cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7498729
title: Dynamical simulation of EBSD master pattern of silicon

description: Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of silicon (<em>Fd\(\bar{3}\)m</em>, <em>a</em> = 5.4307 Å). The master pattern was simulated with EMsoft v5.0. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 5 to 20 kV with an increment of 1 kV. The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python 3.7 or higher and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands: <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/si_mc_mp_20kv.h5") s.plot()</code></pre> The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: si.xtal), EMMCOpenCL (input: si.xtal, mcopencl.nml; output: si_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, si_mc_mp_20kv.h5; output: added to existing si_mc_mp_20kv.h5).
description: The master pattern was simulated while Håkon Wiik Ånes received financial support from the Norwegian University of Science and Technology (NTNU) through the NTNU Aluminium Product Innovation Centre (NAPIC).
DOI: 10.5281/zenodo.7498728
title: Dynamical simulation of EBSD master pattern of silicon

description: Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of silicon (<em>Fd\(\bar{3}\)m</em>, <em>a</em> = 5.4307 Å). The master pattern was simulated with EMsoft v5.0. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 5 to 20 kV with an increment of 1 kV. The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python 3.7 or higher and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands: <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/si_mc_mp_20kv.h5") s.plot()</code></pre> The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: si.xtal), EMMCOpenCL (input: si.xtal, mcopencl.nml; output: si_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, si_mc_mp_20kv.h5; output: added to existing si_mc_mp_20kv.h5).
description: The master pattern was simulated while Håkon Wiik Ånes received financial support from the Norwegian University of Science and Technology (NTNU) through the NTNU Aluminium Product Innovation Centre (NAPIC).
DOI: 10.17909/jmz7-4r27
title: JWST data from the Proplyd paper (Berné et al. 2023)
DOI: 10.5281/zenodo.7455894
title: Data set on district heating potentials in EU-27 countries

description: The provided data set includes the input data for assessment of the district heating potential in EU-27 countries under evolving DH market shares and ambitious heat demand reduction scenario. The data set also includes the output data of the assessment obtained based on the input data from the following sources: Best-Case scenario from: "<em>L. Kranzl et al., Renewable space heating under the revised Renewable Energy Directive:<br> ENER/C1/2018 494 : final report. LU: Publications Office of the European Union, 2022 [Online].<br> Available: https://data.europa.eu/doi/10.2833/525486. [Accessed: Jul. 29, 2022]</em>" BL2050 scenario from sEEnergies project: "<em>B. Möller, E. Wiechers, L. Sánchez-García, and U. Persson, ‘Spatial models and spatial analytics results’, Mar. 2022, doi: 10.5281/zenodo.6524594. [Online]. Available: https://zenodo.org/record/6524594. [Accessed: Dec. 14, 2022]</em>"
DOI: 10.5281/zenodo.7455893
title: Data set on district heating potentials in EU-27 countries

description: The provided data set includes the input data for assessment of the district heating potential in EU-27 countries under evolving DH market shares and ambitious heat demand reduction scenario. The data set also includes the output data of the assessment obtained based on the input data from the following sources: Best-Case scenario from: "<em>L. Kranzl et al., Renewable space heating under the revised Renewable Energy Directive:<br> ENER/C1/2018 494 : final report. LU: Publications Office of the European Union, 2022 [Online].<br> Available: https://data.europa.eu/doi/10.2833/525486. [Accessed: Jul. 29, 2022]</em>" BL2050 scenario from sEEnergies project: "<em>B. Möller, E. Wiechers, L. Sánchez-García, and U. Persson, ‘Spatial models and spatial analytics results’, Mar. 2022, doi: 10.5281/zenodo.6524594. [Online]. Available: https://zenodo.org/record/6524594. [Accessed: Dec. 14, 2022]</em>"
DOI: 10.5281/zenodo.7473517
title: Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV1

description: <strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV1)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.5281/zenodo.7473516
title: Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV1

description: <strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV1)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.5281/zenodo.7473601
title: Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV2

description: <strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV2)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.5281/zenodo.7473600
title: Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 1 FOV2

description: <strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 1 FOV2)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.5281/zenodo.7473316
title: Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV2

description: <strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV2)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.5281/zenodo.7473315
title: Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV2

description: <strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV2)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
DOI: 10.6084/m9.figshare.21520848
title: Horse track displaying Frog

description: Horse footprint showing a 'frog' to the rear. Photos collected with Galaxy Fold 3, model generated with Reality Capture
DOI: 10.5281/zenodo.7498163
title: Spiders (Araneae) of the "Kamennye Mogily" Reserve (Donetsk Region)

