cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13738077
title: Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)

description: This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the preprint article - Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.

####Code####

Source code from the above github project :  ML4SicilyTsunami-NPJ2024.tar.gz

The post-processed numpy dat files for the two test location of Catania(CT) and Siracusa(SR) are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):

####Model Checkpoints#### ML Model checkpoints from all training experiments.- outSR.tar.gz (Siracusa)- outCT.tar.gz (Catania)

####Training Datasets####- t.tar.gz: Offshore waveforms for gauge sites in a 3D numpy file- dZ.tar.gz: Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids- d.tar.gz: Maximum inundation depth in 2D numpy files or reduced 1D flat files

####Flood Mask####- Identifies floodable and non-floodable areas in the domain. - zero_mask_SR_961.npy.gz - zero_mask_CT_892.npy.gz

####Flood Indices####Links between 2D and 1D numpy files:- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:  - zero_indices_SR_961.npy.gz  - zero_indices_CT_892.npy.gz- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:  - lat_lon_idx_CT_892.npy.gz  - lat_lon_idx_SR_961.npy.gz

The filenames follow nomenclature representing {parameter}_{site}_{size}



The first var represents the parameter of the file.



d - 2 dimensional file for inundation depth (events x m x n)

dZ - 2 dimensional file for local deformation( events x m x n)

dflat - 1 dimensional flat file for inundation ( events x locations)

dZflat - 1 dimensional flat file for local deformation( events x locations)

t - 3 dimension offshore waveform for (events x gauges x timesteps)

lat_lon_idx - index file for matching lat long coordinate with location indices(locations x lat x lon)



The second var represents site of the file.



CT - Catania

SR - Siracusa



The third var represents number of events in the file or the selection size.



892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each




More information on the attached readme.
DOI: 10.15468/dl.7batna
title: Occurrence Download

description: A dataset containing 1266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera morrowii A.Gray",
 "Geometry POLYGON((-105 39, -79 39, -79 57, -105 57, -105 39))"
 ]
}

 The dataset includes 1266 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020881-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bqwzgc
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.74514 41.66863,-6.25835 41.66863,-6.25835 44.09408,-9.74514 44.09408,-9.74514 41.66863))",
 "HasGeospatialIssue is false",
 "TaxonKey is Cauchas rufimitrella (Scopoli, 1763)"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020882-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13741283
title: Processed dataset used for developing tsunami inundation emulators(Part-II Test Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed datasets covering testing datasets covering tsunami inputs and outputs (for parameters of offshore waveforms, local deformation fields and inundation depths) used in the evaluation of machine learning emulator discussed in the preprint article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effect with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.


The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz) typically stored in data/processed:
-test_dZ.tar.gz (local deformation files)

-test_d.tar.gz (inundation depth files)

-test_t.tar.gz (offshore waveform files)

The filename follow nomenclature as below

d_CT_0.dat, dZ_CT_0.dat, dflat_CT_0.dat, dZflat_CT_0.dat, t_CT_0.dat,lat_lon_idx_CT_892.npy where the file names represent {parameter}_{site}_{size}



The first var represent parameter of the file.



d - 2 dimensional file for inundation depth (events x m x n)

dZ - 2 dimensional file for local deformation( events x m x n)

dflat - 1 dimensional flat file for inundation ( events x locations)

dZflat - 1 dimensional flat file for local deformation( events x locations)

t - 3 dimension offshore waveform for (events x gauges x timesteps)

lat_lon_idx - index file for matching lat long coordinate with location indices(locations x lat x lon)



The second var represents site of the file.



CT - Catania

SR - Siracusa



The third var represents number of events in the file or the selection size.



0,1,2,3 - represent the approx 50000 test events divided into 4 splits




More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators
DOI: 10.5281/zenodo.13741284
title: Processed dataset used for developing tsunami inundation emulators(Part-II Test Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed datasets covering testing datasets covering tsunami inputs and outputs (for parameters of offshore waveforms, local deformation fields and inundation depths) used in the evaluation of machine learning emulator discussed in the preprint article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effect with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.


The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz) typically stored in data/processed:
-test_dZ.tar.gz (local deformation files)

-test_d.tar.gz (inundation depth files)

-test_t.tar.gz (offshore waveform files)

The filename follow nomenclature as below

d_CT_0.dat, dZ_CT_0.dat, dflat_CT_0.dat, dZflat_CT_0.dat, t_CT_0.dat,lat_lon_idx_CT_892.npy where the file names represent {parameter}_{site}_{size}



The first var represent parameter of the file.



d - 2 dimensional file for inundation depth (events x m x n)

dZ - 2 dimensional file for local deformation( events x m x n)

dflat - 1 dimensional flat file for inundation ( events x locations)

dZflat - 1 dimensional flat file for local deformation( events x locations)

t - 3 dimension offshore waveform for (events x gauges x timesteps)

lat_lon_idx - index file for matching lat long coordinate with location indices(locations x lat x lon)



The second var represents site of the file.



