cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4553136
title: Time-series Photometric Lightcurves of Episodically Active Asteroid (6478) Gault in an Inactive State

description: Differential Magnitudes of Active Asteroid (6478) Gault over time in Autumn of 2020 using telescopes in North America and Asia
description: MLO = Mount Laguna Observatory 1.0-meter telescope; LOT = Lulin One-meter Telescope; TMO = Table Mountain Observatory 1.0-meter telescope; GIT = GROWTH India 0.7-meter Telelscope; P200 = Palomar Observatory 200-inch telescope
DOI: 10.5281/zenodo.4553135
title: Time-series Photometric Lightcurves of Episodically Active Asteroid (6478) Gault in an Inactive State

description: Differential Magnitudes of Active Asteroid (6478) Gault over time in Autumn of 2020 using telescopes in North America and Asia
description: MLO = Mount Laguna Observatory 1.0-meter telescope; LOT = Lulin One-meter Telescope; TMO = Table Mountain Observatory 1.0-meter telescope; GIT = GROWTH India 0.7-meter Telelscope; P200 = Palomar Observatory 200-inch telescope
DOI: 10.6084/m9.figshare.14067779.v1
title: A nontechical guide for conducting open and reproducible PLS-SEM analyses using the "cSEM" package in R/RStudio

description: <br> # Install the fillowing packages and recall libraries in R using this command: if (!require(cSEM, quietly = TRUE)) { install.packages("devtools") library(devtools) install_github("M-E-Rademaker/cSEM") } library(cSEM) # Now Set a working directory using this command: setwd("D:/Secondary") # Specify your model satisfaction_mod # Reflective measurement model IMAG =~ imag1 + imag2 + imag3 SAT =~ sat1 + sat2 + sat3 + sat4 LOY =~ loy1 + loy2 + loy3 + loy4 # Structural model LOY ~ SAT + IMAG " # Estimate model satisfaction_res .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = FALSE, .tolerance = 1e-07) summarize(satisfaction_res) assess(satisfaction_res) # We note that the output of the summarize() function contains a lot of NAs. This is due to the fact that we did not explicitly require to use the bootstrap to perform inference. To do that, we need to include the .resample_method = "bootstrap" argument as in the next example: satisfaction_boot .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = TRUE, .tolerance = 1e-07, .resample_method = "bootstrap", .R = 1000, .seed = 1406) summarize(satisfaction_boot, .ci = "CI_percentile")
DOI: 10.6084/m9.figshare.14067779
title: A non-technical guide for conducting open and reproducible PLS-SEM analyses using the "cSEM" package in R/RStudio

description: <br> # Install the fillowing packages and recall libraries in R using this command: if (!require(cSEM, quietly = TRUE)) { install.packages("devtools") library(devtools) install_github("M-E-Rademaker/cSEM") } library(cSEM) # Now Set a working directory using this command: setwd("D:/Secondary") # Specify your model satisfaction_mod # Reflective measurement model IMAG =~ imag1 + imag2 + imag3 SAT =~ sat1 + sat2 + sat3 + sat4 LOY =~ loy1 + loy2 + loy3 + loy4 # Structural model LOY ~ SAT + IMAG " # Estimate model satisfaction_res .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = FALSE, .tolerance = 1e-07) summarize(satisfaction_res) assess(satisfaction_res) # We note that the output of the summarize() function contains a lot of NAs. This is due to the fact that we did not explicitly require to use the bootstrap to perform inference. To do that, we need to include the .resample_method = "bootstrap" argument as in the next example: satisfaction_boot .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = TRUE, .tolerance = 1e-07, .resample_method = "bootstrap", .R = 1000, .seed = 1406) summarize(satisfaction_boot, .ci = "CI_percentile")
DOI: 10.6084/m9.figshare.14067779.v3
title: A non-technical guide for conducting open and reproducible PLS-SEM analyses using the "cSEM" package in R/RStudio

description: <br> # Install the fillowing packages and recall libraries in R using this command: if (!require(cSEM, quietly = TRUE)) { install.packages("devtools") library(devtools) install_github("M-E-Rademaker/cSEM") } library(cSEM) # Now Set a working directory using this command: setwd("D:/Secondary") # Specify your model satisfaction_mod # Reflective measurement model IMAG =~ imag1 + imag2 + imag3 SAT =~ sat1 + sat2 + sat3 + sat4 LOY =~ loy1 + loy2 + loy3 + loy4 # Structural model LOY ~ SAT + IMAG " # Estimate model satisfaction_res .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = FALSE, .tolerance = 1e-07) summarize(satisfaction_res) assess(satisfaction_res) # We note that the output of the summarize() function contains a lot of NAs. This is due to the fact that we did not explicitly require to use the bootstrap to perform inference. To do that, we need to include the .resample_method = "bootstrap" argument as in the next example: satisfaction_boot .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = TRUE, .tolerance = 1e-07, .resample_method = "bootstrap", .R = 1000, .seed = 1406) summarize(satisfaction_boot, .ci = "CI_percentile")
DOI: 10.6084/m9.figshare.14067779.v2
title: A non-technical guide for conducting open and reproducible PLS-SEM analyses using the "cSEM" package in R/RStudio

