cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1217381.10fu
title: SH1217381.10FU

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.vrqjn2
title: Occurrence Download

description: A dataset containing 129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhodiola quadrifida"
 ]
}

 The dataset includes 129 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002812-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1217382.10fu
title: SH1217382.10FU

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/sh1217384.10fu
title: SH1217384.10FU

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/sh1217385.10fu
title: SH1217385.10FU

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: 10.3886/e243239
title: Excess Deaths Associated with COVID-19

description: Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected.

Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.
Read less
description: (Downloaded 1/10/2026)
DOI: 10.15156/bio/sh1217388.10fu
title: SH1217388.10FU

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: 10.3886/e243239v1
title: Excess Deaths Associated with COVID-19

description: Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected.

Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.
Read less
description: (Downloaded 1/10/2026)
DOI: 10.15156/bio/sh1217389.10fu
title: SH1217389.10FU

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/sh1217391.10fu
title: SH1217391.10FU

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/sh1217392.10fu
title: SH1217392.10FU

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/sh1217393.10fu
title: SH1217393.10FU

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/sh1217394.10fu
title: SH1217394.10FU

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: 10.3886/e242619
title: BEAM Dashboard â€“ Top 30 Most Common Serotypes

description: The BEAM (Bacteria, Enterics, Amoeba, and Mycotics) Dashboard is an interactive tool to access and visualize data from the System for Enteric Disease Response, Investigation, and Coordination (SEDRIC). The BEAM Dashboard provides timely data on pathogen trends and serotype details to inform work to prevent illnesses from food and animal contact.
description: (Downloaded 1/7/2026)
DOI: 10.3886/e242619v1
title: BEAM Dashboard â€“ Top 30 Most Common Serotypes

description: The BEAM (Bacteria, Enterics, Amoeba, and Mycotics) Dashboard is an interactive tool to access and visualize data from the System for Enteric Disease Response, Investigation, and Coordination (SEDRIC). The BEAM Dashboard provides timely data on pathogen trends and serotype details to inform work to prevent illnesses from food and animal contact.
description: (Downloaded 1/7/2026)
DOI: 10.15468/dl.5rxqy5
title: Occurrence Download

description: A dataset containing 572 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eucalyptus astringens"
 ]
}

 The dataset includes 572 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080488-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ckg5x8
title: Occurrence Download

description: A dataset containing 572 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eucalyptus astringens"
 ]
}

 The dataset includes 572 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080489-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qxsipz
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017839-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fmkowj
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000699-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.iblrgy
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000719-160430102343922/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qeysyq
title: Occurrence Download

description: A dataset containing 2135416126 species occurrences available in GBIF matching the query:
{
 "Year" : [
 "1000-2022"
 ]
}

 The dataset includes 2135416126 records from 40955 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0146595-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/713unkgd
title: Energy Reconstruction from TAIGA-IACT Images using Deep Learning Methods

description: The dataset used for energy reconstruction in TAIGA-IACT images using deep learning methods.
DOI: 10.57702/yur9bod4
title: 3D-Chairs dataset

description: 3D-Chairs dataset consists of 30,000 training and 10,000 testing samples.
DOI: 10.57702/ld4du07r
title: Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning

description: This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL).
DOI: 10.57702/4vwrx3tu
title: SEER dataset

description: The dataset used in this paper is a collection of patient data from the SEER database, including variables such as age, gender, grade, and treatment information.
DOI: 10.57702/i3077efo
title: Dmoz dataset

description: The dataset used in the paper is a web directory dataset, with 15159 instances, 17659 attributes, and 354 categories. The dataset is used for clustering and evaluation of the proposed method.
DOI: 10.57702/imoy7637
title: Serie A Soccer Player Ratings

description: The dataset consists of soccer player ratings assigned by three Italian sports newspapers.
DOI: 10.57702/d3shyq48
title: Viola

description: Unified codec language models for speech recognition, synthesis, and translation.
DOI: 10.57702/wki28hpk
title: Rotating Machine Dataset

description: Vibration sensor data from five rotating machines
DOI: 10.57702/fsox36xy
title: Java-Only Monolingual Triple Dataset

description: A multilingual Java-only monolingual triple dataset for jointly repairing code and generating commit messages.
DOI: 10.57702/khihokvv
title: CARMENES M dwarfs dataset

description: The dataset used in this paper is a collection of 286 M dwarfs observed by the CARMENES survey. The dataset contains the stellar parameters Teff, log g, [M/H], and v sin i for each star.
DOI: 10.57702/gefadviy
title: Incomplete Multi-view Clustering via Cross-view Relation Transfer

description: Incomplete multi-view clustering on incomplete views
DOI: 10.57702/eh5fkxzt
title: Microsoft Research Paraphrase Corpus

description: The Microsoft Research Paraphrase Corpus (MRPC) consists of sentence pairs that have been labeled for whether they are paraphrases. It is used as a benchmark for evaluating paraphrase identification models.
DOI: 10.6084/m9.figshare.31017895
title: <b>Physical activity modifies the association between source-specific noise exposure and chronic pain: A cross-sectional analysis of NHANES 1999–2004</b>

description: Background: Chronic pain is a prevalent public health problem, and environmental noise exposure has been implicated as a potential risk factor. However, evidence remains limited regarding different sources of noise exposure and whether lifestyle factors, such as physical activity, modify the association between noise exposure and chronic pain. Methods: We conducted a cross-sectional analysis of adults aged ≥18 years using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004. Noise exposure was assessed by self-reported occupational noise exposure, non-occupational noise exposure, and firearms noise exposure. Chronic pain was defined as self-reported pain lasting more than one year. Total physical activity was quantified as metabolic equivalent of task minutes per week (MET-min/week) and categorized into tertiles. Survey-weighted logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for demographic, socioeconomic, behavioral, and clinical covariates. Subgroup analyses and sensitivity analyses were performed to assess the robustness of the findings and potential effect modification. Results: Among 1,869 participants included in the analysis, 790 reported chronic pain. In fully adjusted models, firearms noise exposure was significantly associated with higher odds of chronic pain (OR 1.77, 95% CI 1.25–2.51; P = 0.003), whereas occupational and non-occupational noise exposures were not. Physical activity significantly modified the association between firearms noise exposure and chronic pain (P for interaction = 0.012). Stratified analyses showed that firearms noise exposure was strongly associated with chronic pain among individuals in the lowest physical activity tertile (OR 3.42, 95% CI 2.11–5.55; P &lt; 0.001), but not among those in the middle or highest tertiles. Consistent patterns were observed in analyses of weighted chronic pain prevalence. Conclusion: Firearms noise exposure was associated with chronic pain among U.S. adults, particularly among individuals with low physical activity.