acoustic telemetry has been used extensively to study the behavior of aquatic animals. The Juvenile Salmon Acoustic Telemetry System (JSATS) is one such system; it was developed for studying juvenile salmonids but has been used to study numerous species. A recent innovation of the JSATS system is an acoustic transmitter that is small enough to be implanted through injection or small incision that doesn’t require sutures. Use of the JSATS system involves deploying cabled acoustic receivers at hydroelectric dams, or other structures, and autonomous acoustic receivers in free-flowing sections of a river. The raw detections from acoustic-tagged fish are processed to remove potential false positives. The clean detections (5,147,996 total) are used to generate detection events and to compute 3-D trajectories (403,900 total), which are used to assign fish to a passage route through a dam. Controlled field testing involving a high-accuracy Global Positioning System receiver is done to validate the submeter accuracy of the trajectories. The JSATS dataset could be reused for expanding the understanding of near-dam fish behavior.

59 BASIC BIOLOGICAL SCIENCES↗

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗

A thermoelectric materials database auto-generated from the scientific literature using ChemDataExtractor

An auto-generated thermoelectric-materials database is presented, containing 22,805 data records, automatically generated from the scientific literature, spanning 10,641 unique extracted chemical names. Each record contains a chemical entity and one of the seminal thermoelectric properties: thermoelectric figure of merit, ZT; thermal conductivity, κ; Seebeck coefficient, S; electrical conductivity, σ; power factor, PF; each linked to their corresponding recorded temperature, T. The database was auto-generated using the automatic sentence-parsing capabilities of the chemistry-aware, natural language processing toolkit, ChemDataExtractor 2.0, adapted for application in the thermoelectric-materials domain, following a rule-based sentence-simplification step. Data were mined from the text of 60,843 scientific papers that were sourced from three scientific publishers: Elsevier, the Royal Society of Chemistry, and Springer. To the best of our knowledge, this is the first automatically-generated database of thermoelectric materials and their properties from existing literature. The database was evaluated to have a precision of 82.25% and has been made publicly available to facilitate the application of data science in the thermoelectric-materials domain, for analysis, design, and prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata

Abstract We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

The Princess Project: From Differential to Integral Experiments

Following the shutdown of the CEA Valduc experimental facilities, where, for more than 50 years, IRSN used to perform experiments related to criticality safety, IRSN initiated a new project named PRINCESS (PRoject for IRSN Neutron physics and Criticality Experimental data Supporting Safety). The objective is to continue collecting experimental data necessary for the IRSN missions in nuclear safety. For this purpose, collaborations with various national and international laboratories have been established. The PRINCESS project covers various nuclear physics fields from nuclear data to criticality-safety and reactor physics providing information to both differential and integral data improvements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Implementing interdisciplinary sustainability education with the food-energy-water (FEW) nexus

Growth in the green jobs sector has increased demand for college graduates who are prepared to enter the workforce with interdisciplinary sustainability skills. Simultaneously, scholarly calls for interdisciplinary collaboration in the service of addressing the societal challenges of enhancing resilience and sustainability have also increased in recent years. However, developing, executing, and assessing interdisciplinary content and skills at the post-secondary level has been challenging. The objective of this paper is to offer the Food-Energy-Water (FEW) Nexus as a powerful way to achieve sustainability competencies and matriculate graduates who will be equipped to facilitate the transformation of the global society by meeting the targets set by the United Nations Sustainable Development Goals. The paper presents 10 curricular design examples that span multiple levels, including modules, courses, and programs. These modules enable clear evaluation and assessment of key sustainability competencies, helping to prepare graduates with well-defined skillsets who are equipped to address current and future workforce needs.

54 ENVIRONMENTAL SCIENCES↗

FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

There is growing support and interest in postsecondary interdisciplinary environmental education which integrate concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here are a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise for explaining changing water usage than discussing tradeoffs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as more nuanced understanding of student comprehension of complex interdisciplinary concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

A New Era of Nuclear Criticality Experiments: The First 10 Years of Radiation Test Object Operations at NCERC

The work presented in this paper focuses on the first 10 years (2011–2020) of radiation test object (RTO) operations at the National Criticality Experiments Research Center. RTOs are subcritical configurations of special nuclear material that are built by hand. These configurations are utilized for benchmark experiments, detector testing/characterization, and training. An overview of the types of measurements used in RTO operations is given as well as a history of RTO operations at Los Alamos National Laboratory from 1944–2011.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A New Era of Nuclear Criticality Experiments: The First 10 Years of Godiva IV Operations at NCERC

