cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10814009
title: Dataset for "Three-dimensional spin-wave dynamics, localization and interference  in a synthetic antiferromagnet"

description: Data availability for the article titled "Three-dimensional spin-wave dynamics, localization and interference in a synthetic antiferromagnet" by Girardi et al. published in Nature Communications.

This data repository contains the following folders and files:•    TR Lamni Raw Data - Co: Files with extension .hdf5 are Time-Resolved Soft X-Ray Laminography Raw Data for all projections acquired to obtain the 3D reconstruction for the CoFeB layer. The projections’ data have been organized in sub-folders for the 7 frames acquired during the measurements. For each frame, a final folder “analysis” contains an aligned_projection.mat file with the angles of all measured projections and the sinogram information.•    TR Lamni Raw Data - Ni: Files with extension .hdf5 are Time-Resolved Soft X-Ray Laminography Raw Data for all projections acquired to obtain the 3D reconstruction for the NiFe layer. The projections’ data have been organized in sub-folders for the 7 frames acquired during the measurements. For each frame, a final folder “analysis” contains an aligned_projection.mat file with the angles of all measured projections and the sinogram information.•    Magnetic reconstruction – Ni edge: contains the .mat files of the 3D magnetic reconstruction obtained for Ni-edge for all 7 frames. In each file, the Mx, My and Mz components of the magnetization associated with the 3D reconstruction of the volume of the sample are present. •    Magnetic reconstruction – Co edge: contains the .mat files of the 3D magnetic reconstruction obtained for Co-edge for all 7 frames. In each file, the Mx, My and Mz components of the magnetization associated with the 3D reconstruction of the volume of the sample are present. •    vtk files: contains the post-processed files for all 7 frames in the .vtk format for the 3D visualization with the Paraview software. In each file, both vectorial and scalar data associated with the 3D reconstruction of the volume of the sample and analyzed in the paper are present.•    Mumax3 simulation dispersion relation: contains .ovf files (containing information about the Mx, My, Mz components of the magnetization as a function of the spatial position) obtained from the mumax3 simulation to study the spin wave dispersion relation of the sample investigated. The total simulation runtime is 10 ns, and each .ovf file corresponds to a frame saved every 0.01 ns.•    Mumax3 simulation 3D interference: contains the .ovf files (containing information about the Mx, My, Mz components of the magnetization as a function of the spatial position) for the static magnetization and 3 frames used to simulate the 3D interference pattern observed experimentally.
DOI: 10.5066/p9q4ho2m
title: Bathymetric and Topographic Surveys at Lucky Peak Lake Intake Structures, Boise County, Idaho, 2023

description: In October 2023, the U.S. Geological Survey Idaho Water Science Center (IDWSC), in cooperation with Lucky Peak Power Plant Project (LPPPP), completed bathymetric and topographic surveys at two dam intake structures using multibeam bathymetry and boat-mounted Light Detection and Ranging (LiDAR). Dam operators indicated a possibility that sediment aggradation was occurring near the intake structures that allow water to pass through the dam. The bathymetric and topographic data generally include complete coverage near the intake structures and banklines near Lucky Peak Dam.
DOI: 10.5281/zenodo.10781300
title: HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020

description: This dataset group comprises subsets of two HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020, as used in the paper “Observations and modeling of areal surface albedo and surface types in the Arctic” by Jäkel et al. (2024, https://doi.org/10.5194/tc-18-1185-2024).

The simulation for 2018 was originally carried out for the period from January to September 2018. It was started from rest with temperature, salinity, ice thickness, and ice concentration fields from ORAS5. The simulation for 2020 was originally carried out for the period 2019-2020 and initialized with fields from 1 January 2019, 00 UTC of an earlier long-term simulation. The prognostic atmospheric fields of the two simulations were nudged to the corresponding ERA5 fields with a vertically uniform nudging time scale of 16.67 h. Sea ice and ocean fields were initialized with fields from an earlier long-term simulation. Further details on the simulations are given in "On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice" by Foth et al. (2023, https://doi.org/10.5194/egusphere-2023-634).
DOI: 10.5281/zenodo.10781301
title: HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020

description: This dataset group comprises subsets of two HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020, as used in the paper “Observations and modeling of areal surface albedo and surface types in the Arctic” by Jäkel et al. (2024, https://doi.org/10.5194/tc-18-1185-2024).

