cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.w39cex
title: Occurrence Download

description: A dataset containing 36143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (January, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Larus delawarensis Ord, 1815",
 "Year 2011-2011"
 ]
}

 The dataset includes 36143 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043043-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/27957579
title: On-Chip Nanofabrication Techniques for the Manufacturing of Nanoscale Objects Using Nanometal Templating, Shape-Directing Ligands, Atomic Layer Deposition, and Selective Etching

description: The work presented here focuses on the advancement of substrate-based nanofabrication techniques that are robust, scalable, and yield nanostructures that are not readily obtained using existing techniques. The nanofabrication techniques presented rely on (i) gold nanoparticle arrays that function as templates for fabricating more complex nanostructures, (ii) growth-shaping ligands that define the geometries present after a liquid-based synthesis, (iii) atomic layer deposition for the uniform and atomically thin deposition of metal-oxides, and/or (iv) etching (both wet and dry) to selectively control which components remain in the nanostructures.
DOI: 10.7274/27957579.v1
title: On-Chip Nanofabrication Techniques for the Manufacturing of Nanoscale Objects Using Nanometal Templating, Shape-Directing Ligands, Atomic Layer Deposition, and Selective Etching

description: The work presented here focuses on the advancement of substrate-based nanofabrication techniques that are robust, scalable, and yield nanostructures that are not readily obtained using existing techniques. The nanofabrication techniques presented rely on (i) gold nanoparticle arrays that function as templates for fabricating more complex nanostructures, (ii) growth-shaping ligands that define the geometries present after a liquid-based synthesis, (iii) atomic layer deposition for the uniform and atomically thin deposition of metal-oxides, and/or (iv) etching (both wet and dry) to selectively control which components remain in the nanostructures.
DOI: 10.15468/dl.m5ppjf
title: Occurrence Download

description: A dataset containing 401 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (TUR Amazon research team collections, The New York Botanical Garden Herbarium (NY), The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris))",
 "Geometry POLYGON((-61.6783 16.02896,-61.655 16.02896,-61.655 16.05029,-61.6783 16.05029,-61.6783 16.02896))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 401 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043044-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t9fgrb
title: Occurrence Download

description: A dataset containing 10779258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((109.01817 43.19689,99.41883 -1.66358,129.87187 -2.32518,136.4921 42.9551,109.01817 43.19689))",
 "Year 2021-2024"
 ]
}

 The dataset includes 10779258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043039-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/27949083
title: Investigating Plasma Catalyst Interactions for N<sub>2 </sub>Reactions

description: The potential for non-thermal plasma to activate strong chemical bonds has provided alternative routes for chemical transformations at milder reaction conditions. The choice of catalytic material is pivotal in harnessing the synergistic relationship observed when stimulated by plasma. However, material design that can effectively harness the plasma-catalyst synergy requires a fundamental understanding of relevant plasma-activated surface reaction pathways. Moreover, in these plasma-catalytic systems, plasma phase reactions can dominate, which often complicates the analysis and contribution of the catalyst reactivity to the overall product yields. 
This thesis focuses on the investigation of surface catalyst pathways for plasma N<sub>2 </sub>transformation. A sequential exposure of the catalyst surface to the different reactant gases in which the plasma-activated nitrogen is subjected to temperature-programmed reactions with another reactant is employed to 1) highlight the opportunities for catalytic coupling and 2) decouple plasma-catalytic interactions for nitrogen reduction and oxidation chemistry. Initial studies focus on the evaluation of the surface reactivity of supported metal catalysts (Pt, Co, Ni, Fe) in plasma-assisted NH<sub>3 </sub>synthesis. Under these hydrogenation conditions, the formation of NH<sub>3</sub> over different silica-supported metal catalysts is observed, showing metal surface hydrogenation of plasma-activated nitrogen species. Additionally, microkinetic modeling calculations show that the temperature of NH<sub>3</sub> formation scales with the activation barrier for surface hydrogenation. Under oxidative conditions over Pt and Au catalysts, the formation of NO and N<sub>2</sub>O was observed. Interestingly, the metal affinity towards oxygen activation was observed to be critical in directing product selectivity, with Pt being more selective to NO and Au more selective to N<sub>2</sub>O. Additionally, temperature regimes where product selectivity can be controlled on Pt catalyst are explored. Also, the influence of catalyst particle size on produce selectivity for N<sub>2</sub>O synthesis was evaluated. Lastly, the modification of oxide material under plasma stimulation was studied as an avenue for chemical looping synthesis.
DOI: 10.7274/27949083.v1
title: Investigating Plasma Catalyst Interactions for N<sub>2 </sub>Reactions

