cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4300895
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)
DOI: 10.5519/qd.p1f35sfu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.15468/255bvp
title: The ‘ red-tailed’ Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic

description: This dataset contains the digitized treatments in Plazi based on the original journal article Gardner, Joel, Gibbs, Jason (2020): The ‘ red-tailed’ Lasioglossum (Dialictus) (Hymenoptera: Halictidae) of the western Nearctic. European Journal of Taxonomy 725: 1-242, DOI: https://doi.org/10.5852/ejt.2020.725.1167
DOI: 10.5281/zenodo.3637652
title: Expanded Groove MIDI Dataset

description: The Expanded Groove MIDI Dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity. Additional information is available on the Magenta website: The Expanded Groove MIDI Dataset If you use the E-GMD dataset in your work, please cite the paper where it was introduced: Lee Callender, Curtis Hawthorne, and Jesse Engel. "Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset." 2020. arXiv:2004.00188. You can also use the following BibTeX entry: <pre>@misc{callender2020improving, title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset}, author={Lee Callender and Curtis Hawthorne and Jesse Engel}, year={2020}, eprint={2004.00188}, archivePrefix={arXiv}, primaryClass={cs.SD} } </pre> Please also make sure to specify which version of the dataset you are using.
DOI: 10.6084/m9.figshare.12814307
title: Multinomial Logistic Regression of increase use of bike in the next 12 months in Puerto Madryn – Analysis factor: Difficulties in cycling

description: Table with Multinomial Logistic Regression of increase use of bike in the next 12 months (dependent) with difficulties in cycling (factors). In the experiment, the dependent variable was the potential increment in the use of bike in the next 12 months.<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.12814307.v3
title: Multinomial Logistic Regression of increase use of bike in the next 12 months in Puerto Madryn – Analysis factor: Difficulties in cycling

description: Table with Multinomial Logistic Regression of increase use of bike in the next 12 months (dependent) with difficulties in cycling (factors). In the experiment, the dependent variable was the potential increment in the use of bike in the next 12 months.<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.12814307.v2
title: Multinomial Logistic Regression of increase use of bike in the next 12 months in Puerto Madryn – Analysis factor: Difficulties in cycling

description: Table with Multinomial Logistic Regression of increase use of bike in the next 12 months (dependent) with difficulties in cycling (factors). In the experiment, the dependent variable was the potential increment in the use of bike in the next 12 months.<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.12814295
title: Multinomial Logistic Regression of increase use of bike in the next 12 months in Puerto Madryn – Analysis factor: Social Agreement

description: Table with Multinomial Logistic Regression of increase use of bike in the next 12 months (dependent) with social agreement (factors). In the experiment, the dependent variable was chosen to be the potential increment in the use of bike in the next 12 months and the factors were the cycling social approval of different membership groups.<br><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.
DOI: 10.6084/m9.figshare.12814310
title: Multinomial Logistic Regression of Impact of biklane implementation in Puerto Madryn – Analysis factor: Difficulties

description: Table with Multinomial Logistic Regression of impact of biklane implementation with factors being difficulties. In the experiment, the dependent variable is the potential impact of cycling infrastructure and the factors are all the difficulties commonly found when cycling.<br> <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.12814310.v2
title: Multinomial Logistic Regression of Impact of biklane implementation in Puerto Madryn – Analysis factor: Difficulties

description: Table with Multinomial Logistic Regression of impact of biklane implementation with factors being difficulties. In the experiment, the dependent variable is the potential impact of cycling infrastructure and the factors are all the difficulties commonly found when cycling.<br> <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.12814310.v1
title: Multinomial Logistic Regression of Impact of biklane implementation in Puerto Madryn – Analysis factor: Difficulties

description: Table with Multinomial Logistic Regression of impact of biklane implementation with factors being difficulties. In the experiment, the dependent variable is the potential impact of cycling infrastructure and the factors are all the difficulties commonly found when cycling.<br> <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.4300968
title: Dataset - Lithium content in Indonesia

description: Dataset - Lithium content in Indonesia The following tables contains datasets of Lithium content at various locations in Indonesia. This dataset was compiled from sevaral sources as described in column "Sources".
DOI: 10.6084/m9.figshare.13312799.v1
title: Colby et al. (in review) - Scientific Reports Data

description: Data used in Colby et al. - Scientific Reports (in review).
DOI: 10.6084/m9.figshare.13312799.v2
title: Colby et al. (in review) - Scientific Reports Data

description: Data and code used in Colby et al. - Scientific Reports (in review).
DOI: 10.5281/zenodo.4300977
title: Simulation data for the model of the Sagittarius stream in the presence of the Large Magellanic Cloud

