cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0158011.10fu
title: SH0158011.10FU

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: 10.60507/fk2/ox9xtm/bzijww
title: point_clouds_UAV1-RGB_plot162.zip
DOI: 10.60507/fk2/ox9xtm/nct8qr
title: point_clouds_UAV1-RGB_plot163.zip
DOI: 10.15156/bio/sh0158012.10fu
title: SH0158012.10FU

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: 10.60507/fk2/ox9xtm/ebwyoz
title: point_clouds_UAV1-RGB_plot164.zip
DOI: 10.60507/fk2/ox9xtm/spfx2h
title: point_clouds_UAV1-RGB_plot165.zip
DOI: 10.15156/bio/sh0158013.10fu
title: SH0158013.10FU

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: 10.60507/fk2/ox9xtm/waez9a
title: point_clouds_UAV1-RGB_plot166.zip
DOI: 10.15156/bio/sh0158014.10fu
title: SH0158014.10FU

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: 10.60507/fk2/ox9xtm/dbdcw3
title: point_clouds_UAV1-RGB_plot168.zip
DOI: 10.60507/fk2/ox9xtm/w1rqhm
title: point_clouds_UAV1-RGB_plot167.zip
DOI: 10.15156/bio/sh0158015.10fu
title: SH0158015.10FU

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: 10.60507/fk2/ox9xtm/krbe5k
title: point_clouds_UAV1-RGB_plot170.zip
DOI: 10.60507/fk2/ox9xtm/kxvwq7
title: point_clouds_UAV1-RGB_plot169.zip
DOI: 10.15156/bio/sh0158016.10fu
title: SH0158016.10FU

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: 10.60507/fk2/ox9xtm/6mbsal
title: point_clouds_UAV1-RGB_plot172.zip
DOI: 10.60507/fk2/ox9xtm/ndlyda
title: point_clouds_UAV1-RGB_plot171.zip
DOI: 10.15156/bio/sh0158017.10fu
title: SH0158017.10FU

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.uc424q
title: Occurrence Download

description: A dataset containing 1195 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Mexico",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1195 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018525-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8w9yfd
title: Occurrence Download

description: A dataset containing 99553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Lissotriton vulgaris",
 "Year 2020-2025"
 ]
}

 The dataset includes 99553 records from 246 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018509-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16227106
title: High sensitivity title and abstract screening pipeline

description: This dataset contains the data of a study that developed and evaluated an automated pipeline for screening titles and abstracts in systematic reviews. The primary goal of this research was to create a highly sensitive and efficient screening process using fine-tuned, open-source large language models (LLMs) combined with a PICOS (Population, Intervention/Exposure, Comparison, Outcome, Study Design) framework.

Dataset Contents

The dataset is organized into four main folders, each corresponding to one of the four systematic reviews used in the study:





Study 1: A systematic review and meta-analysis of randomized controlled trials.




Study 2: A systematic review and meta-analysis of cohort studies.




Study 3: A systematic review and meta-analysis of cohort studies or studies with data derived from randomized controlled trials.




Study 4: A systematic review of observational studies.



Each study folder contains a set of CSV files that document the screening process at different stages:





Total articles: This file lists all the articles retrieved from the initial literature search for the respective systematic review.




Study design screen: This file contains the articles that were excluded by the automated pipeline based on their study design.




PICO screen: This file includes the articles that were excluded after applying the simplified PICOS criteria.




Final included: This file lists the articles that were ultimately included for full-text screening after the automated pipeline was applied.



 

Methods

The data was generated using a multi-step automated pipeline:





PICOS Summarization: A fine-tuned Mistral-Nemo-Instruct-2407 model was used to generate structured PICOS summaries from the titles and abstracts of the retrieved articles.




Exclusion of Irrelevant Study Designs: A fine-tuned Qwen3-14B model was employed to exclude non-human studies, reviews, and other non-primary literature.




Stepwise PICOS-based Screening: The same Qwen3-14B model, trained on a stepwise screening strategy, was used to apply the simplified PICOS inclusion criteria to identify relevant articles.



