cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13149389
title: Binary black-hole simulation SXS:BBH:1719

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13149390
title: Binary black-hole simulation SXS:BBH:1719

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.5xydau
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Triplaris longifolia Huber)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044072-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26425042
title: Annotation of prophage used to characterise the interaction with pneumococcal competence.

description: Prophage annotations for the analysis of the interactions between prophage and pneumococcal competence.
DOI: 10.6084/m9.figshare.26425042.v1
title: Annotation of prophage used to characterise the interaction with pneumococcal competence.

description: Prophage annotations for the analysis of the interactions between prophage and pneumococcal competence.
DOI: 10.15468/dl.yvddu3
title: Occurrence Download

description: A dataset containing 506 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Triplaris melaenodendron (Bertol.) Standl. & Steyerm.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 506 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044073-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12934520
title: Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains a sample of the input data for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).

This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

This sample dataset also includes files relative to metadata, static data, normalization, and plotting.

To use the data, clone the corresponding repository and unzip this zip file in the data folder.
DOI: 10.5281/zenodo.12934521
title: Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains a sample of the input data for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).

This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

This sample dataset also includes files relative to metadata, static data, normalization, and plotting.

To use the data, clone the corresponding repository and unzip this zip file in the data folder.
DOI: 10.15468/dl.cmuwaa
title: Occurrence Download

description: A dataset listing the 4017473 species recorded in GBIF matching the query:
{ }

 The dataset's 4017473 records were derived from 75277 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044059-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12945065
title: 2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 7/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945066
title: 2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 7/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.13149403
title: Binary black-hole simulation SXS:BBH:1594

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13149404
title: Binary black-hole simulation SXS:BBH:1594

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.12945057
title: 2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 6/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945058
title: 2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 6/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945049
title: 2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 5/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945050
title: 2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 5/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945039
title: 2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 4/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945040
title: 2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 4/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945027
title: 2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 3/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945028
title: 2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 3/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945013
title: 2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 2/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12945014
title: 2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 2/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.18150/2tk6rb
title: Geometrical selection of GaN nanowires grown by plas-ma-assisted MBE on polycrystalline ZrN layers

description: The results contained in the repository show that unidirectional supply of material fluxes in molecular beam epitaxy (MBE) together with the shadow effect favor growth of well-oriented NWs even despite random orientation of grains to which the NWs are epitaxially linked [1]. We propose a geometrical model describing an impact of MBE chamber geometry on the orientation of the NWs. Specifically, we calculated effective area of the nanowire’s top facet Seff that is in the line of sight of an effusion cell as a function of nanowire tilt relative to substrate normal (α) and substrate rotation angle (ω). The Seff value is equivalent to the number of atoms incorporated on the NW top facet, which controls axial growth rate of the NW if adatom surface diffusion from the NW sidewalls is negligible, as assumed in the model. For example, this is the case of metal-limited MBE growth of nitride nanowires. As expected, the maximum of Seff corresponds to the NW facing directly in the direction of the Ga effusion cell (α = 40º, ω = 0º). However, during rotation of the substrate the fastest growing nanowires are those perpendicular to the substrate. To verify the model we grew GaN NWs on ZrN polycrystalline buffers. Without substrate rotation the NWs grew uniformly arranged at α = 40º, ω = 0º, exactly as predicted by our model. However, standard growth with rotation of the substrate led to densely packed NWs with significantly limited tilt dispersion. We attribute this behavior to geometrical selection of NWs in densely packed NW array via the shadowing of strongly tilted NWs by preferentially grown vertical ones. A description of the results can be found in the file "readme.txt".
DOI: 10.5281/zenodo.12944959
title: 2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 1/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.12944960
title: 2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

description: This repository contains part 1/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". 

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.
DOI: 10.5281/zenodo.13149316
title: ICM data for Maitri, Antarctica

description: ICM data for the Indian Antarctic station Maitri, for the event on 13 April 2017.
DOI: 10.5281/zenodo.13149317
title: ICM data for Maitri, Antarctica

description: ICM data for the Indian Antarctic station Maitri, for the event on 13 April 2017.
DOI: 10.15468/dl.peadnf
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Triplaris peruviana Fisch. & C.A.Mey. ex C.A.Mey.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 20 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044074-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6092/unibo/amsacta/7813
title: Data – Calibration of aseptic loosening simulation for coatings osteoinductive effect

description: The dataset contains a comprehensive set of input and output parameters, featuring 43 distinct parameter combinations utilised in the finite element (FE) analysis reported in the linked paper (DOI: 10.1007/s10439-024-03588-9). It includes measures from the in vivo experiment, such as initial Bone-to-Implant Contact (BIC) percentage and gap values for cylindrical titanium alloys implanted in rabbits’ tibiae, the different maximum bridgeable gap values used for model calibration, and the final BIC% obtained as a FE analysis output. Moreover, the dataset contains the other parameters used in the simulations within the range of values documented in the literature. Then, the push-out strength in the different scenarios was estimated.