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

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((121.76152 31.37823,122.04719 31.37823,122.04719 31.66913,121.76152 31.66913,121.76152 31.37823))",
 "HasGeospatialIssue is false",
 "TaxonKey is Sibirionetta formosa (Georgi, 1775)"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008256-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48477/coccon.pf10.fairbanks.r01
title: COCCON Version 1 dataset from atmospheric observatory of Fairbanks (Alaska, USA) available at the EVDC Data Handling Facilities covering start date Apr 7th 2018 to end date Oct 18th 2021

description: COCCON uses the EM27/SUN spectrometer commercially available from Bruker Company. The spectrometer performs solar absorption measurements in the NIR for the measurement of column-averaged abundances of atmospheric greenhouse gases (CO2, CH4, CO, and H2O). COCCON requires the use of calibrated spectrometers and of common data analyses tools (see http://www.imk-asf.kit.edu/english/COCCON.php).
DOI: 10.15468/dl.9sbmyw
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Callisia laui (D.R.Hunt) D.R.Hunt"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008258-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qqtfrv
title: Occurrence Download

description: A dataset containing 27057 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((121.67674 31.31562,122.10526 31.31562,122.10526 31.70943,121.67674 31.70943,121.67674 31.31562))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 27057 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008257-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14916831
title: AI-based fish detections at OBSEA Underwater Observatory during a dolphin carcass experiment, March-July 2023

description: This dataset contains AI-based fish detections at the OBSEA Underwater Observatory (NW Mediterranean sea). The model is a YOLOv8 xlarge trained with labeled underwater pictures from OBSEA. The model can be found here.

A dolphin carcass found in the catalan coast was placed in front of the underwater camera to monitor the changes in the ecosystem produced by the presence of a big mammal carcass. The time coverage of the dataset is from 2023-03-16 to 2023-07-17. 

Data

The data file in CSV format includes the following columns:



timestamp: Time instance when the picture was taken (in UTC)

SourceImage: URL of the source image

ProcessedImage: URL of the processed image with detections + bounding boxes

SENSOR_ID: Name of the camera that took the picture

Diplodus puntazzo: Number of specimens detected

Oblada melanurus:  Number of specimens detected

Parablennius gattorugine: Number of specimens detected

Sarpa salpa: Number of specimens detected 

Seriola dumerili:  Number of specimens detected

Serranus cabrilla: Number of specimens detected

Sparus aurata: Number of specimens detected

Symphodus mediterraneus:  Number of specimens detected

Diplodus sargus: Number of specimens detected

Diplodus vulgaris: Number of specimens detected

Epinephelus costae: Number of specimens detected

Aetomylaeus bovinus: Number of specimens detected

Chromis chromis: Number of specimens detected

Coris julis: Number of specimens detected

Dentex dentex: Number of specimens detected

Diplodus cervinus:  Number of specimens detected

Epinephelus marginatus:  Number of specimens detected

Mullus surmuletus:  Number of specimens detected

Muraena helena: Number of specimens detected

Oblada melanurus:  Number of specimens detected

Parablennius gattorugine: Number of specimens detected

Sarpa salpa: Number of specimens detected 

Seriola dumerili:  Number of specimens detected

Serranus cabrilla: Number of specimens detected

Sparus aurata: Number of specimens detected

Symphodus mediterraneus:  Number of specimens detected


Spatial coverage

All pictures where taken at OBSEA underwater observatory, off-the-coast of Vilanova i la Geltrú, Spain. 




 Longitude
  Latitude
depth


1.75257
41.18212
20 m
DOI: 10.5281/zenodo.14916832
title: AI-based fish detections at OBSEA Underwater Observatory during a dolphin carcass experiment, March-July 2023

description: This dataset contains AI-based fish detections at the OBSEA Underwater Observatory (NW Mediterranean sea). The model is a YOLOv8 xlarge trained with labeled underwater pictures from OBSEA. The model can be found here.

