cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5082117
title: A novel method to obtain three-dimensional urban surface temperature from ground-based thermography - Figure 10 median brightness temperatures

description: Median brightness temperatures as shown in Figure 10 of Morrison et al. (2018) <em>A novel method to obtain three-dimensional urban surface temperature from ground-based thermography </em>https://doi.org/10.1016/j.rse.2018.05.004 Details on the files can be found below. Further details can be found in the linked paper and references. <strong>Thermal infrared camera brightness temperature measurements</strong> <strong>02082014_COSMOTIRsunShade.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade)<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>02082014_COSMOTIRorientations.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade) also stratified by surface orientation i.e. &lt;sun_status&gt;_&lt;orientation&gt;<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>002082014_COSMOTIRorientationsAndSunShade.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade) also stratified by surface orientation i.e. &lt;sun_status&gt;_&lt;orientation&gt;<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>Supplementary standard meteorological and radiation measurements.</strong> <strong>MET_RAD_Morrisonetal_2018.csv</strong>: <strong>TIME</strong>: Local time. 10 min end of average of 1 min samples.<br> <strong>press</strong>: Atmospheric pressure (hPa) (site: JMA Kumagaya AMeDAS)<br> <strong>Tair</strong>: Air temperature (degrees celsius) (site: JMA Kumagaya AMeDAS)<br> <strong>RH</strong>: Relative humidity (%) (site: JMA Kumagaya AMeDAS)<br> <strong>WS</strong>: Wind speed (m/s) (site: JMA Kumagaya AMeDAS)<br> <strong>SWdn_direct</strong>: Direct incoming shortwave radiation (W m-2) (site: COSMO, Eko MS-56)<br> <strong>SWdn</strong>: Hemispherical incoming shortwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>SWup</strong>: Hemispherical outgoing shortwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>LWdn</strong>: Hemispherical incoming longwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>LWup</strong>: Hemispherical outgoing longwave radiation (W m-2) (site: COSMO, Eko MR-40)
DOI: 10.5281/zenodo.5078387
title: A novel method to obtain three-dimensional urban surface temperature from ground-based thermography - Figure 10 median brightness temperatures

description: Median brightness temperatures as shown in Figure 10 of Morrison et al. (2018) <em>A novel method to obtain three-dimensional urban surface temperature from ground-based thermography </em>https://doi.org/10.1016/j.rse.2018.05.004 Details on the files can be found below. Further details can be found in the linked paper and references. <strong>Thermal infrared camera brightness temperature measurements</strong> <strong>02082014_COSMOTIRsunShade.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade)<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>02082014_COSMOTIRorientations.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade) also stratified by surface orientation i.e. &lt;sun_status&gt;_&lt;orientation&gt;<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>002082014_COSMOTIRorientationsAndSunShade.csv:</strong><br> <strong>TIME</strong>: Local time. Timestep indicates end of 60 min aggregation interval using 1 min image samples.<br> <strong>class</strong>: sunlit (observed surface is in direct sun) or shaded (observed surface is in shade) also stratified by surface orientation i.e. &lt;sun_status&gt;_&lt;orientation&gt;<br> <strong>median_Tb_K</strong>: median brightness temperature across all images and image pixels for the given class and timestep (K) <strong>Supplementary standard meteorological and radiation measurements.</strong> <strong>MET_RAD_Morrisonetal_2018.csv</strong>: <strong>TIME</strong>: Local time. 10 min end of average of 1 min samples.<br> <strong>press</strong>: Atmospheric pressure (hPa) (site: JMA Kumagaya AMeDAS)<br> <strong>Tair</strong>: Air temperature (degrees celsius) (site: JMA Kumagaya AMeDAS)<br> <strong>RH</strong>: Relative humidity (%) (site: JMA Kumagaya AMeDAS)<br> <strong>WS</strong>: Wind speed (m/s) (site: JMA Kumagaya AMeDAS)<br> <strong>SWdn_direct</strong>: Direct incoming shortwave radiation (W m-2) (site: COSMO, Eko MS-56)<br> <strong>SWdn</strong>: Hemispherical incoming shortwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>SWup</strong>: Hemispherical outgoing shortwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>LWdn</strong>: Hemispherical incoming longwave radiation (W m-2) (site: COSMO, Eko MR-40)<br> <strong>LWup</strong>: Hemispherical outgoing longwave radiation (W m-2) (site: COSMO, Eko MR-40)
DOI: 10.6084/m9.figshare.16399419
title: Untitled Item

