cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/plate/dr.3/plates/3_67047
title: Digital object identifier (DOI) for the plate 67047 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67048
title: Digital object identifier (DOI) for the plate 67048 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67049
title: Digital object identifier (DOI) for the plate 67049 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67050
title: Digital object identifier (DOI) for the plate 67050 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67051
title: Digital object identifier (DOI) for the plate 67051 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67052
title: Digital object identifier (DOI) for the plate 67052 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67053
title: Digital object identifier (DOI) for the plate 67053 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67054
title: Digital object identifier (DOI) for the plate 67054 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67055
title: Digital object identifier (DOI) for the plate 67055 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67056
title: Digital object identifier (DOI) for the plate 67056 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67057
title: Digital object identifier (DOI) for the plate 67057 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67058
title: Digital object identifier (DOI) for the plate 67058 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67059
title: Digital object identifier (DOI) for the plate 67059 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67060
title: Digital object identifier (DOI) for the plate 67060 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67061
title: Digital object identifier (DOI) for the plate 67061 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67062
title: Digital object identifier (DOI) for the plate 67062 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/3_67063
title: Digital object identifier (DOI) for the plate 67063 in archive 3 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70372
title: Digital object identifier (DOI) for the plate 70372 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70373
title: Digital object identifier (DOI) for the plate 70373 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70374
title: Digital object identifier (DOI) for the plate 70374 in archive 401 from the APPLAUSE DR3
DOI: 10.5281/zenodo.8260266
title: Dataset for "Optimal Central Bank Digital Currency (CBDC) Design for Emerging Economies"

description: Dataset for "Optimal Central Bank Digital Currency (CBDC) Design for Emerging Economies"
DOI: 10.5281/zenodo.8260267
title: Dataset for "Optimal Central Bank Digital Currency (CBDC) Design for Emerging Economies"

description: Dataset for "Optimal Central Bank Digital Currency (CBDC) Design for Emerging Economies"
DOI: 10.17876/plate/dr.3/plates/401_70375
title: Digital object identifier (DOI) for the plate 70375 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70376
title: Digital object identifier (DOI) for the plate 70376 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70377
title: Digital object identifier (DOI) for the plate 70377 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70378
title: Digital object identifier (DOI) for the plate 70378 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70379
title: Digital object identifier (DOI) for the plate 70379 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70380
title: Digital object identifier (DOI) for the plate 70380 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70381
title: Digital object identifier (DOI) for the plate 70381 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70382
title: Digital object identifier (DOI) for the plate 70382 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70383
title: Digital object identifier (DOI) for the plate 70383 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70384
title: Digital object identifier (DOI) for the plate 70384 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70385
title: Digital object identifier (DOI) for the plate 70385 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70386
title: Digital object identifier (DOI) for the plate 70386 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70387
title: Digital object identifier (DOI) for the plate 70387 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70388
title: Digital object identifier (DOI) for the plate 70388 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70389
title: Digital object identifier (DOI) for the plate 70389 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70390
title: Digital object identifier (DOI) for the plate 70390 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70391
title: Digital object identifier (DOI) for the plate 70391 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70392
title: Digital object identifier (DOI) for the plate 70392 in archive 401 from the APPLAUSE DR3
DOI: 10.25442/hku.23808639
title: Supporting Data for "Advancing Biophysical Cytometry with Deep-learning (ABCD) for New-Generation Cell-based Diagnostics"

description: There are in total of 7 types of lung cancer cells (i.e. H69, H358, H520, H526, H1975, H2170 and HCC827). All the data were collected on 7 days using multi-ATOM setup, giving 3 batches per cell lines. Both single-cell brightfield and quantitative phase images (QPI) were collected.For training and testing the beGAN model, the data were separated into "Train", "Valid" and "Test" set. They are subsampled and contain 500 cells respectively as a demonstration in this repository (Folder <code>Dataset</code>). Data was uploaded in <code>.mat</code> format with brightfield images in <code>_BF.mat</code> and QPI in <code>_QPI.mat</code>. The images are stored in format of <code>ImageHeight * ImageWidth * NoOfCells</code> with a field of view of 45μm.
DOI: 10.25442/hku.23808639.v1
title: Supporting Data for "Advancing Biophysical Cytometry with Deep-learning (ABCD) for New-Generation Cell-based Diagnostics"

