cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vk4zvw
title: Occurrence Download

description: A dataset containing 816 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Amazilia yucatanensis (S.Cabot, 1845)",
 "Year 1871-1980"
 ]
}

 The dataset includes 816 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0236488-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aptsx4
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hermbstaedtia linearis Schinz"
 ]
}

 The dataset includes 90 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020708-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9vd4xn
title: Occurrence Download

description: A dataset containing 11174821 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-1000.0",
 "Country is Spain",
 "Geometry POLYGON((-7.89188 44.37348,-9.57981 44.13168,-10.93016 40.57822,-11.52094 36.82551,-8.14507 35.59987,-4.00963 35.87389,-1.14015 36.35116,1.22296 38.69357,2.6577 40.64229,3.93084 42.42549,2.87476 43.14751,-1.89972 44.07107,-7.89188 44.37348))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 11174821 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013146-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8253898
title: Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II.

description: This image dataset: "Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II", is the second part of the image dataset for train and validate deep learning models.

This part contains only the training and validation images for No Oil and Lookalike.

the dataset comprises Sentinel-1 SAR images in Sigma0, in decibels (db), along with their ground truth. The images are 2048x2048x2, also the ground truth is 2048x2048; all of them are in TIFF format.

The files are organized in the following manner:



01_Train_Val_No_Oil_Images: There are 685 images of oil-free Sentinel-1 SAR Sigma0 images in the database.

01_Train_Val_No_Oil_mask: There are 685 images in this dataset that represent the ground truth of the Sentinel-1 SAR Sigma0 images in db of the oil-free scenes. In these images, the foreground has a value of 1, and the background has a value of 0. Because the focus of this dataset is on oil spills, the ground truth of oil-free images is assigned a value of 0.

01_Train_Val_Lookalike_images: The database contains 685 Sentinel-1 SAR Sigma0 images that correspond to oil-like surfaces, also known as look-alikes.

01_Train_Val_Lookalike_mask: It includes 685 images that correspond to the ground truth of the Sentinel-1 SAR Sigma0 images in the database of the look-alike images, where the foreground has a value of 1 and the background has a value of 0. Since the focus was on oil spills, the value of the ground truth of the look-alike images is only 0.


Each corresponding ground truth has the same number as its respective image. For instance, the image of an oil spill has a corresponding number of 0001, as well as its ground truth.

 

The complete dataset consists of three parts:

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I. (10.5281/zenodo.8346860)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II. (10.5281/zenodo.8253899)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III. (10.5281/zenodo.13761290)
description: Note that only the Sentinel-1 Sigma0 images in decibels (db) with two polarizations (VV, VH) and dimensions of 2048x2048x2 are georeferenced. The masks or ground truth of each of these images are not georeferenced because they were treated as matrices for the purpose of training, validation, and testing of deep learning models.
DOI: 10.5281/zenodo.8253899
title: Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II.

description: This image dataset: "Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II", is the second part of the image dataset for train and validate deep learning models.

This part contains only the training and validation images for No Oil and Lookalike.

the dataset comprises Sentinel-1 SAR images in Sigma0, in decibels (db), along with their ground truth. The images are 2048x2048x2, also the ground truth is 2048x2048; all of them are in TIFF format.

The files are organized in the following manner:



01_Train_Val_No_Oil_Images: There are 685 images of oil-free Sentinel-1 SAR Sigma0 images in the database.

01_Train_Val_No_Oil_mask: There are 685 images in this dataset that represent the ground truth of the Sentinel-1 SAR Sigma0 images in db of the oil-free scenes. In these images, the foreground has a value of 1, and the background has a value of 0. Because the focus of this dataset is on oil spills, the ground truth of oil-free images is assigned a value of 0.

01_Train_Val_Lookalike_images: The database contains 685 Sentinel-1 SAR Sigma0 images that correspond to oil-like surfaces, also known as look-alikes.

01_Train_Val_Lookalike_mask: It includes 685 images that correspond to the ground truth of the Sentinel-1 SAR Sigma0 images in the database of the look-alike images, where the foreground has a value of 1 and the background has a value of 0. Since the focus was on oil spills, the value of the ground truth of the look-alike images is only 0.


Each corresponding ground truth has the same number as its respective image. For instance, the image of an oil spill has a corresponding number of 0001, as well as its ground truth.

 

The complete dataset consists of three parts:

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I. (10.5281/zenodo.8346860)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II. (10.5281/zenodo.8253899)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III. (10.5281/zenodo.13761290)
description: Note that only the Sentinel-1 Sigma0 images in decibels (db) with two polarizations (VV, VH) and dimensions of 2048x2048x2 are georeferenced. The masks or ground truth of each of these images are not georeferenced because they were treated as matrices for the purpose of training, validation, and testing of deep learning models.
DOI: 10.5281/zenodo.13761289
title: Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III

description: This image dataset: "Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III", is the third part of the image dataset for train and validate deep learning models for oil spill detection and segmentation.

