cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0199192.10fu
title: SH0199192.10FU

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/e242917
title: Impaired Driving Death Rate, by Age and Sex, 2012 & 2014, Region 10 - Seattle

description: Rate of deaths by age/sex (per 100,000 population) for people killed in crashes involving a driver with BAC =&gt;0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Updated on April 28, 2025, to comply with the President’s Executive Order 14168.
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description: (Downloaded 1/9/2026)
DOI: 10.15156/bio/sh0199193.10fu
title: SH0199193.10FU

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/sh0199194.10fu
title: SH0199194.10FU

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/e242917v1
title: Impaired Driving Death Rate, by Age and Sex, 2012 & 2014, Region 10 - Seattle

description: Rate of deaths by age/sex (per 100,000 population) for people killed in crashes involving a driver with BAC =&gt;0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Updated on April 28, 2025, to comply with the President’s Executive Order 14168.
Read less
description: (Downloaded 1/9/2026)
DOI: 10.15156/bio/sh0199195.10fu
title: SH0199195.10FU

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/sh0199196.10fu
title: SH0199196.10FU

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/sh0199197.10fu
title: SH0199197.10FU

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/sh0199198.10fu
title: SH0199198.10FU

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/sh0199199.10fu
title: SH0199199.10FU

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.15233312
title: Dataset for the Systematic Review: "Machine Learning and Generative AI in Learning Analytics for Higher Education: A Systematic Review of Models, Trends, and Challenges"

description: This dataset contains the structured data used in the systematic review titled "Machine Learning and Generative AI in Learning Analytics for Higher Education: A Systematic Review of Models, Trends, and Challenges". The dataset includes metadata extracted from 101 studies published between 2018 and 2025, covering variables such as year, country, educational context, AI models, application types, techniques, and methodological categories. It was used for descriptive, thematic, and cluster-based analyses reported in the article. The dataset is shared to support transparency, reproducibility, and further research in the field of Learning Analytics and Artificial Intelligence.
DOI: 10.15468/dl.74p5xk
title: Occurrence Download

description: A dataset containing 70292 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhinella marina (Linnaeus, 1758)"
 ]
}

 The dataset includes 70292 records from 472 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000916-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14873726
title: Inversion of carbonate-rock properties based on an acoustoelasticity-squirt flow model

description: These data are the P- and S-wave velocities obtained by Ba et al. through ultrasonic experimental measurements and the seismic measurements on a rock sample at different pressures. 

In the package of data, wave velocities measured at different Samples and frequencies are saved as different xlsx files. The title of each xlsx file includes the sample number and measurement frequency. For instance, “Sample A_Seismic” denotes that wave velocities of sample A are measured at seismic frequency, while “Sample B_Ultrasonic” denotes that wave velocities of sample B are measured at ultrasonic frequency. The package also includes the txt file of sample properties.
DOI: 10.5281/zenodo.14873727
title: Inversion of carbonate-rock properties based on an acoustoelasticity-squirt flow model

description: These data are the P- and S-wave velocities obtained by Ba et al. through ultrasonic experimental measurements and the seismic measurements on a rock sample at different pressures. 

In the package of data, wave velocities measured at different Samples and frequencies are saved as different xlsx files. The title of each xlsx file includes the sample number and measurement frequency. For instance, “Sample A_Seismic” denotes that wave velocities of sample A are measured at seismic frequency, while “Sample B_Ultrasonic” denotes that wave velocities of sample B are measured at ultrasonic frequency. The package also includes the txt file of sample properties.
DOI: 10.15468/dl.xyrf8s
title: Occurrence Download

description: A dataset containing 5165 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anaxyrus microscaphus (Cope, 1867)"
 ]
}

 The dataset includes 5165 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000919-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cuvy7u
title: Occurrence Download

description: A dataset containing 2608 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Craugastor mexicanus (Brocchi, 1877)"
 ]
}

 The dataset includes 2608 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000920-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hv7tep
title: Occurrence Download

description: A dataset containing 5790 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Craugastor rhodopis (Cope, 1867)"
 ]
}

 The dataset includes 5790 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000921-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jz2y2u
title: Occurrence Download

description: A dataset containing 890 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Craugastor berkenbuschii (Peters, 1870)"
 ]
}

 The dataset includes 890 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000922-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e218981v1
title: Integrated Postsecondary Education Data System, Complete 1980-2023

description: Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.<br><br>From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):<br><br>Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.
DOI: 10.3886/e218981
title: Integrated Postsecondary Education Data System, Complete 1980-2023

description: Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.<br><br>Updated on 2/14/2025 to add Microsoft Access Database files.<br><br>From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):<br><br>Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.
DOI: 10.3886/e218981v2
title: Integrated Postsecondary Education Data System, Complete 1980-2023

description: Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.<br><br>Updated on 2/14/2025 to add Microsoft Access Database files.<br><br>From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):<br><br>Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.
DOI: 10.21952/wtr/1573029
title: Sample Collection Metadata for Soil Cores from the East River Watershed, Colorado collected in 2017

description: This data package contains sample collection metadata for soil cores from the East River Watershed in Colorado used in biogeochemical analyses by the Watershed Function SFA. Soil cores were collected seasonally during autumn, winter, snowmelt, and spring at a predominately montane meadow, high altitude field site. Information provided for each soil core sample assigned an International Geo Sample Number (IGSN) includes: sample name, IGSN, material, description, collection method, core depth, location, field program, collectors, and date. The full list of 53 IGSN's is listed in the Methods section. There is also a complete core sample list and metadata for the set of baseline data.
DOI: 10.15485/1577267
title: Soil Nitrogen, Water Content, Microbial Biomass, and Archaeal, Bacterial and Fungal Communities from the East River Watershed, Colorado collected in 2016-2017

description: This data set contains soil measurements made at the Pumphouse Hillslope to Floodplain transect at the locations of the early snowmelt-timing manipulation experiments in the East River Watershed in Colorado, USA. The data were collected in 2016 and 2017 to determine soil microbial responses to snow accumulation, snowmelt, and transition from winter to spring. Some additional data from the Washington Gulch snowmelt manipulation experiment lcoations are also included here.Data were collected on soil cores collected in the field at three soil depths (0 to 5 cm, 5 to 15 cm, 15 cm +) below the soil surface and analyzed in the on homogenized samples with roots and gravel removed. Data include gravimetric soil water content (48 hr oven-dried) and soil pH measurements (1:1 w/vol in ddH2O), dissolved nitrogen pools measured in soil extracts (2M KCl extractant), microbial biomass carbon and nitrogen (0.5M K2SO4 extractant), and archaeal, bacterial, and fungal species determined by Illumina 300 bp paired-read marker gene sequencing (16S rDNA for archaea and bacteria and ITS for fungi).