cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15128/r1k3569440m
title: Preparation of one 87Rb and one 133Cs atom in a single optical tweezer [dataset]

description: We report the preparation of exactly one 87Rb atom and one 133Cs atom in the same optical tweezer as the essential first step towards the construction of a tweezer array of individually trapped 87Rb133Cs molecules. Through careful selection of the tweezer wavelengths, we show how to engineer species-selective trapping potentials suitable for high-fidelity preparation of Rb + Cs atom pairs. Using a wavelength of 814 nm to trap Rb and 938 nm to trap Cs, we achieve loading probabilities of 0.508(6) for Rb and 0.547(6) for Cs using standard red-detuned molasses cooling. Loading the traps sequentially yields exactly one Rb and one Cs atom in 28.4(6) % of experimental runs. Using a combination of an acousto-optic deflector and a piezo-controlled mirror to control the relative position of the tweezers, we merge the two tweezers, retaining the atom pair with a probability of 0.99(+0.01)(−0.02).We use this capability to study hyperfine-state-dependent collisions of Rb and Cs in the combined tweezer and compare the measured two-body loss rates with coupled-channel quantum scattering calculations.
DOI: 10.6084/m9.figshare.13026530
title: A dataset on wave propagation through vegetation with coexisting currents

description: This dataset was prepared to explore the effect of currents on wave dissipation by vegetation (hereafter referred as WDV) by conducting flume experiments with stiff plant mimics in combined wave-flow conditions. Two flume experiments with following and adverse currents were carried out respectively in a 40-m-long and 0.8-m-wide wave flume of the Fluid Mechanics Laboratory at Delft University of Technology and in a 26-m-long, 0.6-m-wide, 0.6-m-high wave flume in Sun Yat-sen University. Wave height data, velocity data and force data were obtained in these two flume experiments. Notably, drag coefficient data were calculated using calibration method and direct measurement method. The data sets presented here includes the wave data, velocity data, force data, velocity profile and drag coefficient.
DOI: 10.6084/m9.figshare.13026530.v1
title: A dataset on wave propagation through vegetation with coexisting currents

description: This dataset was prepared to explore the effect of currents on wave dissipation by vegetation (hereafter referred as WDV) by conducting flume experiments with stiff plant mimics in combined wave-flow conditions. Two flume experiments with following and adverse currents were carried out respectively in a 40-m-long and 0.8-m-wide wave flume of the Fluid Mechanics Laboratory at Delft University of Technology and in a 26-m-long, 0.6-m-wide, 0.6-m-high wave flume in Sun Yat-sen University. Wave height data, velocity data and force data were obtained in these two flume experiments. Notably, drag coefficient data were calculated using calibration method and direct measurement method. The data sets presented here includes the wave data, velocity data, force data, velocity profile and drag coefficient.
DOI: 10.5281/zenodo.4672028
title: Vectors to drive hACE2 expression in the zebrafish embryo

description: Vectors to drive hACE2 expression in the zebrafish embryo : The two different vectors use pTol2zS263CmCF as a backbone. Its sequence (.xdna; text format), map and construction (pdf format) are included. This plasmid drives the exprression of a farnesylated version of mCherry under the control of the promoter of the ubiquitous ribosomal protein RPS26. The RPS26 proteins of the zebrafish are encoded by two different genes, one on chromosome 6 and one on chromosome 23. The promoter present on pTol2zS263CmCF is the one from chromosome 23. The promoter includes a few kb upstream of the first exon, the complete first intron and the start of the second exon. The start condon present on exon1 has been mutated ATG &gt; CTG. The Tol2zS263ChACE2mCF vector drives the expression of <em>hACE2</em> in fusion with mCherryF. Its sequence (.xdna; text format), map and construction (pdf format) are included. The pTol2zS263C-hACE2.2A.mCF vector includes a self-cleaving 2A peptide between hACE2 and mCherry ORFs in order to lead to the production of two different proteins. Its sequence (.xdna; text format), map and construction (pdf format) are included.
description: These expression vectors have been used in the work reported in the work "Exploring zebrafish larvae as a COVID-19 model: probable SARS-COV-2 replication in the swim bladder" by Valerio Laghi, Veronica Rezelj, Laurent Boucontet, Pierre Boudinot, Irene Salinas, Georges Lutfalla, Marco Vignuzzi and Jean-Pierre Levraud
DOI: 10.5281/zenodo.4672027
title: Vectors to drive hACE2 expression in the zebrafish embryo

