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

description: A dataset containing 1182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Choerospondias axillaris",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 1182 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007948-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18607445
title: Daten des Übersterblichkeitsberichts

description: Im Datensatz "Daten des Übersterblichkeitsberichts" des Robert Koch-Instituts werden wöchentliche Daten zur Übersterblichkeit in Deutschland bereitgestellt. Der Datensatz enthält Angaben zum Wochendatum und zur jeweiligen Gruppe (z. B. Altersgruppen, Geschlecht oder Region). Für jede Gruppe wird die beobachtete Sterbefallzahl den modellbasiert erwarteten Sterbefallzahlen gegenübergestellt.

Zur Bewertung von Abweichungen zwischen beobachteter und erwarteter Sterbefallzahl wird der z-Score berechnet, der zur Einordnung einer etwaigen Übersterblichkeit genutzt wird.

Der Datensatz dient der kontinuierlichen Überwachung von Sterbefallzahlen in Deutschland und ermöglicht die frühzeitige Erkennung von ungewöhnlich hohen Sterbefallzahlen in den betrachteten Gruppen.
DOI: 10.25549/usctheses-ouc11399nf04
title: FLEX DISTRIBUTION FOR BOUNDED-SUBOPTIMAL MULTI-AGENT PATH FINDING
DOI: 10.15468/dl.335cjq
title: Occurrence Download

description: A dataset containing 7340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Andropogon virginicus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is not null",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 7340 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007949-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jxrk33
title: Occurrence Download

description: A dataset containing 117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aloe marlothii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 117 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007950-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24092323
title: The immunogenicity of the ChAdOx1 nCoV-19 vaccination in participants with underlying comorbidity diseases: A prospective cohort study

description: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) increases mortality rates in older adults and those with comorbidities. Individuals with certain comorbidities may have a poor immune response and require early booster vaccines. We aimed to assess the immune response after two doses of ChAdOx1 nCoV-19 vaccine, at 84-day intervals, in participants with the following comorbidities; diabetes mellitus; obesity; cardiovascular disease; chronic kidney disease; rheumatological disease; cirrhosis; hematological disease; hematological malignancy; or solid malignancy. The study was conducted at Chulabhorn Hospital in Thailand, with healthy healthcare workers serving as the control group. Of the 769 participants, 352 were in the healthy cohort and 417 were in the comorbidity cohort, all received at least one dose of vaccine. Anti-RBD total antibody levels were evaluated on Day 0, Day 84, and Day 112. The results at Day 112 (4 weeks after the second dose) showed that individuals with comorbidities had a poor immune response compared to healthy individuals, especially those with hematological malignancy and solid malignancy. The geometric mean concentration (GMC) of anti-RBD antibody in the comorbidity cohort was significantly lower than that in the healthy cohort: 433.66 BAU/ml (95% CI 334.62–562.01) versus 1096.14 BAU/ml (95% CI 1010.26–1189.33), respectively. The geometric mean ratio (GMR) between the two cohorts was 0.40 (95% CI 0.30–0.52, <i>p</i> &lt; .001). This study concluded that individuals with comorbidities, particularly hematological and solid malignancies, had poor immune responses and may require an early booster vaccine to prevent infection and death.
DOI: 10.48580/dg953.v13
title: First occurrence of brachyopid temnospondyls in Southeast Asia and review of the Mesozoic amphibians from Thailand
DOI: 10.5281/zenodo.18607427
title: LINF_320044300

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18607426
title: LINF_320044300

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.gwfcta
title: Occurrence Download

description: A dataset containing 3871 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cortaderia jubata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is not null",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 3871 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007954-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ykmzs
title: Occurrence Download

description: A dataset containing 2291 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cinnamomum burmanni",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 2291 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007953-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58132/cpqpru
title: Codebook from qualitative analysis of in-depth interviews IDI with Ukrainian migrants

description: Codebook from the qualitative analysis of the IDIs conducted with Ukrainian migrants in Poland in 2023 in the project "International mobility and disruptions in fertility patterns – towards new insights from a low fertility context (MIGFERT)", NCN SONATA16 no 2020/39/D/HS4/02325.Regarding the IDI data, interview transcripts are not shared due to the very sensitive topics related to the private and family life of the respondents, many of whom are vulnerable migrants. Such was the condition guaranteed to the participants in the informed consent form.
DOI: 10.5281/zenodo.17936842
title: Performance of high-resolution precipitation products in capturing sub-daily spatial-temporal evolution of the recent record-breaking rainfall events in Chinese cities

description: This repository contains the datasets and code (R language) used in the study evaluating the spatial-temporal performance of seven high-resolution precipitation products (CMORPH, GPM, PERSIANN, PERSIANN-CCS, ERA5 single-level, ERA5-Land, and MSWEP) in capturing record-breaking extreme rainfall events (REREs). The analysis covers sub-daily (30 minutes to 3 hours) temporal scales and fine spatial resolutions (0.04° to 0.25°), focusing on four cities with varying climatic conditions (e.g., Beijing, Longnan, Zhengzhou, and Hong Kong).

