Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO 3
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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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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From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation
Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.
AI-driven discovery↗
Plant Bioengineering Atlas: A Knowledge Graph of Genes, DNA Constructs, and Plant Traits.
Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.
, Genes, DNA Constructs↗
DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases
A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.
BERT↗
Leveraging large language models to address data scarcity in machine learning for graphene synthesis
Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.
Chemical vapor deposition↗
Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties
The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.
30 DIRECT ENERGY CONVERSION↗
Mining experimental data from materials science literature with large language models: an evaluation study
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Foppiano, Luca (ORCID:0000000261146164)↗
High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort
Human hands play a significant role during extravehicular activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. It is because of this high frequency usage that hand- and arm-related injuries and discomfort are known to occur during training in the NBL and while conducting EVAs. Hand-related injuries and discomforts have been occurring to crewmembers since the days of Apollo. While there have been numerous engineering changes to the glove design, hand-related issues still persist. The primary objectives of this study are therefore to: 1) document all known EVA glove-related injuries and the circumstances of these incidents, 2) determine likely risk factors, and 3) recommend ergonomic mitigations or design strategies that can be implemented in the current and future glove designs. METHODS: The investigator team conducted an initial set of literature reviews, data mining of Lifetime Surveillance of Astronaut Health (LSAH) databases, and data distribution analyses to understand the ergonomic issues related to glove-related injuries and discomforts. The investigation focused on the injuries and discomforts of U.S. crewmembers who had worn pressurized suits and experienced glove-related incidents during the 1980 to 2010 time frame, either during training or on-orbit EVA. In addition to data mining of the LSAH database, the other objective of the study was to find complimentary sources of information such as training experience, EVA experience, suit-related sizing data, and hand-arm anthropometric data to be tied to the injury data from LSAH. RESULTS: Past studies indicated that the hand was the most frequently injured part of the body during both EVA and NBL training. This study effort thus focused primarily on crew training data in the NBL between 2002 and 2010. Of the 87 recorded training incidents, 19 occurred to women and 68 to men. While crew ages ranged from thirties to fifties, the age category most affected was in the forties range. Incident rate calculations (incidents per 100 training runs) revealed that the 2002, 2003, and 2004 time periods registered the highest reported incident rate levels (3.4, 6.1, and 4.1 respectively) when compared to the following years (all ≤ 1.0). In addition to general hand-arm discomfort being the highest reported result from training, specific types of hand injuries or symptoms included erythema, fingernail delamination, abrasions, muscle soreness/fatigue, paresthesia, bruising, blanching, and edema. Specific body locations most affected by hand injuries included the metacarpophalangeal joints, fingernails, finger crotches, fingers in general, interphalangeal joints, and fingertips. Causes of injuries reported in the LSAH data were primarily attributed to the forces that the gloved hands were exposed to due to hand intensive tasks and/or poor glove sizing. DISCUSSION: Although the age data indicate that most injuries are reported by male crewmembers in their forties, that is also the dominant gender and age range of most EVA crew therefore it is not an unexpected finding. Age and gender analysis will continue as more details on the uninjured population is accrued. While there is a reasonable mechanism to link training quantity to injury, the results were inconsistent and point to the need for a consistent method of suit-related injury screening and documentation. For instance, the high-incident rate levels for the years 2002 to 2004 could be attributed to a comprehensive medical review of crewmembers post-NBL EVA training that occurred from July 19, 2002 to January 16, 2004. Furthermore, there could have been increased awareness from an investigation at the NBL. These investigations may have temporarily increased the fidelity of reported injuries and discomforts during these dates as compared to surrounding years, when injury signs and symptom were no longer actively being investigated but rather voluntarily reported. Data mining for possible mechanistic factors continues and includes more detailed training timelines, hand anthropometry, and suit sizing information. The limited published data looking at hand-arm anthropometry correlated hand-anthropometry metrics with injuries stemming from glove design and operation. Future work will include further evaluation of body sizing and fit in relation to hand injury incidents.
Reid, C. R.↗
Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis
Abstract Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO 3 ) 4 and LiFePO 4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized.
