cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jh69s5
title: Occurrence Download

description: A dataset containing 770 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Mauligobius maderensis (Valenciennes, 1837)"
 ]
}

 The dataset includes 770 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000417-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7266/re20k34s
title: Stakeholder survey assessment of Disaster Impact Estimation Prediction (DIEP) system
DOI: 10.15468/dl.we3awn
title: Occurrence Download

description: A dataset containing 721651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is e2e717bf-551a-4917-bdc9-4fa0f342c530",
 "TaxonKey is Pycnonotus jocosus (Linnaeus, 1758)"
 ]
}

 The dataset includes 721651 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000367-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27613242.v2
title: clustering and survival analysis on multi-omics datasets

description: multi-omics data: the input data of the analysis, including miRNA, gene expression data, DNA methylation data, and survival outcome data. All the data were downloaded from TCGA.code: 1. data preprocessing. 2. clustering patients in each omics layer and performing Kaplan-Meier survival analysis to determine the association between patient clusters and survival outcomes. 3. differential expression analysis to identify features that are associated with patients with consistent survival outcomes.
DOI: 10.3886/e207909v1
title: ECIN Replication Package for "Does nature shape economic preferences? Evidence from Chile, Norway, and Tanzania"

description: Does exposure to a more risky environment affect risk preferences?<br>Going beyond single-case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread.<br><br>
DOI: 10.3886/e207909v2
title: ECIN Replication Package for "Does nature shape economic preferences? Evidence from Chile, Norway, and Tanzania"

description: Does exposure to a more risky environment affect risk preferences?<br>Going beyond single-case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread.<br><br>
DOI: 10.3886/e207909v3
title: ECIN Replication Package for "Does nature shape economic preferences? Evidence from Chile, Norway, and Tanzania"

description: Does exposure to a more risky environment affect risk preferences?<br>Going beyond single-case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread.<br><br>
DOI: 10.3886/e207909
title: ECIN Replication Package for "Does nature shape economic preferences? Evidence from Chile, Norway, and Tanzania"

description: Does exposure to a more risky environment affect risk preferences?<br>Going beyond single-case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread.<br><br>
DOI: 10.3886/e207909v4
title: ECIN Replication Package for "Does nature shape economic preferences? Evidence from Chile, Norway, and Tanzania"

description: Does exposure to a more risky environment affect risk preferences?<br>Going beyond single-case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread.<br><br>
DOI: 10.15468/dl.55a4ft
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grewia suffruticosa K.Schum.",
 "Continent is Africa"
 ]
}

 The dataset includes 25 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000429-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e4uxky
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is A photographic type catalogue of Platygastroidea (Insecta, Hymenoptera) in the Natural History Museum Vienna",
 "Country is Croatia"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000424-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hmn8bx
title: Occurrence Download

description: A dataset containing 768 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Picea orientalis (L.) Peterm."
 ]
}

 The dataset includes 768 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007763-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rueszu
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rattus exulans (Peale, 1848)",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 14 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007765-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnujte
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lissotriton vulgaris (Linnaeus, 1758)",
 "Year is 33",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007766-241107131044228/datasets/export for details.
DOI: 10.7274/25944454
title: MPC Formulation and Numerical Optimization for Online Synthesis of Athletic Maneuvers with Quadruped Robots