description: PSS scores for PGY1/housemenstarting work during the COVID19 pandemic2020 results
DOI: 10.6084/m9.figshare.16399419.v1
title: Untitled Item

description: PSS scores for PGY1/housemenstarting work during the COVID19 pandemic2020 results
DOI: 10.6084/m9.figshare.16399107
title: RawQualitativedata_PGY1ConcernsChallenges_May2020.docx

description: Qualtiative responses for concerns and challengesd
DOI: 10.6084/m9.figshare.16399107.v1
title: RawQualitativedata_PGY1ConcernsChallenges_May2020.docx

description: Qualtiative responses for concerns and challengesd
DOI: 10.5281/zenodo.5234886
title: Untersuchungen zur Adhäsion von Cyanacrylaten auf Siliziumdioxid

description: Cyanacrylate, besser bekannt als Sekundenkleber, spielen eine zunehmend wichtige Rolle in der Fertigungstechnik. Sie ermöglichen das Fügen von Werkstoffen mit geringer Schweißeignung wie z.B. Aluminium und den Einsatz von hybriden Werkstoffverbunden. Die schnelle Aushärtereaktion des Cyanacrylats mit Feuchtigkeit hat darüber hinaus kurze Prozesszeiten zur Folge.<br> Trotz dieser Bedeutung sind die genauen Adhäsionsmechanismen an der Grenzfläche zwischen Klebstoff und Metall bzw. Metalloxid noch nicht hinreichend verstanden. Die hohe Reaktivität des Klebstoffs erschwert die Probenpräparation und Untersuchung. Im Rahmen des Sonderforschungsbereichs „Sauerstofffreie Produktion“ (SFB 1368) werden die Abscheidetechniken des thermischen Verdampfens und Spin Coatings unter-sucht, um dünne Klebstoffschichten auf dem Modellsubstrat Siliziumdioxid abzuscheiden.<br> Mittels Mikroskopiemethoden sollen die Schichtdicke und Morphologie der Kleb-stofffilme untersucht werden. Mit der Röntgenphotoelektronenspektroskopie (XPS) sollen darüber hinaus die zugrundeliegenden Wechselwirkungen an der Grenzfläche zwischen Klebstoff und Siliziumdioxid verstanden werden. Dieses Wissen dient als Grundlage für weitergehende Untersuchungen an technisch relevanten Oberflächen wie z.B. Aluminium(oxid) und Titan(oxid).
DOI: 10.5281/zenodo.5234885
title: Untersuchungen zur Adhäsion von Cyanacrylaten auf Siliziumdioxid

description: Cyanacrylate, besser bekannt als Sekundenkleber, spielen eine zunehmend wichtige Rolle in der Fertigungstechnik. Sie ermöglichen das Fügen von Werkstoffen mit geringer Schweißeignung wie z.B. Aluminium und den Einsatz von hybriden Werkstoffverbunden. Die schnelle Aushärtereaktion des Cyanacrylats mit Feuchtigkeit hat darüber hinaus kurze Prozesszeiten zur Folge.<br> Trotz dieser Bedeutung sind die genauen Adhäsionsmechanismen an der Grenzfläche zwischen Klebstoff und Metall bzw. Metalloxid noch nicht hinreichend verstanden. Die hohe Reaktivität des Klebstoffs erschwert die Probenpräparation und Untersuchung. Im Rahmen des Sonderforschungsbereichs „Sauerstofffreie Produktion“ (SFB 1368) werden die Abscheidetechniken des thermischen Verdampfens und Spin Coatings unter-sucht, um dünne Klebstoffschichten auf dem Modellsubstrat Siliziumdioxid abzuscheiden.<br> Mittels Mikroskopiemethoden sollen die Schichtdicke und Morphologie der Kleb-stofffilme untersucht werden. Mit der Röntgenphotoelektronenspektroskopie (XPS) sollen darüber hinaus die zugrundeliegenden Wechselwirkungen an der Grenzfläche zwischen Klebstoff und Siliziumdioxid verstanden werden. Dieses Wissen dient als Grundlage für weitergehende Untersuchungen an technisch relevanten Oberflächen wie z.B. Aluminium(oxid) und Titan(oxid).
DOI: 10.6084/m9.figshare.14995314.v6
title: Effect of behavioural sampling methods on local and global social network metrics: A case-study of three macaque species

description: Data set and R-codes
DOI: 10.5281/zenodo.4618057
title: Supplementary Material for "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP"

