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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A meta-analysis of semantic classification of citations

The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, pre-processing methods, extraction of contextual and non-contextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Analyzing Citation-Distance Networks for Evaluating Publication Impact

Studying citation patterns of scholarly articles has been of interest to many researchers from various disciplines. While the relationship of citations and scientific impact has been widely studied in the literature, in this paper we develop the idea of analyzing the semantic distance of scholarly articles in a citation network (citation-distance network) to uncover patterns that reflect scientific impact. More specifically, we compare two types of publications in terms of their citation-distance patterns, seminal publications and literature reviews, and focus on their referencing patterns as well as on publications which cite them. We show that seminal publications are associated with a larger semantic distance, measured using the content of the articles, between their references and the citing publications, while literature reviews tend to cite publications from a wider range of topics. Our motivation is to understand and utilize this information to create new research evaluation metrics which would better reflect scientific impact.

Herrmannova, Dasha↗
DOE OSTI2018

Do citations and readership identify seminal publications?

Here, this work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Finally, our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Software Citation in HEP: Current State and Recommendations for the Future

In November 2022, the HEP Software Foundation and the Institute for Research and Innovation for Software in High-Energy Physics organized a workshop on the topic of Software Citation and Recognition in HEP. The goal of the workshop was to bring together different types of stakeholders whose roles relate to software citation, and the associated credit it provides, in order to engage the community in a discussion on: the ways HEP experiments handle citation of software, recognition for software efforts that enable physics results disseminated to the public, and how the scholarly publishing ecosystem supports these activities. Reports were given from the publication board leadership of the ATLAS, CMS, and LHCb experiments and HEP open source software community organizations (ROOT, Scikit-HEP, MCnet), and perspectives were given from publishers (Elsevier, JOSS) and related tool providers (INSPIRE, Zenodo). This paper summarizes key findings and recommendations from the workshop as presented at the 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Unlocking Scholarly Insights: Leveraging Machine Learning Approaches for Citation Analysis and Intent Classification

Publicly funded organizations, notably institutions like the Los Alamos National Laboratory (LANL), are deeply vested in acquiring robust productivity metrics to gauge the entirety of their research output. Motivated by the imperative to enhance institutional productivity assessment, this study investigates the utilization of Large Language Models (LLM) such as BERT-based models, as well as local LLaMa-30b-instruct and Mixtral-8x7b-instruct architectures for classifying type of URL referenced resources in academic papers such as software, dataset, as well as authorship intent. Challenges in discerning resource types from context are highlighted, along with the potential of BERT and LLMs to address these challenges. Through comprehensive analysis, this research unveils a notable surge in documents featuring URL citations, indicative of the escalating importance of digital resources in scholarly publications. Moreover, citations to datasets and software demonstrate consistent growth over time, underscoring their increasing significance. Our findings also reveal that LANL authors contribute substantially to accessible science, comprising about 10% of dataset and software mentions in LANL

Large Language Models, BERT, citation classificati↗
DOE OSTI2021

Overview of the 2021 SDP 3C Citation Context Classification Shared Task

This paper provides an overview of the 2021 3C Citation Context Classification shared task. The second edition of the shared task was organised as part of the 2nd Workshop on Scholarly Document Processing (SDP 2021). The task is composed of two subtasks: classifying citations based on their (Subtask A) purpose and (Subtask B) influence. As in the previous year, both tasks were hosted on Kaggle and used a portion of the new ACT dataset. A total of 22 teams participated in Subtask A, and 19 teams competed in Subtask B. All the participated systems were ranked based on their achieved macro f-score. The highest scores of 0.26973 and 0.60025 were reported for sub-task A and B, respectively.

Kunnath, Suchetha N.↗
DOE OSTI2024

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↗
DOE OSTI2026

Citation network datasets for benchmarking spiking graph neural networks on experimental neuromorphic hardware

Spiking neural networks (SNNs) running on neuromorphic computers offer an energy-efficient alternative for AI tasks. Recently, spiking graph neural networks (S-GNNs) have been shown to produce encouraging results on benchmark citation network datasets such as Cora, CiteSeer, and PubMed for node classification tasks. These S-GNNs were run on SNN simulators only because they contain up to tens of thousands of neurons and up to millions of synapses, translating poorly to neuromorphic hardware. Therefore, in this paper, we create a suite of benchmark datasets from the CiteSeer dataset that can be accommodated on current neuromorphic hardware platforms. Our contribution consists of a collection of three datasets. First, we have an induced subgraph of CiteSeer, which we call MiniSeer, containing 2110 papers, 3604 binary features, and 6 topics. Second, MicroSeer is a very small dataset consisting of 84 papers, 1227 features, and 6 topics. Lastly, BiteSeer is a collection of 15 binary classification datasets. We present creation of these datasets along with accuracies, running times, and spike counts when simulated. We believe that our results in this paper will be used by the neuromorphic community to benchmark, test, and develop neuromorphic hardware and simulators.

