cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7565489
title: Ulex (Ulex europaeus) UAV imagery and reference data (raw)

description: This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =4) of Ulex europaeus acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Ulex cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Ulex canopies are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publication: Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73. https://doi.org/10.1016/j.rse.2019.03.025 https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166
DOI: 10.5281/zenodo.7565500
title: Pinus (Pinus radiata) UAV imagery and reference data (raw)

description: This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =4) of Pinus radiata acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Pinus cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Pinus canopies are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publication: Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73. https://doi.org/10.1016/j.rse.2019.03.025 https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166
DOI: 10.5281/zenodo.7565499
title: Pinus (Pinus radiata) UAV imagery and reference data (raw)

description: This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =4) of Pinus radiata acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Pinus cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Pinus canopies are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publication: Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73. https://doi.org/10.1016/j.rse.2019.03.025 https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166
DOI: 10.5281/zenodo.7584492
title: Kolakovic-Supplementary_Materials

description: Input data set and results of study: Estimation of large river designed-floods using the peaks-over-threshold (POT) method.
DOI: 10.5281/zenodo.7584491
title: Kolakovic-Supplementary_Materials

description: Input data set and results of study: Estimation of large river designed-floods using the peaks-over-threshold (POT) method.
DOI: 10.5281/zenodo.6389957
title: Dataset for an ML model to predict diffusivity in ZIF variants

description: This dataset accompanies the publication titled "Data Mining for Predicting Gas Diffusivity in Zeolitic-imidazolate Frameworks (ZIFs)" All ZIFs in pdb format can be found in: <pre>10.5281/zenodo.7584374</pre>
DOI: 10.5281/zenodo.7565591
title: Knotweed (Reynoutria japonica) UAV imagery and reference data (raw)

description: This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =3) of Reynoutria japonica (Knotweed) acquired in 2021 in Germany. The resolution (ground sampling distance) of the orthomosaics is below 1 cm. The orthomosaics are labelled (polygon shapefiles) in terms of Knotweedcover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Knotweedcanopies are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publication: Soltani, S., Feilhauer, H., Duker, R., & Kattenborn, T. (2022). Transfer learning from citizen science photographs enables plant species identification in UAVs imagery. <em>ISPRS Open Journal of Photogrammetry and Remote Sensing</em>, 100016. https://doi.org/10.1016/j.ophoto.2022.100016 https://www.sciencedirect.com/science/article/pii/S2667393222000059
DOI: 10.5281/zenodo.7565590
title: Knotweed (Reynoutria japonica) UAV imagery and reference data (raw)

description: This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =3) of Reynoutria japonica (Knotweed) acquired in 2021 in Germany. The resolution (ground sampling distance) of the orthomosaics is below 1 cm. The orthomosaics are labelled (polygon shapefiles) in terms of Knotweedcover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Knotweedcanopies are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publication: Soltani, S., Feilhauer, H., Duker, R., & Kattenborn, T. (2022). Transfer learning from citizen science photographs enables plant species identification in UAVs imagery. <em>ISPRS Open Journal of Photogrammetry and Remote Sensing</em>, 100016. https://doi.org/10.1016/j.ophoto.2022.100016 https://www.sciencedirect.com/science/article/pii/S2667393222000059
DOI: 10.5281/zenodo.7153351
title: Data to reproduce the results: Statistical power of spatial earthquake forecast tests

description: We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".
DOI: 10.5281/zenodo.7153350
title: Data to reproduce the results: Statistical power of spatial earthquake forecast tests

description: We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".
DOI: 10.34740/kaggle/dsv/4919224
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.7579354
title: Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters

description: The input and main output files used for the paper <em><strong>"Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters"</strong></em> are separated in the different tar files depending the MD stage they belong to. <strong>Content</strong> 00_AlphaFold2: The models predicted from AlphaFold2 01_build_system: The parameters for ATP and the initial pdb file used to build each system 02_minimization: Minimization input files for each variant 03_equilibration: Equilibration input files for each variant 04_long_equilibration: ATP restrained equilibration input files for each variant 05_free_equilibration: Free equilibration input files for each variant 06_production: Free production input files for each variant 07_caver: Caver calculations for each variant and replica 08_transport_tools: Analysis of tunnel networks using TransportTools software 09_tunnel_selection: Selection of the tunnels with widest bottleneck radius to perform CaverDock experiments 10_caverdock: CaverDock calculations for each variant and for each ligand tested 11_membrane_patch: MD simulations for liquiritigenin and POPC membrane only 12_MD_analysis: Calculations of RMSD, RMSF of each system. Calculation of helical parameters for trans-membrane helices 2, 5, 8 and 11 (not for APO). Calculation of X1 and X2 angles for residue N1331 in each variant (not for APO) 13_US_closed_to_open: Umbrella Sampling simulations to obtain the inward facing (IF) open state of each variant. Not used for PMF analysis. 14_US_opening_energy: Umbrella Sampling simulations to obtain the Potential of Mean Force for the transition from IF-closed to IF-open conformations. 15_US_equilibration: Equilibration input files for WT and F562L variants in IF-open states. 16_US_production: Production input files for WT and F562L variants in IF-open states. 17_US_caver: Caver calculations for WT and F562L variants in IF-open states. 18_US_transport_tools: Analysis of tunnel networks using TransportTools software of WT and F562L variants in IF-open states. 19_US_tunnel_selection: Selection of the tunnels with widest bottleneck radius to perform CaverDock experiments for WT and F562L variants in IF-open states. 20_US_caverdock: CaverDock calculations for WT and F562L variants in IF-open states. 21_US_MD_analysis: Calculations of RMSD, RMSF of each system. Calculation of helical parameters for trans-membrane helices 2, 5, 8 and 11. ABC_Sequences.fasta: Sequences from 1KP analysis
description: This project was funded by the National Science Centre, Poland - grant no. 2017/25/B/NZ1/01307..
DOI: 10.5281/zenodo.7002738
title: Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters

description: The input and main output files used for the paper <em><strong>"Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters"</strong></em> are separated in the different tar files depending the MD stage they belong to. <strong>Content</strong> 00_AlphaFold2: The models predicted from AlphaFold2 01_build_system: The parameters for ATP and the initial pdb file used to build each system 02_minimization: Minimization input files for each variant 03_equilibration: Equilibration input files for each variant 04_long_equilibration: ATP restrained equilibration input files for each variant 05_free_equilibration: Free equilibration input files for each variant 06_production: Free production input files for each variant 07_caver: Caver calculations for each variant and replica 08_transport_tools: Analysis of tunnel networks using TransportTools software 09_tunnel_selection: Selection of the tunnels with widest bottleneck radius to perform CaverDock experiments 10_caverdock: CaverDock calculations for each variant and for each ligand tested 11_membrane_patch: MD simulations for liquiritigenin and POPC membrane only 12_MD_analysis: Calculations of RMSD, RMSF of each system. Calculation of helical parameters for trans-membrane helices 2, 5, 8 and 11 (not for APO). Calculation of X1 and X2 angles for residue N1331 in each variant (not for APO) 13_US_closed_to_open: Umbrella Sampling simulations to obtain the inward facing (IF) open state of each variant. Not used for PMF analysis. 14_US_opening_energy: Umbrella Sampling simulations to obtain the Potential of Mean Force for the transition from IF-closed to IF-open conformations. 15_US_equilibration: Equilibration input files for WT and F562L variants in IF-open states. 16_US_production: Production input files for WT and F562L variants in IF-open states. 17_US_caver: Caver calculations for WT and F562L variants in IF-open states. 18_US_transport_tools: Analysis of tunnel networks using TransportTools software of WT and F562L variants in IF-open states. 19_US_tunnel_selection: Selection of the tunnels with widest bottleneck radius to perform CaverDock experiments for WT and F562L variants in IF-open states. 20_US_caverdock: CaverDock calculations for WT and F562L variants in IF-open states. 21_US_MD_analysis: Calculations of RMSD, RMSF of each system. Calculation of helical parameters for trans-membrane helices 2, 5, 8 and 11. ABC_Sequences.fasta: Sequences from 1KP analysis
description: This project was funded by the National Science Centre, Poland - grant no. 2017/25/B/NZ1/01307..
DOI: 10.6084/m9.figshare.21975869
title: Data for Wright, Elsden - 2023 - Resonant Fast-Alfven wave coupling in a 3-D Coronal Arcade

description: IDL save file containing slices of the data used to make the publication figures. Use the 'restore' command to read in the data.
DOI: 10.6084/m9.figshare.21975869.v1
title: Data for Wright, Elsden - 2023 - Resonant Fast-Alfven wave coupling in a 3-D Coronal Arcade

description: IDL save file containing slices of the data used to make the publication figures. Use the 'restore' command to read in the data.
DOI: 10.6084/m9.figshare.21975872
title: Peptide