description: <br> # Install the fillowing packages and recall libraries in R using this command: if (!require(cSEM, quietly = TRUE)) { install.packages("devtools") library(devtools) install_github("M-E-Rademaker/cSEM") } library(cSEM) # Now Set a working directory using this command: setwd("D:/Secondary") # Specify your model satisfaction_mod # Reflective measurement model IMAG =~ imag1 + imag2 + imag3 SAT =~ sat1 + sat2 + sat3 + sat4 LOY =~ loy1 + loy2 + loy3 + loy4 # Structural model LOY ~ SAT + IMAG " # Estimate model satisfaction_res .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = FALSE, .tolerance = 1e-07) summarize(satisfaction_res) assess(satisfaction_res) # We note that the output of the summarize() function contains a lot of NAs. This is due to the fact that we did not explicitly require to use the bootstrap to perform inference. To do that, we need to include the .resample_method = "bootstrap" argument as in the next example: satisfaction_boot .model = satisfaction_mod, .PLS_weight_scheme_inner = "path", .disattenuate = TRUE, .tolerance = 1e-07, .resample_method = "bootstrap", .R = 1000, .seed = 1406) summarize(satisfaction_boot, .ci = "CI_percentile")
DOI: 10.15161/oar.it/73535
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4487116
title: Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows

description: Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter &gt; 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).
description: This dataset is a supplement to: Van Den Berge S, Vangansbeke P, Calders K, Vanneste T, Baeten L, Verbeeck H, Krishna Moorthy SP, Verheyen K (2021) Biomass expansion factors for hedgerow-grown trees derived from terrestrial LiDAR. BioEnergy Research. https://doi.org/10.1007/s12155-021-10250-y
DOI: 10.5281/zenodo.4487115
title: Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows

description: Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter &gt; 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).
description: This dataset is a supplement to: Van Den Berge S, Vangansbeke P, Calders K, Vanneste T, Baeten L, Verbeeck H, Krishna Moorthy SP, Verheyen K (2021) Biomass expansion factors for hedgerow-grown trees derived from terrestrial LiDAR. BioEnergy Research. https://doi.org/10.1007/s12155-021-10250-y
DOI: 10.25550/1-yk9j
title: Dataset sample

description: Yadda yadda yadda
DOI: 10.5281/zenodo.3844172
title: Figure-maker code for 'The imaginary part of the high-harmonic cutoff'

description: <strong>Figure-maker code for 'The imaginary part of the high-harmonic cutoff'</strong> This collection contains the code and experimental data used to produce the figures in the paper The imaginary part of the high-harmonic cutoff. Emilio Pisanty, Marcelo F. Ciappina and Maciej Lewenstein. J. Phys. Photonics 2, 034013 (2020), arXiv:2003.00277. The collection consists of the following: The Mathematica notebooks used to produce the data as well as to generate the figures, together with pdf printouts of those notebooks. The Mathematica package file RB-SFA.m, taken from the RB-SFA package. The intermediate data where the calculation is impractical to run quickly and flexibly. Copies of all the figures in the paper. The code here is also used to generate the figures for the Supplementary Material for the paper, which is available at imaginary-harmonic-cutoff.github.io and has been archived separately as doi:10.5281/zenodo.3758483. This collection is presented in an as-is manner in the hope that it will be useful. The copyright of this collection rests with the authors (2020). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license; reuse and redistribution is allowed (and encouraged) under the terms of that license. However, in addition to that legal constraint, if your use of this material results in a publication, you have the academic obligation to cite the paper as quoted above.
description: {"references": ["The imaginary part of the high-harmonic cutoff. Emilio Pisanty, Marcelo F. Ciappina and Maciej Lewenstein. arXiv:2003.00277\u00a0(2020)."]}
DOI: 10.5281/zenodo.3692562
title: Figure-maker code for 'The imaginary part of the high-harmonic cutoff'

