cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.scw7gu
title: Occurrence Download

description: A dataset containing 274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alouatta palliata (Gray, 1849)"
 ]
}

 The dataset includes 274 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070694-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ynxa5y
title: Occurrence Download

description: A dataset containing 368018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "DatasetKey is International Barcode of Life project (iBOL)",
 "OccurrenceStatus is Present",
 "TaxonKey is Diptera"
 ]
}

 The dataset includes 368018 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070689-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zzf5bc
title: Occurrence Download

description: A dataset containing 20603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Romania",
 "DatasetKey is A global database for the distributions of crop wild relatives"
 ]
}

 The dataset includes 20603 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070696-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n2jjzv
title: Occurrence Download

description: A dataset containing 25595 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Bulgaria",
 "DatasetKey is A global database for the distributions of crop wild relatives"
 ]
}

 The dataset includes 25595 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070697-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7mkxhf
title: Occurrence Download

description: A dataset containing 2254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Croatia",
 "DatasetKey is A global database for the distributions of crop wild relatives"
 ]
}

 The dataset includes 2254 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070699-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15750918
title: FRmatch Lung Reference small
DOI: 10.5281/zenodo.15750919
title: FRmatch Lung Reference small
DOI: 10.15468/dl.7xt47p
title: Occurrence Download

description: A dataset containing 1052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Formicivora iheringi Hellmayr, 1909"
 ]
}

 The dataset includes 1052 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070700-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rqc9s
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Mormonilla phasma Giesbrecht, 1891"
 ]
}

 The dataset includes 55 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070701-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tnbqaq
title: Occurrence Download

description: A dataset containing 1382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Penelope albipennis Taczanowski, 1878"
 ]
}

 The dataset includes 1382 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070703-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15750880
title: Topography restoration in white-light interferometry using an instrument transfer function evaluated with binary pseudo-random arrays: Dataset and Codes

description: This dataset contains all files needed to reproduce the figures in the paper “Topography restoration in white-light interferometry using an instrument transfer function evaluated with binary pseudo-random arrays.”

From the repository’s root directory, run `make` to generate the figures; the outputs will be saved in the `figs/` folder.
DOI: 10.17608/k6.auckland.14173982
title: CANDID-PTX

description: 19,237 anonymized adult chest x-ray datasets in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Images were manually annotated by RANZCR radiology trainee and radiologists with respect to pneumothorax, acute rib fracture, and chest tubes. Segmentation annotations were converted to run-length-coded (RLE) format in csv files. In the provided metadata, image filenames contain patient index (enabling analysis requiring patient grouping of images), as well as anonymized date of acquisition information where the temporal relationship between images is preserved.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID PTX dataset is to be accessed by the external users. Therefore, the CANDID PTX dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.14173982.v2
title: CANDID-PTX

description: 19,237 anonymized adult chest x-ray datasets in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Images were manually annotated by RANZCR radiology trainee and radiologists with respect to pneumothorax, acute rib fracture, and chest tubes. Segmentation annotations were converted to run-length-coded (RLE) format in csv files. In the provided metadata, image filenames contain patient index (enabling analysis requiring patient grouping of images), as well as anonymized date of acquisition information where the temporal relationship between images is preserved.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID PTX dataset is to be accessed by the external users. Therefore, the CANDID PTX dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.14173982.v1
title: CANDID-PTX

description: 19,237 anonymized adult chest x-ray datasets in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Images were manually annotated by RANZCR radiology trainee and radiologists with respect to pneumothorax, acute rib fracture, and chest tubes. Segmentation annotations were converted to run-length-coded (RLE) format in csv files. In the provided metadata, image filenames contain patient index (enabling analysis requiring patient grouping of images), as well as anonymized date of acquisition information where the temporal relationship between images is preserved.To access the data, an ethics training process is required and is divided into 2 steps:1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to sijingfeng@gmail.com.2. Signing the Data Use Agreement. It can be accessed at Data Use Agreement.pdf. Once you signed the Data Use Agreement, please also send the signed copy to sijingfeng@gmail.comAfter you have successfully completed both of above steps, you will receive a link to download the dataset.
DOI: 10.5281/zenodo.15750932
title: NIKOLAI LESKOV AND HIS ARTISTIC STYLE
DOI: 10.5281/zenodo.15750933
title: NIKOLAI LESKOV AND HIS ARTISTIC STYLE
DOI: 10.6084/m9.figshare.29421361.v1
title: Dexmedetomidine protects against cerebral ischemia-reperfusion injury in mice by interfering with the crosstalk between autophagy and ferroptosis