description: The potential of legged robots lies in their ability to replicate the mobility and agility of humans and animals. Despite notable advancements in recent decades, existing control methods have yet to fully capture the highly dynamic maneuvers observed in nature, such as athletic parkour and jumping. The primary goal of this dissertation is to elevate the dynamic capabilities of quadruped robots to new heights. A major contribution toward this goal is the conceptual development, benchmarking, and successful implementation of a Cascaded-Fidelity Model Predictive Control (CAFE-MPC) framework, which enables a running barrel roll on quadruped robots for the first time. Model Predictive Control (MPC) is a key control technique for legged locomotion, which functions by repeatedly solving Trajectory Optimization (TO) problems. Dynamic behaviors such as a barrel roll involve fast dynamics, demanding a fast udpate rate and reasoning over the whole-body dynamics, posing computational challenges to MPC. This dissertation proposes to address these challenges using two approaches. The first approach is concerned with developing a real-time TO solver. A particular interest herein is on Differential Dynamic Programming (DDP), which naturally has linear computational complexity relative to the prediction horizon. We leverage this favorable property and extend DDP to handle a multiple-shooting formulation and to be applicable to hybrid systems (e.g., legged robots). The second approach seeks to mitigate the computational burden of MPC from a formulation perspective. CAFE-MPC is proposed to relax the planning problem along the prediction horizon, posing a TO problem that is computationally more tractable. In addition, a value-based whole-body controller (VWBC) is formulated for enhanced bandwidth without additional tuning. The DDP-based solver, CAFE-MPC and VWBC synergize to streamline the MPC process, enabling highly dynamic maneuvers like a running barrel roll in experiments with MIT Mini Cheetah.
DOI: 10.7274/25944454.v1
title: MPC Formulation and Numerical Optimization for Online Synthesis of Athletic Maneuvers with Quadruped Robots

description: The potential of legged robots lies in their ability to replicate the mobility and agility of humans and animals. Despite notable advancements in recent decades, existing control methods have yet to fully capture the highly dynamic maneuvers observed in nature, such as athletic parkour and jumping. The primary goal of this dissertation is to elevate the dynamic capabilities of quadruped robots to new heights. A major contribution toward this goal is the conceptual development, benchmarking, and successful implementation of a Cascaded-Fidelity Model Predictive Control (CAFE-MPC) framework, which enables a running barrel roll on quadruped robots for the first time. Model Predictive Control (MPC) is a key control technique for legged locomotion, which functions by repeatedly solving Trajectory Optimization (TO) problems. Dynamic behaviors such as a barrel roll involve fast dynamics, demanding a fast udpate rate and reasoning over the whole-body dynamics, posing computational challenges to MPC. This dissertation proposes to address these challenges using two approaches. The first approach is concerned with developing a real-time TO solver. A particular interest herein is on Differential Dynamic Programming (DDP), which naturally has linear computational complexity relative to the prediction horizon. We leverage this favorable property and extend DDP to handle a multiple-shooting formulation and to be applicable to hybrid systems (e.g., legged robots). The second approach seeks to mitigate the computational burden of MPC from a formulation perspective. CAFE-MPC is proposed to relax the planning problem along the prediction horizon, posing a TO problem that is computationally more tractable. In addition, a value-based whole-body controller (VWBC) is formulated for enhanced bandwidth without additional tuning. The DDP-based solver, CAFE-MPC and VWBC synergize to streamline the MPC process, enabling highly dynamic maneuvers like a running barrel roll in experiments with MIT Mini Cheetah.
DOI: 10.15468/dl.p3exjr
title: Occurrence Download

description: A dataset containing 1833 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eschweilera ovata (Cambess.) Mart. ex Miers"
 ]
}

 The dataset includes 1833 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007767-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ccaqyj
title: Occurrence Download

description: A dataset containing 87447 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bidens coronata Fisch. ex Steud., 1840, Bidens crocata Cav., Bidens cuneata Sherff, Bidens pinnata Noronha, Bidens rotata Sherff, Bidens frondosa L., Bidens frondosa Buch.-Ham., Bidens frondosa Buch.-Ham. ex Hook.fil., Bidens subalternans DC., Bidens vulgata Greene)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 87447 records from 1035 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007764-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/26061358
title: Radiative Transfer Verification Solutions

description: Physical processes involving fluids in which radiation is strong enough to convey significant energy require some radiative heat transfer (RT) description to model. It is standard to assume that photons may be described as particles and that they travel in straight lines between collisions. This allows the radiation to be modeled by the linear Boltzmann transport equation, often called simply ``the transport equation,'' or ``transport.'' 

