cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.61pkeu
title: Occurrence Download

description: A dataset containing 723142 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Amphibia",
 "TaxonKey is Aves",
 "TaxonKey is Insecta",
 "TaxonKey is Reptilia",
 "TaxonKey is Mammalia",
 "TaxonKey is Chilopoda",
 "TaxonKey is Diplopoda",
 "TaxonKey is Arachnida"
 ]
 },
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation"
 ]
 },
 "HasCoordinate is true",
 "DecimalLongitude is greater than or equal to 120.000000",
 "DecimalLongitude is less than 150.000000",
 "DecimalLatitude is greater than or equal to 30.000000",
 "DecimalLatitude is less than 60.000000",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2018"
 ]
}

 The dataset includes 723142 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034647-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fc8cbc
title: Occurrence Download

description: A dataset containing 739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Ipomoea purpurea (L.) Roth, Ipomoea hederacea Jacq., Ipomoea indica (Burm.) Merr.)"
 ]
}

 The dataset includes 739 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017877-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8b3a2p
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xenodon werneri (Eiselt, 1963)"
 ]
}

 The dataset includes 21 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005910-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hc6wpy
title: Occurrence Download

description: A dataset containing 14152340 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Mexico"
 ]
}

 The dataset includes 14152340 records from 2344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034647-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60613/el000015
title: Bedarfe der Energieforschung: Community- und Anforderungsanalyse für eine digitale Serviceplattform
title: NFDI4Energy
DOI: 10.5281/zenodo.15022460
title: Open Set Testing Audios

description: Contains ~500 real audios outside of FMA and the SunoCaps generated audios. Folder structure is the same as the original audios folder, but the 'train' and 'validate' folders are empty (forgot to delete those oops). Everything is in the 'test' folder.
DOI: 10.5281/zenodo.15022461
title: Open Set Testing Audios

description: Contains ~500 real audios outside of FMA and the SunoCaps generated audios. Folder structure is the same as the original audios folder, but the 'train' and 'validate' folders are empty (forgot to delete those oops). Everything is in the 'test' folder.
DOI: 10.15468/dl.bkqbu6
title: Occurrence Download

description: A dataset containing 896 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Philonthus sanguinolentus (Gravenhorst, 1802)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 896 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007806-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/d088b25e36a13a8bf89fa412fc90a0dc
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 14 meteorological station: 30-minute soil volumetric water content data: 2013 - ongoing

description: 30-minute Soil Moisture data at 10 cm at CSIS Block-14. Collection of soil volumetric water content data at Jornada LTER Cross-scale Interactions Study (CSIS) blocks, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Soil volumetric water content is measured at 10 cm depth at 3 locations at each of the 4 plots per block. The locations in each plot are under bare soil, under perennial grass, and under shrub. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are recorded every 30 minutes at an automated meteorological station installed at Jornada LTER CSIS Block-14 site. This is an ONGOING dataset.
DOI: 10.5281/zenodo.1215753
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., 2017. This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.
DOI: 10.5281/zenodo.1215824
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD 95, 104023, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., PRD 96, 024058 (2017). This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.
DOI: 10.5281/zenodo.1420586
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD 95, 104023, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., PRD 96, 024058 (2017). This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.
DOI: 10.5281/zenodo.1420589
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD 95, 104023, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., PRD 96, 024058 (2017). This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.
DOI: 10.5281/zenodo.1420594
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD 95, 104023, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., PRD 96, 024058 (2017). This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.
DOI: 10.5281/zenodo.1421321
title: Binary Black-Hole Surrogate Waveform Catalog

description: This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models can be found in Field et al., PRX 4, 031006 (2014).

Several numerical relativity surrogate models are currently available in this catalog:


	
	SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is described in Blackman et al., PRL 115, 121102 (2015). It is evaluated with the gwsurrogate python package, which can be found on PyPI. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this example IPython code.
	
	
	NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in Blackman et al., PRD 95, 104023, (2017), and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this example IPython code.
	
	
	NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be found at Blackman et al., PRD 96, 024058 (2017). This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this example IPython code.
	


 

If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).

Caveats:


	
	Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.
	
	
	The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.