description: Previous studies show DEX protects against focal cerebral ischemia by hindering autophagy. However, the exact mechanisms are unclear. This study hypothesizes DEX may protect neurons by regulating the crosstalk between autophagy and ferroptosis. In this study, male C57/BL6 mice were used in an MCAO model divided into five groups. After MCAO and reperfusion, treatments with DEX, DEX+Erastin, or DEX+rapamycin were administered. Neurological deficits and cerebral infarction volumes were measured 24 hours later. Electron microscopy and molecular techniques were used to analyze autophagy and ferroptosis markers to explore DEX’s neuroprotective mechanism. Compared to sham controls, the model group showed significantly increased cerebral infarction volume and neurobehavioral scores (<i>p</i> &lt; 0.05). DEX treatment reversed these effects, demonstrating neuroprotection against ischemic injury. Simultaneously, DEX treatment safeguards mitochondrial integrity by suppressing autophagy lysosomes. It also upregulates the expression of glutathione (GSH) protein, solute carrier family 7 member 11 (SLC7A11) protein, and glutathione peroxidase 4 (GPX4). Moreover, DEX inhibits the levels of malondialdehyde (MDA), ferrous ion (Fe2+), microtubule-associated protein 1A/1B-light chain 3 (LC3), and Beclin1 proteins (all <i>p</i> values &lt; 0.05), thereby intervening in the crosstalk between the autophagy and ferroptosis pathways and ultimately exerting its neuroprotective function. Notably, DEX’s effects were negated by ferroptosis inducer Erastin or autophagy inducer RAPA. Dexmedetomidine modulates the crosstalk between autophagy and ferroptosis pathways, thereby protecting the mouse brain against ischemia-reperfusion injury and providing novel insights for the future treatment of ischemic stroke.
DOI: 10.6084/m9.figshare.29421361
title: Dexmedetomidine protects against cerebral ischemia-reperfusion injury in mice by interfering with the crosstalk between autophagy and ferroptosis

description: Previous studies show DEX protects against focal cerebral ischemia by hindering autophagy. However, the exact mechanisms are unclear. This study hypothesizes DEX may protect neurons by regulating the crosstalk between autophagy and ferroptosis. In this study, male C57/BL6 mice were used in an MCAO model divided into five groups. After MCAO and reperfusion, treatments with DEX, DEX+Erastin, or DEX+rapamycin were administered. Neurological deficits and cerebral infarction volumes were measured 24 hours later. Electron microscopy and molecular techniques were used to analyze autophagy and ferroptosis markers to explore DEX’s neuroprotective mechanism. Compared to sham controls, the model group showed significantly increased cerebral infarction volume and neurobehavioral scores (<i>p</i> &lt; 0.05). DEX treatment reversed these effects, demonstrating neuroprotection against ischemic injury. Simultaneously, DEX treatment safeguards mitochondrial integrity by suppressing autophagy lysosomes. It also upregulates the expression of glutathione (GSH) protein, solute carrier family 7 member 11 (SLC7A11) protein, and glutathione peroxidase 4 (GPX4). Moreover, DEX inhibits the levels of malondialdehyde (MDA), ferrous ion (Fe2+), microtubule-associated protein 1A/1B-light chain 3 (LC3), and Beclin1 proteins (all <i>p</i> values &lt; 0.05), thereby intervening in the crosstalk between the autophagy and ferroptosis pathways and ultimately exerting its neuroprotective function. Notably, DEX’s effects were negated by ferroptosis inducer Erastin or autophagy inducer RAPA. Dexmedetomidine modulates the crosstalk between autophagy and ferroptosis pathways, thereby protecting the mouse brain against ischemia-reperfusion injury and providing novel insights for the future treatment of ischemic stroke.
DOI: 10.17608/k6.auckland.19606921
title: CANDID-II Dataset

