cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0186899.10fu
title: SH0186899.10FU

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/sh0186900.10fu
title: SH0186900.10FU

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/sh0186901.10fu
title: SH0186901.10FU

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/sh0186902.10fu
title: SH0186902.10FU

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: 10.7910/dvn/f2bvlu
title: US Geological Survey (USGS) Landsat Collection 2 Surface Reflectance-derived Spectral Indices

description: The Landsat Collection 2 Surface Reflectance-derived Spectral Indices offers data for vegetation, moisture, burn ratio, and snow indices that are derived from satellite-based observations of global surface reflectance. The data are available upon request and are generated at 30 m spatial resolution.
description: <b>Intended Use</b>Geospatial exposure assessment for public health studies on shorter-term or longer-term outcomesGeospatial exposure assessment at the individual-level or population-levelLong-term (multi-decade) health cohort studiesGeospatial exposure model development<br><b>Strengths</b>Dataset supports analysis of variation in greenness across global locations and multiple decades in a consistent wayDataset supports analysis of finely-resolved spatial variation (e.g., within-neighborhood differences) in vegetation indices<br><b>Limitations</b>Temporal discontinuities between different Landsat satellite missions (e.g., gap in available data in 2012 between Landsat 7 and Landsat 8 missions)Data quality varies based on cloud cover, air quality, sunlight angle, and other factors that impact satellite-based sensing of land surface reflectanceLimited data quality information (e.g., pixel saturation) provided for each indexTemporal discontinuities between different Landsat satellite missions (e.g., gap in available data in 2012 between Landsat 7 and Landsat 8 missions)Data quality varies based on cloud cover, air quality, sunlight angle, and other factors that impact satellite-based sensing of land surface reflectanceLimited data quality information (e.g., pixel saturation) provided for each index<br><b>Suggested Audience</b>Researcher<br>
DOI: 10.15156/bio/sh0186903.10fu
title: SH0186903.10FU

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/sh0186904.10fu
title: SH0186904.10FU

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/sh0186905.10fu
title: SH0186905.10FU

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/sh0186906.10fu
title: SH0186906.10FU

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: 10.6084/m9.figshare.26568439.v1
title: Additional file 6 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 6: Table S4. Mann-Whitney U statistical analysis between PCSTs related to athletes and sedentary samples (Sheet_1). Statistical Kruskall-Wallis analysis of bacterial species between PCST clusters (Sheet_2).
DOI: 10.6084/m9.figshare.26568439
title: Additional file 6 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 6: Table S4. Mann-Whitney U statistical analysis between PCSTs related to athletes and sedentary samples (Sheet_1). Statistical Kruskall-Wallis analysis of bacterial species between PCST clusters (Sheet_2).
DOI: 10.6084/m9.figshare.26568442
title: Additional file 7 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 7: Table S5. Bray-Curtis dissimilarity matrix based on taxonomical data (Sheet_1). Bray-Curtis dissimilarity matrix based on functional data (Sheet_2).
DOI: 10.6084/m9.figshare.26568451
title: Additional file 10 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 10: Table S8. Correlation analysis results of all the HBIS against EFC_1 and EFC_4.
DOI: 10.15468/dl.6f22w7
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.229643 -36.89901, 149.229643 -36.901255, 149.231889 -36.901255, 149.231889 -36.89901, 149.229643 -36.89901))"
 ]
}

 The dataset includes 94 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066643-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5678/y2rt-xx81
title: Wet seasonal climate measurements of temperature, relative humidity and wind speed at the mobile AWS 1 and 2
title: aws mob1 and 2 wet season

description: SITE,LAT,LON<br>
MOBIL1,4.04534,-52.67833<br>
MOBIL2,4.03938,-52.67379<br>
TURM,4.04232,-52.67672
DOI: 10.6084/m9.figshare.26568508.v1
title: Additional file 1 of Timing and delivery route effects of cecal microbiome transplants on Salmonella Typhimurium infections in chickens: potential for in-hatchery delivery of microbial interventions

description: Additional file 1. The main feature, taxonomic and metadata tables used to analyse and describe the data collected across the two CMT experiments.
DOI: 10.6084/m9.figshare.26568454
title: Additional file 11 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 11: Table S9. EC Back-tracing report of the extracted nine HBIS manually analyzed on MetaCyc and related to EFC_4.
DOI: 10.15468/dl.uawtd8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.722092 -32.477031, 148.722092 -32.483769, 148.726584 -32.483769, 148.726584 -32.477031, 148.722092 -32.477031))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066644-240626123714530/datasets/export for details.
DOI: 10.5678/34e6-ap63
title: Dry seasonal climate measurements of temperature, relative humidity and wind speed at the mobile AWS 1 and 2
title: aws mob1 and 2

description: SITE,LAT,LON<br>
MOBIL1,4.04534,-52.67833<br>
MOBIL2,4.03938,-52.67379<br>
TURM,4.04232,-52.67672
DOI: 10.6084/m9.figshare.26568448
title: Additional file 9 of The human gut microbiome of athletes: metagenomic and metabolic insights

description: Additional file 9: Table S7. Full Spearman correlation analysis result of all the EC numbers Vs. all the EFC (1-4) identified.
DOI: 10.15468/dl.5sgmnb
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Leucaena leucocephala (Lam.) de Wit"
 ]
}