description: Vectors to drive hACE2 expression in the zebrafish embryo : The two different vectors use pTol2zS263CmCF as a backbone. Its sequence (.xdna; text format), map and construction (pdf format) are included. This plasmid drives the exprression of a farnesylated version of mCherry under the control of the promoter of the ubiquitous ribosomal protein RPS26. The RPS26 proteins of the zebrafish are encoded by two different genes, one on chromosome 6 and one on chromosome 23. The promoter present on pTol2zS263CmCF is the one from chromosome 23. The promoter includes a few kb upstream of the first exon, the complete first intron and the start of the second exon. The start condon present on exon1 has been mutated ATG &gt; CTG. The Tol2zS263ChACE2mCF vector drives the expression of <em>hACE2</em> in fusion with mCherryF. Its sequence (.xdna; text format), map and construction (pdf format) are included. The pTol2zS263C-hACE2.2A.mCF vector includes a self-cleaving 2A peptide between hACE2 and mCherry ORFs in order to lead to the production of two different proteins. Its sequence (.xdna; text format), map and construction (pdf format) are included.
description: These expression vectors have been used in the work reported in the work "Exploring zebrafish larvae as a COVID-19 model: probable SARS-COV-2 replication in the swim bladder" by Valerio Laghi, Veronica Rezelj, Laurent Boucontet, Pierre Boudinot, Irene Salinas, Georges Lutfalla, Marco Vignuzzi and Jean-Pierre Levraud
DOI: 10.5281/zenodo.4659953
title: Replication package for: Can autocracy promote literacy? evidence from a cultural alignment success story

description: The replication materials contain a README file, a single STATA dataset ("AGE_PalmaReis_final.dta"), and a single do-file ("PalmaReis.do"). The do-file in this replication package for Palma and Reis (2021) constructs the entire analysis from the data sources described in the published paper, using STATA. There is a single do-file that runs the code for all the figures and tables. The replicator should expect the code to run for less than 10 minutes. Palma, N. and J. Reis (2021). Can autocracy promote literacy? evidence from a cultural alignment success story. Journal of Economic Behavior and Organization, forthcoming<br>
DOI: 10.5281/zenodo.4659952
title: Replication package for: Can autocracy promote literacy? evidence from a cultural alignment success story

description: The replication materials contain a README file, a single STATA dataset ("AGE_PalmaReis_final.dta"), and a single do-file ("PalmaReis.do"). The do-file in this replication package for Palma and Reis (2021) constructs the entire analysis from the data sources described in the published paper, using STATA. There is a single do-file that runs the code for all the figures and tables. The replicator should expect the code to run for less than 10 minutes. Palma, N. and J. Reis (2021). Can autocracy promote literacy? evidence from a cultural alignment success story. Journal of Economic Behavior and Organization, forthcoming<br>
DOI: 10.5281/zenodo.4671794
title: 3-dimensional closure of field-aligned currents in the polar ionosphere

description: This contains the original simulation data used by the paper '3-dimensional closure of field-aligned currents in the polar ionosphere', which will appear in Journal of Geophysical Research - Space Physics. The data were obtained by the Hall MHD simulation. The VTK file (Visualization Tookkit format), named “CaseN-M.vtk”, contains the following variables for Case N, where N=1, 2, and 3: number density (d in 1/km^3), magnetic field vector (B in microT), electric field vector (E in V/km), velocity vector (V in km/s), current density vector (J in A/km^2). M stands for time. M of 1, 2, 3, and 4 stands for t = 0, 1.8, 2.5, and 4.0 sec, respectively. The VTK file, named “CaseN_thin.vtk”, contains the following variables under the thin-layer approximation: number density (d in 1/km^3), electric field vector (E in V/km), current density vector (J in A/km2).
DOI: 10.5281/zenodo.4671324
title: 3-dimensional closure of field-aligned currents in the polar ionosphere