description: This archive contains simulations of the disrupting Sagittarius galaxy in the combined potential of the Milky Way and the Large Magellanic Cloud. <strong>Sgr_snapshot</strong><br> contains the final (present-day) snapshot from the fiducial simulation with a triaxial Milky Way halo and M_LMC=1.5e11 Msun (see the readme file in that folder for details). <strong>Sgr_snapshot_noLMC</strong><br> contains the same data but for a model without the LMC (which does not reproduce some aspects of the observations, but is nevertheless useful for a comparison with the other one). <strong>potentials_triax</strong><br> contains the initial and subsequently evolving potentials of both the Milky Way and LMC, represented by multipole expansions, as well as the trajectory of the LMC and the reflex motion-induced acceleration of the Milky Way -- everything that is needed to study the dynamics of the Sgr stream and other objects in a time-dependent potential of the interacting Milky Way and LMC. This model corresponds to the stream simulation in the previous folder. <strong>potentials_axisym</strong><br> contains the same data, but for another Milky Way halo model, which is axisymmetric rather than triaxial (note that in either case, its axis ratios vary with radius). It may be more convenient in certain applications, and produces a stream that fits the observations almost as well as the triaxial model. <strong>scripts</strong><br> contains the Python scripts illustrating how to integrate orbits in a time-dependent potential, and how to construct initial conditions for these simulations (the parameter files for various choices of Milky Way halo potentials are also included). These scripts use the Agama framework, available at http://agama.software
description: version 2 adds another N-body snapshot for a model without the LMC, and is otherwise identical to the version 1
DOI: 10.5281/zenodo.4038140
title: Simulation data for the model of the Sagittarius stream in the presence of the Large Magellanic Cloud

description: This archive contains simulations of the disrupting Sagittarius galaxy in the combined potential of the Milky Way and the Large Magellanic Cloud. <strong>Sgr_snapshot</strong><br> contains the final (present-day) snapshot from the fiducial simulation with a triaxial Milky Way halo and M_LMC=1.5e11 Msun (see the readme file in that folder for details). <strong>Sgr_snapshot_noLMC</strong><br> contains the same data but for a model without the LMC (which does not reproduce some aspects of the observations, but is nevertheless useful for a comparison with the other one). <strong>potentials_triax</strong><br> contains the initial and subsequently evolving potentials of both the Milky Way and LMC, represented by multipole expansions, as well as the trajectory of the LMC and the reflex motion-induced acceleration of the Milky Way -- everything that is needed to study the dynamics of the Sgr stream and other objects in a time-dependent potential of the interacting Milky Way and LMC. This model corresponds to the stream simulation in the previous folder. <strong>potentials_axisym</strong><br> contains the same data, but for another Milky Way halo model, which is axisymmetric rather than triaxial (note that in either case, its axis ratios vary with radius). It may be more convenient in certain applications, and produces a stream that fits the observations almost as well as the triaxial model. <strong>scripts</strong><br> contains the Python scripts illustrating how to integrate orbits in a time-dependent potential, and how to construct initial conditions for these simulations (the parameter files for various choices of Milky Way halo potentials are also included). These scripts use the Agama framework, available at http://agama.software
description: version 2 adds another N-body snapshot for a model without the LMC, and is otherwise identical to the version 1
DOI: 10.17632/wz7rx9vz24.1
title: Data for: Thermal performance of brick and stone masonry: Cumulative heat flux dataset for main orientations and under diverse seasonal conditions

description: This dataset consists of the hourly heat flux for four seasons and orientations of 15 different construction configurations of brick and stone masonry combined with insulation system solutions. The analysis was conducted with the use of Finite Element Modelling (FEM). The development of the models and the investigation of their thermal performance was conducted with the use of thermal modelling and numerical simulation analysis with COMSOL Multiphysics. For this purpose, a transient 2D multi- dimensional, time- dependent simulation model on finite elements was developed. The governing equations of heat transfer were considered as well as the convection and radiation heat transfer coefficients in accordance to the ISO 6946:2017.
DOI: 10.3886/e113769v1-71240
title: Replication data for: Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform

description: Does trade policy influence schooling and child labor in low-income countries? We examine this question in the context of India's 1991 tariff reforms. While schooling increased and child labor declined in rural India in the 1990s, these trends are attenuated in districts with employment concentrated in industries losing tariff protection. As the loss of protection causes a relative rise in poverty in affected districts, families reduce schooling to save schooling costs. Girls disproportionately bear the burden of helping their families cope with poverty. (JEL F13, F16, I21, J13, J82, O15, O19)
DOI: 10.3886/e126161
title: Data and Code for: Do Pre-analysis Plans Hamper Publication?

description: We examine publication outcomes of NBER working papers with and without pre-analysis plans to examine whether the existence of a pre-analysis plan hampers publication and downstream citations. We find that papers with pre-analysis plans are slightly less likely to be published but, conditional on being published, more likely to land in top-five journals and more likely to be cited. Our findings suggest that the alleged trade-off between career concerns and the scientific credibility that comes from pre-registering and adhering to a pre-analysis plan may be less stark than is sometimes alleged and may even tilt in favor of preregistration for researchers most concerned about publishing in the most prestigious journals and maximizing citations to their work.
DOI: 10.3886/e126161v1
title: Data and Code for: Do Pre-analysis Plans Hamper Publication?