 The dataset's 3 records were derived from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066628-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26600602
title: Additional file 2 of 12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

description: Additional file 2. The detailed information of all the identified serum metabolite among T2DM patients with or without AMI detected by untargeted metabonomic. N = 222. D, Type II diabetes. A, acute myocardial infarction. C, healthy control. Screening methods: In OPLS-DA model, Variable important in projection (VIP) &gt; 1, P &lt; 0.05. T2DM, Type II diabetes; AMI, Acute myocardial infarction.
DOI: 10.6084/m9.figshare.26600605
title: Additional file 3 of 12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

description: Additional file 3. The detailed information of 146’s serum differential metabolites among T2DM patients with or without AMI detected by untargeted metabonomic. Screening methods: In OPLS-DA model, Variable important in projection (VIP) &gt; 1, P &lt; 0.05. T2DM, Type II diabetes; AMI, Acute myocardial infarction.
DOI: 10.6084/m9.figshare.26600608
title: Additional file 4 of 12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

description: Additional file 4. The detailed information of 16’s serum specific differential metabolites between T2DM group and T2DM + AMI group detected by untargeted metabonomic.
DOI: 10.6084/m9.figshare.26600608.v1
title: Additional file 4 of 12,13-diHOME and noradrenaline are associated with the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

description: Additional file 4. The detailed information of 16’s serum specific differential metabolites between T2DM group and T2DM + AMI group detected by untargeted metabonomic.
DOI: 10.15468/dl.xnzty2
title: Occurrence Download

description: A dataset containing 291265 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Reduviidae"
 ]
}

 The dataset includes 291265 records from 1002 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066594-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5678/hj8x-0c36
title: Cold Air Drainage basins for the whole study area with a merging threshold of 100 meters.
title: Cold air drainage basins

description: Raster file containing Cold Air Drainage basins for the whole study area with a merging threshold of 100 meters.
DOI: 10.5281/zenodo.10535521
title: The Rising Influence of AI in Higher Education: Trends and Insights from a Bibliometric Analysis

description: The purpose of this research was to examine the evolution, scope, and orientation of the scientific production on artificial intelligence applications in university students. The methodology, with a non-experimental design and qualitative approach, involved a search in Scopus, identifying 643 documents between 1975-2024, analyzed through VOSviewer and Bibliometrix. The results show an emerging field, but with rapid growth (4.59% per year), with notoriety of Kong, Abdulrahman and Chai. Research is predominantly in computer science (61%), social sciences (33%) and engineering (23%) from China, USA, Spain and Taiwan. Current applications focus on the use of AI in education, machine learning, support for academic decisions and student mental health. However, it is necessary to expand the approach towards ethical and regulatory aspects and the evaluation of multifaceted effects on different student profiles. In conclusion, although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience.
DOI: 10.5281/zenodo.13323332
title: The Rising Influence of AI in Higher Education: Trends and Insights from a Bibliometric Analysis

description: The purpose of this research was to examine the evolution, scope, and orientation of the scientific production on artificial intelligence applications in university students. The methodology, with a non-experimental design and qualitative approach, involved a search in Scopus, identifying 643 documents between 1975-2024, analyzed through VOSviewer and Bibliometrix. The results show an emerging field, but with rapid growth (4.59% per year), with notoriety of Kong, Abdulrahman and Chai. Research is predominantly in computer science (61%), social sciences (33%) and engineering (23%) from China, USA, Spain and Taiwan. Current applications focus on the use of AI in education, machine learning, support for academic decisions and student mental health. However, it is necessary to expand the approach towards ethical and regulatory aspects and the evaluation of multifaceted effects on different student profiles. In conclusion, although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience.
DOI: 10.5678/h2sh-3r49
title: Raster file containing accumulated flows into downslope cells for the whole study area
title: accumulated flows

description: Raster file containing accumulated flows into downslope cells for the whole study area.
DOI: 10.15468/dl.tpd2vh
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.485664 -32.474785, 149.492401 -32.474785, 149.492401 -32.477031, 149.48791 -32.477031, 149.48791 -32.481523, 149.492401 -32.481523, 149.492401 -32.483769, 149.501384 -32.483769, 149.501384 -32.481523, 149.496893 -32.481523, 149.496893 -32.477031, 149.494647 -32.477031, 149.494647 -32.474785, 149.496893 -32.474785, 149.496893 -32.47254, 149.492401 -32.47254, 149.492401 -32.468048, 149.48791 -32.468048, 149.48791 -32.47254, 149.485664 -32.47254, 149.485664 -32.474785)), ((149.485664 -32.474785, 149.481172 -32.474785, 149.481172 -32.479277, 149.483418 -32.479277, 149.483418 -32.481523, 149.485664 -32.481523, 149.485664 -32.474785)))"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066647-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.