The simulation for 2018 was originally carried out for the period from January to September 2018. It was started from rest with temperature, salinity, ice thickness, and ice concentration fields from ORAS5. The simulation for 2020 was originally carried out for the period 2019-2020 and initialized with fields from 1 January 2019, 00 UTC of an earlier long-term simulation. The prognostic atmospheric fields of the two simulations were nudged to the corresponding ERA5 fields with a vertically uniform nudging time scale of 16.67 h. Sea ice and ocean fields were initialized with fields from an earlier long-term simulation. Further details on the simulations are given in "On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice" by Foth et al. (2023, https://doi.org/10.5194/egusphere-2023-634).
DOI: 10.5281/zenodo.10799114
title: STCGAN: a novel Cycle-Consistent Generative Adversarial Network for Spatial Transcriptomics Cellular Deconvolution
DOI: 10.5281/zenodo.10799113
title: STCGAN: a novel Cycle-Consistent Generative Adversarial Network for Spatial Transcriptomics Cellular Deconvolution
DOI: 10.6084/m9.figshare.25420708
title: Multifaceted Self dataset.xlsx

description: this is the dataset of the study
DOI: 10.6084/m9.figshare.25420708.v1
title: Multifaceted Self dataset.xlsx

description: this is the dataset of the study
DOI: 10.5281/zenodo.10816830
title: Generating high fidelity surface meshes of neocortical neurons using skin modifiers

description: Summary

The dataset contained in this repository is the resulting meshes from applying the Skin Modifier in Blender on all neuronal morphological types (mtypes) identified for comastosensory cortical cells by the Blue Brain Project. 

The papser, Generating high fidelity surface meshes of neocortical neurons using skin modifiers, is published in the 2019 EG Computer Graphics & Visual Computing (CGVC) conference. 

https://doi.org/10.2312/cgvc.20191257https://diglib.eg.org:443/handle/10.2312/cgvc20191257

Paper abstract 

We present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.

BibTex citation



@inproceedings {10.2312:cgvc.20191257,       booktitle = {Computer Graphics and Visual Computing (CGVC)},       editor = {Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.},       title = {{Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers}},       author = {Abdellah, Marwan and Favreau, Cyrille and Hernando, Juan and Lapere, Samuel and Schürmann, Felix},       year = {2019},       publisher = {The Eurographics Association},       ISBN = {978-3-03868-096-3},       DOI = {10.2312/cgvc.20191257}    }
DOI: 10.5281/zenodo.10816831
title: Generating high fidelity surface meshes of neocortical neurons using skin modifiers

description: Summary

The dataset contained in this repository is the resulting meshes from applying the Skin Modifier in Blender on all neuronal morphological types (mtypes) identified for comastosensory cortical cells by the Blue Brain Project. 

The papser, Generating high fidelity surface meshes of neocortical neurons using skin modifiers, is published in the 2019 EG Computer Graphics & Visual Computing (CGVC) conference. 

https://doi.org/10.2312/cgvc.20191257https://diglib.eg.org:443/handle/10.2312/cgvc20191257

Paper abstract 

We present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.

BibTex citation



@inproceedings {10.2312:cgvc.20191257,       booktitle = {Computer Graphics and Visual Computing (CGVC)},       editor = {Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.},       title = {{Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers}},       author = {Abdellah, Marwan and Favreau, Cyrille and Hernando, Juan and Lapere, Samuel and Schürmann, Felix},       year = {2019},       publisher = {The Eurographics Association},       ISBN = {978-3-03868-096-3},       DOI = {10.2312/cgvc.20191257}    }
DOI: 10.25345/c5542jk89
title: MassIVE MSV000094327 - Biofunctionalized dissolvable hydrogel microbeads enable sensitive characterization of native protein complexes
title: Evaluation of the target recovery rate and purification specificity for protein purification using affinity beads
DOI: 10.5281/zenodo.8237173
title: X-ray diffraction dataset for experimental noise filtering