The transport equation has seven independent variables, one for time, three for direction, two for angle, and one for energy. Until recently, even advanced computers did not have the computational power to solve the full transport equation. This compelled simplifications. One of these, the P1 approximation, involves taking angular moments of the transport equation with spherical harmonics and solving the resulting angular-independent coupled system. Monte Carlo methods were developed to circumvent the dimensionality question, but they converge slowly. Now, however, advances in computing allow ambitious codes that simulate the full transport equation in all of the independent variables to be coupled with complex physics simulations and run on state of the art machines. 

The advancement of simulation code makes the issue of verification even more pertinent. Analytic solution methods have historically been the focus of verification in RT and from these methods have come many invaluable solutions. However, it can be argued that much of the low hanging fruit on the analytic solution tree has been picked. A modification in approach therefore is in order. This approach, presented here, is a discretized benchmark generator which is able to achieve the same levels of accuracy as the semi-analytic benchmarks in the field.

To solve the one-dimensional, energy-independent transport equation, which is still a challenging integro-differential equation, the angular dependence is discretized by discrete ordinates (SN). Spatial variation is resolved by a spectral elements method with moving mesh edges. The integration of the resulting ordinary differential equations in time is accomplished with a Runge-Kutta integrator. In addition to this, the uncollided part of the solution is separated out and solved analytically. 

This method is tested on existing analytic solutions and novel solutions developed specifically for this purpose and extended to provide new benchmarks for a nonlinear RT problem and an uncoupled blast wave imaging problem on one dimensional Cartesian coordinates, also called ``slab geometry''. Derivation of a one dimensional spherical geometry version of the method is also proven on known solutions. Additionally, new asymptotic analysis is used to verify that the method is asymptotic preserving in the early time initial layer and late time diffusive regimes.
DOI: 10.7274/26061358.v1
title: Radiative Transfer Verification Solutions

description: Physical processes involving fluids in which radiation is strong enough to convey significant energy require some radiative heat transfer (RT) description to model. It is standard to assume that photons may be described as particles and that they travel in straight lines between collisions. This allows the radiation to be modeled by the linear Boltzmann transport equation, often called simply ``the transport equation,'' or ``transport.'' 

The transport equation has seven independent variables, one for time, three for direction, two for angle, and one for energy. Until recently, even advanced computers did not have the computational power to solve the full transport equation. This compelled simplifications. One of these, the P1 approximation, involves taking angular moments of the transport equation with spherical harmonics and solving the resulting angular-independent coupled system. Monte Carlo methods were developed to circumvent the dimensionality question, but they converge slowly. Now, however, advances in computing allow ambitious codes that simulate the full transport equation in all of the independent variables to be coupled with complex physics simulations and run on state of the art machines. 

The advancement of simulation code makes the issue of verification even more pertinent. Analytic solution methods have historically been the focus of verification in RT and from these methods have come many invaluable solutions. However, it can be argued that much of the low hanging fruit on the analytic solution tree has been picked. A modification in approach therefore is in order. This approach, presented here, is a discretized benchmark generator which is able to achieve the same levels of accuracy as the semi-analytic benchmarks in the field.

To solve the one-dimensional, energy-independent transport equation, which is still a challenging integro-differential equation, the angular dependence is discretized by discrete ordinates (SN). Spatial variation is resolved by a spectral elements method with moving mesh edges. The integration of the resulting ordinary differential equations in time is accomplished with a Runge-Kutta integrator. In addition to this, the uncollided part of the solution is separated out and solved analytically. 