description: We examine publication outcomes of NBER working papers with and without pre-analysis plans to examine whether the existence of a pre-analysis plan hampers publication and downstream citations. We find that papers with pre-analysis plans are slightly less likely to be published but, conditional on being published, more likely to land in top-five journals and more likely to be cited. Our findings suggest that the alleged trade-off between career concerns and the scientific credibility that comes from pre-registering and adhering to a pre-analysis plan may be less stark than is sometimes alleged and may even tilt in favor of preregistration for researchers most concerned about publishing in the most prestigious journals and maximizing citations to their work.
DOI: 10.34740/kaggle/ds/997253
title: 20 newsgroup preprocessed
DOI: 10.5281/zenodo.4300943
title: Expanded Groove MIDI Dataset

description: The Expanded Groove MIDI Dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity. Additional information is available on the Magenta website: The Expanded Groove MIDI Dataset If you use the E-GMD dataset in your work, please cite the paper where it was introduced: Lee Callender, Curtis Hawthorne, and Jesse Engel. "Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset." 2020. arXiv:2004.00188. You can also use the following BibTeX entry: <pre>@misc{callender2020improving, title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset}, author={Lee Callender and Curtis Hawthorne and Jesse Engel}, year={2020}, eprint={2004.00188}, archivePrefix={arXiv}, primaryClass={cs.SD} } </pre> Please also make sure to specify which version of the dataset you are using.
DOI: 10.5281/zenodo.3637651
title: Expanded Groove MIDI Dataset

description: The Expanded Groove MIDI Dataset (E-GMD), a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum dataset with annotations of velocity. Additional information is available on the Magenta website: The Expanded Groove MIDI Dataset If you use the E-GMD dataset in your work, please cite the paper where it was introduced: Lee Callender, Curtis Hawthorne, and Jesse Engel. "Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset." 2020. arXiv:2004.00188. You can also use the following BibTeX entry: <pre>@misc{callender2020improving, title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset}, author={Lee Callender and Curtis Hawthorne and Jesse Engel}, year={2020}, eprint={2004.00188}, archivePrefix={arXiv}, primaryClass={cs.SD} } </pre> Please also make sure to specify which version of the dataset you are using.
DOI: 10.6084/m9.figshare.13315664
title: Tug-of-war

description: Data file for the paper "Pulling for the team: Competition between political partisans" published at the Evolutionary Psychological Science
DOI: 10.21227/360j-tb92
title: EEG dataset of 7-day Motor Imagery BCI

description: In this dataset, we performed a seven-day motor imagery (MI) based BCI experiment without feedback training on 20 healthy subjects. The MI tasks include left hand, right hand, feet and idle task.
DOI: 10.21227/r9n4-xz17
title: Indoor Received Signal Strength Data generated from ray-tracing

description: The dataset consists a training and testing folder with received signal strength (RSS) data, obtained from a ray-tracing software (Wireless Insite). There are K=8 anchor nodes and N=12 regions.- In the folder _training, it contains 8 * 12 = 96 separate .p2m files, each file corresponds to a RSS data collected from a grid number of user locations (coordinate is given in the .p2m file) with respect to a certain Anchor node- In the folder _testing, it contains 8 separate .p2m files corresponds to a RSS data set collected from a moving user trajectory locations (coordinate is given in the .p2m file) with respect to a certain Anchor node
DOI: 10.21227/rabs-vg77
title: Indoor Received Signal Strength Data generated from ray-tracing

description: The dataset consists a training and testing folder with received signal strength (RSS) data, obtained from a ray-tracing software (Wireless Insite). There are K=8 anchor nodes and N=12 regions.- In the folder _training, it contains 8 * 12 = 96 separate .p2m files, each file corresponds to a RSS data collected from a grid number of user locations (coordinate is given in the .p2m file) with respect to a certain Anchor node- In the folder _testing, it contains 8 separate .p2m files corresponds to a RSS data set collected from a moving user trajectory locations (coordinate is given in the .p2m file) with respect to a certain Anchor node
DOI: 10.6084/m9.figshare.12178656
title: Data for "Retrieving distributed aquifer hydraulic parameters from distributed strain sensing"

description: The time-depth-value strain data in csv format for the manuscript "Towards retrieving distributed aquifer hydraulic parameters from distributed strain sensing_Zhang et al. 2020". The first and second columns give the time and time interval between each measurement (each row). The other columns give the strain data. The first row of these columns gives the depth of each strain measurement.
DOI: 10.6084/m9.figshare.12178656.v1
title: Data for "Retrieving distributed aquifer hydraulic parameters from distributed strain sensing"

description: The time-depth-value strain data in csv format for the manuscript "Towards retrieving distributed aquifer hydraulic parameters from distributed strain sensing_Zhang et al. 2020". The first and second columns give the time and time interval between each measurement (each row). The other columns give the strain data. The first row of these columns gives the depth of each strain measurement.
DOI: 10.21227/qmgt-9r54
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/hkme-xg09
title: EEG dataset of 7-day Motor Imagery BCI

description: In this dataset, we performed a seven-day motor imagery (MI) based BCI experiment without feedback training on 20 healthy subjects. The MI tasks include left hand, right hand, feet and idle task.