A dolphin carcass found in the catalan coast was placed in front of the underwater camera to monitor the changes in the ecosystem produced by the presence of a big mammal carcass. The time coverage of the dataset is from 2023-03-16 to 2023-07-17. 

Data

The data file in CSV format includes the following columns:



timestamp: Time instance when the picture was taken (in UTC)

SourceImage: URL of the source image

ProcessedImage: URL of the processed image with detections + bounding boxes

SENSOR_ID: Name of the camera that took the picture

Diplodus puntazzo: Number of specimens detected

Oblada melanurus:  Number of specimens detected

Parablennius gattorugine: Number of specimens detected

Sarpa salpa: Number of specimens detected 

Seriola dumerili:  Number of specimens detected

Serranus cabrilla: Number of specimens detected

Sparus aurata: Number of specimens detected

Symphodus mediterraneus:  Number of specimens detected

Diplodus sargus: Number of specimens detected

Diplodus vulgaris: Number of specimens detected

Epinephelus costae: Number of specimens detected

Aetomylaeus bovinus: Number of specimens detected

Chromis chromis: Number of specimens detected

Coris julis: Number of specimens detected

Dentex dentex: Number of specimens detected

Diplodus cervinus:  Number of specimens detected

Epinephelus marginatus:  Number of specimens detected

Mullus surmuletus:  Number of specimens detected

Muraena helena: Number of specimens detected

Oblada melanurus:  Number of specimens detected

Parablennius gattorugine: Number of specimens detected

Sarpa salpa: Number of specimens detected 

Seriola dumerili:  Number of specimens detected

Serranus cabrilla: Number of specimens detected

Sparus aurata: Number of specimens detected

Symphodus mediterraneus:  Number of specimens detected


Spatial coverage

All pictures where taken at OBSEA underwater observatory, off-the-coast of Vilanova i la Geltrú, Spain. 




 Longitude
  Latitude
depth


1.75257
41.18212
20 m
DOI: 10.17909/nf8g-sn84
title: TESS data for planet fits of WASP-102 b, WASP-116 b, WASP-149 b WASP-154 b, WASP-155 b, WASP-188 b, WASP-194 b/HAT-P-71 b, WASP-195 b, and WASP-197 b

description: TESS data used in the paper "A Swarm ofWASP Planets: Nine giant planets identified by the WASP survey"
Abstract: The Wide Angle Search for Planets (WASP) survey provided some of the first transiting hot Jupiter candidates. With the addition of the Transiting Exoplanet Survey Satellite (TESS), many WASP planet candidates have now been revisited and given updated transit parameters. Here we present 9 transiting planets orbiting FGK stars that were identified as candidates by the WASP survey and measured to have planetary masses by radial velocity measurements. Subsequent space-based photometry taken by TESS as well as ground-based photometric and spectroscopic measurements have been used to jointly analyze the planetary properties of WASP-102 b, WASP-116 b, WASP-149 b WASP-154 b, WASP-155 b, WASP-188 b, WASP-194 b/HAT-P-71 b, WASP-195 b, and WASP-197 b. These planets have radii between 0.9 R_Jup and 1.4 R_Jup, masses between 0.1 M_Jup and 1.5 M_Jup, and periods between 1.3 and 6.6 days.
DOI: 10.5281/zenodo.15113972
title: Time series of "Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow" (Docquier et al., 2024)

description: This repository contains the time series of the 5 different large model ensembles as well as observations used in Docquier et al. (2024), Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow, Scientific Reports, https://doi.org/10.1038/s41598-024-76056-y.

We also provide the Python script fig1.py to reproduce Fig. 1 of that paper based on the different time series.

In each .npy file, the 8 variables are (by order):

1) September Arctic sea-ice extent (in million km²);

2) March Arctic sea-ice extent (in million km²);

3) March Arctic sea-ice volume (in thousand km³);

4) Annual mean Arctic surface air temperature (in °C);

5) Annual mean Atlantic Ocean heat transport (sum of Barents Sea Opening, Fram Strait and Davis Strait; in TW);

6) Annual mean Pacific Ocean heat transport (Bering Strait; in TW);

7) Annual mean net shortwave radiation (in W/m²);

8) Annual mean downward longwave radiation (in W/m²).