description: The dataset is based on the results of a long-term research project (1993–2008). Samples come from the “Kamennye Mogily” Reserve (Nikolskyi District, Donetsk Region) and providing about 2 511 records in the dataset with more than 14400 specimens recorded. The dataset can help to explore regional fauna, regional and general species distribution, their monitoring, conservation, phenology, and habitat preferences.
DOI: 10.5281/zenodo.7498162
title: Spiders (Araneae) of the "Kamennye Mogily" Reserve (Donetsk Region)

description: The dataset is based on the results of a long-term research project (1993–2008). Samples come from the “Kamennye Mogily” Reserve (Nikolskyi District, Donetsk Region) and providing about 2 511 records in the dataset with more than 14400 specimens recorded. The dataset can help to explore regional fauna, regional and general species distribution, their monitoring, conservation, phenology, and habitat preferences.
DOI: 10.5517/ccdc.csd.cc27wzy2
title: CCDC 2082485: Experimental Crystal Structure Determination

description: Related Article: Yicun Liang, Wenling Feng, Xiaozhan Liang|2021|CSD Communication|||
description: Related Article: Song Xie, Jing Shi, Yicun Liang, Xiaozhan Yang, Wenlin Feng|2023|Opt.Mater.|135|113345|doi:10.1016/j.optmat.2022.113345
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/4798689
title: YouTube Daily
DOI: 10.6084/m9.figshare.20067332.v7
title: Dataset and supplemental material for "Overcoming barriers for seed-based restoration in the world’s largest tropical wetland"

description: (1) Raw data sheet: database comprising all the references and the items used in this review. <br> (2) Tabla S2 sheet: list of the species and their respective families included in the articles on seed ecology in the Pantanal wetland. Data about the number of cases (frequency), number of articles and percentage of articles over the total of published articles for each species are included. <br> (3) Table S3 sheet: data on seed-related functional attributes relevant for achievable restoration plans of the species included in the articles on seed ecology in the Pantanal wetland: growth-form, substrate where the seed germinates, substrate where the plant grows (plant life-form), type of fruit, dispersal mode, studied topics for each species, presence or absence of dormancy, and type of dormancy are included.<br>
DOI: 10.6084/m9.figshare.21763211
title: Raw data of Gynostemma pentaphyllum Makino extract-PJ.docx

description: Raw data of Gynostemma pentaphyllum Makino extract
DOI: 10.6084/m9.figshare.21763211.v4
title: Raw data of Gynostemma pentaphyllum Makino extract-PJ.docx

description: Raw data of Gynostemma pentaphyllum Makino extract
DOI: 10.5281/zenodo.7063344
title: MUSES Normalized Difference Vegetation Index (NDVI) 16-Day 30m Geographic Grid over Beijing Since 1984

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES NDVI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES NDVI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from the Landsat collection 2 surface reflectance data using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N Temporal Coverage: 1984 – 2021 Spatial Resolution: 0.000269469º (approximately 30 m) Temporal Resolution: 16 days Projection: Geographic Data Format: HDF Scale: 0.0001 Valid Range: 0 – 10000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864 Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568 If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7063343
title: MUSES Normalized Difference Vegetation Index (NDVI) 16-Day 30m Geographic Grid over Beijing Since 1984

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES NDVI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES NDVI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from the Landsat collection 2 surface reflectance data using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao <em>et al</em>., 2015; Xiao <em>et al</em>., 2017). The MUSES NDVI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N Temporal Coverage: 1984 – 2021 Spatial Resolution: 0.000269469º (approximately 30 m) Temporal Resolution: 16 days Projection: Geographic Data Format: HDF Scale: 0.0001 Valid Range: 0 – 10000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. <em>Remote Sensing</em>, 7, 9844-9864 Xiao Zhiqiang, <em>et al</em>. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 10, 5551-5568 If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7159053
title: MUSES Leaf Area Index (LAI) 16-Day 30m Geographic Grid over Beijing Since 1984

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES LAI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES LAI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from time-series Landsat surface reflectance data using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N Temporal Coverage: 1984 – 2021 Spatial Resolution: 0.000269469º (approximately 30 m) Temporal Resolution: 16 days Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7159052
title: MUSES Leaf Area Index (LAI) 16-Day 30m Geographic Grid over Beijing Since 1984

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES LAI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES LAI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from time-series Landsat surface reflectance data using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N Temporal Coverage: 1984 – 2021 Spatial Resolution: 0.000269469º (approximately 30 m) Temporal Resolution: 16 days Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).