cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0825027.10fu
title: SH0825027.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0825028.10fu
title: SH0825028.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0825029.10fu
title: SH0825029.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0825030.10fu
title: SH0825030.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0825031.10fu
title: SH0825031.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0825032.10fu
title: SH0825032.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.49jsw2
title: Occurrence Download

description: A dataset containing 769082 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Gabon"
 ]
}

 The dataset includes 769082 records from 731 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077475-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25573/data.21391068
title: Heart rate monitoring of captive scimitar-horned oryx

description: Heart rate biologging has been successfully used in wildlife to study changes in metabolic rates, responses to natural or human-caused stressors (e.g., hunting, landscape of fear), stress, and animal welfare. Yet, applications of heart rate monitoring to inform wildlife management and conservation, especially for threatened and endangered species, are still rare. We successfully deployed subcutaneous heart rate monitors in eight captive scimitar-horned oryx, a species extinct in the wild, to assess their potential usefulness for monitoring the species during reintroduction activities. Detection accuracy was high for six of the individuals, ranging from 83 to 99%. We excluded data from two individuals with a right lateral placement of the biologger because accuracies were below 60%. Average heart rate across all six scimitar-horned oryx was 60.3 ± 12.7 bpm, and varied by about 12 bpm between individuals, with a minimum of 31 and a maximum of 188 bpm. Long-term patterns in heart rate were most strongly influenced by activity, and modulated by hour, season, and temperature. Circadian rhythms closely tracked activity with values increasing during the day and peaking near dusk. Seasonal heart rate patterns were reversed compared to activity. During winter, oryx were less active but exhibited higher relative heart rates, likely due to a combination of thermal stress from low temperatures and psychological stress from confined conditions in captivity. Patterns in activity for captive oryx were different from observations in the wild, where the species shows strong seasonal shifts from diurnal activity in the cool season to predominantly nocturnal activity in the hot dry season. We expect a similar pattern in heart rate in wild scimitar-horned oryx because of reduced overall activity and metabolic rate during the hot dry season, when food is scarce and evaporative water loss is high. When linked with movement data, the use of heart rate monitors in reintroduced scimitar-horned oryx would allow managers to assess how released animals adapt to their new environment and advance our knowledge about behavioral and physiological adaptations of desert ungulates to their extreme environment.
DOI: 10.25573/data.21391068.v3
title: Heart rate monitoring of captive scimitar-horned oryx

description: Heart rate biologging has been successfully used in wildlife to study changes in metabolic rates, responses to natural or human-caused stressors (e.g., hunting, landscape of fear), stress, and animal welfare. Yet, applications of heart rate monitoring to inform wildlife management and conservation, especially for threatened and endangered species, are still rare. We successfully deployed subcutaneous heart rate monitors in eight captive scimitar-horned oryx, a species extinct in the wild, to assess their potential usefulness for monitoring the species during reintroduction activities. Detection accuracy was high for six of the individuals, ranging from 83 to 99%. We excluded data from two individuals with a right lateral placement of the biologger because accuracies were below 60%. Average heart rate across all six scimitar-horned oryx was 60.3 ± 12.7 bpm, and varied by about 12 bpm between individuals, with a minimum of 31 and a maximum of 188 bpm. Long-term patterns in heart rate were most strongly influenced by activity, and modulated by hour, season, and temperature. Circadian rhythms closely tracked activity with values increasing during the day and peaking near dusk. Seasonal heart rate patterns were reversed compared to activity. During winter, oryx were less active but exhibited higher relative heart rates, likely due to a combination of thermal stress from low temperatures and psychological stress from confined conditions in captivity. Patterns in activity for captive oryx were different from observations in the wild, where the species shows strong seasonal shifts from diurnal activity in the cool season to predominantly nocturnal activity in the hot dry season. We expect a similar pattern in heart rate in wild scimitar-horned oryx because of reduced overall activity and metabolic rate during the hot dry season, when food is scarce and evaporative water loss is high. When linked with movement data, the use of heart rate monitors in reintroduced scimitar-horned oryx would allow managers to assess how released animals adapt to their new environment and advance our knowledge about behavioral and physiological adaptations of desert ungulates to their extreme environment.
DOI: 10.25573/data.21391068.v2
title: Heart rate monitoring of captive scimitar-horned oryx