Zhu, Kevin [George Mason University, Virginia]↗
DOE OSTI2024

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↗
DOE OSTI2020

Citation for the 2019 Victor M. Goldschmidt award to Donald J. DePaolo

Perhaps the comments most commonly heard when a colleague receives professional recognition are “well-deserved” and “long-overdue”. As such, it is my pleasure to introduce Donald DePaolo as the recipient of this year’s Goldschmidt Award as it is both long-overdue and so well-deserved. While Don has received many honors over the course of his career, the Goldschmidt best recognizes his broad impact on geochemistry. There are few who have contributed as much to geochemistry as Don DePaolo. Finally, his scientific contributions are voluminous and broad, ranging from pioneering efforts to establish the Sm/Nd isotopic system, and its applications to mantle evolution and basalt petrogenesis, to studies of magma mixing, assimilation and crystal fractionation, to evolution of the lower crust and granite petrogenesis, to chemical oceanography, diagenesis and seafloor alteration, to reactive transport accompanying rock-water interaction, and to kinetic isotopic fractionation associated with crystal growth, diffusion and evaporation.

58 GEOSCIENCES↗
DOE OSTI2019

Data Mining Scientific Literature Demonstrates Use of Biological and Medical Data Across Scientific Disciplines

The Protein Data Bank (PDB) was established as the 1st open access digital data resource in biology and medicine. Today, the PDB archive contains over 140,000 atomic‐level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. About 20 new structures are deposited daily. PDB data is used in basic and applied research, patent applications, discovery of lifesaving drugs, product innovation, and education. About 80% of all PDB structures deposited have a “primary citation,” which is the first paper to describe the molecule and its structure and function. In turn, these primary citations have contributed to more than 1 million publications. An initial study by Clarivate Analytics on the Nucleic Acids Research publication demonstrated that PDB data drive high‐impact research in diverse scientific fields. To build on this study, we used Python programming to perform several data mining studies to understand the impact of these structures in fundamental biology, biomedicine, and energy in the scientific literature. The scientific literature database, Web of Science, tracks scientific article metrics such as the number of times cited, subject category, year published, funding source, and keywords. Web of Science data for PDB primary citations was compiled as used as the data set. Two main investigations were performed. In the first, we examined the number of citations per article (data overall and by individual subject categories) to develop “top 10 cited structures” lists and analyzed for trends. In the second investigation, we extended our data mining analysis to consider for each main subject category, the papers with the most citations and the structures they cite, and the most frequently occuring keywords. We have observed different aspects of biological data utility in various subject areas, at times in ways unanticipated from the structures' primary citation. This research demonstrates the value of biological data from the PDB by showing the diverse ways in which the data are being used. Support or Funding Information RCSB PDB is funded by a grant (DBI‐1338415) from the National Science Foundation, the National Institutes of Health, and the US Department of Energy. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Verdiguel, Natalie↗
DOE OSTI2017

Topical Pathogens, Topical (Skin) Infections, and Cosmetics Periodic Update (July 1, 2016 to June 30, 2017)

A literature search was conducted of PubMed/Medline and MESH databases to support analyses and investigations of the relationship between microorganisms associated with human topical (skin) infections (including opportunistic pathogens) and cosmetics. Key terms for literature searches included; microbial genera and species of interest (bacteria, viruses, fungi, protozoans, parasites), opportunistic pathogens, human skin disease and infection, specific skin diseases (e.g., dermatomycoses, candidiasis, ecthyma, impetigo, etc.), surgery, infants, pregnant women, geriatric, immunocompromised, HIV and polymicrobial. The number of journal articles for defined searches was summarized and journal citations and abstracts were cataloged and incorporated into a searchable information system (MS Access). A periodic update to literature searches was completed in July 2017 and observations from the update indicate the most commonly associated bacteria with topical (skin) infections/diseases were Staphylococcus spp., Streptococcus spp., and Pseudomonas spp. Also, the literature searches indicate the most commonly associated fungi with topical (skin) infections/diseases were Tinea spp., Candida spp., and Aspergillus spp. A relative rank of occurrence of microorganisms associated with topical (skin) infections/disease in the literature citations was: Herpes simplex virus, Staphylococcus aureus, Tinea spp., Candida spp., Human papillomavirus, Pseudomonas aeruginosa, Leishmania spp., Streptococcus spp., Aspergillus spp., and Treponema pallidum. Literature searches for opportunistic microorganisms and emerging pathogens for this update identified several articles on dermatomycotic species including Candida yeasts (Candida albicans), dermatophytes (Aspergillus, and Trichophyton) and other opportunistic yeasts (Malassezia spp. and Rhodotorula spp.) associated with topical (skin) infections/diseases. A few of the opportunistic pathogenic Enterobacteriaceae more prevalent in citations were Klebsiella spp. and Enterobacter spp. Although the literature searches focused on microorganisms, key topical (skin) diseases noted in the citations were dermatomycoses, leishmaniasis, eczema, candidiasis, and impetigo.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Ocular Pathogens, Eye Infections, and Cosmetics Periodic Update (July 1, 2016 to June 30, 2017)