description: Implantology biomatrials
DOI: 10.6084/m9.figshare.21975872.v1
title: Peptide

description: Implantology biomatrials
DOI: 10.5281/zenodo.7583707
title: Numerical Investigation of Interface Passivation Strategies for Sb2Se3/CdS Solar Cells

description: Sb<sub>2</sub>Se<sub>3</sub> is an emerging earth-abundant material praised for its promising optoelectronic properties, although the presence of interfacial defects at the vicinity of the p–n junction limit its performance as photovoltaic absorber. Using a device modeling approach and a realistic set of material parameters, it unravels pathways mitigating the impact of interfacial defects with a baseline Sb<sub>2</sub>Se<sub>3</sub>/CdS. Two straightforward strategies are devised and tested against the baseline. First, a thin front surface sulfurization of the Sb<sub>2</sub>Se<sub>3</sub> absorber allowing a local lowering of the valence band and creating a “front surface field,” resulting in an increased carrier selectivity and limiting the density of holes available for interface recombination, leading to a significant efficiency improvement for optimized conditions. Second, the use of an ultrathin insulating Al<sub>2</sub>O<sub>3</sub> layer between the absorber and the buffer layer is considered, helping in preventing detrimental chemical interdiffusion at the junction. This strategy provides a direct interface passivation, though the interlayer thickness needs a fine tuning to balance the benefits of reduced interface recombination and a detrimental Al<sub>2</sub>O<sub>3</sub> low-conductivity layer. In each case, an analysis covering a broad range of parameters is presented, and conclusions are made in the frame of past numerical and experimental results.
DOI: 10.5281/zenodo.7583706
title: Numerical Investigation of Interface Passivation Strategies for Sb2Se3/CdS Solar Cells

description: Sb<sub>2</sub>Se<sub>3</sub> is an emerging earth-abundant material praised for its promising optoelectronic properties, although the presence of interfacial defects at the vicinity of the p–n junction limit its performance as photovoltaic absorber. Using a device modeling approach and a realistic set of material parameters, it unravels pathways mitigating the impact of interfacial defects with a baseline Sb<sub>2</sub>Se<sub>3</sub>/CdS. Two straightforward strategies are devised and tested against the baseline. First, a thin front surface sulfurization of the Sb<sub>2</sub>Se<sub>3</sub> absorber allowing a local lowering of the valence band and creating a “front surface field,” resulting in an increased carrier selectivity and limiting the density of holes available for interface recombination, leading to a significant efficiency improvement for optimized conditions. Second, the use of an ultrathin insulating Al<sub>2</sub>O<sub>3</sub> layer between the absorber and the buffer layer is considered, helping in preventing detrimental chemical interdiffusion at the junction. This strategy provides a direct interface passivation, though the interlayer thickness needs a fine tuning to balance the benefits of reduced interface recombination and a detrimental Al<sub>2</sub>O<sub>3</sub> low-conductivity layer. In each case, an analysis covering a broad range of parameters is presented, and conclusions are made in the frame of past numerical and experimental results.
DOI: 10.6084/m9.figshare.21975878
title: ERnet datasets

description: supporting datasets of ERnet construction and characterisation
DOI: 10.6084/m9.figshare.21975773
title: LMMS-SP ALBERT-XXLARGE

description: Includes SP-USM and SP-WSD variants, at sensekey and synset level, and SVD-300d version.
DOI: 10.6084/m9.figshare.21975773.v1
title: LMMS-SP ALBERT-XXLARGE

description: Includes SP-USM and SP-WSD variants, at sensekey and synset level, and SVD-300d version.
DOI: 10.6084/m9.figshare.21975563
title: RawData - Hypoxia-inducible factor-2alpha affects the MEK/ERK signaling pathway via primary cilia in connection with the intraflagellar transport protein 88 homolog

description: Data that support the findings of this study
DOI: 10.6084/m9.figshare.21975563.v1
title: RawData - Hypoxia-inducible factor-2alpha affects the MEK/ERK signaling pathway via primary cilia in connection with the intraflagellar transport protein 88 homolog

description: Data that support the findings of this study
DOI: 10.5281/zenodo.7582808
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2007

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 global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product 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. This dataset is the MUSES LAI product in 2007. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2006</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2008</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2007 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal 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, <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, 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>. (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.7582807
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2007

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 global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product 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. This dataset is the MUSES LAI product in 2007. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2006</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2008</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2007 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal 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, <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, 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>. (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).