cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13981207
title: Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence

description: This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model, 2024 (submitted to Artificial Intelligence for the Earth Systems, preprint available at https://arxiv.org/abs/2409.20087).
description: We introduce three ML models in the paper, namely SALAMA0D, SALAMA1D-2021, and SALAMA1D-2022. For all three models, this repository provides the following HDF5 files:



training: dataset for training

validation: dataset for monitoring training progress


The repository also provides data sets for testing and evaluating skill scores, namely:



test: dataset for testing and evaluating skill scores

test_00: dataset with forecasts of a fixed lead time 0h

test_01: dataset with forecasts of a fixed lead time 1h

...

test_11: dataset with forecasts of a fixed lead time 11h


SALAMA1D-2021 and SALAMA1D-2022 have been evaluated on the same test sets; therefore, the repository provides only one set of data sets for testing for the SALAMA1D models.

Each file contains the following datasets:



predictors: 400,000 (training) or 100,000 (validation, test) samples of  atmospheric variables, obtained from operational forecasts of the ICON-D2-EPS weather model and evaluated for a random grid point in Central Europe.

labels: The corresponding piece of information whether any stroke of lightning occured in the neighborhood of the grid point.


Additional attributes provide information on field names, units etc.

Furthermore, to offer the possibility to reproduce the bootstrap resampling with day-wise blocking from the paper, we provide a 1D-NumPy array (saved as .npy) for each test set, called "bootstrap_test.npy" or f"bootstrap_test_lead{LEAD:02d}.npy", LEAD=0,1,...,11. The length of each array corresponds to the number of samples of the corresponding data set. The array assigns an integer, the block index, to each sample. Each block index stands for a random test day (even days of July-August 2023). Samples with the same block index originate from the same test day. The bootstrap arrays are valid for the SALAMA0D and SALAMA1D test sets simultaneously as the test sets have been sampled from the same grid points and times.

Data sources:



predictors: Deutscher Wetterdienst

labels: nowcast GmbH
DOI: 10.5281/zenodo.13981208
title: Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence

description: This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model, 2024 (submitted to Artificial Intelligence for the Earth Systems, preprint available at https://arxiv.org/abs/2409.20087).
description: The repository contains datasets for all three ML models presented in the paper. For each model, the following HDF5 files are provided:



training: datasets for training

test: datasets for testing and evaluating skill scores

validation: datasets for monitoring training progress

test_00: datasets with forecasts of a fixed lead time 0h

test_01: datasets with forecasts of a fixed lead time 1h

...

test_11: datasets with forecasts of a fixed lead time 11h


Each file contains the following datasets:



predictors: 400,000 class-balanced atmospheric variables, obtained from operational forecasts of the ICON-D2-EPS weather model and evaluated for a random grid point in Central Europe.

labels: The corresponding piece of information whether any stroke of lightning occured in the neighborhood of the grid point.


Additional attributes provide information on field names, units etc.

Data sources:



predictors: Deutscher Wetterdienst

labels: nowcast GmbH
DOI: 10.5281/zenodo.15284879
title: Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence

description: This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model, 2024 (submitted to Artificial Intelligence for the Earth Systems, preprint available at https://arxiv.org/abs/2409.20087).
description: We introduce three ML models in the paper, namely SALAMA0D, SALAMA1D-2021, and SALAMA1D-2022. For all three models, this repository provides the following HDF5 files:



training: dataset for training

validation: dataset for monitoring training progress


The repository also provides data sets for testing and evaluating skill scores, namely:



test: dataset for testing and evaluating skill scores

test_00: dataset with forecasts of a fixed lead time 0h

test_01: dataset with forecasts of a fixed lead time 1h

...

test_11: dataset with forecasts of a fixed lead time 11h


SALAMA1D-2021 and SALAMA1D-2022 have been evaluated on the same test sets; therefore, the repository provides only one set of data sets for testing for the SALAMA1D models.

Each file contains the following datasets:



predictors: 400,000 (training) or 100,000 (validation, test) samples of  atmospheric variables, obtained from operational forecasts of the ICON-D2-EPS weather model and evaluated for a random grid point in Central Europe.

labels: The corresponding piece of information whether any stroke of lightning occured in the neighborhood of the grid point.


Additional attributes provide information on field names, units etc.

Furthermore, to offer the possibility to reproduce the bootstrap resampling with day-wise blocking from the paper, we provide a 1D-NumPy array (saved as .npy) for each test set, called "bootstrap_test.npy" or f"bootstrap_test_lead{LEAD:02d}.npy", LEAD=0,1,...,11. The length of each array corresponds to the number of samples of the corresponding data set. The array assigns an integer, the block index, to each sample. Each block index stands for a random test day (even days of July-August 2023). Samples with the same block index originate from the same test day. The bootstrap arrays are valid for the SALAMA0D and SALAMA1D test sets simultaneously as the test sets have been sampled from the same grid points and times.