description: X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Each data group contains sequences of 41 consecutive frames, corresponding to a scan along the reciprocal h-direction. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network. The data has been recorded on a La<sub>1.88</sub>Sr<sub>0.12</sub>CuO<sub>4</sub> single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.
description: Additional funding: ONR (N00014-20-1-2352) European Union's Horizon 2020 research and innovation program (ERC-StG-Neupoert-757867-PARATOP) EU framework program for research and innovation Horizon 2020 CALIPSOplus (730872)
DOI: 10.5281/zenodo.10816877
title: X-ray diffraction dataset for experimental noise filtering

description: X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Each data group contains sequences of 41 consecutive frames, corresponding to a scan along the reciprocal h-direction. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network.

Additionally, files containing the trained model weights are included for two different architectures described in the main article (10.1038/s42256-024-00790-1).

The data has been recorded on a La1.88Sr0.12CuO4 single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.
description: Additional funding:
ONR (N00014-20-1-2352)
European Union's Horizon 2020 research and innovation program (ERC-StG-Neupoert-757867-PARATOP)
EU framework program for research and innovation Horizon 2020 CALIPSOplus (730872)
DOI: 10.5281/zenodo.6674076
title: X-ray diffraction dataset for experimental noise filtering

description: X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Each data group contains sequences of 41 consecutive frames, corresponding to a scan along the reciprocal h-direction. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network.

Additionally, files containing the trained model weights are included for two different architectures described in the main article (10.1038/s42256-024-00790-1).

The data has been recorded on a La1.88Sr0.12CuO4 single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.
description: Additional funding:
ONR (N00014-20-1-2352)
European Union's Horizon 2020 research and innovation program (ERC-StG-Neupoert-757867-PARATOP)
EU framework program for research and innovation Horizon 2020 CALIPSOplus (730872)
DOI: 10.5281/zenodo.6674077
title: X-ray diffraction dataset for experimental noise filtering

description: X-ray diffraction data set for the training of noise filtering algorithms. The data set contains more than 200 groups of low/high counting statistics pairs. The exposure times are mostly 2 (21) seconds for low (high) counting data. Every folder in a group consists of 41 consecutive frames with a resolution of 195 x 487 pixels and a bit-depth of 32. The text file "groups.txt" defines the names of the grouped folders containing low/high counting statistics pairs with the corresponding exposure times. The individual groups are separated by blank lines. The data has been recorded on a La<sub>1.88</sub>Sr<sub>0.12</sub>CuO<sub>4</sub> single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.
description: Additional funding: ONR (N00014-20-1-2352) European Union's Horizon 2020 research and innovation program (ERC-StG-Neupoert-757867-PARATOP) EU framework program for research and innovation Horizon 2020 CALIPSOplus (730872)
DOI: 10.5281/zenodo.7851929
title: X-ray diffraction dataset for experimental noise filtering

description: X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network. The data has been recorded on a La<sub>1.88</sub>Sr<sub>0.12</sub>CuO<sub>4</sub> single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.
description: Additional funding: ONR (N00014-20-1-2352) European Union's Horizon 2020 research and innovation program (ERC-StG-Neupoert-757867-PARATOP) EU framework program for research and innovation Horizon 2020 CALIPSOplus (730872)
DOI: 10.5281/zenodo.7919733
title: Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules

description: Related publication: Katarzyna Świderek, Susana Velasco-Lozano, Miquel À. Galmés, Ion Olazabal, Haritz Sardon, Fernando López-Gallego, Vicent Moliner. Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules. Nature Communications 2023, 14  - 1 (3556), doi: 10.1038/s41467-023-39201-1
DOI: 10.5281/zenodo.7919732
title: Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules

description: Related publication: Katarzyna Świderek, Susana Velasco-Lozano, Miquel À. Galmés, Ion Olazabal, Haritz Sardon, Fernando López-Gallego, Vicent Moliner. Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules. Nature Communications 2023, 14  - 1 (3556), doi: 10.1038/s41467-023-39201-1
DOI: 10.5281/zenodo.10817282
title: Spherical harmonic models of the gravity field of the Galilean satellites [Galileo]

description: This archive contains spherical harmonic models of the gravitational potential of the Galilean satellites derived from data collected by the Galileo mission. For all models, the spherical harmonic coefficients are to be used with unnormalized spherical harmonics that exclude the Condon-Shortley phase factor of (-1)^m. The fist line of each file is a header that contains the reference radius (in km), the GM and its uncertainty (in km^3/s^2), and the k2 Love number and its uncertainty (for Io only).