Wang, Zheren (ORCID:0000000227429451)↗
Extraction and Analysis of the Properties of Shape Memory Alloys
Shape Memory Alloys (SMA) are materials of high industrial importance, increasingly used for the aerospace applications. Use of SMA allows to simplify flight systems and reduce the total weight. Through the literature-based data mining, we extracted the computed SMA properties from the high-impact journals. We plotted the selected pairs of properties, found and parametrized correlations.
Jason Ernesto Diaz↗
Reports From EXPORTS Modeling and Data-Mining Activities
EXPORTS -EXport Processes in the Ocean from RemoTe Sensing is NASA’s large field campaign focusing on development of a predictive understanding of the export, fate and carbon cycle impacts of the global net primary production. This co-funded program (NSF, private funding1, and international participation) was conceived in 2013, with EXPORTS Science plan published in 2016 (Siegel et al 2016, EXPORTS Writing Team 2015) and its implementation plan finalized in 2016 (EXPORTS Science Definition Team 2016). More details are presented in Siegel et al (2021). EXPORTS campaign is structured as a multiyear effort (Figure 1). It started with “Pre-EXPORTS” modeling and data-mining activity followed by a first phase with two major field programs and a second synthesis and modeling phase. The “Pre-EXPORTS” projects, total of 6 of them (Table 1), funded under A.3 Ocean Biology and Biogeochemistry 2015 call, helped to plan the field campaign (Resplandy et al 2019, Rousseaux & Gregg 2017), and supporting further global synthesis with datasets mined from the literature (e.g. Bisson et al (2020), Bisson et al (2018), Kramer and Siegel (2019)), directly responding to objectives outlined in Science Plan (see section 6 in Team (2015)) and Implementation team (see Figure 1 in Team (2016)). This document presents a compilation of the final reports of the Pre-EXPORTS funded projects, in hope of synthesizing the outcomes, and insuring the legacy of this program. Each of these reports contains a list of published papers, and reader should refer to them to see results in details.
Brandi J. McCarty↗
Spacesuit Glove-Induced Hand Trauma and Analysis of Potentially Related Risk Variables
Injuries to the hands are common among astronauts who train for extravehicular activity (EVA). When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. Glove injuries, both anecdotal and recorded, have been reported during EVA training and flight persistently through NASA's history regardless of mission or glove model. Theories as to causation such as glove-hand fit are common but often lacking in supporting evidence. Previous statistical analysis has evaluated onycholysis in the context of crew anthropometry only (Opperman et al 2010). The purpose of this study was to analyze all injuries (as documented in the medical records) and available risk factor variables with the goal to determine engineering and operational controls that may reduce hand injuries due to the EVA glove in the future. A literature review and data mining study were conducted between 2012 and 2014. This study included 179 US NASA crew who trained or completed an EVA between 1981 and 2010 (crossing both Shuttle and ISS eras) and wore either the 4000 Series or Phase VI glove during Extravehicular Mobility Unit (EMU) spacesuit EVA training and flight. All injuries recorded in medical records were analyzed in their association to candidate risk factor variables. Those risk factor variables included demographic characteristics, hand anthropometry, glove fit characteristics, and training/EVA characteristics. Utilizing literature, medical records and anecdotal causation comments recorded in crewmember injury data, investigators were able to identify several risk factors associated with increased risk of glove related injuries. Prime among them were smaller hand anthropometry, duration of individual suited exposures, and improper glove-hand fit as calculated by the difference in the anthropometry middle finger length compared to the baseline EVA glove middle finger length.
McFarland, Shane M.↗
Spacesuit Glove-Induced Hand Trauma and Analysis of Potentially Related Risk Variables
Injuries to the hands are common among astronauts who train for extravehicular activity (EVA). When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. Glove injuries, both anecdotal and recorded, have been reported during EVA training and flight persistently through NASA's history regardless of mission or glove model. Theories as to causation such as glove-hand fit are common but often lacking in supporting evidence. Previous statistical analysis has evaluated onycholysis in the context of crew anthropometry only. The purpose of this study was to analyze all injuries (as documented in the medical records) and available risk factor variables with the goal to determine engineering and operational controls that may reduce hand injuries due to the EVA glove in the future. A literature review and data mining study were conducted between 2012 and 2014. This study included 179 US NASA crew who trained or completed an EVA between 1981 and 2010 (crossing both Shuttle and ISS eras) and wore either the 4000 Series or Phase VI glove during Extravehicular Mobility Unit (EMU) spacesuit EVA training and flight. All injuries recorded in medical records were analyzed in their association to candidate risk factor variables. Those risk factor variables included demographic characteristics, hand anthropometry, glove fit characteristics, and training/EVA characteristics. Utilizing literature, medical records and anecdotal causation comments recorded in crewmember injury data, investigators were able to identify several risk factors associated with increased risk of glove related injuries. Prime among them were smaller hand anthropometry, duration of individual suited exposures, and improper glove-hand fit as calculated by the difference in the anthropometry middle finger length compared to the baseline EVA glove middle finger length.
