cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4300427
title: A database of plant-pollinator networks

description: This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.
DOI: 10.21227/4pvs-4s70
title: Biomedical KEH B

description: This dataset is in support of my research paper submitted to "IEEE Transactions on Circuits and Systems I: Regular Papers".
DOI: 10.21227/pah5-8x79
title: Biomedical KEH B

description: This dataset is in support of my research paper submitted to "IEEE Transactions on Circuits and Systems I: Regular Papers".Author extends this to artificial leg(project which was abandoned by the author due to finances) or artifical handIn this paper and dataset, mass-spring system
DOI: 10.25505/fiz.icsd.cc26qr3t
title: CSD 2047646: Experimental Crystal Structure Determination

description: Related Article: Joel Mague, K. Chkirate|2020|ICSD Communication|||
DOI: 10.15482/usda.adc/1520556
title: Meta-data for Phenolic Compound Induction in Plant-Microbe and Plant-Insect Interactions
DOI: 10.5281/zenodo.4300845
title: Sentinel-3A and Sentinel-3B radar altimetry water surface elevation in the Zambezi catchment

description: This dataset contains the data presented in Kittel et al. (2020): Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B, https://doi.org/10.5194/hess-2020-165 The dataset is the full Sentinel-3A and 3B radar altimetry records for water surface elevation in the Zambezi catchment for all virtual stations containing at least 80% of the expected observations and predominantly single-peak waveforms. Not all VS have been manually checked or validated. Additionally the repository contains a shapefile with all Sentinel-3 virtual stations in the Zambezi.
DOI: 10.5281/zenodo.4300844
title: Sentinel-3A and Sentinel-3B radar altimetry water surface elevation in the Zambezi catchment

description: This dataset contains the data presented in Kittel et al. (2020): Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B, https://doi.org/10.5194/hess-2020-165 The dataset is the full Sentinel-3A and 3B radar altimetry records for water surface elevation in the Zambezi catchment for all virtual stations containing at least 80% of the expected observations and predominantly single-peak waveforms. Not all VS have been manually checked or validated. Additionally the repository contains a shapefile with all Sentinel-3 virtual stations in the Zambezi.
DOI: 10.5519/qd.foxxyzfw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (67 records)
DOI: 10.5281/zenodo.4300883
title: Vortex input files -- Florida scrub-jay PVA

description: Input files for Vortex PVA of Florida scrub-jay, a collaboration of NASA, USFWS, TNC, SCTI, and others
DOI: 10.6084/m9.figshare.13315421
title: SDSU Fanfiction Creator Survey 2020 minus free text responses

description: Results of the August 27 - November 1, 2020 SDSU Fanfiction Creator Survey. Participants were asked about personal preferences for fanfiction:<br> tagging and metadata, <br>search & discovery,<br>and platform functionality<br>
DOI: 10.21227/na8z-n616
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.6084/m9.figshare.13315454
title: SDSU Fanfiction User Survey 2020 minus free text responses

description: Results of the SDSU Fanfiction User Survey 2020. Participants were asked their personal preferences on fanfiction platforms for:<br>tagging and metadata,search and discovery,and platform functionality.<br>
DOI: 10.6084/m9.figshare.13315454.v1
title: SDSU Fanfiction User Survey 2020 minus free text responses

description: Results of the SDSU Fanfiction User Survey 2020. Participants were asked their personal preferences on fanfiction platforms for:<br>tagging and metadata,search and discovery,and platform functionality.<br>
DOI: 10.21227/4fxf-xy91
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.21227/t856-5x05
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.21227/jjac-x932
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.21227/edqk-9a31
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.6084/m9.figshare.12960161.v1
title: Striatal direct and indirect pathway neurons differentially control the encoding and updating of goal-directed learning.

description: Source data related to the article "Striatal direct and indirect pathway neurons differentially control the encoding and updating of goal-directed learning" by Peak, Chieng, Hart & Balleine (2020). Each source data file corresponds to each figure in the paper. E.g. Source Data File 1 relates to Figure 1 and Figure 1 - figure supplements.
DOI: 10.6084/m9.figshare.12960161
title: Striatal direct and indirect pathway neurons differentially control the encoding and updating of goal-directed learning.