description: The potential for non-thermal plasma to activate strong chemical bonds has provided alternative routes for chemical transformations at milder reaction conditions. The choice of catalytic material is pivotal in harnessing the synergistic relationship observed when stimulated by plasma. However, material design that can effectively harness the plasma-catalyst synergy requires a fundamental understanding of relevant plasma-activated surface reaction pathways. Moreover, in these plasma-catalytic systems, plasma phase reactions can dominate, which often complicates the analysis and contribution of the catalyst reactivity to the overall product yields. 
This thesis focuses on the investigation of surface catalyst pathways for plasma N<sub>2 </sub>transformation. A sequential exposure of the catalyst surface to the different reactant gases in which the plasma-activated nitrogen is subjected to temperature-programmed reactions with another reactant is employed to 1) highlight the opportunities for catalytic coupling and 2) decouple plasma-catalytic interactions for nitrogen reduction and oxidation chemistry. Initial studies focus on the evaluation of the surface reactivity of supported metal catalysts (Pt, Co, Ni, Fe) in plasma-assisted NH<sub>3 </sub>synthesis. Under these hydrogenation conditions, the formation of NH<sub>3</sub> over different silica-supported metal catalysts is observed, showing metal surface hydrogenation of plasma-activated nitrogen species. Additionally, microkinetic modeling calculations show that the temperature of NH<sub>3</sub> formation scales with the activation barrier for surface hydrogenation. Under oxidative conditions over Pt and Au catalysts, the formation of NO and N<sub>2</sub>O was observed. Interestingly, the metal affinity towards oxygen activation was observed to be critical in directing product selectivity, with Pt being more selective to NO and Au more selective to N<sub>2</sub>O. Additionally, temperature regimes where product selectivity can be controlled on Pt catalyst are explored. Also, the influence of catalyst particle size on produce selectivity for N<sub>2</sub>O synthesis was evaluated. Lastly, the modification of oxide material under plasma stimulation was studied as an avenue for chemical looping synthesis.
DOI: 10.15468/dl.9kj9ss
title: Occurrence Download

description: A dataset containing 759763 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lonchura punctulata (Linnaeus, 1758)"
 ]
}

 The dataset includes 759763 records from 223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043041-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/27949068
title: Forests in Response to Changing Environments: Understanding the Roles of Nutrient and Disturbance with Computational Models

description: Forests play a vital role in mitigating climate change by sequestering carbon, conserving biodiversity, and providing other ecosystem services. However, they face significant threats from anthropogenic climate change, land-use changes and subsequential disturbances, which alter the forest dynamics and carbon cycling. This dissertation investigates forest responses to these environmental challenges, focusing on carbon allocation, biomass turnover, and disturbance legacies across diverse ecosystems.

The first study explores nutrient fertilization effects on carbon allocation in tropical dry forests. Using the ED2 model and Costa Rica fertilization experiment data, it demonstrates how phosphorus availability shapes carbon allocation to leaves, wood, and roots. The findings highlight the sensitivity of carbon sink estimates to nutrient dynamics, emphasizing the need for refined model representations of nutrient effects.

The second study evaluates biomass turnover in Amazonian forests under current and future climates. Through Bayesian calibration and sensitivity analyses, it addresses biases in the ED2 model’s mortality simulations. The study reveals the critical importance of turnover processes in improving biomass projection accuracy under climate change scenarios.

The third study examines the legacy effects of Emerald Ash Borer outbreaks on temperate forests in the U.S. Midwest and Northeast. Using the Forest Vegetation Simulator, it identifies long-lasting impacts on forest structure and function, revealing the ecological and management challenges of invasive species.

By integrating empirical data with advanced modeling, this research advances the understanding of forest responses to environmental stressors. It underscores the importance of refining models to accurately represent nutrient dynamics, turnover processes, and biotic disturbances, providing valuable insights for predicting and managing forest carbon dynamics under global change.
DOI: 10.7274/27949068.v1
title: Forests in Response to Changing Environments: Understanding the Roles of Nutrient and Disturbance with Computational Models

description: Forests play a vital role in mitigating climate change by sequestering carbon, conserving biodiversity, and providing other ecosystem services. However, they face significant threats from anthropogenic climate change, land-use changes and subsequential disturbances, which alter the forest dynamics and carbon cycling. This dissertation investigates forest responses to these environmental challenges, focusing on carbon allocation, biomass turnover, and disturbance legacies across diverse ecosystems.