Charvat, Chacqueline M.↗
Progress of the NASA/USGS Lunar Regolith Simulant Project
Beginning in 2004 personnel at MSFC began serious efforts to develop a new generation of lunar simulants. The first two products were a replication of the previous JSC-1 simulant under a contract to Orbitec and a major workshop in 2005 on future simulant development. Beginning in 2006 the project refocused its efforts and approached simulant development in a new and more comprehensive manner, examining new approaches in simulant development and ways to more accurately compare simulants to actual lunar materials. This led to a multi-year effort with five major tasks running in parallel. The five tasks are Requirements, Lunar Analysis, Process Development, Feed Stocks, and Standards. Major progress has been made in all five areas. A substantial draft of a formal requirements document now exists and has been largely stable since 2007. It does evolve as specific details of the standards and Lunar Analysis efforts proceed. Lunar Analysis has turned out to be vastly more difficult than anticipated. After great effort to mine existing published and gray literature, the team has realized the necessity of making new measurements of the Apollo samples, an effort that is currently in progress. Process development is substantially ahead of expectations in 2006. It is now practical to synthesize glasses of appropriate composition and purity. It is also possible to make agglutinate particles in significant quantities. A series of minerals commonly found on the Moon has been synthesized. Separation of mineral constituents from starting rock material is also proceeding. Customized grinding and mixing processes have been developed and tested are now being documented. Identification and development of appropriate feedstocks has been both easier and more difficult than anticipated. The Stillwater Mining Company, operating in the Stillwater layered mafic intrusive complex of Montana, has been an amazing resource for the project, but finding adequate sources for some of the components remains a difficult problem. For example the ratio of clino- to ortho-pyroxenes in the Stillwater is not an exact match for lunar materials. One of the sources being examined as an alternative pyroxene source is the Bushveld Complex in South Africa. Standards have been a major success for the project. The Figure of Merit algorithms have been created, tested, and are being considered for an ISO standard. Agreement has been reached in the community about how to make many of the critical measurements. There remains much work to do: (1) driving down the cost of simulants remains a major obstacle; (2) documentation and cost data analysis have not kept up with progress; (3) educating users in the complexity of the lunar regolith and the use of simulants remains a major task. In summary the project has made enormous progress and is successfully placing simulant development and use on a rigorous, scientifically defensible, engineering basis.
Rickman, Doug↗
Identifying Performance Advantaged Biobased Chemicals Utilizing Bioprivileged Molecules
Two technology areas were advanced; a) novel molecules with improved performance in the end use application of organic corrosion inhibitors and flame retardant nylon polymers and b) development of a systematic process for identifying biomass-derived molecules with improved performance in end use applications. In total 17 novel organic corrosion inhibitors were identified that had significantly better performance than the commercial reference organic corrosion inhibitor and 7 novel nylons were synthesized with improved flame retardant properties relative to standard nylon-6,6. While an end-to-end systematic process for identifying biomass-derived molecules with improved end use performance was not completed, important progress was made computational tools for mining chemical structures from the literature and databases as well as establishing reaction network generation algorithms to aid in the discovery of novel molecules.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mars Target Encyclopedia: Information Extraction for Planetary Science
We created a new reference database called the Mars Target Encyclopedia (MTE) that contains compositional information about science targets (usually rocks or soils) on Mars. Users can search for all targets that contain an element (e.g., “calcium”) or mineral (e.g., “hematite”) and see a map view of their spatial locations (see Fig. 1). Clicking on a search result or searching for a specific target of interest (e.g., “Dillinger”) brings up a page that compiles previous findings about its composition (see Fig. 2). The information that populates the MTE was mined from the planetary science literature using information extraction technology. All information in the MTE is linked to the source publication from which it was extracted, so users can easily browse the full context in the original document (Fig. 2).