description: This contains the original simulation data used by the paper '3-dimensional closure of field-aligned currents in the polar ionosphere', which will appear in Journal of Geophysical Research - Space Physics. The data were obtained by the Hall MHD simulation. The VTK file (Visualization Tookkit format), named “CaseN-M.vtk”, contains the following variables for Case N, where N=1, 2, and 3: number density (d in 1/km^3), magnetic field vector (B in microT), electric field vector (E in V/km), velocity vector (V in km/s), current density vector (J in A/km^2). M stands for time. M of 1, 2, 3, and 4 stands for t = 0, 1.8, 2.5, and 4.0 sec, respectively. The VTK file, named “CaseN_thin.vtk”, contains the following variables under the thin-layer approximation: number density (d in 1/km^3), electric field vector (E in V/km), current density vector (J in A/km2).
DOI: 10.5281/zenodo.4672070
title: A High-Resolution Global-Scale Model for COVID-19 Infection Rate

description: This dataset contains all information to reproduce our experiment to produce a high-resolution global map (0.1°) of infection-rate risk for COVID-19, based on temperature, precipitation, and CO2. The produced risk index map predicts most of the areas with an actual high risk (87% accuracy), which are characterized by a moderate-high level of CO2, moderate-low temperatures, and a moderate level of precipitation. With respect to our previous model (https://zenodo.org/record/3945495#.YG7UEugzaUk) - which had a coarser 0.5° resolution - this new model is much more accurate at predicting real-world scenarios that reported both high and low infection rates in 2020 (80% accuracy). <strong>Explanation of data and images:</strong> comparisonvert.png -&gt; Visualisation of the output produced by our model: (a) distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008), (b) probability peak areas (0.13 threshold), (c) overlap between low infection rate countries extracted from real data and our risk map, and (d) highlight of low infection rate countries not predicted by our model<br> countries_high_rate.csv-&gt; high-infection-rate countries<br> countries_low_rate.csv-&gt; low-infection-rate countries<br> countries_low_rate_mispredicted.csv-&gt; low-infection-rate countries mispredicted by our model<br> covid_derivatives.csv-&gt; extracted average derivatives of world countries covid_risk.csv-&gt;Risk Map dataset<br> gp.asc-&gt; MaxEnt distribution raster<br> LowDerivativeRegions.png-&gt;low-infection-rate countries - image<br> MaxEnt distribution.png-&gt;distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008) - image<br> MaxEnt peaks.png-&gt; MaxEnt probability peak areas (0.13 threshold)<br> Precipitation.png-&gt;Average precipitation 2000-2005<br> RiskMap.png-&gt; New high-infection-rate risk map based on a 0.1° resolution MaxEnt model<br> RiskMap05.png-&gt;our previous risk map based on a 0.5° resolution MaxEnt model<br> riskmapcomparison.png-&gt; Visual comparison between (a) our new high-infection-rate risk map based on a 0.1° resolution MaxEnt model and (b) our previous risk map based on a 0.5° resolution MaxEnt model.<br> RiskMapOverlap_mispredicted.png-&gt;highlight of low infection rate countries not predicted by our model<br> Temperature.png-&gt;Average Surface Air Temperature 2000-2005<br> time_series_covid19_confirmed_global.csv-&gt;World COVID-19 reports up to April 2021
DOI: 10.5281/zenodo.4671981
title: A High-Resolution Global-Scale Model for COVID-19 Infection Rate