The performance of this pipeline was evaluated by comparing its results to the gold standard of included articles from the original published systematic reviews. The dataset provides the complete outputs of this automated process, allowing for full transparency and reproducibility of the study's findings. This data can be used to validate the results, explore the performance of the models in detail, and serve as a foundation for future research in AI-assisted evidence synthesis.
DOI: 10.5281/zenodo.16228378
title: High sensitivity title and abstract screening pipeline

description: This dataset contains the data of a study that developed and evaluated an automated pipeline for screening titles and abstracts in systematic reviews. The primary goal of this research was to create a highly sensitive and efficient screening process using fine-tuned, open-source large language models (LLMs) combined with a PICOS (Population, Intervention/Exposure, Comparison, Outcome, Study Design) framework.

Dataset Contents

The dataset is organized into four main folders, each corresponding to one of the four systematic reviews used in the study:





Study 1: A systematic review and meta-analysis of randomized controlled trials.




Study 2: A systematic review and meta-analysis of cohort studies.




Study 3: A systematic review and meta-analysis of cohort studies or studies with data derived from randomized controlled trials.




Study 4: A systematic review of observational studies.



Each study folder contains a set of CSV files that document the screening process at different stages:





Total articles: This file lists all the articles retrieved from the initial literature search for the respective systematic review.




Study design screen: This file contains the articles that were excluded by the automated pipeline based on their study design.




PICO screen: This file includes the articles that were excluded after applying the simplified PICOS criteria.




Final included: This file lists the articles that were ultimately included for full-text screening after the automated pipeline was applied.



 

Methods

The data was generated using a multi-step automated pipeline:





PICOS Summarization: A fine-tuned Mistral-Nemo-Instruct-2407 model was used to generate structured PICOS summaries from the titles and abstracts of the retrieved articles.




Exclusion of Irrelevant Study Designs: A fine-tuned Qwen3-14B model was employed to exclude non-human studies, reviews, and other non-primary literature.




Stepwise PICOS-based Screening: The same Qwen3-14B model, trained on a stepwise screening strategy, was used to apply the simplified PICOS inclusion criteria to identify relevant articles.



The performance of this pipeline was evaluated by comparing its results to the gold standard of included articles from the original published systematic reviews. The dataset provides the complete outputs of this automated process, allowing for full transparency and reproducibility of the study's findings. This data can be used to validate the results, explore the performance of the models in detail, and serve as a foundation for future research in AI-assisted evidence synthesis.
DOI: 10.15468/dl.9bdmkn
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudorasbora parva",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 80 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018527-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.em55dx
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dreissena polymorpha",
 "Country is Lithuania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 108 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018528-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7yzj37
title: Occurrence Download

description: A dataset containing 12355 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agrostis perennans"
 ]
}

 The dataset includes 12355 records from 310 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018526-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5dpug
title: Occurrence Download

description: A dataset containing 345 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gyalidea asteriscus"
 ]
}

 The dataset includes 345 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018529-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u873v8
title: Occurrence Download

description: A dataset containing 107 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudorasbora parva",
 "Country is Serbia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 107 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018530-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ncnvdk
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ACE8813"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018531-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.csy7ku
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dreissena polymorpha",
 "Country is Mexico",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018532-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qe8ebc
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudorasbora parva",
 "Country is Slovakia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018533-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gbk9u7
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Mozambique",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018534-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15829721
title: Database Knowledge needs and Initiatives: Qualitative results
DOI: 10.5281/zenodo.15829722
title: Database Knowledge needs and Initiatives: Qualitative results
DOI: 10.15468/dl.28pysv
title: Occurrence Download

description: A dataset containing 4896 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dreissena polymorpha",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4896 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018535-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kphvmw
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Myanmar",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018536-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qkrb67
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Millettia irvinei"
 ]
}

 The dataset includes 25 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018537-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.72mqhk
title: Occurrence Download

description: A dataset containing 1905 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galinsoga parviflora",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1905 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018538-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tsmbh5
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynodon dactylon",
 "Country is Kazakhstan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018539-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eu8dqu
title: Occurrence Download

description: A dataset containing 263 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galinsoga parviflora",
 "Country is Slovakia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 263 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018540-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sva3xq
title: Occurrence Download

description: A dataset containing 5117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample, Specimen, Living Specimen, Material citation, Occurrence evidence)",
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Erthesina fullo",
 "Year 1970-2025"
 ]
}

 The dataset includes 5117 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018541-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u4x3en
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Nauru",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018542-250717081556266/datasets/export for details.