description: <strong>Figure-maker code for 'The imaginary part of the high-harmonic cutoff'</strong> This collection contains the code and experimental data used to produce the figures in the paper The imaginary part of the high-harmonic cutoff. Emilio Pisanty, Marcelo F. Ciappina and Maciej Lewenstein. J. Phys. Photonics 2, 034013 (2020), arXiv:2003.00277. The collection consists of the following: The Mathematica notebooks used to produce the data as well as to generate the figures, together with pdf printouts of those notebooks. The Mathematica package file RB-SFA.m, taken from the RB-SFA package. The intermediate data where the calculation is impractical to run quickly and flexibly. Copies of all the figures in the paper. The code here is also used to generate the figures for the Supplementary Material for the paper, which is available at imaginary-harmonic-cutoff.github.io and has been archived separately as doi:10.5281/zenodo.3758483. This collection is presented in an as-is manner in the hope that it will be useful. The copyright of this collection rests with the authors (2020). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license; reuse and redistribution is allowed (and encouraged) under the terms of that license. However, in addition to that legal constraint, if your use of this material results in a publication, you have the academic obligation to cite the paper as quoted above.
description: {"references": ["The imaginary part of the high-harmonic cutoff. Emilio Pisanty, Marcelo F. Ciappina and Maciej Lewenstein. arXiv:2003.00277\u00a0(2020)."]}
DOI: 10.15131/shef.data.14068538
title: Data for the paper: "Patient-specific finite element models of posterior pedicle screw fixation: effect of screw's size and geometry"

description: Data used in the paper: "Patient-specific finite element models of posterior pedicle screw fixation: effect of screw's size and geometry"Sensale, Vendeuvre, Schilling, Grupp, Rochette and Dall'Ara published in Frontiers Bioengineering and Biotechnology<br>We share here the results and one example of FE models for the implanted vertebrae.<br>Short description of the files included:<br>-) the results of the paper are reported in "Results_file.xlsx". In this file, there are different sheets:.NumElements_CompTime - this contains the number of elements and computational time to solve the FE models, reported in Table 2 and 3;<br>.MeshConvergenceStudy - this contains the results of the Mesh convergence study reported in Figure 3.<br>.Sensitivity Deflection - Stress AND Sensitivity Strain - these contain the results of the sensitivity analysis reported in Table 4, 5 and 6;.Correlation - this contains the results of the correlation analysis reported in Figure 6;<br><br>-) In the "Frequency_plot_files" folder, the result files for the frequency plots reported in Figure 7 are reported:.MinStrainMeshPoints(...).txt - these files contains the values of minimum principal strain used in the frequency plots in Figure 7.<br><br>-) The "Example_Model" folder contains an example of the models used in the sensitivity analysis for Patient #3 with simplified pedicle screw with diamter D6.5mm and length L45mm<br>The folder contains:- the Ansys workbench 2020R2 project file corresponding to the model in the name of the folder;- the corresponding geometry SpaceClaim file;- the Ansys cdb mesh file exported from Bonemat;- the calibration xml file used in Bonemat;- the Ansys apdl command snippet used in Mechanical to build an heterogeneous model of the bone.<br>In case the reader is interested in the whole database they should contact Dr Enrico Dall'Ara (e.dallara@sheffield.ac.uk).
DOI: 10.15131/shef.data.14068538.v1
title: Data for the paper: "Patient-specific finite element models of posterior pedicle screw fixation: effect of screw's size and geometry"

description: Data used in the paper: "Patient-specific finite element models of posterior pedicle screw fixation: effect of screw's size and geometry"Sensale, Vendeuvre, Schilling, Grupp, Rochette and Dall'Ara published in Frontiers Bioengineering and Biotechnology<br>We share here the results and one example of FE models for the implanted vertebrae.<br>Short description of the files included:<br>-) the results of the paper are reported in "Results_file.xlsx". In this file, there are different sheets:.NumElements_CompTime - this contains the number of elements and computational time to solve the FE models, reported in Table 2 and 3;<br>.MeshConvergenceStudy - this contains the results of the Mesh convergence study reported in Figure 3.<br>.Sensitivity Deflection - Stress AND Sensitivity Strain - these contain the results of the sensitivity analysis reported in Table 4, 5 and 6;.Correlation - this contains the results of the correlation analysis reported in Figure 6;<br><br>-) In the "Frequency_plot_files" folder, the result files for the frequency plots reported in Figure 7 are reported:.MinStrainMeshPoints(...).txt - these files contains the values of minimum principal strain used in the frequency plots in Figure 7.<br><br>-) The "Example_Model" folder contains an example of the models used in the sensitivity analysis for Patient #3 with simplified pedicle screw with diamter D6.5mm and length L45mm<br>The folder contains:- the Ansys workbench 2020R2 project file corresponding to the model in the name of the folder;- the corresponding geometry SpaceClaim file;- the Ansys cdb mesh file exported from Bonemat;- the calibration xml file used in Bonemat;- the Ansys apdl command snippet used in Mechanical to build an heterogeneous model of the bone.<br>In case the reader is interested in the whole database they should contact Dr Enrico Dall'Ara (e.dallara@sheffield.ac.uk).
DOI: 10.25646/8045
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.14058185
title: QUAN Strand Dataset - Occupational Burnout and Job Satisfaction Among Physicians in Times of COVID-19 Crisis: A Convergent Parallel Mixed-Method Study