description: Source data related to the article "Striatal direct and indirect pathway neurons differentially control the encoding and updating of goal-directed learning" by Peak, Chieng, Hart & Balleine (2020). Each source data file corresponds to each figure in the paper. E.g. Source Data File 1 relates to Figure 1 and Figure 1 - figure supplements.
DOI: 10.6084/m9.figshare.13315499
title: San Diego State University Fanfiction User and Creator Surveys README

description: README for the San Diego State University Fanfiction User and Creator Surveys.<br>
DOI: 10.6084/m9.figshare.13315448.v1
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: Novel traits arise from genome evolution, but it remains ambiguous which evolutionary events modulate trait diversity in yeast subphylum. Here, we performed a multi-dimensional evolution analysis to investigate the general mechanism shaping yeast phenome diversity. We reconstructed genome-scale metabolic models for 332 yeast species to characterize versatile metabolic functions. Subsequent model simulation and selection analysis indicate that most genes are negatively selected across species, while the evolution rate of gene and codon are pathway- and function-dependent. Maping expanded and horizontal transferred genes onto pathways verify that the extended metabolic network through gene duplication and enzyme promiscuity are prominent mechanism for innovations in substrates utilization. We further find that the positive selection on multiple genes, converging on EMP pathway, contribute to the formation of more complex traits, i.e., thermotolerance and Crabtree-effect. Our findings testify that for phenotypic diversification, multi-dimensional evolution from both metabolic network structure and individual protein elements are crucial.
DOI: 10.6084/m9.figshare.13315448.v2
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: Novel traits arise from genome evolution, but it remains ambiguous which evolutionary events modulate trait diversity in yeast subphylum. Here, we performed a multi-dimensional evolution analysis to investigate the general mechanism shaping yeast phenome diversity. We reconstructed genome-scale metabolic models for 332 yeast species to characterize versatile metabolic functions. Subsequent model simulation and selection analysis indicate that most genes are negatively selected across species, while the evolution rate of gene and codon are pathway- and function-dependent. Maping expanded and horizontal transferred genes onto pathways verify that the extended metabolic network through gene duplication and enzyme promiscuity are prominent mechanism for innovations in substrates utilization. We further find that the positive selection on multiple genes, converging on EMP pathway, contribute to the formation of more complex traits, i.e., thermotolerance and Crabtree-effect. Our findings testify that for phenotypic diversification, multi-dimensional evolution from both metabolic network structure and individual protein elements are crucial.
DOI: 10.6084/m9.figshare.13315448.v3
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: Novel traits arise from genome evolution, but it remains ambiguous which evolutionary events modulate trait diversity in yeast subphylum. Here, we performed a multi-dimensional evolution analysis to investigate the general mechanism shaping yeast phenome diversity. We reconstructed genome-scale metabolic models for 332 yeast species to characterize versatile metabolic functions. Subsequent model simulation and selection analysis indicate that most genes are negatively selected across species, while the evolution rate of gene and codon are pathway- and function-dependent. Maping expanded and horizontal transferred genes onto pathways verify that the extended metabolic network through gene duplication and enzyme promiscuity are prominent mechanism for innovations in substrates utilization. We further find that the positive selection on multiple genes, converging on EMP pathway, contribute to the formation of more complex traits, i.e., thermotolerance and Crabtree-effect. Our findings testify that for phenotypic diversification, multi-dimensional evolution from both metabolic network structure and individual protein elements are crucial.
DOI: 10.6084/m9.figshare.3761562.v1696
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5286/isis.e.rb2010673
title: In-situ neutron diffraction study of TWIP in a Ni based superalloy

