cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14796947
title: DMI warning data to support the paper "Incorporating compound flood events in the weather service value chain"

description: This dataset is comprising ten years of meteorological warnings issued by the Danish Meteorological Institute (DMI). The dataset includes detailed records of warnings issued at the municipal level, covering various hazard types and their temporal distributions. Additionally, the repository contains code for extracting and processing the warning data, along with a spatial mapping file for municipalities to facilitate geographic analysis. A supplementary dataset within the archive summarises the annual number of warnings issued for each municipality, enabling further analysis of spatial and temporal trends in warning dissemination. This dataset offers valuable insights for researchers and practitioners aiming to enhance warning systems for compound flood events and other meteorological hazards.
DOI: 10.5281/zenodo.14796948
title: DMI warning data to support the paper "Incorporating compound flood events in the weather service value chain"

description: This dataset is comprising ten years of meteorological warnings issued by the Danish Meteorological Institute (DMI). The dataset includes detailed records of warnings issued at the municipal level, covering various hazard types and their temporal distributions. Additionally, the repository contains code for extracting and processing the warning data, along with a spatial mapping file for municipalities to facilitate geographic analysis. A supplementary dataset within the archive summarises the annual number of warnings issued for each municipality, enabling further analysis of spatial and temporal trends in warning dissemination. This dataset offers valuable insights for researchers and practitioners aiming to enhance warning systems for compound flood events and other meteorological hazards.
DOI: 10.15468/dl.rvq6jh
title: Occurrence Download

description: A dataset containing 678 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "Geometry POLYGON((-82.78 41.9,-73.02 41.9,-73.02 45.82,-82.78 45.82,-82.78 41.9))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Macropis nuda (Provancher, 1882), Dufourea novaeangliae (Robertson, 1897), Megachile campanulae (Robertson, 1903), Andrena erigeniae Robertson, 1891, Colletes latitarsis Robertson, 1891, Colletes simulans Cresson, 1868, Andrena fragilis Smith, 1853, Andrena persimulata Viereck, 1917, Andrena platyparia Robertson, 1895)"
 ]
}

 The dataset includes 678 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010835-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10154920
title: GHALogs: Large-Scale Dataset of GitHub Actions Runs

description: In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the open-source community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes—knowledge that is essential for identifying bottlenecks and optimization opportunities.Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages.This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories.We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).
DOI: 10.5281/zenodo.10154919
title: GHALogs: Large-Scale Dataset of GitHub Actions Runs

description: In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the open-source community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes—knowledge that is essential for identifying bottlenecks and optimization opportunities.Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages.This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories.We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).
DOI: 10.5281/zenodo.14796970
title: GHALogs: Large-Scale Dataset of GitHub Actions Runs

description: In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the open-source community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes—knowledge that is essential for identifying bottlenecks and optimization opportunities.Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages.This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories.We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).
DOI: 10.15468/dl.8yug6v
title: Occurrence Download

description: A dataset containing 76634954 species occurrences available in GBIF matching the query:
{
 "Continent" : [
 "is Africa"
 ]
}

 The dataset includes 76634954 records from 5802 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010824-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tvoaqv
title: Occurrence Download

description: A dataset containing 6703 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Arremonops rufivirgatus (Lawrence, 1851)"
 ]
}

 The dataset includes 6703 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003867-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vhxkmv
title: Occurrence Download

description: A dataset containing 207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bourreria mollis Standl.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 207 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003868-150615163101818/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7j6gw9
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cousinia kuramensis Bornm."
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0149773-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mr8sje
title: Occurrence Download

description: A dataset containing 28796420 species occurrences available in GBIF matching the query:
{
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 28796420 records from 4498 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027305-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15734585
title: A New Approach to Nowcasting Thunderstorm Gusts Based on a Spatiotemporal Decoupling Architecture and Multi-scale Feature Learning

description: The data, code scripts, and figures related to the paper 'A New Approach to Nowcasting Thunderstorm Gusts Based on a Spatiotemporal Decoupling Architecture and Multi-scale Feature Learning' are provided to support the conclusions of the study. The scripts used in this work are developed in Python (version 3.9) and NCL (version 6.6.2).
DOI: 10.5281/zenodo.15734586
title: A New Approach to Nowcasting Thunderstorm Gusts Based on a Spatiotemporal Decoupling Architecture and Multi-scale Feature Learning

description: The data, code scripts, and figures related to the paper 'A New Approach to Nowcasting Thunderstorm Gusts Based on a Spatiotemporal Decoupling Architecture and Multi-scale Feature Learning' are provided to support the conclusions of the study. The scripts used in this work are developed in Python (version 3.9) and NCL (version 6.6.2).
DOI: 10.4121/4464ab86-9214-49b3-a808-10ca655385a6.v5
title: Data underlying the publication: MOSAIC: a highly efficient, one-step recombineering approach to plasmid editing and diversification

description: Data underlying the publication: van den Brink, M., Althuis, T. Y., Danelon, C. & Claassens, N. J. MOSAIC: A Highly Efficient, One-Step Recombineering Approach to Plasmid Editing and Diversification. <em>ACS Synthetic Biology</em> <strong>14</strong>, 3880–3889 (2025). DOI: 10.1021/acssynbio.4c00657.<br>This dataset contains data collected during experiments as part of Marijn van den Brink's and Timotheus Althuis' PhD projects. The raw data consists of Nanopore sequencing data. Nanopore sequencing was performed by Plasmidsaurus (Oregon, US). The FASTQ files were then mapped to reference sequences, resulting in .bam/.bai files. These were further analyzed using an in-house developed R script. All data processing and analysis steps are described in detail in the methods section of the publication.<br>The data is grouped in folders based on the figures in the publication. The folder name specifies the figure number and contains a short description of the data. Each folder contains raw data (FASTQ) and processed data. The processed data consists of (i) the mapped reads (file format: .bam and .bai), (ii) the analysis results of the mapped reads (file format: .csv and .xlsx), (iii) the graphs plotting the results (file format: .pzfx), and (iv) the R scripts used to produce the results incl. the required .xlsx or .csv files.<br>To analyze your own data using our R script, go to "General Nanopore data analysis scripts &gt; MOSAIC-library-analysis-script.R". This R script contains information on how to adapt the script to your own data.
DOI: 10.15468/dl.nwfqpp
title: Occurrence Download

description: A dataset containing 7137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spodoptera frugiperda",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2014",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 7137 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041290-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2bv2fe
title: Occurrence Download

description: A dataset containing 1064 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Fish Occurrence Database of the Family Aphaniidae"
 ]
}

 The dataset includes 1064 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041292-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9979p
title: Occurrence Download

description: A dataset containing 596 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epigonus denticulatus Dieuzeide, 1950",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen, Material sample)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 596 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004361-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfbprd
title: Occurrence Download

description: A dataset containing 10900 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eleutherodactylus planirostris (Cope, 1862)"
 ]
}

 The dataset includes 10900 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005171-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sb6kkg
title: Occurrence Download

description: A dataset containing 423392100 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "IucnRedListCategory is one of (CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 423392100 records from 14270 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005164-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0177451.10fu
title: SH0177451.10FU

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/sh0177452.10fu
title: SH0177452.10FU

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/sh0177453.10fu
title: SH0177453.10FU

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/sh0177454.10fu
title: SH0177454.10FU

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/sh0177455.10fu
title: SH0177455.10FU

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/sh0177456.10fu
title: SH0177456.10FU

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.