This method is tested on existing analytic solutions and novel solutions developed specifically for this purpose and extended to provide new benchmarks for a nonlinear RT problem and an uncoupled blast wave imaging problem on one dimensional Cartesian coordinates, also called ``slab geometry''. Derivation of a one dimensional spherical geometry version of the method is also proven on known solutions. Additionally, new asymptotic analysis is used to verify that the method is asymptotic preserving in the early time initial layer and late time diffusive regimes.
DOI: 10.15468/dl.k82df3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Troglodytes troglodytes (Linnaeus, 1758)",
 "Year is 34",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007768-241107131044228/datasets/export for details.
DOI: 10.7274/26082544
title: Advancing Additive Manufacturing of Biomedical Sensing Devices through Hybrid Printing and Artificial Intelligence

description: Additive manufacturing (AM) has emerged as a transformative manufacturing process in modern production, enabling the creation of complex 3D structures with minimal waste. Despite recent advancements, AM still encounters significant challenges, including limited material options and the trade-offs between feature sizes and resolution that can be achieved by a single AM technology, extensive post-processing that compromises efficiency and material compatibility, and the time-consuming printing parameter optimizations.

This dissertation introduces innovative solutions to address these challenges by developing hybrid printing techniques and artificial intelligence enabled autonomous AM processes. Central to this research is the development of a hybrid printing system that integrates a novel high-throughput combinatorial aerosol jet printing and extrusion printing on a single platform. This integration facilitates the streamlined manufacturing of complex devices that incorporate a diverse range of materials and feature sizes, achieving high performance, high efficiency, and high resolution.

The effectiveness of this hybrid system is demonstrated through the fabrication and characterization of novel wearable devices and biosensors. These include a tellurium nanowire-based wearable piezoelectric sensor, which offers high sensitivity and stretchability for monitoring physiological signals such as heart rate, and a microfluidic sweat sensor that enables the amperometric sensing of lactate in sweat. Moreover, the development of an in-situ plasma jet sintering technique eliminates the need for post-processing and enhances process compatibility through ambient temperature processings.

To further enhance AM's capabilities, this work integrates a hybrid machine learning algorithm to autonomously optimize printing parameters based on user-defined target output properties in a data-efficient manner. Additionally, the dissertation explores the application of an environment-adaptive robotic printer equipped with environmental perception capabilities, enabling precise printing on irregular and dynamically changing surfaces. These advancements have the potential to revolutionize personalized healthcare by allowing the direct printing of biomedical devices on living biological materials.
DOI: 10.7274/26082544.v1
title: Advancing Additive Manufacturing of Biomedical Sensing Devices through Hybrid Printing and Artificial Intelligence

description: Additive manufacturing (AM) has emerged as a transformative manufacturing process in modern production, enabling the creation of complex 3D structures with minimal waste. Despite recent advancements, AM still encounters significant challenges, including limited material options and the trade-offs between feature sizes and resolution that can be achieved by a single AM technology, extensive post-processing that compromises efficiency and material compatibility, and the time-consuming printing parameter optimizations.

This dissertation introduces innovative solutions to address these challenges by developing hybrid printing techniques and artificial intelligence enabled autonomous AM processes. Central to this research is the development of a hybrid printing system that integrates a novel high-throughput combinatorial aerosol jet printing and extrusion printing on a single platform. This integration facilitates the streamlined manufacturing of complex devices that incorporate a diverse range of materials and feature sizes, achieving high performance, high efficiency, and high resolution.

The effectiveness of this hybrid system is demonstrated through the fabrication and characterization of novel wearable devices and biosensors. These include a tellurium nanowire-based wearable piezoelectric sensor, which offers high sensitivity and stretchability for monitoring physiological signals such as heart rate, and a microfluidic sweat sensor that enables the amperometric sensing of lactate in sweat. Moreover, the development of an in-situ plasma jet sintering technique eliminates the need for post-processing and enhances process compatibility through ambient temperature processings.

To further enhance AM's capabilities, this work integrates a hybrid machine learning algorithm to autonomously optimize printing parameters based on user-defined target output properties in a data-efficient manner. Additionally, the dissertation explores the application of an environment-adaptive robotic printer equipped with environmental perception capabilities, enabling precise printing on irregular and dynamically changing surfaces. These advancements have the potential to revolutionize personalized healthcare by allowing the direct printing of biomedical devices on living biological materials.
DOI: 10.15468/dl.6c75a8
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Polylepis pauta Hieron."
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007769-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.