description: There are in total of 7 types of lung cancer cells (i.e. H69, H358, H520, H526, H1975, H2170 and HCC827). All the data were collected on 7 days using multi-ATOM setup, giving 3 batches per cell lines. Both single-cell brightfield and quantitative phase images (QPI) were collected.For training and testing the beGAN model, the data were separated into "Train", "Valid" and "Test" set. They are subsampled and contain 500 cells respectively as a demonstration in this repository (Folder <code>Dataset</code>). Data was uploaded in <code>.mat</code> format with brightfield images in <code>_BF.mat</code> and QPI in <code>_QPI.mat</code>. The images are stored in format of <code>ImageHeight * ImageWidth * NoOfCells</code> with a field of view of 45μm.
DOI: 10.17876/plate/dr.3/plates/401_70393
title: Digital object identifier (DOI) for the plate 70393 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70394
title: Digital object identifier (DOI) for the plate 70394 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70395
title: Digital object identifier (DOI) for the plate 70395 in archive 401 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/401_70396
title: Digital object identifier (DOI) for the plate 70396 in archive 401 from the APPLAUSE DR3
DOI: 10.15468/dl.2prtcx
title: Occurrence Download

description: A dataset containing 549 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Acrolejeunea (Spruce) Steph."
 ]
}

 The dataset includes 549 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078786-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a37xe5
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Punotia D.R.Hunt"
 ]
}

 The dataset includes 54 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078787-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yk9q4
title: Occurrence Download

description: A dataset containing 6497 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Strelitziaceae"
 ]
}

 The dataset includes 6497 records from 343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078781-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60635/c37p4h
title: Smithsonian ForestGEO San Lorenzo and Panama Small Plots aerial photogrammetry orthomosaics,digital surface models, point clouds and raw images for 2015-2024.

description: Data are available for download at: https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThe Panama Forest Drone Dataset is a dataset part of an initiative focused on monitoring and understanding forest ecosystems in Panama through the use of drone technology. This project is led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute, with the primary goal of capturing high-resolution imagery and comprehensive data across various forest study plots located in central Panama. The dataset is a part of an ongoing effort to document and analyze changes in forest conditions, contributing valuable insights into tropical forest dynamics.This extensive dataset covers nine macrosites, including well-known areas such as the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa, San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots, each representing distinct areas of interest. Data collection has been conducted over multiple dates, with a total of 84 drone flights contributing to the dataset, capturing critical information across these diverse landscapes.The data products available within this dataset include orthomosaics, digital surface models (DSMs), point clouds, raw images, and detailed processing reports. All these products are provided in the UTM Zone 17N coordinate reference system (EPSG: 32617).Orthomosaics and DSMs are available in GeoTIFF format, while point clouds are provided as LAS files in versions 1.2 and 1.4. The dataset primarily consists of RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.The drones used in this project include a variety of platforms, each equipped with specific sensors suited to the data collection needs. The DJI Phantom 4 Pro, denoted as P4P in the dataset, was frequently used, equipped with an FC6310 camera. Other platforms include the Solo 3DR, outfitted with a Peau Productions MAPIR sensor(SOLO) and a MAPIR Survey 2 sensor(SOLO2). The eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset. The DJI Mavic 3 Enterprise (M3E), featured a M3M multispectral sensor. The Hobbyest drones doneted as DR1 carried a Canon PowerShot ELPH 520 HS.AcknoledgementsWe extend our sincere gratitude to Jonathan Dandois, Ryan Nolin, and Marino Ramirez for their exceptional work as drone pilots during the early flights. We also deeply appreciate the invaluable field support provided by Paulino Villarreal and Pablo Ramos. A special thanks goes to Milton Solano for his support as a GIS specialist.
DOI: 10.15468/dl.34j7bp
title: Occurrence Download

description: A dataset containing 610 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aotus lemurinus I.Geoffroy Saint-Hilaire, 1843"
 ]
}

 The dataset includes 610 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078788-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqcwfh
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Acanthopetalum carinatum (Brandt, 1840)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078789-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xfzt56
title: Occurrence Download

description: A dataset containing 2044 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhinolophus mehelyi Matschie, 1901",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 2044 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078790-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13363332
title: Supplementary Datasets to Höps et al. 2024

description: Supplementary Data files to Höps et al. 2024: Impact and characterization of serial structural variations across humans and great apes 

Supplementary Data 1: A table with coordinates and genotypes of 37 human sSVs across 56 haplotypes.

Supplementary Data 2: A table with NAHRwhals calls in 336 CNV-associated regions of interest.

Supplementary Data 3: A table with Genomic elements overlapping with sSVs (inversions, cups, mcnvs).

Supplementary Data 4: A table with NAHRwhals calls in 48 morbid CNV-associated regions.

 

Please consult the manuscript for further information. 

contact: wolfram.hoeps@gmail.com
DOI: 10.5281/zenodo.13363333
title: Supplementary Datasets to Höps et al. 2024

description: Supplementary Data files to Höps et al. 2024: Impact and characterization of serial structural variations across humans and great apes 

Supplementary Data 1: A table with coordinates and genotypes of 37 human sSVs across 56 haplotypes.

Supplementary Data 2: A table with NAHRwhals calls in 336 CNV-associated regions of interest.