This part contains the test images.

The dataset comprises Sentinel-1 SAR images in Sigma0, in decibels (db), along with their ground truth. The images are 2048x2048x2, also the ground truth is 2048x2048; all of them are in TIFF format.

The files are organized in the following manner:



Images: Sentinel-1 SAR images in Sigma0 in decibels.



Lookalike: There are 150 images corresponding to look-alike surfaces

No oil: There are 150 oil free images

Oil: There are 150 oil spill images



Mask: Gorund truth masks images for each Sentinel-1 SAR images



Lookalike: There are 150 images corresponding to the ground truth for look-alike images

No oil: There are 150 images to corresponds oil free ground truth images

Oil: There are 150 images for the ground truth for oil spill images




Each corresponding ground truth has the same number as its respective image. For instance, the image of an oil spill has a corresponding number of 0001, as well as its ground truth.

 

The complete dataset consists of three parts:

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I. (10.5281/zenodo.8346860)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II. (10.5281/zenodo.8253899)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III. (10.5281/zenodo.13761290)
description: Note that only the Sentinel-1 Sigma0 images in decibels (db) with two polarizations (VV, VH) and dimensions of 2048x2048x2 are georeferenced. The masks or ground truth of each of these images are not georeferenced.
DOI: 10.5281/zenodo.13761290
title: Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III

description: This image dataset: "Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III", is the third part of the image dataset for train and validate deep learning models for oil spill detection and segmentation.

This part contains the test images.

The dataset comprises Sentinel-1 SAR images in Sigma0, in decibels (db), along with their ground truth. The images are 2048x2048x2, also the ground truth is 2048x2048; all of them are in TIFF format.

The files are organized in the following manner:



Images: Sentinel-1 SAR images in Sigma0 in decibels.



Lookalike: There are 150 images corresponding to look-alike surfaces

No oil: There are 150 oil free images

Oil: There are 150 oil spill images



Mask: Gorund truth masks images for each Sentinel-1 SAR images



Lookalike: There are 150 images corresponding to the ground truth for look-alike images

No oil: There are 150 images to corresponds oil free ground truth images

Oil: There are 150 images for the ground truth for oil spill images




Each corresponding ground truth has the same number as its respective image. For instance, the image of an oil spill has a corresponding number of 0001, as well as its ground truth.

 

The complete dataset consists of three parts:

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I. (10.5281/zenodo.8346860)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II. (10.5281/zenodo.8253899)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III. (10.5281/zenodo.13761290)
description: Note that only the Sentinel-1 Sigma0 images in decibels (db) with two polarizations (VV, VH) and dimensions of 2048x2048x2 are georeferenced. The masks or ground truth of each of these images are not georeferenced.
DOI: 10.6084/m9.figshare.25142804
title: Common commercially available parasiticides do not cause fatal changes in the microbiome of the dung beetle <i>Onthophagus binodis</i>–a pilot study

description: Dung beetles perform vital ecosystem functions, but their survival is threatened by parasiticide use in veterinary practices. Currently, it's unclear if parasiticides directly harm dung beetles or cause damage via secondary effects on crucial microbiome components. We investigated the impact of three commercial parasiticides (P1–levamisole and oxfendazole; P2–abamectin, levamisole and oxfendazole; P3–moxidectin) on <i>Onthophagus binodis</i> dung beetle gut and exoskeleton microbiomes. Dung beetles were subjected to parasiticides mixed with fresh dung for 14 days, and we used 16S rRNA genes to detect bacterial diversity changes. The tested parasiticides didn't alter dung beetle gut communities, but slightly affected the exoskeleton microbiota composition. No mortality difference was observed. <i>Onthophagus binodis</i> microbiomes are mainly diet-influenced, and their exoskeleton is more adaptable than their gut. Our study suggests that adjustable dung beetle microbiomes aren't susceptible to parasiticides due to their innate resilience.
DOI: 10.6084/m9.figshare.25142804.v1
title: Common commercially available parasiticides do not cause fatal changes in the microbiome of the dung beetle <i>Onthophagus binodis</i>–a pilot study

description: Dung beetles perform vital ecosystem functions, but their survival is threatened by parasiticide use in veterinary practices. Currently, it's unclear if parasiticides directly harm dung beetles or cause damage via secondary effects on crucial microbiome components. We investigated the impact of three commercial parasiticides (P1–levamisole and oxfendazole; P2–abamectin, levamisole and oxfendazole; P3–moxidectin) on <i>Onthophagus binodis</i> dung beetle gut and exoskeleton microbiomes. Dung beetles were subjected to parasiticides mixed with fresh dung for 14 days, and we used 16S rRNA genes to detect bacterial diversity changes. The tested parasiticides didn't alter dung beetle gut communities, but slightly affected the exoskeleton microbiota composition. No mortality difference was observed. <i>Onthophagus binodis</i> microbiomes are mainly diet-influenced, and their exoskeleton is more adaptable than their gut. Our study suggests that adjustable dung beetle microbiomes aren't susceptible to parasiticides due to their innate resilience.
DOI: 10.6084/m9.figshare.21994951
title: The mycobiota of faeces from the critically endangered kākāpō and associated nest litter

