cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1064151.10fu
title: SH1064151.10FU

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/sh1064152.10fu
title: SH1064152.10FU

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/sh1064155.10fu
title: SH1064155.10FU

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: 10.5281/zenodo.17383804
title: A deep learning framework based on pragmatic analysis for inferring peptide-protein non-covalent interactions
title: KGIPA

description: This dataset accompanies the research presented in the paper "A deep learning framework based on pragmatic analysis for inferring peptide-protein non-covalent interactions".

Natural language processing provides a systematic route for studying the functions of biological sequences such as proteins. However, modeling residue-level interactions remains challenging due to their intrinsic complexity and environment dependence. In this context, we introduce KGIPA, a deep learning framework based on pragmatic analysis for inferring peptide-protein interactions. KGIPA employs graph neural networks and a bilinear network to represent intra- and extra-linguistic contexts, thereby integrating multimodal molecular features and capturing environment-dependent interaction patterns. It further adopts a knowledge-guided strategy to coordinate diverse types of non-covalent bonds, revealing the non-covalent binding mechanisms between peptides and proteins. The model consistently outperforms state-of-the-art methods across cross-validation, unseen evaluation, and multi-label assessments, and demonstrates robust performance in peptide-protein binding affinity prediction and virtual screening of peptide-based therapeutics. This work opens up opportunities for utilizing deep learning to infer complex molecular interactions.
DOI: 10.5281/zenodo.17383805
title: A deep learning framework based on pragmatic analysis for inferring peptide-protein non-covalent interactions
title: KGIPA

description: This dataset accompanies the research presented in the paper "A deep learning framework based on pragmatic analysis for inferring peptide-protein non-covalent interactions".

Natural language processing provides a systematic route for studying the functions of biological sequences such as proteins. However, modeling residue-level interactions remains challenging due to their intrinsic complexity and environment dependence. In this context, we introduce KGIPA, a deep learning framework based on pragmatic analysis for inferring peptide-protein interactions. KGIPA employs graph neural networks and a bilinear network to represent intra- and extra-linguistic contexts, thereby integrating multimodal molecular features and capturing environment-dependent interaction patterns. It further adopts a knowledge-guided strategy to coordinate diverse types of non-covalent bonds, revealing the non-covalent binding mechanisms between peptides and proteins. The model consistently outperforms state-of-the-art methods across cross-validation, unseen evaluation, and multi-label assessments, and demonstrates robust performance in peptide-protein binding affinity prediction and virtual screening of peptide-based therapeutics. This work opens up opportunities for utilizing deep learning to infer complex molecular interactions.
DOI: 10.15156/bio/sh1064156.10fu
title: SH1064156.10FU

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/sh1064157.10fu
title: SH1064157.10FU

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/sh1064162.10fu
title: SH1064162.10FU

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: 10.34740/kaggle/dsv/14452129
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15156/bio/sh1064163.10fu
title: SH1064163.10FU

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.x8vj3y
title: Occurrence Download

description: A dataset containing 21058 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-67.9278 44.94251,-74.61797 44.94251,-81.30813 41.55392,-84.2169 44.32152,-85.67129 41.98779,-86.12666 41.31659,-87.42396 41.19468,-88.83969 43.9052,-90.63624 46.02341,-93.92991 48.16342,-98.79527 48.53948,-115.96023 48.66421,-121.619 48.78864,-124.07114 48.16342,-124.07114 46.37171,-125.01427 42.60592,-123.88251 39.477,-121.43038 34.35305,-118.03511 33.09805,-116.5261 30.04526,-115.65617 28.74406,-115.04033 26.78238,-112.94644 25.34406,-111.59158 23.32412,-109.8672 22.30233,-107.77332 20.92827,-105.18676 18.72696,-100.99899 16.61466,-96.93439 15.54953,-95.71941 15.49416,-94.34027 15.77085,-92.90367 14.71747,-91.92679 14.77304,-90.49019 16.04717,-90.31779 17.80627,-89.68569 18.57056,-88.7088 19.27721,-88.40788 20.25242,-86.58458 20.79935,-86.38836 21.34476,-86.98488 22.12579,-83.66675 25.92246,-81.20834 24.60878,-79.42041 25.11572,-79.19692 27.42023,-80.20263 30.93179,-77.52073 33.67046,-74.95058 35.87319,-73.60963 39.06515,-70.48075 40.69432,-68.35758 43.26883,-67.9278 44.94251))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sylvilagus"
 ]
}

 The dataset includes 21058 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053457-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uv92tr
title: Occurrence Download

description: A dataset containing 472 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Romerolagus diazi"
 ]
}

 The dataset includes 472 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053458-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6075/j0zw1jfx
title: Data from: Feasibility of continuous fever monitoring using wearable devices

description: Sixty-five continuous days of data at one-minute resolution covering fifty individuals reporting COVID-19 infections starting on day 45 in the data set. Heart rate, heart rate variability, respiratory rate, and skin temperature are included, as generated by the second generation Oura ring. No PII included. Duration of symptoms, and presence of fever in symptoms included.