Singh, K.↗
High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort
Human hands play a significant role during Extravehicular Activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. Because of this high frequency usage, hand and arm related injuries are known to occur during EVA and EVA training in the NBL. The primary objectives of this investigation were to: 1) document all known EVA glove related injuries and circumstances of these incidents, 2) determine likely risk factors, and 3) recommend interventions where possible that could be implemented in the current and future glove designs. METHODS: The investigation focused on the discomforts and injuries of U.S. crewmembers who had worn the pressurized Extravehicular Mobility Unit (EMU) spacesuit and experienced 4000 Series or Phase VI glove related incidents during 1981 to 2010 for either EVA ground training or in-orbit flight. We conducted an observational retrospective case-control investigation using 1) a literature review of known injuries, 2) data mining of crew injury, glove sizing, and hand anthropometry databases, 3) descriptive statistical analyses, and finally 4) statistical risk correlation and predictor analyses to better understand injury prevalence and potential causation. Specific predictor statistical analyses included use of principal component analyses (PCA), multiple logistic regression, and survival analyses (Cox proportional hazards regression). Results of these analyses were computed risk variables in the forms of odds ratios (likelihood of an injury occurring given the magnitude of a risk variable) and hazard ratios (likelihood of time to injury occurrence). Due to the exploratory nature of this investigation, we selected predictor variables significant at p≤0.15. RESULTS: Through 2010, there have been a total of 330 NASA crewmembers, from which 96 crewmembers performed 322 EVAs during 1981-2010, resulting in 50 crewmembers being injured inflight and 44 injured during 11,704 ground EVA training events. Of the 196 glove related injury incidents, 106 related to EVA and 90 to EVA training. Over these 196 incidents, 277 total injuries (126 flight; 151 training) were reported and were then grouped into 23 types of injuries. Of EVA flight injuries, 65% were commonly reported to the hand (in general), metacarpophalangeal (MCP) joint, and finger (not including thumb) with fatigue, abrasion, and paresthesia being the most common injury types (44% of total flight injuries). Training injuries totaled to more than 70% being distributed to the fingernail, MCP joint, and finger crotch with 88% of the specific injuries listed as pain, erythema, and onycholysis. Of these training injuries, when reporting pain or erythema, the most common location was the index finger, but when reporting onycholysis, it was the middle finger. Predictor variables specific to increased risk of onycholysis included: female sex (OR=2.622), older age (OR=1.065), increased duration in hours of the flight or training event (OR=1.570), middle finger length differences in inches between the finger and the EVA glove (OR=7.709), and use of the Phase VI glove (OR=8.535). Differentiation between training and flight and injury reporting during 2002-2004 were significant control variables. For likelihood of time to first onycholysis injury, there was a 24% reduction in rate of reporting for each year increase in age. Also, more experienced crewmembers, based on number of EVA flight or training events completed, were less likely to report an onycholysis injury (3% less for every event). Longer duration events also found reporting rates to occur 2.37 times faster for every hour of length. Crewmembers with larger hand size reported onycholysis 23% faster than those with smaller hand size. Finally, for every 1/10th of an inch increase in difference between the middle finger length and the glove, the rate of reporting increased by 60%. DISCUSSION: One key finding was that the Series 4000 glove had a lower injury risk than the Phase VI, which provides a platform for further evaluation. General interventions that reduce hand overexertion and repetitive use exposure through tool development, procedural changes and shorter exposures may be one mitigation path, but due to the way the training event times were reported, we cannot provide a guideline for a specific event duration change. When the finger length was different from the glove length, the risk of injury increased indicating that the use of larger finger take-ups could be contributing to injury and therefore may not be recommended. Prior to this investigation, there was one previous investigation indicating hand anthropometry may be related to onycholysis. We found different hand anthropometry variables indicated by this investigation as compared to the prior, specifically differences in middle finger length compared to glove finger length, which point more towards a sizing issue than a specific anthropometry issue. Additionally, although this investigation has identified sizing as an issue, the force and environmental-related variables of the EVA glove that could also cause injury were not accounted for.
Reid, C. R.↗
PNNL-Predictive-Phenomics/DancePartner
Mining multi-omics relationship networks from literature and databases
Degnan, David [Pacific Northwest National Laborato↗