cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13218634
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_122-182)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 122-182). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 183-244).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-05-01 ~ 2012-06-30

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.5281/zenodo.13218635
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_122-182)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 122-182). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 183-244).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-05-01 ~ 2012-06-30

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.5281/zenodo.10878384
title: FloodSformer: Dam-Break datasets&checkpoints

description: Data used for the paper: Pianforini et al. (2024). Real-Time Flood Maps Forecasting for Dam-Break Scenarios with a Transformer-Based Deep Learning Model. Journal of Hydrology, 635, 131169. https://doi.org/10.1016/j.jhydrol.2024.131169

 





The repository contains the training/testing datasets and the checkpoints for two test cases:

1.   Dam-break in a channel with a parabolic cross section

2.   Dam-break in a rectangular tank

The data for the third test case are unavailable due to restrictions on data permissions.


More detailed information on the repository content are provided in the README.md file.



 

Acknowledgements

This research was granted by University of Parma through the action “Bando di Ateneo 2022 per la ricerca” co-funded by MUR-Italian Ministry of Universities and Research - D.M. 737/2021 - PNR - PNRR – NextGenerationEU.

Renato Vacondio and Susanna Dazzi acknowledge financial support from the PNRR MUR project ECS_00000033_ECOSISTER.
DOI: 10.5281/zenodo.13218547
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_061-121)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 061-121). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 122-182).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-03-01 ~ 2012-04-30

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.5281/zenodo.13218548
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_061-121)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 061-121). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 122-182).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-03-01 ~ 2012-04-30

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.6084/m9.figshare.24628533
title: AI-based fault recognition and classification in the IEEE 9-bus system interconnected to PV systems

description: PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach.
DOI: 10.6084/m9.figshare.24628533.v1
title: AI-based fault recognition and classification in the IEEE 9-bus system interconnected to PV systems

description: PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach.
DOI: 10.5281/zenodo.13218475
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_001-060)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 001-060). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 061-121).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-01-01 ~ 2012-02-29

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.5281/zenodo.13218476
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2012_001-060)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2012 (DOY: 001-060). Please click here to download the ELITE Daily SLUR product in 2012 (DOY: 061-121).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2012-01-01 ~ 2012-02-29

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.15468/dl.z6q2jh
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Collection Porifera SMF",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetractinellida"
 ]
}

 The dataset includes 102 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048685-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g79sn2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.48791 -32.719578, 149.48791 -32.721823, 149.492401 -32.721823, 149.492401 -32.728561, 149.496893 -32.728561, 149.496893 -32.721823, 149.494647 -32.721823, 149.494647 -32.719578, 149.48791 -32.719578))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048686-240626123714530/datasets/export for details.
DOI: 10.15468/dl.sh87yf
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Echinopsis schickendantzii F.A.C.Weber)",
 "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 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048687-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3wheh
title: Occurrence Download

description: A dataset containing 269 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.171252 -36.099505, 149.177989 -36.099505, 149.177989 -36.095013, 149.180235 -36.095013, 149.180235 -36.099505, 149.19371 -36.099505, 149.19371 -36.088276, 149.186973 -36.088276, 149.186973 -36.083784, 149.19371 -36.083784, 149.19371 -36.077047, 149.189218 -36.077047, 149.189218 -36.072555, 149.186973 -36.072555, 149.186973 -36.07031, 149.189218 -36.07031, 149.189218 -36.065818, 149.186973 -36.065818, 149.186973 -36.061326, 149.180235 -36.061326, 149.180235 -36.063572, 149.177989 -36.063572, 149.177989 -36.072555, 149.180235 -36.072555, 149.180235 -36.083784, 149.177989 -36.083784, 149.177989 -36.088276, 149.173498 -36.088276, 149.173498 -36.092768, 149.171252 -36.092768, 149.171252 -36.099505), (149.180235 -36.083784, 149.182481 -36.083784, 149.182481 -36.08603, 149.180235 -36.08603, 149.180235 -36.083784), (149.180235 -36.072555, 149.180235 -36.065818, 149.182481 -36.065818, 149.182481 -36.072555, 149.180235 -36.072555)), ((149.171252 -36.099505, 149.15104 -36.099505, 149.15104 -36.101751, 149.155531 -36.101751, 149.155531 -36.103997, 149.162269 -36.103997, 149.162269 -36.101751, 149.164515 -36.101751, 149.164515 -36.103997, 149.171252 -36.103997, 149.171252 -36.099505)))"
 ]
}

 The dataset includes 269 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048688-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.usewy8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Echinopsis schieliana (Backeb.) D.R.Hunt)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048690-240626123714530/datasets/export for details.
DOI: 10.13120/hetp-v628
title: COLLAPSE -  Cook glacier-Ocean system, sea LeveL and Antarctic Past Stability

description: The COLLAPSE dataset is a comprehensive collection of geophysical measurements and sediment core data collected in the continental margin facing the Cook, Ninnis and Mertz glaciers (East Antarctica), in water depth of 500-3000 meters, 158°E-152°E and 65°S-67°S. The aim is to reconstruct the modalities of the glaciers advance, and retreat during the glacial and interglacial cycles and past depositional processes. The data set includes 229.79 km of multichannel seismic profiles on March 17th and 18th 2022. (see attached table with details). In the navigation files, Latitude and Longitude are expressed in geographical coordinates and refer to the position of the GPS antenna on the Bassi. The "navigation" for the seismic was managed with the PDS 2000 Software (version 3.8.1.2), capable of generating the Trigger for the Gun Controller and SERCEL system. The projection used is UTM 55 S.
DOI: 10.15468/dl.cwn9sx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.669819 -32.719578, 149.669819 -32.721823, 149.672065 -32.721823, 149.672065 -32.719578, 149.669819 -32.719578))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048691-240626123714530/datasets/export for details.
DOI: 10.15468/dl.2uwcrh
title: Occurrence Download

description: A dataset containing 59355 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Myanmar",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Bryophyta, Tracheophyta)"
 ]
}

 The dataset includes 59355 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048689-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nnddmw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Echinopsis schreiteri (A.Cast.) Werderm.)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048692-240626123714530/datasets/export for details.
DOI: 10.15468/dl.x6ekpm
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.013039 -36.101751, 147.013039 -36.106242, 147.017531 -36.106242, 147.017531 -36.101751, 147.013039 -36.101751))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048693-240626123714530/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.26495830
title: Marasmiaceae MAT: Phylogenetics

description: Phylogenetics of Marasmiaceae, Omphalotaceae, and Physalacriaceae where the sexual compatibility system is known.
DOI: 10.6084/m9.figshare.26495830.v1
title: Marasmiaceae MAT: Phylogenetics

description: Phylogenetics of Marasmiaceae, Omphalotaceae, and Physalacriaceae where the sexual compatibility system is known.