description: This dataset contains all information to reproduce our experiment to produce a high-resolution global map (0.1°) of infection-rate risk for COVID-19, based on temperature, precipitation, and CO2. The produced risk index map predicts most of the areas with an actual high risk (87% accuracy), which are characterized by a moderate-high level of CO2, moderate-low temperatures, and a moderate level of precipitation. With respect to our previous model (https://zenodo.org/record/3945495#.YG7UEugzaUk) - which had a coarser 0.5° resolution - this new model is much more accurate at predicting real-world scenarios that reported both high and low infection rates in 2020 (80% accuracy). <strong>Explanation of data and images:</strong> comparisonvert.png -&gt; Visualisation of the output produced by our model: (a) distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008), (b) probability peak areas (0.13 threshold), (c) overlap between low infection rate countries extracted from real data and our risk map, and (d) highlight of low infection rate countries not predicted by our model<br> countries_high_rate.csv-&gt; high-infection-rate countries<br> countries_low_rate.csv-&gt; low-infection-rate countries<br> countries_low_rate_mispredicted.csv-&gt; low-infection-rate countries mispredicted by our model<br> covid_derivatives.csv-&gt; extracted average derivatives of world countries covid_risk.csv-&gt;Risk Map dataset<br> gp.asc-&gt; MaxEnt distribution raster<br> LowDerivativeRegions.png-&gt;low-infection-rate countries - image<br> MaxEnt distribution.png-&gt;distribution of high-infection-rate areas using the MaxEnt balanced threshold (0.008) - image<br> MaxEnt peaks.png-&gt; MaxEnt probability peak areas (0.13 threshold)<br> Precipitation.png-&gt;Average precipitation 2000-2005<br> RiskMap.png-&gt; New high-infection-rate risk map based on a 0.1° resolution MaxEnt model<br> RiskMap05.png-&gt;our previous risk map based on a 0.5° resolution MaxEnt model<br> riskmapcomparison.png-&gt; Visual comparison between (a) our new high-infection-rate risk map based on a 0.1° resolution MaxEnt model and (b) our previous risk map based on a 0.5° resolution MaxEnt model.<br> RiskMapOverlap_mispredicted.png-&gt;highlight of low infection rate countries not predicted by our model<br> Temperature.png-&gt;Average Surface Air Temperature 2000-2005<br> time_series_covid19_confirmed_global.csv-&gt;World COVID-19 reports up to April 2021
DOI: 10.5281/zenodo.4638711
title: Predicción de series de datos medioambientales mediante redes complejas

description: Aplicación de métodos de análisis y predicción de series temporales basados en sistemas dinámicos no lineales y redes complejas. Aplicados a una serie temporal multidimensional obtenida del servicio OpenData de la AEMET, compuesta por diversas variables meteorológicas (temperatura media, mínima, máxima, velocidad media del viento, irradiación solar, y presión atmosférica, entre otras) diarias registradas por la estación de Murcia (código 7178I) desde el 2016-03-01 hasta el 2021-02-01. Estos datos se han recopilado para el proyecto<em> BitBlue: Investigación de técnicas de difusión, procesado y análisis de Big Data del medio marino</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["datos obtenidos de AEMET (OpenData)."]}
DOI: 10.5281/zenodo.4638710
title: Predicción de series de datos medioambientales mediante redes complejas

description: Aplicación de métodos de análisis y predicción de series temporales basados en sistemas dinámicos no lineales y redes complejas. Aplicados a una serie temporal multidimensional obtenida del servicio OpenData de la AEMET, compuesta por diversas variables meteorológicas (temperatura media, mínima, máxima, velocidad media del viento, irradiación solar, y presión atmosférica, entre otras) diarias registradas por la estación de Murcia (código 7178I) desde el 2016-03-01 hasta el 2021-02-01. Estos datos se han recopilado para el proyecto<em> BitBlue: Investigación de técnicas de difusión, procesado y análisis de Big Data del medio marino</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["datos obtenidos de AEMET (OpenData)."]}
DOI: 10.5281/zenodo.4638740
title: Reconstrucción de tráfico marítimo a partir de datos AIS incompletos mediante Deep Learning

description: Reconstrucción de rutas marítimas a partir de datos AIS incompletos. Se presenta la predicción del tráfico marítimo a partir de estos datos mediante algoritmos de aprendizaje profundo. Estos datos se han recopilado para el proyecto<em> BitBlue: Investigación de técnicas de difusión, procesado y análisis de Big Data del medio marino</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["Marine Cadastre: https://marinecadastre.gov/ais", "F. Mazzarella, V. Fernandez Arguedas y M. Vespe, \u00abKnowledge-based vessel position prediction using historical AIS data,\u00bb 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1-6, 2015", "Alvaro Orgaz Exp\u00f3sito - Deep Learning & Graph clustering for Maritime Logistics - Predicting destination and Expected time of Arrival for vessels across Europe (2020)"]}
DOI: 10.5281/zenodo.4638739
title: Reconstrucción de tráfico marítimo a partir de datos AIS incompletos mediante Deep Learning

