cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ueez8v
title: Occurrence Download

description: A dataset containing 27628 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phaeotremella simplex (H.S.Jacks. & G.W.Martin) Millanes & Wedin, Trichaptum abietinum (Pers. ex J.F.Gmel.) Ryvarden)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 27628 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329720-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fm6fcw
title: Occurrence Download

description: A dataset containing 1629 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "TaxonKey is one of (Vultur gryphus Linnaeus, 1758)"
 ]
}

 The dataset includes 1629 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002825-220831081235567/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.29293460
title: Base de datos Revisión

description: Revisión sistemática de los hemosporidios encontrados en hospederos aviares en Colombia<br>
DOI: 10.6084/m9.figshare.29293460.v1
title: Base de datos Revisión

description: Revisión sistemática de los hemosporidios encontrados en hospederos aviares en Colombia<br>
DOI: 10.5281/zenodo.15634206
title: Data for the submitted paper "Electron Spin Resonance Thermochronometry Indicates Quaternary Activity of the Brenner Fault (Eastern Alps)" by Argante et al

description: In this folder, we have stored all raw ESR data, Matlab scripts for the thermokinematic modelling, and a short description of the scripts for "Electron Spin Resonance Thermochronometry Indicates Quaternary Activity of the Brenner Fault (Eastern Alps)" by Argante et al. which was submittted to G-cubed.
DOI: 10.15468/dl.8349sv
title: Occurrence Download

description: A dataset containing 635 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((4.70964 52.97844,4.44946 52.32138,5.10898 52.22882,5.09688 52.59053,5.34495 52.73732,5.08478 52.96386,4.70964 52.97844))",
 "HasGeospatialIssue is false",
 "TaxonKey is Turdus merula Linnaeus, 1758"
 ]
}

 The dataset includes 635 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036425-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cg3kvx
title: Occurrence Download

description: A dataset containing 12496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Phalacrocorax aristotelis (Linnaeus, 1761)"
 ]
}

 The dataset includes 12496 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036426-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d56zdj
title: Occurrence Download

description: A dataset containing 56483412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "License is CC-BY 4.0",
 "Month is one of (May, June)",
 "Year 2019-2020"
 ]
}

 The dataset includes 56483412 records from 223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036399-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.29293685
title: Supplementary information files for "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"

description: Supplementary files for article "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"<br><br>Deep learning-based flood prediction methods have demonstrated significant potential for rapid simulation and early warning of flood disasters. Existing flood surrogate models typically require developing diverse deep-learning architectures based on multiple flood-driving factors, making it challenging to apply these models to different flood scenarios within a consistent network architecture. The temporal resolution of predicted flood maps is also inherently constrained by input flood-driving factors. This paper conceptualizes flood modeling as the translation from coarse-grid to fine-grid flood maps and proposes a novel spatiotemporal flood simulation method termed SwinFlood. The flood-driving factors are unified into two-dimensional coarse-grid hydrodynamic features and fused with fine-grid static terrain features. Utilizing the Swin Transformer for deep feature extraction, the model ultimately outputs fine-grid flood maps. A multi-level model evaluation approach is implemented to systematically assess the performance of the SwinFlood model at global, local, and pixel levels. The proposed model is applied to a complex urban–rural catchment in the upper reaches of the Shenzhen River. Compared to physics-based models, the results demonstrate that the SwinFlood model effectively captures the spatiotemporal variations of water depth, velocity, and river discharge, achieving a speed-up ratio exceeding 1900. The SwinFlood model outperforms traditional purely CNN-based models with comparable parameters. This study provides an efficient and accurate deep-learning method for real-time flood simulation and management.<br><br>© The Author(s), CC BY-NC-ND 4.0
DOI: 10.17028/rd.lboro.29293685.v1
title: Supplementary information files for "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"