This code and data are those used to generate figures and statistics used in the publication "Feasibility of continuous fever monitoring using wearable devices."
description: Code written for Matlab 2019b; data generated on second generation Oura rings made by Oura Health Oy, 2020.
DOI: 10.3886/e239138
title: NSSP Emergency Department Visit Trajectories by State and Sub State Regions- COVID-19, Flu, RSV, Combined

description: Data from 2022-2025 in the United States sorted by state, and county and time period covering percent visits of common diseases such as covid-19, flu, RSV combined visits. This is combined with trends seen in these diseases based off past results.
DOI: 10.3886/e239138v1
title: NSSP Emergency Department Visit Trajectories by State and Sub State Regions- COVID-19, Flu, RSV, Combined

description: Data from 2022-2025 in the United States sorted by state, and county and time period covering percent visits of common diseases such as covid-19, flu, RSV combined visits. This is combined with trends seen in these diseases based off past results.
DOI: 10.15468/dl.jk4mkk
title: Occurrence Download

description: A dataset containing 4125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Harmonia axyridis",
 "HasCoordinate is true",
 "Country is Poland"
 ]
}

 The dataset includes 4125 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053459-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17419926
title: MPFL Reproducible Package

description: This record provides the reproducible analysis package for the MPFL literature study: R/Python scripts, summarized data, and result figures. Raw data are anonymized/aggregated due to privacy/ownership constraints. See README_TR_EN.md for details. The package replicates the analyses of “A Quarter-Century of MPFL Reconstruction Research (2000–2024): Global Trends, Citation Dynamics, and Thematic Evolution”; once available, the article DOI will be added under “Related identifiers” as IsSupplementTo.
DOI: 10.5281/zenodo.17419925
title: MPFL Reproducible Package

description: This record provides the reproducible analysis package for the MPFL literature study: R/Python scripts, summarized data, and result figures. Raw data are anonymized/aggregated due to privacy/ownership constraints. See README_TR_EN.md for details. The package replicates the analyses of “A Quarter-Century of MPFL Reconstruction Research (2000–2024): Global Trends, Citation Dynamics, and Thematic Evolution”; once available, the article DOI will be added under “Related identifiers” as IsSupplementTo.
DOI: 10.15468/dl.utkhdt
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Centriscus cristatus, Heraldia nocturna)"
 ]
}

 The dataset includes 176 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053460-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25612/837.3qvn987o75og
title: State of Housing (2024) - Data Archive

description: Collection of data from various sources used in the 2024 Kinder Institute for Urban Research's State of Housing Report in Harris County and Houston
DOI: 10.15468/dl.smfzh6
title: Occurrence Download

description: A dataset containing 1614591 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "InstitutionCode is iNaturalist",
 "Country is United States of America",
 "Year is greater than or equal to 2010"
 ]
}

 The dataset includes 1614591 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053419-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13481610
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.3886/e239137
title: CDC - 2023 Respiratory Virus Response - NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined

description: Each row is a percentage of emergency department patient vists for the specified pathogen of all ED patient visits for the specified geography that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP)<br>
description: Smallest Geographic Unit: State
DOI: 10.3886/e239137v1
title: CDC - 2023 Respiratory Virus Response - NSSP Emergency Department Visits - COVID-19, Flu, RSV, Combined

description: Each row is a percentage of emergency department patient vists for the specified pathogen of all ED patient visits for the specified geography that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP)<br>
description: Smallest Geographic Unit: State
DOI: 10.15468/dl.ppvxb4
title: Occurrence Download

description: A dataset containing 3490 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acer rufinerve",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3490 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053464-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8pd97y
title: Occurrence Download

description: A dataset containing 6002 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Harmonia axyridis",
 "HasCoordinate is true",
 "Country is Sweden"
 ]
}

 The dataset includes 6002 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053463-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.am3f23
title: Occurrence Download

description: A dataset containing 48626 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is one of (Argentina, Falkland Islands (Malvinas), South Georgia and the South Sandwich Islands)",
 {
 "and" : [
 "Country is one of (Argentina, Falkland Islands (Malvinas), South Georgia and the South Sandwich Islands)",
 "HasGeospatialIssue is false"
 ]
 },
 "Geometry POLYGON((-74 -60, -25 -60, -25 -90, -74 -90, -74 -60))"
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Plantae [GBIF Backbone Taxonomy])",
 "Country is one of (Argentina)",
 "InstitutionKey is one of (90771a5f-afbb-4d61-9b25-09a0ff3bc3d7, d7475b3a-52a4-4fa7-8136-5311900cdfcd, 1eeead7c-9721-4319-8c20-41b4e5837262, 8c4ae816-dfd9-4bc8-a2fd-f25a56bf82e9, eabef514-1abb-4fb9-90dc-9ef3da1f96dc)",
 "EstablishmentMeans is one of (native)"
 ]
 }
 ]
}

 The dataset includes 48626 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053462-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6075/j0wq024c
title: EarthCube User Scenario Collection

description: This collection holds a set of 50 user scenarios collected from end-user scientists that detail the specific cyberinfrastructure (CI) challenges and barriers that those geoscientists experience. The scenarios were collected by the Use Case Working Group within the National Science Foundation-funded EarthCube geosciences cyberinfrastructure program. The purpose of the user scenarios was to understand the CI needs and priorities of the geosciences community. In interviews, geoscientists were asked to describe their research, and identify those parts that were difficult or impossible in a way that new or better CI could help. These user scenarios are structured, thorough, in-depth expressions of how each scientist does his or her work, and what the pain points are CI might be able to address.
DOI: 10.15468/dl.jmkefw
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus leucotrichophora",
 "HasCoordinate is true"
 ]
}

 The dataset includes 148 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053466-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.