cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5906749
title: ICI-4 E-N Collisional Current Simulations

description: This dataset contains probe current data of PIC simulations decribed in the<br> paper "Electron-Neutral collisions, effects on Langmuir Probes in the Lower<br> E-Region Ionosphere". The simulations are done for plasma parameters<br> representative of the lower E-region ionosphere, with probe biases in the<br> electron saturation regime. Each simulation has its own folder denoting the power of the electron-neutral<br> collision frequency, and probe bias. For example the subfolder 2-4.5V has a<br> history.xy.h5 file (in HDF5 format) containing data for a simulation using a<br> electron-neutral collision frequency proportional to 10^2, and a probe bias of<br> 4.5V. "nocoll" in a folder name means it's a simulation with no collisions. Plasma parameters used for simulations: Density (e,i) (m−3) 9.597e10<br> Te(K) 441<br> Ti(K) 475<br> me(kg) 9.109e-31<br> mi(kg) 6.646e-27 The hierarchical structure of the history.xy.h5 files: -current<br> -electrons<br> -dataset<br> -ions<br> -dataset<br> -energy<br> -kinetic<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -dataset The top-level group "current" is the current to the probe given for electrons<br> and ions. Values are in Ampere. "energy" is the total kinetic and potential energies within the whole simulated<br> domain for electrons (specie 0), ions (specie 1), and both species combined<br> (total). The potential energy is not calculated per species for these<br> simulations, and is therefore set to zero. Values are given in Joule. The top-level group "potential" is the electric potential (bias) of the probe<br> and is given in Volts. Each datapoint is given each 5*10^(-10) s, giving it a sampling frequency of 2*10^(9) Hz.
DOI: 10.5281/zenodo.5569352
title: ICI-4 E-N Collisional Current Simulations

description: This dataset contains probe current data of PIC simulations decribed in the<br> paper "Electron-Neutral collisions, effects on Langmuir Probes in the Lower<br> E-Region Ionosphere". The simulations are done for plasma parameters<br> representative of the lower E-region ionosphere, with probe biases in the<br> electron saturation regime. Each simulation has its own folder denoting the power of the electron-neutral<br> collision frequency, and probe bias. For example the subfolder 2-4.5V has a<br> history.xy.h5 file (in HDF5 format) containing data for a simulation using a<br> electron-neutral collision frequency proportional to 10^2, and a probe bias of<br> 4.5V. "nocoll" in a folder name means it's a simulation with no collisions. Plasma parameters used for simulations: Density (e,i) (m−3) 9.597e10<br> Te(K) 441<br> Ti(K) 475<br> me(kg) 9.109e-31<br> mi(kg) 6.646e-27 The hierarchical structure of the history.xy.h5 files: -current<br> -electrons<br> -dataset<br> -ions<br> -dataset<br> -energy<br> -kinetic<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -dataset The top-level group "current" is the current to the probe given for electrons<br> and ions. Values are in Ampere. "energy" is the total kinetic and potential energies within the whole simulated<br> domain for electrons (specie 0), ions (specie 1), and both species combined<br> (total). The potential energy is not calculated per species for these<br> simulations, and is therefore set to zero. Values are given in Joule. The top-level group "potential" is the electric potential (bias) of the probe<br> and is given in Volts. Each datapoint is given each 5*10^(-10) s, giving it a sampling frequency of 2*10^(9) Hz.
DOI: 10.6084/m9.figshare.17082566.v1
title: COI.fas

description: COI alignment
DOI: 10.6084/m9.figshare.17082566
title: COI.fas

description: COI alignment
DOI: 10.6084/m9.figshare.19071197
title: vWF.fas

description: vWF alignment
DOI: 10.6084/m9.figshare.19071197.v1
title: vWF.fas

description: vWF alignment
DOI: 10.25318/3310041701-fra
title: Incidence prévue de l’absence de programmes de soutien gouvernemental sur la survie des entreprises ou des organismes, quatrième trimestre de 2021

description: Incidence prévue de l’absence de programmes de soutien gouvernemental sur la survie des entreprises ou des organismes, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2021.
DOI: 10.6084/m9.figshare.19071277
title: Assessing Nummulites geochemistry as a proxy for Early Eocene palaeotemperature evolution in the North Sea Basin

description: Geochemical data
DOI: 10.6084/m9.figshare.19071277.v1
title: Assessing Nummulites geochemistry as a proxy for Early Eocene palaeotemperature evolution in the North Sea Basin

description: Geochemical data
DOI: 10.5281/zenodo.5906219
title: Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity.

description: Dataset from Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2021 Apr 5:1–15. doi: 10.1007/s40519-021-01173-w. Epub ahead of print. PMID: 33821453; PMCID: PMC8020829. Abstract <strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored. <strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19. <strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P &lt; 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P &lt; 0.001; r = 0.41, P &lt; 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P &lt; 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P &lt; 0.001) as the only independent factors associated with high CXR severity scores. <strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.
DOI: 10.5281/zenodo.5906218
title: Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity.

