cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nkagw2
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agelaia centralis (Cameron, 1907)"
 ]
}

 The dataset's 3 records were derived from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000958-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14825908
title: Dataset: Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers

description: Data and code belonging to the manuscript:  

Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers 

Abstract

Transformative agreements have become an important strategy in the transition to open access, with almost 1,200 such agreements registered by 2025. Despite their prevalence, these agreements suffer from important transparency limitations, most notably article-level metadata indicating which articles are covered by these agreements. Typically, this data is available to libraries but not openly shared, making it difficult to study the impact of these agreements. In this paper, we present a novel, open, replicable method for analyzing transformative agreements using open metadata, specifically the Journal Checker tool provided by cOAlition S and OpenAlex. To demonstrate its potential, we apply our approach to a subset of publications funded by the Dutch Research Council (NWO) and its health research counterpart ZonMw. In addition, the results of this open method are compared with the actual publisher data reported to the Dutch university library consortium UKB. This validation shows that this open method accurately identified 89% of the publications covered by transformative agreements, while the 11% false positives shed an interesting light on the limitations of this method. In the absence of hard, openly available article-level data on transformative agreements, we provide researchers and institutions with a powerful tool to critically track and evaluate the impact of these agreements.

This dataset contains the following files:



Dataset.csv - Data set of unique DOIs (n = 6,610) enriched with data from Crossref, Unpaywall, OpenAlex and the Journal Checker Tool. 

Data dictionary.csv - description of the data in the dataset, its type and sources.  

Google_Apps_Script.js - Google Apps Script for retrieving information from the Journal Checker Tool API.

Data comparison.csv - Data set of DOI's (n= 10,126) retrieved from the UKBsis datahub and used to establish the overlap with the original dataset.

Data dictionary data comparison.csv - description of the data in the data comparison data set, its type and sources.
DOI: 10.5281/zenodo.14825909
title: Dataset: Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers

description: Data and code belonging to the manuscript:  

Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers 

Abstract

Transformative agreements have become an important strategy in the transition to open access, with almost 1,200 such agreements registered by 2025. Despite their prevalence, these agreements suffer from important transparency limitations, most notably article-level metadata indicating which articles are covered by these agreements. Typically, this data is available to libraries but not openly shared, making it difficult to study the impact of these agreements. In this paper, we present a novel, open, replicable method for analyzing transformative agreements using open metadata, specifically the Journal Checker tool provided by cOAlition S and OpenAlex. To demonstrate its potential, we apply our approach to a subset of publications funded by the Dutch Research Council (NWO) and its health research counterpart ZonMw. In addition, the results of this open method are compared with the actual publisher data reported to the Dutch university library consortium UKB. This validation shows that this open method accurately identified 89% of the publications covered by transformative agreements, while the 11% false positives shed an interesting light on the limitations of this method. In the absence of hard, openly available article-level data on transformative agreements, we provide researchers and institutions with a powerful tool to critically track and evaluate the impact of these agreements.

This dataset contains the following files:



Dataset.csv - Data set of unique DOIs (n = 6,610) enriched with data from Crossref, Unpaywall, OpenAlex and the Journal Checker Tool. 

Data dictionary.csv - description of the data in the dataset, its type and sources.  

Google_Apps_Script.js - Google Apps Script for retrieving information from the Journal Checker Tool API.
DOI: 10.5281/zenodo.15000633
title: Dataset: Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers

description: Data and code belonging to the manuscript:  

Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers 

Abstract

Transformative agreements have become an important strategy in the transition to open access, with almost 1,200 such agreements registered by 2025. Despite their prevalence, these agreements suffer from important transparency limitations, most notably article-level metadata indicating which articles are covered by these agreements. Typically, this data is available to libraries but not openly shared, making it difficult to study the impact of these agreements. In this paper, we present a novel, open, replicable method for analyzing transformative agreements using open metadata, specifically the Journal Checker tool provided by cOAlition S and OpenAlex. To demonstrate its potential, we apply our approach to a subset of publications funded by the Dutch Research Council (NWO) and its health research counterpart ZonMw. In addition, the results of this open method are compared with the actual publisher data reported to the Dutch university library consortium UKB. This validation shows that this open method accurately identified 89% of the publications covered by transformative agreements, while the 11% false positives shed an interesting light on the limitations of this method. In the absence of hard, openly available article-level data on transformative agreements, we provide researchers and institutions with a powerful tool to critically track and evaluate the impact of these agreements.

This dataset contains the following files:



Dataset.csv - Data set of unique DOIs (n = 6,610) enriched with data from Crossref, Unpaywall, OpenAlex and the Journal Checker Tool. 

Data dictionary.csv - description of the data in the dataset, its type and sources.  

Google_Apps_Script.js - Google Apps Script for retrieving information from the Journal Checker Tool API.

Data comparison.csv - Data set of DOI's (n= 10,126) retrieved from the UKBsis datahub and used to establish the overlap with the original dataset.