The work presented in this paper focuses on the first 10 years (2011–2020) of Godiva IV operations at the National Criticality Experiments Research Center (NCERC). Godiva IV is a fast burst critical assembly constructed of approximately 65 kg of highly enriched uranium fuel alloyed with 1.5% molybdenum for strength. Godiva is one of the last such critical assemblies in the United States and can be used for studies of super-prompt-critical behavior as well as irradiations and demonstrations. An overview of the startup of Godiva IV at NCERC is given followed by a summary of the experiments performed utilizing Godiva IV over the first 10 years of operation at NCERC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A New Era of Nuclear Criticality Experiments: The First 10 Years of Flattop Operations at NCERC

The Flattop critical assembly was built and operated from 1958 through 2004 at the Pajarito Site (Technical Area-18), home to the Los Alamos Critical Experiments Facility. Flattop was disassembled in 2005 and refurbished over the course of 3 years. In 2008, Flattop was installed in the National Criticality Experiments Research Center (NCERC) located in the Device Assembly Facility at the Nevada National Security Site. Startup of Flattop with a uranium core occurred in 2011. This paper details the first 10 years of Flattop operations at NCERC (2011–present).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A New Era of Nuclear Criticality Experiments: The First 10 Years of Comet Operations at NCERC

Comet is a general-purpose, heavy-duty, vertical-lift assembly designed for flexibility in conducting a variety of critical experiments. It is currently located at the National Criticality Experiments Research Center (NCERC) in Nevada. In the past, Comet resided at Technical Area-18 in Los Alamos, New Mexico, as part of the Los Alamos Critical Experiments Facility (LACEF). The Comet assembly was relocated to NCERC in 2008 and became fully operational in June of 2011. The first critical experiment performed on Comet at NCERC was a verification of one of the previous configurations of the Zeus experiment series. Over the next 10 years, many additional experiments followed including other Zeus configurations as well as completely new designs. This paper discusses the Comet vertical-lift assembly, the transition from LACEF to NCERC, and a selection of experiments that have been performed on Comet during its first 10 years of operation at NCERC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A New Era of Nuclear Criticality Experiments: The First 10 Years of Planet Operations at NCERC

Planet is a vertical-lift assembly machine currently located at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site. In the past, Planet resided at Technical Area-18 in Los Alamos, New Mexico, as part of the Los Alamos Critical Experiments Facility (LACEF). Following the de-inventorying of LACEF, the Planet assembly was relocated to NCERC in 2008 and became fully operational in June of 2011. The Class Foils experiment, which involves stacking highly enriched uranium foils to obtain a critical configuration, was the first critical experiment performed on Planet. As a major component of the Nuclear Criticality Safety Class taught for the U.S. Department of Energy (DOE) Nuclear Criticality Safety Program, the Class Foils experiment allows personnel from all over the DOE complex to handle nuclear material and to complete the approach to critical safely and successfully. This paper describes the Planet vertical assembly and recent engineering upgrade and a selection of the experiments that have been performed on Planet since its transition to NCERC 10 years ago.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Evaluation of Digital Nautical Chart data for confirmation and expansion of GeoNames data

Here, this work examines how Digital Nautical Chart (DNC) data may contribute to the evolution and refinement of GeoNames data for near-shore features. GeoNames features are point data with one or more possible place names. DNC Earth Cover Text (ECRText) objects are map labels positioned nearby their real word counterpart. ECRText feature map position strikes a compromise between association with real features and cartographic readability. This work explores whether ECRText features can confirm (or expand names for) existing locations or contribute new locations through data conflation. Due to name variations and spatial position, conflating these data are nontrivial. Previous work engaged in a brief examination using the trigram string matching algorithm under coarse proximity constraints, indicating that ECRText could provide additional value to GeoNames. This work builds on that study, by engaging in a deeper examination of spatial proximity and exploring conflation agreement across an ensemble of string matching approaches. The result finds strong ensemble agreement about ECRText features which already exist in GeoNames but mixed results about which features contribute new information, as well as exploring why some of these matching techniques fail. With an eye toward automation, computational efficiency was found not to be a constraint in sustaining updates.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