Supplementary Data 3: A table with Genomic elements overlapping with sSVs (inversions, cups, mcnvs).

Supplementary Data 4: A table with NAHRwhals calls in 48 morbid CNV-associated regions.

 

Please consult the manuscript for further information. 

contact: wolfram.hoeps@gmail.com
DOI: 10.60635/c33w2k
title: Smithsonian ForestGEO San Lorenzo and Panama Small Plots aerial photogrammetry orthomosaics, digital surface models, point clouds and raw images for 2015-2024.

description: Data are available for download at: https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThe Panama Forest Drone Dataset is a dataset part of an initiative focused on monitoring and understanding forest ecosystems in Panama through the use of drone technology. This project is led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute, with the primary goal of capturing high-resolution imagery and comprehensive data across various forest study plots located in central Panama. The dataset is a part of an ongoing effort to document and analyze changes in forest conditions, contributing valuable insights into tropical forest dynamics.This extensive dataset covers nine macrosites, including well-known areas such as the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa, San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots, each representing distinct areas of interest. Data collection has been conducted over multiple dates, with a total of 84 drone flights contributing to the dataset, capturing critical information across these diverse landscapes.The data products available within this dataset include orthomosaics, digital surface models (DSMs), point clouds, raw images, and detailed processing reports. All these products are provided in the UTM Zone 17N coordinate reference system (EPSG: 32617).Orthomosaics and DSMs are available in GeoTIFF format, while point clouds are provided as LAS files in versions 1.2 and 1.4. The dataset primarily consists of RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.The drones used in this project include a variety of platforms, each equipped with specific sensors suited to the data collection needs. The DJI Phantom 4 Pro, denoted as P4P in the dataset, was frequently used, equipped with an FC6310 camera. Other platforms include the Solo 3DR, outfitted with a Peau Productions MAPIR sensor(SOLO) and a MAPIR Survey 2 sensor(SOLO2). The eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset. The DJI Mavic 3 Enterprise (M3E), featured a M3M multispectral sensor. The Hobbyest drones doneted as DR1 carried a Canon PowerShot ELPH 520 HS.AcknoledgementsWe extend our sincere gratitude to Jonathan Dandois, Ryan Nolin, and Marino Ramirez for their exceptional work as drone pilots during the early flights. We also deeply appreciate the invaluable field support provided by Paulino Villarreal and Pablo Ramos. A special thanks goes to Milton Solano for his support as a GIS specialist.
DOI: 10.15468/dl.5zwpkj
title: Occurrence Download

description: A dataset containing 1105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sicista betulina (Pallas, 1779)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 1105 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078791-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7m4ch
title: Occurrence Download

description: A dataset containing 330 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is USA.32.2_1",
 "OccurrenceStatus is Present",
 "StateProvince is New Mexico",
 "TaxonKey is Microtus longicaudus (Merriam, 1888)"
 ]
}

 The dataset includes 330 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078792-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xb5sk3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Acanthopetalum cycladicum (Verhoeff, 1901)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078793-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.26814805.v1
title: Vocalization Data

description: Inter- and intraspecific competition for resources is common among individuals which share ecological niches. To avoid physical confrontations, individuals can use various types of signals to demonstrate their dominance, including vocalizations. <i>Kerivoula hardwickii</i> is a solitary bat species that lives in highly ephemeral plant structures, which are therefore a limited resource. So far, it is unknown if individuals of <i>K. hardwickii</i> use vocalizations during competitive encounters for roosts, and if the intention of these vocalizations can be deduced by potential rivals. We hypothesized that the calls emitted during roost competition contain information that influences the ability of an individual to defend its roost. We conducted roost competition experiments in a flight cage, where there was an individual roost owner and an intruder who would attempt to evict the owner from the roost. All the vocalizations emitted during these encounters were recorded and analyzed to determine which acoustic parameters, if any, had an influence on the successful defense of the roost. We found that the calls emitted by males can influence their ability to defend the roost, and that entropy is the parameter that most strongly explains a successful defense. High entropy suggests that encounters between individuals of <i>K. hardwickii</i> escalate to high levels of aggressiveness and explain whether calls influence an individual’s capacity to defend a roost. We suggest that bat vocalizations contain important information about individual characteristics, which in turn help bats make decisions during resource competition
DOI: 10.15468/dl.zjvb8j
title: Occurrence Download

description: A dataset containing 8666 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhinolophus euryale Blasius, 1853",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 8666 records from 262 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078794-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ws2pdy
title: Occurrence Download

description: A dataset containing 29301 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Musaceae"
 ]
}

 The dataset includes 29301 records from 604 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078785-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jhknut
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Acanthopetalum albidicolle (Verhoeff, 1900)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078795-240626123714530/datasets/export for details.