description: The critically endangered kākāpō is a flightless, nocturnal parrot endemic to Aotearoa New Zealand. During the 2019 breeding season multiple kākāpō on Whenua Hou, an island with one of the largest kākāpō populations, contracted the respiratory fungal disease aspergillosis. Infection is caused by members of the genus <i>Aspergillus</i> which can seriously impact avian health and is often fatal to wild and domestic birds. We sought to understand how <i>Aspergillus</i> influenced the gut mycobiota of kākāpō chicks and surrounding nest litter communities over a 10-week period. This study was conducted in parallel with another longitudinal project utilising the same samples where we investigated the influence of management practice on development of gut bacterial communities in kākāpō chicks. Ultimately, aspergillosis infection had no significant impact on the fungal communities of kākāpō chicks or nest litter, and nor did regular management practices (i.e. artificially removing faecal material from nests). Age and time, however, were significantly associated with variation in the mycobiotas of chick and nest samples. Overall, this study represents our first glimpse of the kākāpō gut mycobiota and provides essential information and future research directions for the conservation of this rare species.
DOI: 10.6084/m9.figshare.21994951.v1
title: The mycobiota of faeces from the critically endangered kākāpō and associated nest litter

description: The critically endangered kākāpō is a flightless, nocturnal parrot endemic to Aotearoa New Zealand. During the 2019 breeding season multiple kākāpō on Whenua Hou, an island with one of the largest kākāpō populations, contracted the respiratory fungal disease aspergillosis. Infection is caused by members of the genus <i>Aspergillus</i> which can seriously impact avian health and is often fatal to wild and domestic birds. We sought to understand how <i>Aspergillus</i> influenced the gut mycobiota of kākāpō chicks and surrounding nest litter communities over a 10-week period. This study was conducted in parallel with another longitudinal project utilising the same samples where we investigated the influence of management practice on development of gut bacterial communities in kākāpō chicks. Ultimately, aspergillosis infection had no significant impact on the fungal communities of kākāpō chicks or nest litter, and nor did regular management practices (i.e. artificially removing faecal material from nests). Age and time, however, were significantly associated with variation in the mycobiotas of chick and nest samples. Overall, this study represents our first glimpse of the kākāpō gut mycobiota and provides essential information and future research directions for the conservation of this rare species.
DOI: 10.6084/m9.figshare.24269026
title: Microbial signatures of vertebrate visitation in floral nectar: a case study with two endemic Aotearoa New Zealand plant species

description: Microbes found in nectar and dispersed by animal visitors can mediate pollination and host fitness. While previous studies have characterised floral nectar microbiomes and their effects on invertebrate pollinators, fewer studies have focused on vertebrate pollinators, especially outside of the Northern Hemisphere. In Aotearoa New Zealand, vertebrates, such as birds and bats, are critical pollinators for many native plant species. Here, we present nectar microbiome profiles for two endemic, vertebrate-pollinated plant species in Aotearoa New Zealand, wharariki, mountain flax (<i>Phormium cookianum</i>), and pua o te Rēinga, wood rose (<i>Dactylanthus taylorii</i>). We used vertebrate exclusion treatments and camera traps to monitor visitation. Camera trap footage revealed silvereye (<i>Zosterops lateralis</i>), ship rat (<i>Rattus rattus</i>), and possum (<i>Trichosurus vulpecula)</i> visitation. We detected shifts in microbial species turnover in wood rose and varying relative abundances of fungal and bacterial taxa across the vertebrate exclusion treatments for both mountain flax and wood rose. However, we did not detect strong effects on floral nectar microbiome community composition or richness. Future work should move beyond profiling the microbial communities and identify fitness consequences and pollination outcomes, which could affect conservation and management decisions.
DOI: 10.6084/m9.figshare.24269026.v1
title: Microbial signatures of vertebrate visitation in floral nectar: a case study with two endemic Aotearoa New Zealand plant species