description: Supplementary material to accompany pre-print of "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP" by Jethro Browell and Matteo Fasiolo available on on arXiv and accepted for publication in IEEE Transactions on Smart Grid.
DOI: 10.5281/zenodo.5235052
title: Data: Genetic diversity of Colletotrichum lupini and its virulence on white and Andean lupin

description: DATA Lupin cultivation worldwide is threatened by anthracnose, a destructive disease caused by the seed- and air-borne fungal pathogen <em>Colletotrichum lupini</em>. In this study we explored the intraspecific diversity of 39 <em>C. lupini</em> isolates collected from different lupin cultivating regions around the world, and representative isolates were screened for their pathogenicity and virulence on white and Andean lupin. Multi-locus phylogeny and morphological characterizations showed intraspecific diversity to be greater than previously shown, distinguishing a total of six genetic groups and ten distinct morphotypes. Highest diversity was found across South America, indicating it as the center of origin of <em>C. lupini</em>. The isolates that correspond to the current pandemic belong to a genetic and morphological uniform group, were globally widespread, and showed high virulence on tested white and Andean lupin accessions. Isolates belonging to the other five genetic groups were mostly found locally and showed distinct virulence patterns. Two highly virulent strains were shown to overcome resistance of advanced white lupin breeding material. This stresses the need to be careful with international seed transports in order to prevent spread of currently confined but potentially highly virulent strains. This study improves our understanding of the diversity, phylogeography and pathogenicity of a member of one of the world’s top 10 plant pathogen genera, providing valuable information for breeding programs and future disease management.
DOI: 10.5281/zenodo.5235051
title: Data: Genetic diversity of Colletotrichum lupini and its virulence on white and Andean lupin

description: DATA Lupin cultivation worldwide is threatened by anthracnose, a destructive disease caused by the seed- and air-borne fungal pathogen <em>Colletotrichum lupini</em>. In this study we explored the intraspecific diversity of 39 <em>C. lupini</em> isolates collected from different lupin cultivating regions around the world, and representative isolates were screened for their pathogenicity and virulence on white and Andean lupin. Multi-locus phylogeny and morphological characterizations showed intraspecific diversity to be greater than previously shown, distinguishing a total of six genetic groups and ten distinct morphotypes. Highest diversity was found across South America, indicating it as the center of origin of <em>C. lupini</em>. The isolates that correspond to the current pandemic belong to a genetic and morphological uniform group, were globally widespread, and showed high virulence on tested white and Andean lupin accessions. Isolates belonging to the other five genetic groups were mostly found locally and showed distinct virulence patterns. Two highly virulent strains were shown to overcome resistance of advanced white lupin breeding material. This stresses the need to be careful with international seed transports in order to prevent spread of currently confined but potentially highly virulent strains. This study improves our understanding of the diversity, phylogeography and pathogenicity of a member of one of the world’s top 10 plant pathogen genera, providing valuable information for breeding programs and future disease management.
DOI: 10.6084/m9.figshare.16401072
title: Waterbird counts in the Okavango Delta

description: Compilation of waterbird counts across the Okavango Delta - 1966 to 2019
DOI: 10.5281/zenodo.5229988
title: Analysis of antibody signal loss for elongating RNA polymerase II upon transcription inhibition

description: This data set assesses the loss of antibody signal for elongating RNA polymerase II in zebrafish embryos upon transcription inhibition with flavopiridol. Polymerase was labeled by immunofluorescence, microscopy images were acquired by instant-SIM micrscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.
description: Steps needed to carry out the analysis: 1. Install bfmatlab from OME, add it to the MatLab path 2. Extract the image data by running the script MultiPosition_extraction_nd2.m 3. Analyze the extracted data by running the script ClusterAnalysis.m 4. Generate example images by running the script ExampleImages.m
DOI: 10.5281/zenodo.5229987
title: Analysis of antibody signal loss for elongating RNA polymerase II upon transcription inhibition

description: This data set assesses the loss of antibody signal for elongating RNA polymerase II in zebrafish embryos upon transcription inhibition with flavopiridol. Polymerase was labeled by immunofluorescence, microscopy images were acquired by instant-SIM micrscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.
description: Steps needed to carry out the analysis: 1. Install bfmatlab from OME, add it to the MatLab path 2. Extract the image data by running the script MultiPosition_extraction_nd2.m 3. Analyze the extracted data by running the script ClusterAnalysis.m 4. Generate example images by running the script ExampleImages.m
DOI: 10.6084/m9.figshare.11973693.v10
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections and high speed streams (Solar Orbiter, Parker Solar Probe, STEREO-A/B, MAVEN, Wind, MESSENGER, VEX, Ulysses) 2007-2021