A PubMed literature search was conducted to support analyses and investigations of the relationship between microorganisms associated with human eye infections (including opportunistic pathogens) and cosmetics. Key terms for PubMed literature searches included; microbial genera and species of interest (bacteria, viruses, fungi, protozoans, parasites), opportunistic pathogens, human eye disease and infection, human ocular infections, specific eye diseases (keratitis, conjunctivitis, etc.), eye accidents, eye surgery, contacts, infants, pregnant women, geriatric, immunocompromised, HIV and polymicrobial. The number of journal articles for defined searches was summarized and journal citations and abstracts were cataloged and incorporated into a searchable information system (MS Access). A periodic update to the literature searches was conducted in July 2017 and observations from the update indicate the most commonly associated bacteria with eye infections/diseases were Staphylococcus sp., Chlamydia sp. and Pseudomonas sp. Also, the literature searches indicate the most commonly associated fungi with eye infections/diseases were Fusarium sp., Aspergillus sp. and Candida sp. A relative rank of occurrence of microorganisms associated with eye infections/disease in the literature citations was; Staphylococcus aureus, Chlamydia trachomatis, Cytomegalovirus, Herpes zoster virus, Pseudomonas aeruginosa, Staphylococcus epidermidis, Acanthamoeba sp., Fusarium sp., E. coli and Mycobacterium tuberculosis. Literature searches for opportunistic microorganisms and emerging pathogens for this update identified several articles on fungi, Bacillus sp. (primarily B. cereus), Haemophilus influenzae and Human Papillomavirus (HPV) associated with eye infections/diseases. Although the literature searches focused on microorganisms, key eye diseases noted in the citations were keratitis, conjunctivitis, corneal ulcers, endophthalmitis and keratoconjunctivitis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

An introductory note on Randomized Maximum Likelihood (RML) as it relates to quantitative inference from radiography

The purpose of this report is to identify the source citations for the RML method and clearly delineate the relevant analysis that is contained within, relate the RML paradigm to inference from radiography and thereby contextualize the source citations as limited justification for an intuitive Monte Carlo technique, and identify a potential pitfall of applying RML as it is presented in the source citations and prescribe a deviation from techniques adopted by the AWE and presented by researchers there.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

NCAR/EOL ISFS Data for LASSO-CACTI Overview Paper

5 minute averages of surface meteorology and flux data collected by the NCAR/EOL Integrated Surface Flux System (ISFS) at 15 sites during the RELAMPAGO field campaign. These data have been quality-controlled and are available in NetCDF format. Winds reported by the sonic anemometers have been tilt corrected and rotated into geographic coordinates. Data providence, citation, and acknowledgement This ARM data set is a copy of v2.0 of the NCAR data set obtained on 6-Jun-2024 from https://doi.org/10.26023/ZPHJ-JW9W-2B0Y. The citation for the original data source is: NCAR/EOL In-situ Sensing Facility, Oncley, S. 2021. NCAR/EOL ISFS Surface Meteorology and Flux Products, 5-minute. Version 2.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/ZPHJ-JW9W-2B0Y Accessed 06 Jun 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: "Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/"

atmosphere: surface↗
DOE OSTI2025

NCAR-RAL Surface Hydrometeorological Observation Network Data for LASSO-CACTI Overview Paper

This data set contains the 15 minute resolution surface meteorology and soils data from the 15 NCAR/RAL weather stations that were operated around central Argentina during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations) Extended Observing Period (EOP). Data providence, citation, and acknowledgement This ARM data set is a copy of v1.0 of the NCAR data set obtained in June 2024 from https://doi.org/10.26023/KW8Z-F2WX-H0Y. The citation for the original data source is: Gochis, D., et al. 2019. NCAR-RAL Surface Hydrometeorological Observation Network Data. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/KW8Z-F2WX-H0Y Accessed June 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: “Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/”

air temperature↗
DOE OSTI2020

CiteSoft_Py

There is a need to provide a way for dev-users (Scientists, Engineers, and other Software Developers) to get credit (citations) when they contribute to an important software package - particularly a large package that is already established. Various solutions (including cross package) solutions do exist, but a simple standard format that can work between multiple packages and multiple languages in a modular way is not widely available. CiteSoft_py is a python implementation of CiteSoft. CiteSoft is a plain text standard consisting of a format and a protocol that exports the citations for the end-users for whichever softwares they have used. CiteSoft has been designed so that software dev-users can rely upon it regardless of coding language or platform, and even for cases where multiple codes are working in a coupled manner. The CiteSoft_py implementation includes python decorators to make wrappers for facile use. The purpose is that when dev-users (scientists, engineers and professional programmers) contribute to a collaborative software project, that the appropriate citations (e.g., journal article, conference proceeding) are provided to the end-user. The end-user does not need to know how to use a command line interface, API, etc.

Savara, Aditya↗