These time series were generated from CMIP6 model outputs available on the Earth System Grid Federation (ESGF) nodes (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/) for 4 of the models (EC-Earth3, MPI-ESM1.2-LR, CanESM5, ACCESS-ESM1.5) and from the National Center for Atmospheric Research (NCAR) Climate Data Gateway (https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2le.output.html) for the CESM2 model. Observations were retrieved from different data providers mentioned in the "Data Availability" section of Docquier et al. (2024).

More details about how these data were produced can be found in the "Methods" section of Docquier et al. (2024).
DOI: 10.5281/zenodo.15113973
title: Time series of "Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow" (Docquier et al., 2024)

description: This repository contains the time series of the 5 different large model ensembles as well as observations used in Docquier et al. (2024), Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow, Scientific Reports, https://doi.org/10.1038/s41598-024-76056-y.

We also provide the Python script fig1.py to reproduce Fig. 1 of that paper based on the different time series.

In each .npy file, the 8 variables are (by order):

1) September Arctic sea-ice extent (in million km²);

2) March Arctic sea-ice extent (in million km²);

3) March Arctic sea-ice volume (in thousand km³);

4) Annual mean Arctic surface air temperature (in °C);

5) Annual mean Atlantic Ocean heat transport (sum of Barents Sea Opening, Fram Strait and Davis Strait; in TW);

6) Annual mean Pacific Ocean heat transport (Bering Strait; in TW);

7) Annual mean net shortwave radiation (in W/m²);

8) Annual mean downward longwave radiation (in W/m²).

These time series were generated from CMIP6 model outputs available on the Earth System Grid Federation (ESGF) nodes (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/) for 4 of the models (EC-Earth3, MPI-ESM1.2-LR, CanESM5, ACCESS-ESM1.5) and from the National Center for Atmospheric Research (NCAR) Climate Data Gateway (https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2le.output.html) for the CESM2 model. Observations were retrieved from different data providers mentioned in the "Data Availability" section of Docquier et al. (2024).

More details about how these data were produced can be found in the "Methods" section of Docquier et al. (2024).
DOI: 10.6084/m9.figshare.28450643.v7
title: Urban and Periurban Forests of Italy: Structure, Tree-related Microhabitats, and Deadwood

description: The dataset consists of a total of 180 plots, sampled across three Italian urban areas: Firenze, Roma, and Campobasso. Within each urban area, four distinct locations were selected, with 15 in-situ plots sampled at each site. The plots were arranged along a gradient from urban to peri-urban environments, as follows:<b>Firenze</b>: Parco delle Cascine, Giardino di Boboli, Villa Medicea della Petraia, Villa Demidoff - Parco Mediceo di Pratolino;<b>Roma</b>: Villa Borghese, Villa Ada Savoia, Villa Doria Pamphilj, Riserva Naturale di Decima Malafede;<b>Campobasso</b>: Villa De Capoa, Rocca Monforte, Parco dello Scarafone, Monte Vairano.Per each plot, information regarding (i) forest structure, (ii) tree-related microhabitats, and (iii) deadwood characteristics were collected.
DOI: 10.5281/zenodo.15114147
title: Data for the paper "Self-sorting of bidisperse particles in evaporating sessile droplets"

description: Dataset Description

This dataset contains simulation data corresponding to the study presented in the paper titled:"Self-sorting of bidisperse particles in evaporating sessile droplets"Authors: Aman Kumar Jain, Fabian Denner, and Berend van Wachem

Dataset Structure

The dataset includes six folders, each representing a distinct simulation case (C1 through C6) from Stage 2 of the simulation study, as outlined in Table 3 of the paper.





Cases C1 and C2: Simulate droplets with an initial contact angle of 60°.




Cases C3 and C4: Simulate droplets with an initial contact angle of 90°.




Cases C5 and C6: Correspond to additional conditions as specified in the publication.