description: Reconstrucción de rutas marítimas a partir de datos AIS incompletos. Se presenta la predicción del tráfico marítimo a partir de estos datos mediante algoritmos de aprendizaje profundo. Estos datos se han recopilado para el proyecto<em> BitBlue: Investigación de técnicas de difusión, procesado y análisis de Big Data del medio marino</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["Marine Cadastre: https://marinecadastre.gov/ais", "F. Mazzarella, V. Fernandez Arguedas y M. Vespe, \u00abKnowledge-based vessel position prediction using historical AIS data,\u00bb 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1-6, 2015", "Alvaro Orgaz Exp\u00f3sito - Deep Learning & Graph clustering for Maritime Logistics - Predicting destination and Expected time of Arrival for vessels across Europe (2020)"]}
DOI: 10.5281/zenodo.4638838
title: Aplicación de técnicas de minería de procesos para descubrimiento de procesos

description: Caso de uso aplicado para un ejemplo real en el que CTN se ha basado para capacitarse en la implementación de algoritmos de minería de procesos sobre un conjunto de datos real del sector sanitario. Concretamente, nos centramos en las diferentes fases por las que atraviesa un paciente cuando realiza una consulta médica, de modo que el conjunto de datos se conforma de un listado de 100 pacientes con sus correspondientes citas médicas con los respectivos doctores y las diferentes pruebas a las que se someten. Estos datos se han recopilado para el proyecto<em> MINEPRAFDT - Mining process & analysis for Digital Twin - Aplicación del Process Mining al modelado y análisis de proceso industriales con una alta componente manual para la creación de su Gemelo Digital</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-1-ae02027a050", "https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-3-cc9af986c122", "https://pm4py.fit.fraunhofer.de/", "https://www.futurelearn.com/courses/process-mining-healthcare"]}
DOI: 10.5281/zenodo.4638837
title: Aplicación de técnicas de minería de procesos para descubrimiento de procesos

description: Caso de uso aplicado para un ejemplo real en el que CTN se ha basado para capacitarse en la implementación de algoritmos de minería de procesos sobre un conjunto de datos real del sector sanitario. Concretamente, nos centramos en las diferentes fases por las que atraviesa un paciente cuando realiza una consulta médica, de modo que el conjunto de datos se conforma de un listado de 100 pacientes con sus correspondientes citas médicas con los respectivos doctores y las diferentes pruebas a las que se someten. Estos datos se han recopilado para el proyecto<em> MINEPRAFDT - Mining process & analysis for Digital Twin - Aplicación del Process Mining al modelado y análisis de proceso industriales con una alta componente manual para la creación de su Gemelo Digital</em>, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.
description: {"references": ["https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-1-ae02027a050", "https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-3-cc9af986c122", "https://pm4py.fit.fraunhofer.de/", "https://www.futurelearn.com/courses/process-mining-healthcare"]}
DOI: 10.6084/m9.figshare.13655276.v4
title: Materials Informatics Platform with Three-Dimensional Structures - Workflow and Thermoelectric Applications

description: We present a large dataset of 3D materials. with more than 30,000 entries in the database, which contain the relaxed crystal structures, density of states, and band structures. Calculation of the equations of state and sound velocities is performed for over 12,000 entries. The electrical transport properties for approximately 4,400 entries are also calculated.
DOI: 10.5281/zenodo.4672000
title: A time-resolved paleomagnetic record of Main Group pallasites: Evidence for a Psyche-like parent body

description: Several paleomagnetic studies have been conducted on five main group pallasites: Brenham, Marjalahti, Springwater, Imilac and Esquel. These pallasites have distinct cooling histories, meaning that their paleomagnetic records may have been acquired at different times during the thermal evolution of their parent body. Here we compile new and existing data to present the most complete time-resolved paleomagnetic record for a planetesimal, which includes a period of quiescence prior to core solidification as well as dynamo activity generated by compositional convection during core solidification. We present new paleomagnetic data for the Springwater pallasite, which constrains the timing of core solidification. Our results suggest that in order to generate the observed strong paleointensities (~ 65 - 95 µT), the pallasites must have been relatively close to the dynamo source. Our thermal and dynamo models predict that the main group pallasites originate from a planetesimal with a large core (&gt; 200 km) and a thin mantle (&lt; 70 km). The density of our model large-cored planetesimals is similar to the predicted density of the asteroid (16) Psyche. We therefore suggest that it is plausible that the main group pallasites originated from a Psyche-like parent body.