description: Microbes found in nectar and dispersed by animal visitors can mediate pollination and host fitness. While previous studies have characterised floral nectar microbiomes and their effects on invertebrate pollinators, fewer studies have focused on vertebrate pollinators, especially outside of the Northern Hemisphere. In Aotearoa New Zealand, vertebrates, such as birds and bats, are critical pollinators for many native plant species. Here, we present nectar microbiome profiles for two endemic, vertebrate-pollinated plant species in Aotearoa New Zealand, wharariki, mountain flax (<i>Phormium cookianum</i>), and pua o te Rēinga, wood rose (<i>Dactylanthus taylorii</i>). We used vertebrate exclusion treatments and camera traps to monitor visitation. Camera trap footage revealed silvereye (<i>Zosterops lateralis</i>), ship rat (<i>Rattus rattus</i>), and possum (<i>Trichosurus vulpecula)</i> visitation. We detected shifts in microbial species turnover in wood rose and varying relative abundances of fungal and bacterial taxa across the vertebrate exclusion treatments for both mountain flax and wood rose. However, we did not detect strong effects on floral nectar microbiome community composition or richness. Future work should move beyond profiling the microbial communities and identify fitness consequences and pollination outcomes, which could affect conservation and management decisions.
DOI: 10.6084/m9.figshare.27919129
title: Elusive aetiology of exudative cloacitis in the critically endangered kākāpō

description: The kākāpō (<i>Strigops habroptilus</i>) is a critically endangered, flightless parrot endemic to Aotearoa New Zealand. While an intensive recovery programme has led to considerable successes in kākāpō conservation, management of this iconic species has been complicated since 2002 by the emergence of the inflammatory syndrome exudative cloacitis. Cloacitis is a significant threat to kākāpō health yet its origin remains unknown. With recent increases in prevalence coupled with the first detection of cases originating on islands beyond where it was initially detected, understanding the risk factors that lead to this condition remains a high priority for kākāpō conservation. Here we undertake a pathogen discovery approach incorporating metagenomic (DNA) and metatranscriptomic (RNA) sequencing of faecal and cloacal swab samples from affected and healthy kākāpō. We report, for the first time, differentially higher transcript abundances from five bacterial taxa in cloacitis versus healthy birds, as well as abundant <i>Clostridium sensu stricto 1</i> bacteria in severe cloacitis cases. Furthermore, affected birds exhibited significantly higher expression of <i>hok</i>/<i>gef</i> genes that encode for toxin proteins in bacteria including <i>Escherichia coli</i>, the dominant member of the kākāpō microbiome. While not directly identifying specific risk factors, our results contribute to ongoing efforts to elucidate the underlying causes of exudative cloacitis in kākāpō.
DOI: 10.6084/m9.figshare.27919129.v1
title: Elusive aetiology of exudative cloacitis in the critically endangered kākāpō

description: The kākāpō (<i>Strigops habroptilus</i>) is a critically endangered, flightless parrot endemic to Aotearoa New Zealand. While an intensive recovery programme has led to considerable successes in kākāpō conservation, management of this iconic species has been complicated since 2002 by the emergence of the inflammatory syndrome exudative cloacitis. Cloacitis is a significant threat to kākāpō health yet its origin remains unknown. With recent increases in prevalence coupled with the first detection of cases originating on islands beyond where it was initially detected, understanding the risk factors that lead to this condition remains a high priority for kākāpō conservation. Here we undertake a pathogen discovery approach incorporating metagenomic (DNA) and metatranscriptomic (RNA) sequencing of faecal and cloacal swab samples from affected and healthy kākāpō. We report, for the first time, differentially higher transcript abundances from five bacterial taxa in cloacitis versus healthy birds, as well as abundant <i>Clostridium sensu stricto 1</i> bacteria in severe cloacitis cases. Furthermore, affected birds exhibited significantly higher expression of <i>hok</i>/<i>gef</i> genes that encode for toxin proteins in bacteria including <i>Escherichia coli</i>, the dominant member of the kākāpō microbiome. While not directly identifying specific risk factors, our results contribute to ongoing efforts to elucidate the underlying causes of exudative cloacitis in kākāpō.
DOI: 10.3886/e209244
title: Disparities in Access to Dental Care During COVID-19 Pandemic: An Analysis of RAPID-SF Survey

description: This cross sectional study examined factors influencing access to dental care in the San Francisco Bay Area, using data from 2022 RAPID SF Survey. It assessed prevalence of dental visits in relation to race, household language, insurance type, education level, and income status. <br>
DOI: 10.3886/e209244v1-161761
title: Disparities in Access to Dental Care During COVID-19 Pandemic: An Analysis of RAPID-SF Survey

description: This cross sectional study examined factors influencing access to dental care in the San Francisco Bay Area, using data from 2022 RAPID SF Survey. It assessed prevalence of dental visits in relation to race, household language, insurance type, education level, and income status. <br>
DOI: 10.6084/m9.figshare.28039421
title: mixed_reality_stereo_vision

description: A study on the multimodal characterization of stereoscopic visual fatigue induced by mixed reality environments. Please refer to our GitHub repository in details.