In each pair of cases:





Odd-numbered cases (C1, C3, C5) neglect Marangoni stresses.




Even-numbered cases (C2, C4, C6) include Marangoni stresses.



The suffixes in the case folder names denote particle types:





“_S”: Standard silica particles




“_N”: Neutrally buoyant particles



Each case folder contains:





Subdirectories:





Fields: Fluid field data (e.g., velocity, pressure)




DMs: Particle and domain-related metadata




Meshes: Computational mesh files




Particles: Lagrangian particle data






Visualization files:





results.xmf: XMF wrapper for reading fluid data (velocity, pressure, liquid volume fraction α) in ParaView




results_DEM.xmf: XMF wrapper for particle data visualization (e.g., position, velocity)






Particle position data:





Five CSV files representing particle positions at five distinct time instances.





Analysis Script

A Python script named ParticleCombined.py is included. This script processes the particle position CSV files from each case folder to compute particle surface density over time.

Funding Acknowledgment

This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No. 452916560.
DOI: 10.5281/zenodo.15114148
title: Data for the paper "Self-sorting of bidisperse particles in evaporating sessile droplets"

description: Dataset Description

This dataset contains simulation data corresponding to the study presented in the paper titled:"Self-sorting of bidisperse particles in evaporating sessile droplets"Authors: Aman Kumar Jain, Fabian Denner, and Berend van Wachem

Dataset Structure

The dataset includes six folders, each representing a distinct simulation case (C1 through C6) from Stage 2 of the simulation study, as outlined in Table 3 of the paper.





Cases C1 and C2: Simulate droplets with an initial contact angle of 60°.




Cases C3 and C4: Simulate droplets with an initial contact angle of 90°.




Cases C5 and C6: Correspond to additional conditions as specified in the publication.



In each pair of cases:





Odd-numbered cases (C1, C3, C5) neglect Marangoni stresses.




Even-numbered cases (C2, C4, C6) include Marangoni stresses.



The suffixes in the case folder names denote particle types:





“_S”: Standard silica particles




“_N”: Neutrally buoyant particles



Each case folder contains:





Subdirectories:





Fields: Fluid field data (e.g., velocity, pressure)




DMs: Particle and domain-related metadata




Meshes: Computational mesh files




Particles: Lagrangian particle data






Visualization files:





results.xmf: XMF wrapper for reading fluid data (velocity, pressure, liquid volume fraction α) in ParaView




results_DEM.xmf: XMF wrapper for particle data visualization (e.g., position, velocity)






Particle position data:





Five CSV files representing particle positions at five distinct time instances.





Analysis Script

A Python script named ParticleCombined.py is included. This script processes the particle position CSV files from each case folder to compute particle surface density over time.

Funding Acknowledgment

This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No. 452916560.
DOI: 10.15468/dl.228e4p
title: Occurrence Download

description: A dataset containing 497 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caragana korshinskii Kom."
 ]
}

 The dataset includes 497 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008260-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.53d47m
title: Occurrence Download

description: A dataset containing 1040562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "or" : [
 "Country is Ghana",
 "Country is Togo"
 ]
 }
 ]
}

 The dataset includes 1040562 records from 579 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008250-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a643kn
title: Occurrence Download

description: A dataset containing 28120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "Issue is one of (Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Continent derived from country, Continent derived from coordinates, Elevation min max swapped, Modified date invalid)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Bolitoglossa mexicana Duméril, Bibron & Duméril, 1854, Gastrophryne elegans (Boulenger, 1882), Tlalocohyla picta (Günther, 1901), Tlalocohyla loquax (Gaige & Stuart, 1934), Smilisca cyanosticta (Smith, 1953), Agalychnis callidryas (Cope, 1862), Incilius valliceps (Wiegmann, 1833), Eleutherodactylus leprus (Cope, 1879), Craugastor loki (Shannon & Werler, 1955), Triprion petasatus (Cope, 1865))"
 ]
}