The files in this archive with the asociated references are:



Anderson2001_Io_gravity.sh (Anderson et al. 2001)

Anderson1998_Europa_gravity.sh (Anderson et al. 1998)

Anderson1996_Ganymede_1_gravity.sh (Anderson et al. 1996, encounter 1)

Anderson1996_Ganymede_2_gravity.sh (Anderson et al. 1996, encounter 2)



Anderson2001_Callisto_gravity.sh (Anderson et al. 2001)
DOI: 10.5281/zenodo.10817281
title: Spherical harmonic models of the gravity field of the Galilean satellites [Galileo]

description: This archive contains spherical harmonic models of the gravitational potential of the Galilean satellites derived from data collected by the Galileo mission. For all models, the spherical harmonic coefficients are to be used with unnormalized spherical harmonics that exclude the Condon-Shortley phase factor of (-1)^m. The fist line of each file is a header that contains the reference radius (in km), the GM and its uncertainty (in km^3/s^2), and the k2 Love number and its uncertainty (for Io only).

The files in this archive with the asociated references are:



Anderson2001_Io_gravity.sh (Anderson et al. 2001)

Anderson1998_Europa_gravity.sh (Anderson et al. 1998)

Anderson1996_Ganymede_1_gravity.sh (Anderson et al. 1996, encounter 1)

Anderson1996_Ganymede_2_gravity.sh (Anderson et al. 1996, encounter 2)



Anderson2001_Callisto_gravity.sh (Anderson et al. 2001)
DOI: 10.5281/zenodo.10814297
title: Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover

description: Overview:This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (Duveiller et al. 2018). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (Stengel et al. 2017) and RegCM5 (Giorgi et al. 2023) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.

Contents:

s4t_ESACCI.zip:Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.s4t_RegCM5.zip:Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.Variables:

Cloud Area Fractions:Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.
DOI: 10.6084/m9.figshare.25420779
title: Table S2 from Navigating the Unknown: Assessing Anthropogenic Threats to Beaked Whales, family Ziphiidae

description: Data extracted on concentrations and details of pollution and chemical contaminant studies of beaked whales.
DOI: 10.6084/m9.figshare.25420779.v1
title: Table S2 from Navigating the Unknown: Assessing Anthropogenic Threats to Beaked Whales, family Ziphiidae

description: Data extracted on concentrations and details of pollution and chemical contaminant studies of beaked whales.
DOI: 10.5281/zenodo.10691338
title: Supplementary data - "Rebuilding with sand roses" paper
DOI: 10.5281/zenodo.10817223
title: Supplementary data - "Rebuilding with sand roses" paper
DOI: 10.5281/zenodo.7421246
title: Wikibase knowledge graphs

description: A collection of open source tools, resources and conferences related to Wikibase knowledge graphs (https://github.com/shigapov/wikibase-knowledge-graphs)
DOI: 10.5281/zenodo.7421247
title: Wikibase knowledge graphs

description: A collection of open source tools, resources and conferences related to Wikibase knowledge graphs (https://github.com/shigapov/wikibase-knowledge-graphs)
DOI: 10.48529/0m0t-kj50
title: National Longitudinal Phone Survey 2021-2023
title: NGA_2021-2023_NLPS_v07_M
DOI: 10.48529/5v9w-fy76
title: World Bank Group Country Survey 2023
title: UZB_2023_WBCS_v01_M
DOI: 10.48529/4fyz-hr81
title: World Bank Group Country Survey 2023
title: ROU_2023_WBCS_v01_M