cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.a4vt7p
title: Occurrence Download

description: A dataset containing 856886 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Sturnus vulgaris Linnaeus, 1758"
 ]
}

 The dataset includes 856886 records from 209 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006126-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15103637
title: Research bundle for "The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology"
DOI: 10.15468/dl.5drxw4
title: Occurrence Download

description: A dataset containing 854 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Harpia harpyja (Linnaeus, 1758)",
 "Year 1000-2010"
 ]
}

 The dataset includes 854 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006136-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5jw9su
title: Occurrence Download

description: A dataset containing 111900 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is Asia",
 "Country is Hong Kong",
 "DatasetKey is one of (iNaturalist Research-grade Observations, eButterfly Surveys, Earth Guardians Weekly Feed)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Continent derived from coordinates, The taxon ID was not used in backbone matching, Coordinate rounded)",
 "IucnRedListCategory is one of (NE, LC)",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "Month is one of (December, November, October, September, August, July, June, May, April, March, February, January)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (28eb1a3f-1c15-4a95-931a-4af90ecb574d, b6d09100-919d-4026-b35b-22be3dae7156, 17726825-3aa2-4fbc-b0e3-303d3fc5702f)",
 "TaxonKey is Lepidoptera",
 "Year 2020-2025"
 ]
}

 The dataset includes 111900 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006132-250325103851331/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.28690133
title: Dynamic ultrasound achromatic imaging with a stretchable flexible metalens

description: Ultrasound imaging is widely used in both engineering and medicine due to its advantages of deep penetration and no ionizing radiation. Focusing techniques can improve image resolution, but conventional methods often suffer from chromatic aberration. We present a novel approach to ultrasound imaging that overcomes this challenge, which utilizes a stretchable flexible metalens to experimentally achieve dynamic achromatic imaging. By carefully stretching and releasing the metalens, we can focus ultrasound of different frequencies at the same depth. This method features remarkable focusing resolutions of 0.6λ in the radial direction and 2.3λ in the axial direction, and a 13% deformation of the metalens enables effective compensation for chromatic aberration of ultrasound from 0.81 MHz to 1 MHz. Furthermore, we demonstrate the dynamic achromatic imaging of microstructures, showcasing its ability to capture high-resolution ultrasound images.
DOI: 10.15468/dl.bd778v
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((14.37264 47.4931,15.10347 47.4931,15.10347 47.86799,14.37264 47.86799,14.37264 47.4931))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Apodemus Kaup, 1829"
 ]
}

 The dataset includes 118 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006137-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.te4axf
title: Occurrence Download

description: A dataset containing 636 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lamproptera meges (Zincken, 1831)"
 ]
}

 The dataset includes 636 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006138-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15105517
title: Production of oxygen- and sulfur-containing volatile organic compounds by marine bacteria from coastal seawater

description: This data set contains data associated with the manuscript "Production of oxygen- and sulfur-containing volatile organic compounds by marine bacteria from coastal seawater". Currently under review.
DOI: 10.5281/zenodo.15105518
title: Production of oxygen- and sulfur-containing volatile organic compounds by marine bacteria from coastal seawater

description: This data set contains data associated with the manuscript "Production of oxygen- and sulfur-containing volatile organic compounds by marine bacteria from coastal seawater". Currently under review.
DOI: 10.15468/dl.zqttcs
title: Occurrence Download

description: A dataset containing 6528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is FloraCAT: Banco de datos de los cormófitos de Cataluña",
 "Geometry POLYGON((2.11568 41.49908,2.1154 41.49926,2.11566 41.49951,2.11559 41.49997,2.11528 41.50063,2.11508 41.50082,2.11484 41.50079,2.11466 41.5009,2.11413 41.50104,2.11359 41.5014,2.1135 41.50161,2.11254 41.50233,2.11165 41.50294,2.11108 41.50321,2.11147 41.5047,2.11104 41.5048,2.11022 41.50486,2.10952 41.50537,2.1093 41.50599,2.10916 41.50615,2.109 41.50632,2.10894 41.50669,2.10888 41.50672,2.10885 41.50665,2.10864 41.50652,2.10841 41.50655,2.10752 41.50708,2.1071 41.50735,2.10681 41.50745,2.10529 41.50632,2.1051 41.50652,2.10488 41.50664,2.10479 41.50687,2.10457 41.50705,2.1039 41.50734,2.10325 41.50758,2.10291 41.50786,2.10257 41.50813,2.10211 41.50787,2.10134 41.5083,2.09989 41.50871,2.09797 41.50882,2.09293 41.50851,2.09306 41.50584,2.093 41.50508,2.09281 41.5043,2.09234 41.50306,2.09108 41.50109,2.08779 41.49748,2.08768 41.49726,2.0886 41.49697,2.0889 41.49711,2.0893 41.49715,2.0897 41.49732,2.09056 41.49698,2.09073 41.49697,2.0909 41.49714,2.09111 41.4972,2.09403 41.49677,2.09394 41.49622,2.09434 41.49589,2.09428 41.49559,2.09469 41.49573,2.09511 41.49556,2.09541 41.49523,2.09535 41.49482,2.09517 41.49458,2.09493 41.49437,2.09559 41.49401,2.0966 41.49327,2.09697 41.49314,2.09695 41.49301,2.09812 41.49345,2.09919 41.49373,2.10021 41.49373,2.10126 41.49364,2.10229 41.49361,2.10366 41.49373,2.10526 41.49401,2.10719 41.49449,2.10901 41.49509,2.11009 41.49549,2.11185 41.49646,2.11387 41.49772,2.11462 41.49827,2.11568 41.49908))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6528 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006139-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n2hdqk
title: Occurrence Download

description: A dataset containing 182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Actinidia Lindl.",
 "Year 2015-2025"
 ]
}