 The dataset includes 28120 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008259-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/cigkaj
title: 3D mass spectrometry imaging as a novel screening method for evaluating biocontrol agents

description: Following files contain the results of LARAPPI/CI (Laser Ablation Remote Atmospheric Pressure Photoionization/Chemical Ionization)-3D-MSI performed employing an ultrahigh resolution QToF (quadrupole-time-of-flight) mass spectrometer Bruker Impact II.3D_step0_6_d.zip; 3D_step1_6_d.zip; 3D_step2_6_d.zip: the .zip files contains raw 3D MSI data in .d format including 3 measurents, one from each level of the sample with Fusarium avenaceum and Priestia megaterium. The data can be opened using an open-source R package that is freely available.3D_step0_8_d.zip; 3D_step1_8_d.zip; 3D_step2_8_d.zip: the .zip files contains raw 3D MSI data in .d format including 3 measurents, one from each level of the sample with Fusarium avenaceum and Bacillus licheniformis. The data can be opened using an open-source R package that is freely available.
DOI: 10.6084/m9.figshare.27320355
title: 2.7 GHz Experimental and Modeling Data for McGlasson & Bramson

description: Experimental data and model inputs/outputs for McGlasson & Bramson: "Experimental Analysis of the Radar Response of Analog Martian Dusty Ice Layers", submitted to <i>Icarus</i>. <b>experiments.zip</b> contains raw and processed data for the 20 layer thickness and dust content experiments, as well as files containing the coordinates of the picked reflectors. Each experiment subfolder is as follows: H[xx]cm_DC[yy], where xx is the thickness of the layer in cm (1.4, 2.75, 5.5, or 11) and yy is the %volumetric dust content of the layer (5, 10, 20 , 30, or 40). Each experiment subfolder contains folders "proc" and "raw". Proc contains the processed .gpr files and .txt reflector pick files. Raw contains the unprocessed .DZT and .DZT radar files in the native GSSI file formats. <i>All radar data in these directories (.gpr, .dzt) can be viewed in the open source Python software </i><i>GprPy</i><i>.</i><b>models.zip</b> contains all input and output files for 2D modeling in gprMax forward modeling software. Naming convention is DC[yy]_H[xx]_[mixing model]_[imaginary] where yy is the % volumetric dust content of the layer (5, 10, 20 , 30, or 40), xx is the thickness of the layer in cm (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), mixing model is the mixing model responsible for the real component of dielectric permittivity (brouet, stillman), and imaginary is the material that the imaginary component of dielectric permittivity is equivalent to (iceloss, dustloss).
DOI: 10.6084/m9.figshare.27320355.v2
title: 2.7 GHz Experimental and Modeling Data for McGlasson & Bramson

description: Experimental data and model inputs/outputs for McGlasson & Bramson: "Experimental Analysis of the Radar Response of Analog Martian Dusty Ice Layers", submitted to <i>Icarus</i>. <b>experiments.zip</b> contains raw and processed data for the 20 layer thickness and dust content experiments, as well as files containing the coordinates of the picked reflectors. Each experiment subfolder is as follows: H[xx]cm_DC[yy], where xx is the thickness of the layer in cm (1.4, 2.75, 5.5, or 11) and yy is the %volumetric dust content of the layer (5, 10, 20 , 30, or 40). Each experiment subfolder contains folders "proc" and "raw". Proc contains the processed .gpr files and .txt reflector pick files. Raw contains the unprocessed .DZT and .DZT radar files in the native GSSI file formats. <i>All radar data in these directories (.gpr, .dzt) can be viewed in the open source Python software </i><i>GprPy</i><i>.</i><b>models.zip</b> contains all input and output files for 2D modeling in gprMax forward modeling software. Naming convention is DC[yy]_H[xx]_[mixing model]_[imaginary] where yy is the % volumetric dust content of the layer (5, 10, 20 , 30, or 40), xx is the thickness of the layer in cm (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), mixing model is the mixing model responsible for the real component of dielectric permittivity (brouet, stillman), and imaginary is the material that the imaginary component of dielectric permittivity is equivalent to (iceloss, dustloss).