CT - Catania

SR - Siracusa



The third var represents number of events in the file or the selection size.



0,1,2,3 - represent the approx 50000 test events divided into 4 splits




More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators
DOI: 10.15468/dl.v9xwx6
title: Occurrence Download

description: A dataset containing 8318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Cereus pedunculatus (Pennant, 1777)"
 ]
}

 The dataset includes 8318 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020883-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sfjbzh
title: Occurrence Download

description: A dataset containing 199 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tabebuia rosea (Bertol.) Bertero ex A.DC.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 199 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020886-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwtm8j
title: Occurrence Download

description: A dataset containing 5827 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synaphobranchus kaupii Johnson, 1862",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5827 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020884-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whfhyy
title: Occurrence Download

description: A dataset containing 5908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Frangula alnus Mill.",
 "Geometry POLYGON((-105 39, -79 39, -79 57, -105 57, -105 39))"
 ]
}

 The dataset includes 5908 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020887-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13741057
title: Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.


The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):


Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha

    -pthaSR.tar.gz - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))

    -pthaCT.tar.gz - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))


The filename follows the nomenclature with:
-true - simulation results

-pred - emulator with pretraining

-pred_direct - emulator without pretraining

-pred_nodeform - emulator without pertaining and deformation input





More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators
DOI: 10.5281/zenodo.13741058
title: Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.


The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):


Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha

    -pthaSR.tar.gz - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))

    -pthaCT.tar.gz - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))


The filename follows the nomenclature with:
-true - simulation results

-pred - emulator with pretraining

-pred_direct - emulator without pretraining

-pred_nodeform - emulator without pertaining and deformation input





More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators
DOI: 10.15468/dl.v3cmfn
title: Occurrence Download

description: A dataset containing 419433 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-54.58975 -9.06501,-47.52841 -9.06501,-47.52841 -2.60014,-54.58975 -2.60014,-54.58975 -9.06501))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 419433 records from 542 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020850-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14258985
title: Processed files used for OQ based tsunami loss modelling using HPC based inundation and  emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.

 

The risk calculation and procedure is available in the main repo: https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk 

 


The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):


Processed hazard information used to prepare and run OQ event based risk analysis are as below,

    -hazard.tar.gz - preliminary numpy files with sitcol, eventid and hazard magnitude info

    -loss.tar.gz -final hdf5 files with both event hazard and site info


The filename follows the nomenclature with:


ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5





More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators

https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk

https://github.com/naveenragur/OQ-Tsunami
DOI: 10.5281/zenodo.14258986
title: Processed files used for OQ based tsunami loss modelling using HPC based inundation and  emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)

description: This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078


This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.

 

The risk calculation and procedure is available in the main repo: https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk 

 


The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):


Processed hazard information used to prepare and run OQ event based risk analysis are as below,

    -hazard.tar.gz - preliminary numpy files with sitcol, eventid and hazard magnitude info

    -loss.tar.gz -final hdf5 files with both event hazard and site info


The filename follows the nomenclature with:


ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5





More information on the attached readme, see project structure and code is available at:

https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators

https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk

https://github.com/naveenragur/OQ-Tsunami
DOI: 10.6084/m9.figshare.22595572
title: A scoping review and analysis of a series of country experiences to inform the Chilean health financing reform

description: Supplementary material.
DOI: 10.6084/m9.figshare.22595572.v1
title: A scoping review and analysis of a series of country experiences to inform the Chilean health financing reform

description: Supplementary material.
DOI: 10.15468/dl.4gv29u
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "GadmGid is CHN.28_1",
 "TaxonKey is Ibidorhyncha struthersii Vigors, 1832"
 ]
}

 The dataset includes 39 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020888-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ge974v
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tamarindus indica L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 39 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020890-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.py853k
title: Occurrence Download

description: A dataset containing 1697 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera tatarica L.",
 "Geometry POLYGON((-105 39, -79 39, -79 57, -105 57, -105 39))"
 ]
}

 The dataset includes 1697 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020891-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ux4v4
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.71889 -16.5321,-71.60678 -17.33726,-71.20517 -16.54389,-71.70508 -16.16135,-72.71889 -16.5321))",
 "HasGeospatialIssue is false",
 "TaxonKey is Xenodacnis parina Cabanis, 1873"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020892-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqpf2p
title: Occurrence Download

description: A dataset containing 24920 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aegilops triuncialis L."
 ]
}

 The dataset includes 24920 records from 396 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020889-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.semygn
title: Occurrence Download

description: A dataset containing 1064 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xenodermichthys copei (Gill, 1884)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1064 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020893-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nhn7g4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tamarix aphylla (L.) H.Karst.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020894-241126133413365/datasets/export for details.
DOI: 10.15468/dl.vq7wsq
title: Occurrence Download

description: A dataset containing 5991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Material citation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Primula pedemontana Gaudin"
 ]
}

 The dataset includes 5991 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020896-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.