description: Supplementary files for article "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"<br><br>Deep learning-based flood prediction methods have demonstrated significant potential for rapid simulation and early warning of flood disasters. Existing flood surrogate models typically require developing diverse deep-learning architectures based on multiple flood-driving factors, making it challenging to apply these models to different flood scenarios within a consistent network architecture. The temporal resolution of predicted flood maps is also inherently constrained by input flood-driving factors. This paper conceptualizes flood modeling as the translation from coarse-grid to fine-grid flood maps and proposes a novel spatiotemporal flood simulation method termed SwinFlood. The flood-driving factors are unified into two-dimensional coarse-grid hydrodynamic features and fused with fine-grid static terrain features. Utilizing the Swin Transformer for deep feature extraction, the model ultimately outputs fine-grid flood maps. A multi-level model evaluation approach is implemented to systematically assess the performance of the SwinFlood model at global, local, and pixel levels. The proposed model is applied to a complex urban–rural catchment in the upper reaches of the Shenzhen River. Compared to physics-based models, the results demonstrate that the SwinFlood model effectively captures the spatiotemporal variations of water depth, velocity, and river discharge, achieving a speed-up ratio exceeding 1900. The SwinFlood model outperforms traditional purely CNN-based models with comparable parameters. This study provides an efficient and accurate deep-learning method for real-time flood simulation and management.<br><br>© The Author(s), CC BY-NC-ND 4.0
DOI: 10.15468/dl.g6r57j
title: Occurrence Download

description: A dataset containing 17921 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "OccurrenceStatus is Present",
 "TaxonKey is Silaum silaus (L.) Schinz & Thell."
 ]
}

 The dataset includes 17921 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036427-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6mhvgw
title: Occurrence Download

description: A dataset containing 166136 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Calonectris diomedea (Scopoli, 1769)"
 ]
}

 The dataset includes 166136 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036408-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data2273
title: Mar Mediterrània. Palamós. Perfil vertical de la temperatura, salinitat i sigma-t de l'aigua del mar

description: Observacions de temperatura de l’aigua de mar, salinitat i sigma-t, a diferents fondàries, a Palamós. &lt;/br&gt;Les dades recollides van, amb períodes molt espaiats, de setembre del 2020 a l’agost del 2021. &lt;/br&gt;Alhora, el període de recollida de les dades és irregular. &lt;/br&gt;En complementació, hi ha mesures, a vegades, en dos punts diferents del mar a Palamós. &lt;/br&gt;També tenim dies en els quals s'afegeixen dades obtingudes al mar a la zona de L’Estartit. &lt;/br&gt;La unitat de mesura de fondària és el metre (m) i la unitat de mesura de la variable temperatura són graus centígrades (°C).&lt;/br&gt;
description: Datos de temperaturas del agua de mar, salinidad y sigma-t, recogidos y calculados a distintas profundidades, en Palamós.&lt;/br&gt; Los datos recolectados van de septiembre de 2020 a agosto de 2021. &lt;/br&gt;Así mismo, el período de recogida de los datos es irregular y espaciado en el tiempo. &lt;/br&gt;A veces, tenemos las mesuras de temperatura recogidas en dos puntos distintos del mar en Palamós. &lt;/br&gt;En algunos momentos, referenciamos datos obtenidos en el mar en la zona de L'Estartit. &lt;/br&gt;La unidad de medida de profundidad es metro (m) y la unidad de medida de la variable temperatura son grados centígrados (°C).&lt;/br&gt;
description: Data collection of seawater temperature, salinity and sigma-t, at different depths, in Palamós.&lt;/br&gt; The collected data range from September 2020 to August 2021. &lt;/br&gt;At the same time, the data collection period is irregular with widely spaced periods between them. &lt;/br&gt;Normally, the measurements are taken in two different points of the sea in Palamós. &lt;/br&gt;Some days additional data from water in Estartit are included. &lt;/br&gt;The measurement unit used for depth is Meter (m) and for temperature is Celsius degrees (°C).&lt;/br&gt;
DOI: 10.34810/data2272
title: Mar Mediterrània. Cap de Creus. Perfil vertical de la temperatura, salinitat i sigma-t de l'aigua del mar