description: Dataset from Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2021 Apr 5:1–15. doi: 10.1007/s40519-021-01173-w. Epub ahead of print. PMID: 33821453; PMCID: PMC8020829. Abstract <strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored. <strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19. <strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P &lt; 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P &lt; 0.001; r = 0.41, P &lt; 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P &lt; 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P &lt; 0.001) as the only independent factors associated with high CXR severity scores. <strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.
DOI: 10.17043/ao2016-ceilometer-2
title: Ceilometer backscatter, cloud base height and cloud fraction data from the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-micromet-oden-2
title: Micrometeorological data from icebreaker Oden’s foremast during the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-micromet-oden-3
title: Micrometeorological data from icebreaker Oden’s foremast during the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-micromet-oden-4
title: Micrometeorological data from icebreaker Oden’s foremast during the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-misu-weather-2
title: Weather data from the MISU weather station during the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-navigation
title: Navigation, meteorological and surface seawater data from the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2016-present-weather
title: Present weather sensor visibility and precipitation data from the Arctic Ocean 2016 expedition
DOI: 10.17043/ao2018-ceilometer-2
title: Ceilometer backscatter, cloud base height and cloud fraction data from the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-cloud-water
title: Cloud water samples obtained in summertime during the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-cloudnet-4
title: Cloudnet remote sensing retrievals of cloud properties during the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-micromet-icefloe-5
title: Micrometeorological data from an ice-floe adjacent to an open lead during the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-micromet-icefloe-6
title: Micrometeorological data from an ice-floe adjacent to an open lead during the Arctic Ocean 2018 expedition
DOI: 10.5281/zenodo.5905789
title: Grid-based dataset of Barcelona POIs

description: This is a processed dataset, generated by a) a set of datasets collected from the open portal of Barcelona city containing points-of-interest and b) image of Barcelona city collected from Copernicus Access Hub. The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes: Geometry, describing the spatial dimension of the cell min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as bike stations, zoos, participation spaces, etc. not_vegetation, percentage of space in the cell with no vegetation vegetation, percentage of space in the cell with vegetation water, percentage of space in the cell with water other, percentage of space in the cell with other than vegetation, water
DOI: 10.5281/zenodo.5906634
title: DeepCollision: Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions

description: With the aim to test autonomous driving systems, we propose a novel reinforcement learning (RL)-based approach named <strong>DeepCollision </strong>to learn operating environment configurations of autonomous vehicles, including formalizing environment configuration learning as an MDP and adopting DQN algorithm as the RL solution; <strong>DeepCollision</strong> learns environment configurations to maximize collisions of an Autonomous Vehicle Under Test (AVUT). This dataset contains: <strong>algorithms</strong> - The algorithm of DeepCollision, which includes the network architecture and the DQN hyperparameter settings; <strong>pilot-study</strong> - All the raw data and plots for the pilot study; <strong>formal-experiment</strong> - A dataset contains all the raw data for analysis and the scenarios with detailed demand values; <strong>rest-api</strong> - The REST API endpoints for environment configuration and one <strong>example </strong>to show the usage of the APIs.
DOI: 10.17043/ao2018-misu-weather-3
title: Weather data from MISU weather station during the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-navigation
title: Navigation, meteorological and surface seawater data from the Arctic Ocean 2018 expedition
DOI: 10.5281/zenodo.5905788
title: Grid-based dataset of Barcelona POIs

description: This is a processed dataset, generated by a) a set of datasets collected from the open portal of Barcelona city containing points-of-interest and b) image of Barcelona city collected from Copernicus Access Hub. The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes: Geometry, describing the spatial dimension of the cell min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as bike stations, zoos, participation spaces, etc. not_vegetation, percentage of space in the cell with no vegetation vegetation, percentage of space in the cell with vegetation water, percentage of space in the cell with water other, percentage of space in the cell with other than vegetation, water
DOI: 10.5281/zenodo.5906633
title: DeepCollision: Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions

description: With the aim to test autonomous driving systems, we propose a novel reinforcement learning (RL)-based approach named <strong>DeepCollision </strong>to learn operating environment configurations of autonomous vehicles, including formalizing environment configuration learning as an MDP and adopting DQN algorithm as the RL solution; <strong>DeepCollision</strong> learns environment configurations to maximize collisions of an Autonomous Vehicle Under Test (AVUT). This dataset contains: <strong>algorithms</strong> - The algorithm of DeepCollision, which includes the network architecture and the DQN hyperparameter settings; <strong>pilot-study</strong> - All the raw data and plots for the pilot study; <strong>formal-experiment</strong> - A dataset contains all the raw data for analysis and the scenarios with detailed demand values; <strong>rest-api</strong> - The REST API endpoints for environment configuration and one <strong>example </strong>to show the usage of the APIs.
DOI: 10.17043/ao2018-submicron-aerosol-anions-3
title: Submicron aerosol methanesulfonate, iodide, iodate and sulfate data from the Arctic Ocean 2018 expedition
DOI: 10.17043/ao2018-ultrafine-aerosol-composition
title: Ultrafine aerosol composition measured in summertime during the Arctic Ocean 2018 expedition
DOI: 10.17043/ryder-2019-ctd-bottle-ph-alkalinity
title: Seawater carbonate chemistry from CTD bottle samples during the Ryder 2019 expedition
DOI: 10.17043/ryder-2019-expedition-3
title: Data from expedition Ryder, off North Greenland, 2019
DOI: 10.17043/ryder-2019-glacier-front-imagery
title: Time lapse imagery of the calving front of Ryder Glacier, Northern Greenland, August 2019
DOI: 10.17043/ryder-2019-ladcp
title: Oceanographic LADCP data from the Ryder 2019 expedition
DOI: 10.17043/ryder-2019-metobs-oden-sherard-osborn-fjord
title: Meteorological observations from Sherard Osborn Fjord, Northern Greenland, measured onboard icebreaker Oden, August 2019
DOI: 10.17043/oden-ryder-2019-micromet-oden
title: Micrometeorological data from icebreaker Oden’s foremast during Ryder 2019 and Northwest passage 2019 expeditions
DOI: 10.17043/oden-ryder-2019-misu-weather
title: Weather data from MISU weather station during Ryder 2019 and Northwest passage 2019 expeditions
DOI: 10.17043/martens-2020
title: Bulk organic carbon, isotopes and terrigenous biomarkers for the marine sediment core SWERUS-L2-31-PC1
DOI: 10.17043/ryder-2019-surface-seawater-ph-alkalinity
title: Underway measurements of seawater pH and total alkalinity from the Ryder 2019 expedition
DOI: 10.17043/swerus-2014-cloudnet
title: Cloudnet remote sensing retrievals of cloud properties from the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-d13c
title: Carbon isotope ratio (δ¹³C) in atmospheric methane from the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-ghg
title: Atmospheric methane and carbon dioxide from the SWERUS-C3 Arctic Ocean expedition in 2014, leg 2
DOI: 10.17043/thornton-2016
title: Atmospheric methane and carbon dioxide and surface water methane from the SWERUS-C3 Arctic Ocean expedition in 2014, leg 1
DOI: 10.17043/swerus-2014-methane-isotopes
title: Isotope data (Δ¹⁴C, δ¹³C and δD) for dissolved methane at the Outer Eurasian Arctic Shelf
DOI: 10.17043/swerus-2014-micromet-oden-4
title: Micrometeorological data from icebreaker Oden’s foremast during the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-micromet-oden-5
title: Micrometeorological data from icebreaker Oden’s foremast during the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-misu-weather-2
title: Weather data from the MISU weather station during the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-misu-weather-3
title: Weather data from the MISU weather station during the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-navigation
title: Navigation, meteorological and surface seawater data from the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-navigation-2
title: Navigation, meteorological and surface seawater data from the SWERUS-C3 Arctic Ocean expedition in 2014
DOI: 10.17043/swerus-2014-rcm-metobs
title: Meteorological observations during the SWERUS-C3 Arctic Ocean expedition 2014 and colocated regional climate model output
DOI: 10.17043/muschitiello-2020
title: Age model and physical properties of sediment from the Arctic Siberian margin, core SWERUS-L2-31PC
DOI: 10.17043/west-2021-1
title: Physical properties and age model of sediment core SWERUS-L2-29-GC1 from the Lomonosov Ridge, Arctic Ocean
DOI: 10.5281/zenodo.5906834
title: Usage Testing Project Artifacts

description: artifacts
DOI: 10.6084/m9.figshare.16644922.v2
title: Short neuropeptide F (sNPF) promotes the acquisition and formation of visual memories in honey bees

description: Fig 2 Fig S2 Fig S3
DOI: 10.6084/m9.figshare.16644922
title: Short neuropeptide F (sNPF) promotes the acquisition and formation of visual memories in honey bees

description: Fig 2 Fig S2 Fig S3
DOI: 10.6084/m9.figshare.16644922.v1
title: Short neuropeptide F (sNPF) promotes the acquisition and formation of visual memories in honey bees

description: Fig 2 Fig S2 Fig S3
DOI: 10.5281/zenodo.5906902
title: Grid-based dataset of Paris-Saclay POIs

description: This is a processed dataset, generated by a) a set of datasets collected from the open portal of Paris-Saclay area containing points-of-interest and b) image of Paris-Saclay collected from Copernicus Access Hub. The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 3, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes: Geometry, describing the spatial dimension of the cell min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as sports areas, military areas, bus stops, etc. not_vegetated, percentage of space in the cell with no vegetation vegetation, percentage of space in the cell with vegetation water, percentage of space in the cell with water other, percentage of space in the cell with other than vegetation, water
DOI: 10.5281/zenodo.5906901
title: Grid-based dataset of Paris-Saclay POIs

description: This is a processed dataset, generated by a) a set of datasets collected from the open portal of Paris-Saclay area containing points-of-interest and b) image of Paris-Saclay collected from Copernicus Access Hub. The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 3, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes: Geometry, describing the spatial dimension of the cell min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as sports areas, military areas, bus stops, etc. not_vegetated, percentage of space in the cell with no vegetation vegetation, percentage of space in the cell with vegetation water, percentage of space in the cell with water other, percentage of space in the cell with other than vegetation, water