 The dataset includes 182 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006143-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.guajf2
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Triops vicentinus Korn, Machado, Cristo & Cancela da Fonseca, 2010"
 ]
}

 The dataset includes 34 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006144-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7574266
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.


Here you will find:



	
compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 
	
the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package using one of the alternatives:
	

		
pip install .
		
python setup.py develop
	
	



More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: Update MANIFEST.in for a correct installation as Python package
DOI: 10.5281/zenodo.10128131
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.


Here you will find:



	
compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 
	
the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package using one of the alternatives:
	

		
pip install .
		
python setup.py develop
	
	



More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: Update the Macaques dataset by correcting the weights labels order in the Connectivity
DOI: 10.5281/zenodo.14992335
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.

Here you will find:



compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 

the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package using this command:



pip install .




More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: switch from setup.py to pyproject.toml
DOI: 10.5281/zenodo.3417206
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.

Here you will find:



compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 

the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package using this command:



pip install .




More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: switch from setup.py to pyproject.toml
DOI: 10.5281/zenodo.3688773
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.


Here you will find:



	
compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 
	
the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package.



More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: Fix default Sensors, Projection Matrices and EEGCap after release 2.0 inside Default_Project.zip
NEW FILES: eeg_cap_rotated.h5, eeg_63.txt, meg_248.txt, projection_eeg_63_surface_16k.npy, projection_eeg_248_surface_16k.npy
DOI: 10.5281/zenodo.4263723
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here.


Here you will find:



	
compatible file/folder structures that can be uploaded through the web interface of TVB.  For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from  https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. 
	
the default datasets used when working in console or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package.



More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: Update Default_Project.zip to hold H5 files that describe the configuration of an operation instead the Operation.xml, Update all *.h5 files for compatibility on form (not data content)
DOI: 10.5281/zenodo.8331301
title: tvb-data - Extended Dataset

description: Various demonstration datasets for use with The Virtual Brain project are provided here. Here you will find: compatible file/folder structures that can be uploaded through the web interface of TVB. For this usage scenario, you should just download, and unzip the current package. Independently, download TVB_Distribution from https://www.thevirtualbrain.org/ and from its web GUI upload files from this unzipped folder, at need. the default datasets used when working in <code>console</code> or library mode. For this usage scenario, you should download, unzip, and install in your python distribution this tvb-data as a regular python package using one of the alternatives: pip install . python setup.py develop More information on TVB documentation site: http://docs.thevirtualbrain.org/ or on the main webpage of the project https://www.thevirtualbrain.org/
description: CHANGES: Removed berlinSubjects folder
description: {"references": ["Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013) The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)", "M. Schirner, L. Domide, D. Perdikis, P. Triebkorn, L. Stefanovski, R. Pai, P. Popa, B. Valean, J. Palmer, C. Langford, A. Blickensd\u00f6rfer, M. van der Vlag, S. Diaz-Pier, A. Peyser, W. Klijn, D. Pleiter, A. Nahm, O. Schmid, M. Woodman, L. Zehl, J. Fousek, S. Petkoski, L. Kusch, M. Hashemi, D. Marinazzo, J.-F. Mangin, A. Fl\u00f6el, S. Akintoye, B. C. Stahl, M. Cepic, E. Johnson, G. Deco, A. R. McIntosh, C. C. Hilgetag, M. Morgan, B. Schuller, A. Upton, C. McMurtrie, T. Dickscheid, J. G. Bjaalie, K. Amunts, J. Mersmann, V. Jirsa, P. Ritter (2022) Brain simulation as a cloud service: The Virtual Brain on EBRAINS, NeuroImage 2022, doi.org/10.1016/j.neuroimage.2022.118973"]}
DOI: 10.15468/dl.4f2t8u
title: Occurrence Download

description: A dataset containing 1695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caretta caretta (Linnaeus, 1758)"
 ]
}

 The dataset includes 1695 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006145-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15105594
title: qv_acmg_svnindel_criteria_20250225

description: This file was used as the examle used for "Application of qualifying variants for genomic analysis".
DOI: 10.15468/dl.xpsa3p
title: Occurrence Download

description: A dataset containing 26054210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "Year 2020-2025"
 ]
}

 The dataset includes 26054210 records from 401 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006131-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9tgbqx
title: Occurrence Download

description: A dataset containing 44799483 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "Year 2010-2025"
 ]
}

 The dataset includes 44799483 records from 989 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006133-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1181032.10fu
title: SH1181032.10FU

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/sh1181033.10fu
title: SH1181033.10FU

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/sh1181034.10fu
title: SH1181034.10FU

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/sh1181035.10fu
title: SH1181035.10FU

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.