Data sources:



predictors: Deutscher Wetterdienst

labels: nowcast GmbH
DOI: 10.15468/dl.npcj4r
title: Occurrence Download

description: A dataset containing 798014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cephalophus nigrifrons Gray, 1871, Cephalophus dorsalis Gray, 1846, Cephalophus silvicultor (Afzelius, 1815), Corynebacterium sepedonicum (Spieckermann & Kotthoff, 1914) Skaptason & Burkholder, 1942, Scopiastes perplexus Slater Alex, 1985, Melanagromyza aeneoventris (Fallén, 1823), Tecia solanivora (Povolný, 1973), Limonius californicus (Mannerheim, 1843), Gryllotalpa gryllotalpa (Linnaeus, 1758), Pectobacterium parmentieri Khayi et al., 2016, Globodera ellingtonae, Pectobacterium brasiliense A, Dickeya solani Wolf et al., 2014, Dickeya zeae Samson et al., 2005, Dickeya dianthicola Samson et al., 2005, Trichocera maculipennis Meigen, 1818, Systena frontalis (Fabricius, 1801), Hystrix indica Kerr, 1792, Drosophila melanogaster Meigen, 1830, Scutellonema bradys (Steiner & LeHew, 1933), Merlinius brevidens (Allen, 1955) Siddiqi, 1970, Trichinella spiralis (Owen, 1835) Railliet, 1895, Symmetrischema tangolias (Gyen, 1913), Apethistis insulsa (Meyrick, 1914), Gyrtothripa papuana (Hampson, 1912), Chrysodeixis acuta (Walker), Toya nigra (Crawford, 1914), Aconophora albipennis Dietrich, Empoascanara smuga Dworakowska, 1979, Jacobiasca jamkandensis (Ahmed, Samad & Naheed, 1981), Eupteryx tuberculata Samad & Ahmed, 1979, Chichahua russeola Young, 1977, Alebroides nigroscutellatus (Distant, 1918), Jassargus flori (Fieber, 1869), Hebata distinguenda (Paoli, 1932), Paratanus yusti Young, 1957, Poliocitellus franklinii (Sabine, 1822), Threskiornis molucca (Cuvier, 1829), Zagrammosoma latilineatum Ubaidillah, 2000, Trichogramma lopezandinense Sarmiento, 1993, Dineulophus phthorimaeae De Santis, 1985, Chrysocharis bedius (Walker, 1842), Elasmus funereus Riek, 1967, Metaphycus swirskii Annecke & Mynhardt, 1979, Leptomastix epona (Walker, 1844), Coccophagus berzeliae Annecke & Insley, 1974, Spalangia oviceps Boucek, 1965, Trichomalopsis americana (Gahan, 1933), Coccophagus varius (Silvestri, 1915), Coccophagus capensis Compere, 1931)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 798014 records from 877 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016965-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drwtpe
title: Occurrence Download

description: A dataset containing 3319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Zambia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ceryle rudis (Linnaeus, 1758)"
 ]
}

 The dataset includes 3319 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0273799-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5tw4zk
title: Occurrence Download

description: A dataset containing 3092 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus brevifolioides Thieret & Delahouss."
 ]
}

 The dataset includes 3092 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002271-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.28vt8m
title: Occurrence Download

description: A dataset containing 44782 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Veronica spicata L."
 ]
}

 The dataset includes 44782 records from 563 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029933-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxd5yr
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cotoneaster astrophoros J.Fryer & E.C.Nelson"
 ]
}

 The dataset includes 17 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0273800-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2mtxep
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus kilianii (Muasya & D.A.Simpson) Lye"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002275-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yaub6h
title: Occurrence Download

description: A dataset containing 339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Macao, Hong Kong)",
 "TaxonKey is Sterna dougallii Montagu, 1813"
 ]
}

 The dataset includes 339 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0282285-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fuyuaz
title: Occurrence Download

description: A dataset containing 1618 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hauya Moc. & Sessé ex DC."
 ]
}

 The dataset includes 1618 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002276-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hymuq7
title: Occurrence Download

description: A dataset containing 218 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Umbilicus oppositifolius Ledeb.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 218 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067271-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5xu7a
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ricinulei",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0273787-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8w3mzu
title: Occurrence Download

description: A dataset containing 452526 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Jacobaea vulgaris Gaertn."
 ]
}

 The dataset includes 452526 records from 1790 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029931-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wp3cp7
title: Occurrence Download

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heritiera javanica (Blume) Kosterm."
 ]
}

 The dataset includes 342 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007413-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.quq2u6
title: Occurrence Download

description: A dataset containing 5806 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heteropogon triticeus (R.Br.) Stapf ex Craib"
 ]
}

 The dataset includes 5806 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007414-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q4qnzx
title: Occurrence Download

description: A dataset containing 1687981 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rosa L."
 ]
}

 The dataset includes 1687981 records from 3305 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0245727-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1375454.10fu
title: SH1375454.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375455.10fu
title: SH1375455.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375456.10fu
title: SH1375456.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375457.10fu
title: SH1375457.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375461.10fu
title: SH1375461.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375462.10fu
title: SH1375462.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1375464.10fu
title: SH1375464.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.