The first study explores nutrient fertilization effects on carbon allocation in tropical dry forests. Using the ED2 model and Costa Rica fertilization experiment data, it demonstrates how phosphorus availability shapes carbon allocation to leaves, wood, and roots. The findings highlight the sensitivity of carbon sink estimates to nutrient dynamics, emphasizing the need for refined model representations of nutrient effects.

The second study evaluates biomass turnover in Amazonian forests under current and future climates. Through Bayesian calibration and sensitivity analyses, it addresses biases in the ED2 model’s mortality simulations. The study reveals the critical importance of turnover processes in improving biomass projection accuracy under climate change scenarios.

The third study examines the legacy effects of Emerald Ash Borer outbreaks on temperate forests in the U.S. Midwest and Northeast. Using the Forest Vegetation Simulator, it identifies long-lasting impacts on forest structure and function, revealing the ecological and management challenges of invasive species.

By integrating empirical data with advanced modeling, this research advances the understanding of forest responses to environmental stressors. It underscores the importance of refining models to accurately represent nutrient dynamics, turnover processes, and biotic disturbances, providing valuable insights for predicting and managing forest carbon dynamics under global change.
DOI: 10.7274/27949092
title: Elucidating the Genetic Determinants of Drug Response in <em>Plasmodium falciparum</em>

description: Plasmodium falciparum malaria remains one of the most lethal infectious diseases despite global efforts toward malaria control and elimination. Antimalarial drugs are an invaluable resource for reducing the burden of malaria but recurring emergence of resistance to frontline treatments is constant threat that will reverse progress that has been made. Our understanding of how the malaria parasites survive exposure to antimalarial drugs is limited in most cases but is essential to understanding how resistance emerges and spreads. A proven method to begin elucidating genetic mechanisms of resistance is by using structured genetic crosses and parents with known phenotypes for clinically relevant traits.
In the first study, the underlying genetics of resistance to the antimalarial drug artemisinin is explored utilizing progeny of a genetic cross between NF54, a lab-line originating in West Africa and NHP4026, a recently isolated Southeast Asian parasite clinically resistant to artemisinin. Resistance data is integrated with the genetics of parasites fitness and transcriptional gene regulation for a comprehensive assessment of traits that allow for the emergence of resistance. In the second study, the in vitro assay for quantification of artemisinin resistance is expanded to generate phenotypic data for multiple clinically relevant traits. The improved methodology will provide deeper knowledge of artemisinin resistant parasites identified through surveillance in the field and controlled experiments with lab adapted parasites. In the third study, chromosome regions influencing parasite response to antimalarial drug combinations are detailed using progeny from a genetic cross between MAL31, a recently isolated drug susceptible parasite from Malawi and KH004, a multidrug resistant parasite from Western Cambodia. As artemisinin resistance spread in Africa, the knowledge of genetic background mutations within the African parasite that influence susceptibility to drug combinations currently in clinical traits will help direct future surveillance efforts and patient treatments.
DOI: 10.7274/27949092.v1
title: Elucidating the Genetic Determinants of Drug Response in <em>Plasmodium falciparum</em>

description: Plasmodium falciparum malaria remains one of the most lethal infectious diseases despite global efforts toward malaria control and elimination. Antimalarial drugs are an invaluable resource for reducing the burden of malaria but recurring emergence of resistance to frontline treatments is constant threat that will reverse progress that has been made. Our understanding of how the malaria parasites survive exposure to antimalarial drugs is limited in most cases but is essential to understanding how resistance emerges and spreads. A proven method to begin elucidating genetic mechanisms of resistance is by using structured genetic crosses and parents with known phenotypes for clinically relevant traits.
In the first study, the underlying genetics of resistance to the antimalarial drug artemisinin is explored utilizing progeny of a genetic cross between NF54, a lab-line originating in West Africa and NHP4026, a recently isolated Southeast Asian parasite clinically resistant to artemisinin. Resistance data is integrated with the genetics of parasites fitness and transcriptional gene regulation for a comprehensive assessment of traits that allow for the emergence of resistance. In the second study, the in vitro assay for quantification of artemisinin resistance is expanded to generate phenotypic data for multiple clinically relevant traits. The improved methodology will provide deeper knowledge of artemisinin resistant parasites identified through surveillance in the field and controlled experiments with lab adapted parasites. In the third study, chromosome regions influencing parasite response to antimalarial drug combinations are detailed using progeny from a genetic cross between MAL31, a recently isolated drug susceptible parasite from Malawi and KH004, a multidrug resistant parasite from Western Cambodia. As artemisinin resistance spread in Africa, the knowledge of genetic background mutations within the African parasite that influence susceptibility to drug combinations currently in clinical traits will help direct future surveillance efforts and patient treatments.
DOI: 10.7274/27949062
title: Nanoengineering of Phyllosilicates for Novel Gas Sensing Applications