Data dictionary data comparison.csv - description of the data in the data comparison data set, its type and sources.
DOI: 10.6084/m9.figshare.27111106
title: <b>A global dataset on mungbean for managing seed yield and quality</b>

description: <b>Mungbean, a protein- and nutrient-rich legume widely grown in Asian countries, is now expanding to other regions. With increasing global demand for nutritious foods and more sustainable agricultural practices, mungbean represents a valuable opportunity to diversify food systems and improve global nutrition. However, management practices to optimize seed yield and quality (including seed oil and protein concentration) remain largely unexplored due to the recent introduction of this crop in many parts of the world. This study aims to create a global open dataset of key crop management practices and their impact on seed</b><b> </b><b>yield parameters and quality.</b><b> </b><b>This global dataset included 227 studies published between 1984 and 2023 period, comprising 2,154 observations. The dataset focuses on key agronomic practices, including fertilization, irrigation, row spacing, seeding rate, and planting date. By providing comprehensive insights into mungbean cultivation, this open-access dataset enables researchers and agronomists to optimize management practices, identify research gaps, and enhance seed yield and nutritional quality. It serves as a valuable tool for improving food security and sustainable agriculture in developing regions.</b>
DOI: 10.15468/dl.qghech
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-80.18137 43.47168,-80.1618 43.47168,-80.1618 43.47963,-80.18137 43.47963,-80.18137 43.47168))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000962-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qv669
title: Occurrence Download

description: A dataset containing 1018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Psallus variabilis (Fallen, 1807)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1018 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000963-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00005735.1
title: Available IPD datapackage for study 'Phase II, Multicenter, Randomized, Parallel-Group, Partially Blinded, Placebo and Avonex Controlled Dose Finding Study to Evaluate the Efficacy As Measured by Brain MRI Lesions, and Safety of 2 Dose Regimens of Ocrelizumab in Patients With RRMS'

description: This is a phase II, multicenter, randomized, parallel-group, partially blinded, placebo and Avonex (interferon beta-1a) controlled dose finding study to evaluate the efficacy as measured by brain MRI lesions, and safety of 2 dose regimens of ocrelizumab in participants with Relapsing Remitting Multiple Sclerosis (RRMS).
DOI: 10.15468/dl.a3zege
title: Occurrence Download

description: A dataset containing 37455342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Fabaceae, Salicaceae, Pinaceae, Anacardiaceae, Menispermaceae)"
 ]
}

 The dataset includes 37455342 records from 9133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000923-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15184013
title: BioBrigit supplementary data

description: Supplementary code and data for the the BioBrigit metal binding site predictor for reproducing: 1) the data preprocessing pipeline to obtain the voxelised protein structures from the MetalPDB database, 2) for training the CNN, 3) to get the statistics for the knowledge-based scoring function, and 4) training logs.
DOI: 10.5281/zenodo.15184014
title: BioBrigit supplementary data

description: Supplementary code and data for the the BioBrigit metal binding site predictor for reproducing: 1) the data preprocessing pipeline to obtain the voxelised protein structures from the MetalPDB database, 2) for training the CNN, 3) to get the statistics for the knowledge-based scoring function, and 4) training logs.
DOI: 10.13145/bacdive7367.20251217.10
title: Microbacterium testaceum (Komagata and Iizuka 1964) Takeuchi and Hatano 1998

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7368.20241212.9.2
title: Microbacterium testaceum (Komagata and Iizuka 1964) Takeuchi and Hatano 1998

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075413.10fu
title: SH0075413.10FU

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: 10.13145/bacdive7368.20251217.10
title: Microbacterium testaceum (Komagata and Iizuka 1964) Takeuchi and Hatano 1998

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075414.10fu
title: SH0075414.10FU

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: 10.13145/bacdive7369.20250331.9.3
title: Microbacterium oxydans (Chatelain and Second 1966) Schumann et al. 1999

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075415.10fu
title: SH0075415.10FU

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: 10.13145/bacdive7369.20251217.10
title: Microbacterium oxydans (Chatelain and Second 1966) Schumann et al. 1999

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7370.20250331.9.3
title: Microbacterium arborescens (ex Frankland and Frankland 1889) Imai et al. 1985

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075416.10fu
title: SH0075416.10FU

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: 10.13145/bacdive7370.20251217.10
title: Microbacterium arborescens (ex Frankland and Frankland 1889) Imai et al. 1985

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7371.20250331.9.3
title: Microbacterium imperiale (Steinhaus 1941) Collins et al. 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075417.10fu
title: SH0075417.10FU

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: 10.13145/bacdive7371.20251217.10
title: Microbacterium imperiale (Steinhaus 1941) Collins et al. 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7372.20241212.9.2
title: Microbacterium lacticum Orla-Jensen 1919

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075418.10fu
title: SH0075418.10FU

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: 10.13145/bacdive7372.20251217.10
title: Microbacterium lacticum Orla-Jensen 1919

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0075419.10fu
title: SH0075419.10FU

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: 10.13145/bacdive7373.20250331.9.3
title: Microbacterium lacticum Orla-Jensen 1919

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7373.20251217.10
title: Microbacterium lacticum Orla-Jensen 1919

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.