description: This dataset was generated in a cross-sectional survey that was conducted as a part of a broader mixed-method study to investigate occupational burnout and job satisfaction among physicians in times of COVID-19 pandemic in Jordan.
DOI: 10.5281/zenodo.4451526
title: Libras-34 Dataset (Kinect v1)

description: Brazilian Sign Language data set with 34 signs for sign language and gesture recognition benchmark: (1) person, (2) to spread, (3) to copy, (4) to catch, (5) to gather, (6) to disappear, (7) to look, (8) fair, (9) truth, (10) weight, (11) justice, (12) who, (13) nothing, (14) to believe, (15) to forget, (16) to love, (17) to afflict, (18) to commemorate, (19) rancor, (20) assembly meeting, (21) to compare, (22) to scream, (23) to speak, (24) to absorb, (25) to fatten, (26) to quarrel, (27) perspicacious, (28) to shine, (29) maid, (30) to replace, (31) prison, (32) television, (33) yesterday and (34) future. Each one of these signs was recorded 5 times by 1 signaller, totaling a database of 170 samples. The signs were captured using an RGB-D sensor (Microsoft Kinect) and processed by nuiCaptureAnalyze software. This dataset is also publicly available at Almeida (2014). Some scientific works related to this dataset are Almeida et al. (2013), Almeida et al. (2014a) and Almeida (2014b).
DOI: 10.5281/zenodo.4451525
title: Libras-34 Dataset (Kinect v1)

description: Brazilian Sign Language data set with 34 signs for sign language and gesture recognition benchmark: (1) person, (2) to spread, (3) to copy, (4) to catch, (5) to gather, (6) to disappear, (7) to look, (8) fair, (9) truth, (10) weight, (11) justice, (12) who, (13) nothing, (14) to believe, (15) to forget, (16) to love, (17) to afflict, (18) to commemorate, (19) rancor, (20) assembly meeting, (21) to compare, (22) to scream, (23) to speak, (24) to absorb, (25) to fatten, (26) to quarrel, (27) perspicacious, (28) to shine, (29) maid, (30) to replace, (31) prison, (32) television, (33) yesterday and (34) future. Each one of these signs was recorded 5 times by 1 signaller, totaling a database of 170 samples. The signs were captured using an RGB-D sensor (Microsoft Kinect) and processed by nuiCaptureAnalyze software. This dataset is also publicly available at Almeida (2014). Some scientific works related to this dataset are Almeida et al. (2013), Almeida et al. (2014a) and Almeida (2014b).
DOI: 10.6084/m9.figshare.11357654.v2
title: Does government funding promote or inhibit the financialization of manufacturing enterprises? Evidence from listed Chinese enterprises

description: the financialization of manufacturing enterprises
DOI: 10.6084/m9.figshare.11357654
title: Does government funding promote or inhibit the financialization of manufacturing enterprises? Evidence from listed Chinese enterprises

description: the financialization of manufacturing enterprises
DOI: 10.6084/m9.figshare.11357654.v4
title: Does government funding promote or inhibit the financialization of manufacturing enterprises? Evidence from listed Chinese enterprises

description: the financialization of manufacturing enterprises
DOI: 10.6084/m9.figshare.11357654.v3
title: Does government funding promote or inhibit the financialization of manufacturing enterprises? Evidence from listed Chinese enterprises

description: the financialization of manufacturing enterprises
DOI: 10.5281/zenodo.4519577
title: Top predators govern multitrophic diversity effects: Data S1

description: Code and data required to produce and/or analyze the results presented in "Top predators govern multitrophic diversity effects".
description: Research funded by the Deutsche Forschungsgemeinschaft (DFG), priority programma DynaTrait (GA 401/25-1).
DOI: 10.6084/m9.figshare.14068664
title: Representative_result.zip

description: The representative result of the proposed method.
DOI: 10.6084/m9.figshare.14068664.v2
title: Representative_result.zip

description: The representative result of the proposed method.
DOI: 10.6084/m9.figshare.14068664.v1
title: Representative_result.zip

description: The representative result of the proposed method.
DOI: 10.5063/pn941q
title: Melolontha hippocastani egg-laying, study on female oviposition behavior, North East of France, 2019

description: During 2019 summer, we studied oviposition behavior of Melolontha hippocastani females. Therefore, we dug pits in forest and counted density of egg-laying and eggs in soil. In parallel, we discribed environmental conditions by measuring canopy openness, presence of a dense shrub layer and tree basal area (including shared basal area by each tree specie).
DOI: 10.6084/m9.figshare.14068700.v1
title: Material Suplementario 1. Metadata

description: <b>Material Suplementario 1. </b>Números de acceso de los genomas utilizados en este estudio. La metadata fue obtenida de la base de datos GISAID. Los genomas fueron generados por el Instituto Nacional de Salud del Perú.