description: Observacions de temperatura de l’aigua de mar, salinitat i sigma-t, a diferents fondàries, al Cap de Creus.&lt;/br&gt; Les dades recollides van, en períodes molt espaiats, de juny del 2019 a octubre del 2022.&lt;/br&gt; Alhora, el període de recollida de les dades és irregular. &lt;/br&gt;En complementació, hi ha mesures, a vegades, en dos punts diferents del mar a Cap de Creus (“Mar amunt” fa referència a Cap de Creus Nord i “Mar avall” fa referència a cap de Creus Est). &lt;/br&gt;També tenim dies en els quals es fa una comparació entre dades obtingudes a Palamós i dades obtingudes a la zona de les Illes Medes, a L’Estartit (Municipi de Torroella de Montgrí – L’Estartit).&lt;/br&gt; La unitat de mesura de fondària és el metre (m) i la unitat de mesura de la variable temperatura són graus centígrades (°C).&lt;/br&gt;
description: Datos de temperaturas del agua de mar, salinidad y sigma-t, recogidos y calculados a distintas profundidades del Cap de Creus. Los datos recolectados van desde junio de 2019 hasta octubre del 2022. &lt;/br&gt;Así mismo, el período de recogida de los datos es irregular y espaciado en el tiempo. &lt;/br&gt;A veces, tenemos las mesuras de temperatura recogidas en dos puntos distintos del mar en Cap de Creus Creus (con la expresión “Mar amunt” nos referimos a Cap de Creus Norte i, con la expresión “Mar avall”, nos referimos a cap de Creus Este). &lt;/br&gt;En algunos momentos, referenciamos datos obtenidos en el mar en la zona de las “Illes Medes”, situades en el pueblo de L'Estartit. &lt;/br&gt;La unidad de medida de profundidad es metro (m) y la unidad de medida de la variable temperatura son grados centígrados (°C).&lt;/br&gt;
description: Data on seawater temperatures, salinity and sigma-t, collected and calculated at different depths at Cap de Creus. &lt;/br&gt;The data collected range from June 2019 to October 2022. &lt;/br&gt;Likewise, the period of data collection is irregular and spaced in time. &lt;/br&gt;Sometimes, we have the temperature measurements collected in two different points of the sea in Cap de Creus Creus (with the expression ‘Mar amunt’ we refer to Cap de Creus North and, with the expression ‘Mar avall’, we refer to Cap de Creus East). &lt;/br&gt;At some points, we refer to data obtained at sea in the area of the ‘Illes Medes’, located in the town of L'Estartit.&lt;/br&gt; The unit of measurement for depth is metre (m) and the unit of measurement for the temperature variable is degrees Celsius (°C).&lt;/br&gt;
DOI: 10.15468/dl.j25uv4
title: Occurrence Download

description: A dataset containing 776533 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Mozambique"
 ]
}

 The dataset includes 776533 records from 858 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036409-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data2046
title: Temperatura i cabal de l'aigua del Riu Ter a Torroella de Montgrí

description: Conjunt de dades dels valors de la temperatura (°C) i el cabal (m³/seg) del Riu Ter (Municipi: Torroella de Montgrí; Província: Girona; Comunitat autònoma: Catalunya; País: Espanya) entre els anys 1993 i 2022. &lt;/br&gt;
Periodicitat: mensual.&lt;/br&gt;
Període de cobertura de valors de temperatura: 1993-2022.&lt;/br&gt;
Període de cobertura de valors de cabal: març 1993-octubre 2002; 2005-2022.&lt;/br&gt;
description: Conjunto de valores de temperatura (°C) y caudal (m³/seg) del Río Ter (Municipio: Torroella de Montgrí; Provincia: Girona; Comunidad autónoma: Cataluña; País: España) entre los años 1993 y 2022.&lt;/br&gt;
Periodicidad: mensual.&lt;/br&gt;
Periodo de cobertura de valores de temperatura: 1993-2022.&lt;/br&gt;
Periodo de cobertura de valores de caudal: marzo 1993-octubre 2002; 2005-2022.&lt;/br&gt;
description: Set of temperature (°C) and flow (m³/sec) values of the Ter River (Municipality: Torroella de Montgrí; Province: Girona; Autonomous community: Catalonia; Country: Spain) between 1993 and 2022. &lt;/br&gt;
Regularity: monthly.&lt;/br&gt; 
Coverage of temperature values: 1993-2022.&lt;/br&gt; 
Coverage of flow values: March 1993-October 2002; 2005-2022.&lt;/br&gt;
DOI: 10.34810/data2044
title: Temperatura de l'aigua de l'Estany de Banyoles