description: Twinning induced plasticity (TWIP) has been studied extensively regarding low stacking fault energy (SFE) materials such as steels where the specific deformation mechanism enhances both strength and ductility. In Ni-based alloys, activation of twinning is difficult due to their high SFE. Our recent work revealed that outstanding mechanical properties can be obtained in a Ni-based superalloy by inducing the TWIP effect at a low deformation temperature. Ab initio calculations indicates that SFE is not the controlling effect. To investigate the underlying mechanisms further an in-situ neutron diffraction investigation is proposed. Lattice strain changes and grain rotations with deformation at RT and 77 K derived from the experiment will be correlated to microstructural studies by laboratory techniques to explore the mechanisms and conditions for activating TWIP in high SFE alloys.
DOI: 10.5286/isis.e.rb2010673-1
title: In-situ neutron diffraction study of TWIP in a Ni based superalloy

description: Twinning induced plasticity (TWIP) has been studied extensively regarding low stacking fault energy (SFE) materials such as steels where the specific deformation mechanism enhances both strength and ductility. In Ni-based alloys, activation of twinning is difficult due to their high SFE. Our recent work revealed that outstanding mechanical properties can be obtained in a Ni-based superalloy by inducing the TWIP effect at a low deformation temperature. Ab initio calculations indicates that SFE is not the controlling effect. To investigate the underlying mechanisms further an in-situ neutron diffraction investigation is proposed. Lattice strain changes and grain rotations with deformation at RT and 77 K derived from the experiment will be correlated to microstructural studies by laboratory techniques to explore the mechanisms and conditions for activating TWIP in high SFE alloys.
DOI: 10.6084/m9.figshare.13315448.v5
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: EC number annotation for 332 yeast species, domain annotation for 332 yeast species, essential gene prediction results for 338 yeast species without experimental data
DOI: 10.6084/m9.figshare.13315448
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: EC number annotation for 332 yeast species, domain annotation for 332 yeast species, essential gene prediction results for 338 yeast species without experimental data
DOI: 10.6084/m9.figshare.13315448.v4
title: Expanded metabolic networks combined with accelerated protein evolution has enabled new cellular traits within yeast subphylum

description: Novel traits arise from genome evolution, but it remains ambiguous which evolutionary events modulate trait diversity in yeast subphylum. Here, we performed a multi-dimensional evolution analysis to investigate the general mechanism shaping yeast phenome diversity. We reconstructed genome-scale metabolic models for 332 yeast species to characterize versatile metabolic functions. Subsequent model simulation and selection analysis indicate that most genes are negatively selected across species, while the evolution rate of gene and codon are pathway- and function-dependent. Maping expanded and horizontal transferred genes onto pathways verify that the extended metabolic network through gene duplication and enzyme promiscuity are prominent mechanism for innovations in substrates utilization. We further find that the positive selection on multiple genes, converging on EMP pathway, contribute to the formation of more complex traits, i.e., thermotolerance and Crabtree-effect. Our findings testify that for phenotypic diversification, multi-dimensional evolution from both metabolic network structure and individual protein elements are crucial.
DOI: 10.18112/openneuro.ds003406.v1.0.0
title: 3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe
DOI: 10.6084/m9.figshare.13315589
title: FW GL data 2020.xlsx

description: Citations to grey literature cited by scholarship on food loss and food waste law and policy in the United States, 2013-2019. (Citing articles are on Sheet 2)
DOI: 10.6084/m9.figshare.13315589.v1
title: FW GL data 2020.xlsx

description: Citations to grey literature cited by scholarship on food loss and food waste law and policy in the United States, 2013-2019. (Citing articles are on Sheet 2)
DOI: 10.5281/zenodo.4300939
title: Motor adaptation distorts visual space

description: The folder Visual Adaptation contains the data for the visual adaptation experiment Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trial 2<sup>nd</sup>: Test size 3<sup>rd</sup>: Subject response on test 4<sup>th</sup>: Condition 5<sup>th</sup>: Test side The folder Motor Adaptation contains the data for the visual adaptation experiment Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trial 2<sup>nd</sup>: Test size 3<sup>rd</sup>: Subject response on test The structure “Resp” contains one matrix for each trial with the hand coordinates for the motor adaptation
DOI: 10.5281/zenodo.4300938
title: Motor adaptation distorts visual space