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch by christian.moestl@oeaw.ac.at or twitter @chrisoutofspace concerning usage and possible co-authorship.<br>Made with https://github.com/cmoestl/heliocats
DOI: 10.15129/c3ae80a3-b34e-43cc-b6e6-629eb5cab922
title: Data for: "Design as a Marked Point Process"

description: Dataset used in the article "Design as a Marked Point Process". The file "DesignAsAMarkedPointProcessData.csv" contains a list of hole diameters and positions that were extracted from CAD valve designs – see paper for details. Each row represents a sinlge instance of a feature in a single design. The variables (columns) are: design – an identifier of the design that the hole features were extracted from feature – the diameter of the extracted hole feature x, y, z – the coordinate in 3D space of the hole feature
DOI: 10.5281/zenodo.5172156
title: Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

description: <strong>D-Tensor Data</strong> The reference data for 3500 configurations of [Co(N<sub>2</sub>S<sub>2</sub>O<sub>4</sub>C<sub>8</sub>H<sub>10</sub>)<sub>2</sub>]<sup>2−</sup> (CoSar), [Fe(tpa)<sup>Ph</sup>]<sup>−</sup> (FeTPAPh), and [Ni(HIM<sub>2</sub>−py)<sub>2</sub>NO<sub>3</sub>]<sup>+</sup> (NiComplex) is generated employing Molpro package [1]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the D-Tensor in cm<sup>-1</sup>. The data set contains four \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. D = data['MAT'] # D-Tensor values in cm-1, D = (D11, D12, D13, D22, D23, D33) N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>AIMD Data</strong> To propagate the periodic cell containing four CoSar molecules, for which D-Tensor was computed above, a data set containing total energies as well as atomic forces of 3500 structures was generated employing VASP package [3-6]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the total energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains six \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar_bulk.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. C = data['C'] # Cell vectors in Ang. E = data['E'] # Total energy in kcal/mol F = data['F'] # Atomic forces in kcal/mol/Ang. N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>References</strong> [1] H.-J. Werner, P. J. Knowles, G. Knizia, F. R. Manby, M. Schütz,et al.,“Molpro, version 2020.0, a package of ab initio programs,” (2020), see https://www.molpro.net. [2] V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,”<strong> submitted</strong> (2021). [3] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994). [4] G. Kresse and J. Hafner, “Ab initio molecular dynamics for liquid metals,” Phys. Rev. B 47, 558 (1993). [5] G. Kresse and J. Furthmüller, “Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,” Comput. Mater. Sci. 6, 15 – 50 (1996). [6] G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 11169 (1996).
DOI: 10.5281/zenodo.5172155
title: Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

description: <strong>D-Tensor Data</strong> The reference data for 3500 configurations of [Co(N<sub>2</sub>S<sub>2</sub>O<sub>4</sub>C<sub>8</sub>H<sub>10</sub>)<sub>2</sub>]<sup>2−</sup> (CoSar), [Fe(tpa)<sup>Ph</sup>]<sup>−</sup> (FeTPAPh), and [Ni(HIM<sub>2</sub>−py)<sub>2</sub>NO<sub>3</sub>]<sup>+</sup> (NiComplex) is generated employing Molpro package [1]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the D-Tensor in cm<sup>-1</sup>. The data set contains four \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. D = data['MAT'] # D-Tensor values in cm-1, D = (D11, D12, D13, D22, D23, D33) N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>AIMD Data</strong> To propagate the periodic cell containing four CoSar molecules, for which D-Tensor was computed above, a data set containing total energies as well as atomic forces of 3500 structures was generated employing VASP package [3-6]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the total energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains six \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar_bulk.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. C = data['C'] # Cell vectors in Ang. E = data['E'] # Total energy in kcal/mol F = data['F'] # Atomic forces in kcal/mol/Ang. N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>References</strong> [1] H.-J. Werner, P. J. Knowles, G. Knizia, F. R. Manby, M. Schütz,et al.,“Molpro, version 2020.0, a package of ab initio programs,” (2020), see https://www.molpro.net. [2] V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,”<strong> submitted</strong> (2021). [3] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994). [4] G. Kresse and J. Hafner, “Ab initio molecular dynamics for liquid metals,” Phys. Rev. B 47, 558 (1993). [5] G. Kresse and J. Furthmüller, “Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,” Comput. Mater. Sci. 6, 15 – 50 (1996). [6] G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 11169 (1996).