description: Valors de temperatura (°C) de l'aigua del Llac de Banyoles (Municipi: Banyoles; Província: Girona; Comunitat autònoma: Catalunya; País: Espanya) mesurats a diverses fondàries (m).&lt;/br&gt; 
Periodicitat: mensual. &lt;/br&gt; 
Les dades mensuals estan obtingudes en un dia determinat del mes (dia variable segon mes).&lt;/br&gt; 
Pengen d'aquest Dataset dos documents:&lt;/br&gt; 
un conté els valors de la temperatura de l'Estany de Banyoles mesurats a diferents fondàries (que varien des de -0,5 m fins a -57 m) des de gener de 2018 fins a gener de 2022. L'altre conté els valors mitjans mensuals de la temperatura de l'Estany de Banyoles mesurats a la superfície i a 15 m de profunditat. Període cobert: 1976-2003, 2009, 2012-2015.&lt;/br&gt;
description: Valores de temperatura (°C) del agua del Lago de Banyoles (Municipio: Banyoles; Provincia: Girona; Comunidad autónoma: Cataluña; País: España) medidos en diversas profundidades (m).&lt;/br&gt; 
Periodicidad: mensual.&lt;/br&gt; 
Los datos mensuales se obtuvieron en un día determinado del mes (día variable según el mes).&lt;/br&gt; 
Documentación adjuntada:&lt;/br&gt; 
Un documento contiene los valores de la temperatura del lago de Banyoles medidos a diferentes profundidades (que varían desde -0,5 m hasta -57 m) desde enero de 2018 hasta enero de 2022. El otro, contiene los valores medios mensuales de la temperatura del Lago de Banyoles medidos en superficie y a 15 m de profundidad. Periodo cubierto: 1976-2003, 2009, 2012-2015.&lt;/br&gt;
description: Water temperature values (°C) from Banyoles Lake (Municipality: Banyoles; Province: Girona; Autonomous Community: Catalonia; Country: Spain) measured at various depths (m). 
Regularity: monthly. &lt;/br&gt; 
Monthly data were obtained on a given day of the month (variable day depending on the month). &lt;/br&gt; 
Attached documentation: &lt;/br&gt; 
one document contains the temperature of Banyoles lake measured at different depths (varying from -0.5 m to -57 m) from January 2018 to January 2022. The other contains the monthly average temperatures of the Banyoles Lake, measured at surface and at 15 m depth. Period covered: 1976-2003, 2009, 2012-2015.&lt;/br&gt;
DOI: 10.15468/dl.jdk2az
title: Occurrence Download

description: A dataset containing 299917 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Abida secale (Draparnaud, 1801), Abraxas grossulariata (Linnaeus, 1758), Abraxas sylvata (Scopoli, 1763), Abrostola tripartita (Hufnagel, 1766), Abrostola triplasia (Linnaeus, 1758), Acalles misellus Bohem., 1844, Acalles ptinoides Dej., 1836, Acalypta carinata (Panzer, 1806), Acalypta nigrina (Fallén, 1807), Acalypta parvula (Fallén, 1807))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Year is greater than 1990",
 "Year is less than 2021"
 ]
}

 The dataset includes 299917 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036417-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qek3fs
title: Occurrence Download

description: A dataset listing the 81717 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 81717 records were derived from 1455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036413-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ddt7y
title: Occurrence Download

description: A dataset containing 650 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-1000.0",
 "Country is one of (Portugal, Spain)",
 "OccurrenceStatus is Present",
 "TaxonKey is Tadarida teniotis (Rafinesque, 1814)",
 "Year 2010-2022"
 ]
}

 The dataset includes 650 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0145805-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ifp5ak
title: Occurrence Download

description: A dataset containing 10310 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prionace glauca (Linnaeus, 1758)"
 ]
}

 The dataset includes 10310 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007460-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7vnhc5
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lotononis evansiana Burtt Davy"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013439-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3yrsj
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solenolambrus tenellus Stimpson, 1871"
 ]
}

 The dataset includes 87 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132723-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxrhjf
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dasyprocta kalinowskii Thomas, 1897"
 ]
}

 The dataset includes 15 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051141-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p45rbv
title: Occurrence Download

description: A dataset containing 536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Sri Lanka",
 "DatasetKey is EOD – eBird Observation Dataset",
 "TaxonKey is Otus thilohoffmanni Warakagoda & Rasmussen, 2004"
 ]
}

 The dataset includes 536 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022492-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wfxskd
title: Occurrence Download

description: A dataset containing 98 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solenolambrus typicus Stimpson, 1871"
 ]
}

 The dataset includes 98 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132726-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.