description: Heart rate biologging has been successfully used in wildlife to study changes in metabolic rates, responses to natural or human-caused stressors (e.g., hunting, landscape of fear), stress, and animal welfare. Yet, applications of heart rate monitoring to inform wildlife management and conservation, especially for threatened and endangered species, are still rare. We successfully deployed subcutaneous heart rate monitors in eight captive scimitar-horned oryx, a species extinct in the wild, to assess their potential usefulness for monitoring the species during reintroduction activities. Detection accuracy was high for six of the individuals, ranging from 83 to 99%. We excluded data from two individuals with a right lateral placement of the biologger because accuracies were below 60%. Average heart rate across all six scimitar-horned oryx was 60.3 ± 12.7 bpm, and varied by about 12 bpm between individuals, with a minimum of 31 and a maximum of 188 bpm. Long-term patterns in heart rate were most strongly influenced by activity, and modulated by hour, season, and temperature. Circadian rhythms closely tracked activity with values increasing during the day and peaking near dusk. Seasonal heart rate patterns were reversed compared to activity. During winter, oryx were less active but exhibited higher relative heart rates, likely due to a combination of thermal stress from low temperatures and psychological stress from confined conditions in captivity. Patterns in activity for captive oryx were different from observations in the wild, where the species shows strong seasonal shifts from diurnal activity in the cool season to predominantly nocturnal activity in the hot dry season. We expect a similar pattern in heart rate in wild scimitar-horned oryx because of reduced overall activity and metabolic rate during the hot dry season, when food is scarce and evaporative water loss is high. When linked with movement data, the use of heart rate monitors in reintroduced scimitar-horned oryx would allow managers to assess how released animals adapt to their new environment and advance our knowledge about behavioral and physiological adaptations of desert ungulates to their extreme environment.
DOI: 10.6084/m9.figshare.21903858
title: Proteogenomic characterization of primary and locally recurrent breast cancers

description: We employed proteogenomics to analyze a cohort of 27 primary breast cancers and their matched IBTRs to define proteogenomic determinants of molecular tumor evolution.
DOI: 10.6084/m9.figshare.21903858.v1
title: Proteogenomic characterization of primary and locally recurrent breast cancers

description: We employed proteogenomics to analyze a cohort of 27 primary breast cancers and their matched IBTRs to define proteogenomic determinants of molecular tumor evolution.
DOI: 10.5281/zenodo.7552788
title: Unveiling the Relationship Between Continuous Integration and Code Coverage

description: Artifacts from <strong>Unveiling Relationships Between Continuous Integration and Code Coverage</strong>
DOI: 10.5281/zenodo.7552789
title: Unveiling the Relationship Between Continuous Integration and Code Coverage

description: Artifacts from <strong>Unveiling Relationships Between Continuous Integration and Code Coverage</strong>
DOI: 10.5281/zenodo.7555149
title: neosilurus_mollespiculum_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7555148
title: neosilurus_mollespiculum_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.6426041
title: Pelvic Bone Tumour Dataset CT

description: Datasets of CT images of pelvic bone tumors images and their labels to be used for 3d Segmentation in CNN deep learning artificial intelligence , MONAI (Medical Open Network in Artificial Intelligence)
DOI: 10.5281/zenodo.6459842
title: Pelvic Bone Tumour Dataset CT

description: Datasets of CT images of pelvic bone tumors images and their labels to be used for 3d Segmentation in CNN deep learning artificial intelligence , MONAI (Medical Open Network in Artificial Intelligence)
DOI: 10.5281/zenodo.6426040
title: Pelvic Bone Tumour Dataset CT

description: Datasets of CT images of pelvic bone tumors images and their labels to be used for 3d Segmentation in CNN deep learning artificial intelligence , MONAI (Medical Open Network in Artificial Intelligence)
DOI: 10.5281/zenodo.3926705
title: The Chilean Waiting List Corpus

description: Referrals from the waiting list for several specialty consultations in Chilean public hospitals were used to create a de-identified clinical corpus. A subset of 900 referrals was manually annotated with entities, attributes, and relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. <br> In this work we describe the Waiting List Corpus, the annotation process, and preliminary results for Named Entity Recognition (NER). The best results were obtained using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within the waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.
DOI: 10.5281/zenodo.4287459
title: The Chilean Waiting List Corpus

description: In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 3000 referrals was manually annotated with 27892 entities, 1272 attributes, and 762 pairs of relations with clinical relevance. <br> The corpus is 68 % medical and 32 % dental. A trained medical doctor or dentist annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 35 % of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
DOI: 10.5281/zenodo.5518225
title: The Chilean Waiting List Corpus

description: Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6 % medical and 41.4 % dental) was manually annotated with ten types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17 % of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/. We are releasing the dataset in 2 formats: cwlc_raw.zip: Contains the raw text files for each document along with its annotation file in Standoff format cwlc.zip: Contains the corpus already tokenized and annotated using the IOB2 format. The corpus is separated into train, test and development subsets.
DOI: 10.5281/zenodo.5591011
title: The Chilean Waiting List Corpus

description: Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6 % medical and 41.4 % dental) was manually annotated with ten types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17 % of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/. We are releasing the dataset in 3 formats: cwlc_raw.zip: Contains the raw text files for each document along with its annotation file in Standoff format cwlc.zip: Contains the corpus already tokenized and annotated using the IOB2 format. The corpus is separated into train, test and development subsets. cwlc_folds: Contains the dataset divided into the 10 folds utilized for cross-validation.