description: This work investigates the novel employment of nanoengineered phyllosilicates as chemiresistive gas sensors towards the detection of volatile organic compounds, chemical warfare agents, toxic industrial chemicals, and a plethora of other analytes. By leveraging fundamental theories of metal oxide semiconductor gas sensing mechanism, material nanoengineering, and electrochemistry, the sensing mechanism, sensitivity, and cross-sensitivity of phyllosilicates as chemiresistive gas sensors is elucidated. To overcome the inherent limitations of sensitivity and selectivity, a morphological and compositional study of phyllosilicates is employed, where the morphology is varied to nano sheets, tubes, flowers, and amorphous structures, whereas the composition of the material is tuned via cation substitution and decoration with a breadth of metals. The attained results in this work exhibit the novel and promising applications of phyllosilicate in the field of electrochemical gas sensing by combining mass-transport control, electrical sensitization, and chemical sensitization on the nanoscale.
DOI: 10.7274/27949062.v1
title: Nanoengineering of Phyllosilicates for Novel Gas Sensing Applications

description: This work investigates the novel employment of nanoengineered phyllosilicates as chemiresistive gas sensors towards the detection of volatile organic compounds, chemical warfare agents, toxic industrial chemicals, and a plethora of other analytes. By leveraging fundamental theories of metal oxide semiconductor gas sensing mechanism, material nanoengineering, and electrochemistry, the sensing mechanism, sensitivity, and cross-sensitivity of phyllosilicates as chemiresistive gas sensors is elucidated. To overcome the inherent limitations of sensitivity and selectivity, a morphological and compositional study of phyllosilicates is employed, where the morphology is varied to nano sheets, tubes, flowers, and amorphous structures, whereas the composition of the material is tuned via cation substitution and decoration with a breadth of metals. The attained results in this work exhibit the novel and promising applications of phyllosilicate in the field of electrochemical gas sensing by combining mass-transport control, electrical sensitization, and chemical sensitization on the nanoscale.
DOI: 10.7274/27949080
title: Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning

description: Active control of fluid dynamics in engineering applications is valuable but challenging, as traditional optimization methods struggle with the complex, high-dimensional nature of these problems.
This dissertation extends and applies an adjoint-based machine learning method, the deep learning PDE augmentation method (DPM), for closed-loop active control on both laminar and turbulent flows. The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function, which we construct using algorithmic differentiation applied to the flow solver. 

In one-dimensional Burgers' equation examples with analytic (manufactured) solutions, the DPM framework is validated, demonstrating comparable effectiveness to standard supervised learning for in-sample solutions, and superior performance for out-of-sample cases (i.e., with different analytic control functions). Additionally, the influence of optimization time intervals and neural network complexity is analyzed, providing insights that guide algorithm design and hyperparameter selection to balance control efficacy and computational cost.

The DPM algorithm is then applied to three laminar flow scenarios. First, drag reduction performance and optimization costs are compared between adjoint-based controllers and deep reinforcement learning (DRL)--based controllers for two-dimensional, incompressible, confined flow over a cylinder, with control achieved via synthetic body forces along the cylinder boundary. The DPM-based controller outperforms the DRL-based controller in both computational efficiency and effectiveness. Second, DPM-based control is tested for compressible, unconfined flow over a cylinder, with the controller extrapolated to out-of-sample Reynolds numbers. A simplified, steady, offline controller is also derived from the DPM control law, achieving comparable drag reduction and vortex shedding stabilization to its adaptive counterparts. Third, a more realistic blowing/suction jet controller is developed for drag reductions with sparse sensors and actuators on the cylinder surface. Both the adaptive trained and simplified constant adjoint-based models are effective for in- and out-of-sample Reynolds numbers.

Beyond laminar flows, the DPM algorithm is extrapolated to turbulent airfoil flows to enhance the lift-to-drag ratio while maintaining manageable energy expenditure. Direct numerical simulations and large eddy simulations are conducted for two-dimensional semi-infinite and three-dimensional airfoils at three angles of attack. Control actions, implemented through blowing/suction jets on the upper surface, are informed by local pressure measurements, which serve as inputs to the controller. Both 2D-trained and 3D-trained DPM controllers effectively capture flow dynamics, significantly improving aerodynamic performance.