description: The folder Visual Adaptation contains the data for the visual adaptation experiment Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trial 2<sup>nd</sup>: Test size 3<sup>rd</sup>: Subject response on test 4<sup>th</sup>: Condition 5<sup>th</sup>: Test side The folder Motor Adaptation contains the data for the visual adaptation experiment Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. The columns contain the following information: 1<sup>st</sup>: Number of trial 2<sup>nd</sup>: Test size 3<sup>rd</sup>: Subject response on test The structure “Resp” contains one matrix for each trial with the hand coordinates for the motor adaptation
DOI: 10.48349/asu/qxxwgp/41obkr
title: Final Data Set.tab

description: :unav
DOI: 10.6084/m9.figshare.12814313.v2
title: Multinomial Logistic Regression of Likelihood of cycle commuting in Puerto Madryn – Analysis factor: Bikelane construction

description: Table with Multinomial Logistic Regression of Likelihood of commuting with factor being bikelane construction. In the experiment, the dependent variable is the likelihood of commuting and the factor is bikelane construction. As can be seen, there is a strong relation between the likelihood of commuting by bike and the availability of proper infrastructure (six out of nine cases). This would lead us to think that the saying “you build it, they use it" could be applied in Puerto Madryn.<br>The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables (namely also <i>independent</i> or <i>factors</i>) and the outcome (<i>dependent)</i>, so that the outcome of a new "experiment" can be correctly predicted for a new data point for which the explanatory variables, but not the outcome, are available. In the process, the model attempts to explain the relative effect of differing explanatory variables on the outcome.<br>
DOI: 10.6084/m9.figshare.12814313
title: Multinomial Logistic Regression of Likelihood of cycle commuting in Puerto Madryn – Analysis factor: Bikelane construction

description: Table with Multinomial Logistic Regression of Likelihood of commuting with factor being bikelane construction. In the experiment, the dependent variable is the likelihood of commuting and the factor is bikelane construction. As can be seen, there is a strong relation between the likelihood of commuting by bike and the availability of proper infrastructure (six out of nine cases). This would lead us to think that the saying “you build it, they use it" could be applied in Puerto Madryn.<br>The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables (namely also <i>independent</i> or <i>factors</i>) and the outcome (<i>dependent)</i>, so that the outcome of a new "experiment" can be correctly predicted for a new data point for which the explanatory variables, but not the outcome, are available. In the process, the model attempts to explain the relative effect of differing explanatory variables on the outcome.<br>
DOI: 10.6084/m9.figshare.12814313.v1
title: Multinomial Logistic Regression of Likelihood of cycle commuting in Puerto Madryn – Analysis factor: Bikelane construction

description: Table with Multinomial Logistic Regression of Likelihood of commuting with factor being bikelane construction. In the experiment, the dependent variable is the likelihood of commuting and the factor is bikelane construction. As can be seen, there is a strong relation between the likelihood of commuting by bike and the availability of proper infrastructure (six out of nine cases). This would lead us to think that the saying “you build it, they use it" could be applied in Puerto Madryn.<br>The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables (namely also <i>independent</i> or <i>factors</i>) and the outcome (<i>dependent)</i>, so that the outcome of a new "experiment" can be correctly predicted for a new data point for which the explanatory variables, but not the outcome, are available. In the process, the model attempts to explain the relative effect of differing explanatory variables on the outcome.<br>
DOI: 10.5281/zenodo.4300896
title: 2020 Cascadia rupture models

description: Synthetic rupture scenarios for 32,500 ruptures o the Cascadia subduction zone. This dataset accompanies the paper "The January 26th, 1700 Cascadia Earthquake as Part of an Event Sequence". There are 5 subfolders for ruptures generated assuming different background mean models. The .log file contains details on each rupture, the .rupt file contains the rupture definitions and the .xy file is used for plotting in GMT. The columns fo the .rupt file are as follows: Subfault number Longitude of subfault centroid Latitude of subfault centroid Depth of subfault centroid (km) Strike of subfault (degs) Dip of subfault (degs) Dummy variable Rise time (s) Slip in the rake = 0 direction (m) Slip in the rake = 90 direction (m) Length of subfault (m) Width of subfault (m) Rupture onset time (s) Rigidity at depth of subfault centroid (Pa)