description: <br>53,054 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. Therefore, the CANDID II dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.19606921.v2
title: CANDID-II Dataset

description: <br>53,054 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. Therefore, the CANDID II dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.19606921.v1
title: CANDID-II Dataset

description:    
53,054 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.
<br>
To access the data, an ethics training process is required and is divided into 2 steps:
1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to Sijing.Feng@southerndhb.govt.nz
2. Signing the Data Use Agreement. It can be accessed at Data_Use_Agreement_-_Hospital_Signed.pdf (trello-attachments.s3.amazonaws.com). Once you signed the Data Use Agreement, please also send the signed copy to Sijing.Feng@southerndhb.govt.nz
<br>
After successfully completion of both of above steps, a private link to download the dataset will be sent.
DOI: 10.17608/k6.auckland.22726004
title: CANDID-III Dataset

description: <br>288,776 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 45 common radiological findings mapped to Unified Medical Language System (UMLS) ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. 33,486 studies were manually labeled. 255,290 were labeled by deep learning models. Accuracy of the AI labeled portion of the dataset with respect to each label will be outlined in the published paper. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID III dataset is to be accessed by the external users. Therefore, the CANDID III dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.22726004.v2
title: CANDID-III Dataset

description: <br>288,776 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 45 common radiological findings mapped to Unified Medical Language System (UMLS) ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. 33,486 studies were manually labeled. 255,290 were labeled by deep learning models. Accuracy of the AI labeled portion of the dataset with respect to each label will be outlined in the published paper. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID III dataset is to be accessed by the external users. Therefore, the CANDID III dataset is not available for access by users outside Health New Zealand. Further notice of access will be updated here should access by external users be reopened.
DOI: 10.17608/k6.auckland.22726004.v1
title: CANDID-III Dataset

description: <br>288,776 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 45 common radiological findings mapped to Unified Medical Language System (UMLS) ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. 33,486 studies were manually labeled. 255,290 were labeled by deep learning models. Accuracy of the AI labeled portion of the dataset with respect to each label will be outlined in the published paper. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.To access the data, an ethics training process is required and is divided into 2 steps:1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to sijingfeng@gmail.com2. Signing the Data Use Agreement. It can be accessed at Data Use Agreement- Unanonymised data.pdf. Once you signed the Data Use Agreement, please also send the signed copy to sijingfeng@gmail.comAfter successfully completion of both of above steps, a private link to download the dataset will be sent.
DOI: 10.15468/dl.tvpqck
title: Occurrence Download

description: A dataset containing 1729 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, NZ National Vegetation Survey occurrence data, Allan Herbarium (CHR), Auckland Museum Botany Collection, WELT Herbarium at Museum of New Zealand Te Papa Tongarewa, Dame Ella Campbell Herbarium (MPN) AVH data, New Zealand National Forestry Herbarium)",
 "TaxonKey is Lophomyrtus bullata (Sol. ex A.Cunn.) Burret"
 ]
}

 The dataset includes 1729 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070709-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15751048
title: УЗБЕКИСТАН И ВЕНГРИЯ В СИСТЕМЕ МИРОВОЙ ЦИВИЛИЗАЦИИ
DOI: 10.5281/zenodo.15751049
title: УЗБЕКИСТАН И ВЕНГРИЯ В СИСТЕМЕ МИРОВОЙ ЦИВИЛИЗАЦИИ
DOI: 10.15468/dl.5fntrv
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Urtica dioica L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Japan"
 ]
}

 The dataset includes 120 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070713-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.