Key findings reveal that CMORPH, GPM, and MSWEP generally outperform other datasets, though all show limitations in accurately representing storm center dynamics and rainfall duration. The performance of these precipitation products strongly depends on rainfall intensity, spatial patterns, and regional climate characteristics. This data and code release supports reproducibility and further research on selecting appropriate precipitation datasets for extreme rainfall event analysis.
DOI: 10.5281/zenodo.17936841
title: Performance of high-resolution precipitation products in capturing sub-daily spatial-temporal evolution of the recent record-breaking rainfall events in Chinese cities

description: This repository contains the datasets and code (R language) used in the study evaluating the spatial-temporal performance of seven high-resolution precipitation products (CMORPH, GPM, PERSIANN, PERSIANN-CCS, ERA5 single-level, ERA5-Land, and MSWEP) in capturing record-breaking extreme rainfall events (REREs). The analysis covers sub-daily (30 minutes to 3 hours) temporal scales and fine spatial resolutions (0.04° to 0.25°), focusing on four cities with varying climatic conditions (e.g., Beijing, Longnan, Zhengzhou, and Hong Kong).

Key findings reveal that CMORPH, GPM, and MSWEP generally outperform other datasets, though all show limitations in accurately representing storm center dynamics and rainfall duration. The performance of these precipitation products strongly depends on rainfall intensity, spatial patterns, and regional climate characteristics. This data and code release supports reproducibility and further research on selecting appropriate precipitation datasets for extreme rainfall event analysis.
DOI: 10.5281/zenodo.17222399
title: RNA-SyntHub

description: The structural diversity of RNA plays a pivotal role in molecular medicine, RNA-targeted drug discovery, and synthetic biology, where engineered RNA molecules are increasingly applied in therapeutic, regulatory, and nanotechnology contexts. The rapid development of deep learning methods has further increased the demand for large and diverse structural datasets; however, while protein datasets are abundant, high-quality RNA data remain scarce. Synthetic RNA structure datasets provide a means to alleviate this limitation by enabling the large-scale exploration of conformational space; however, their practical use requires careful curation and validation to ensure reliability. To address this challenge, we present a pipeline specifically designed for the systematic construction of curated synthetic RNA datasets. The workflow makes use of data provided by multiple structure generation approaches and integrates them with a meta-scoring function that discriminates low-quality models and extracts high-confidence candidates. As a proof of concept, we applied this method to a dataset of 447,402 synthetic RNA structures, obtaining a curated set of approximately 16,000 models, further complemented with predictions from Boltz-1 and RNAComposer. This study introduces both a methodological framework for making synthetic RNA datasets usable in computational research and a concrete dataset that can directly support deep learning applications and the advancement of RNA design in synthetic biology and nanotechnology.
DOI: 10.5281/zenodo.18436072
title: RNA-SyntHub

description: The structural diversity of RNA plays a pivotal role in molecular medicine, RNA-targeted drug discovery, and synthetic biology, where engineered RNA molecules are increasingly applied in therapeutic, regulatory, and nanotechnology contexts. The rapid development of deep learning methods has further increased the demand for large and diverse structural datasets; however, while protein datasets are abundant, high-quality RNA data remain scarce. Synthetic RNA structure datasets provide a means to alleviate this limitation by enabling the large-scale exploration of conformational space; however, their practical use requires careful curation and validation to ensure reliability. To address this challenge, we present a pipeline specifically designed for the systematic construction of curated synthetic RNA datasets. The workflow makes use of data provided by multiple structure generation approaches and integrates them with a meta-scoring function that discriminates low-quality models and extracts high-confidence candidates. As a proof of concept, we applied this method to a dataset of 447,402 synthetic RNA structures, obtaining a curated set of approximately 16,000 models, further complemented with predictions from Boltz-1 and RNAComposer. This study introduces both a methodological framework for making synthetic RNA datasets usable in computational research and a concrete dataset that can directly support deep learning applications and the advancement of RNA design in synthetic biology and nanotechnology.
DOI: 10.15468/dl.2g6f33
title: Occurrence Download

description: A dataset containing 603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aloe vera",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 603 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007955-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17844114
title: CMIP7 ScenarioMIP infilling database for simple climate model workflow

description: Infilling database for CMIP7 ScenarioMIP simple climate model assessment

The files here are infilling databases.
They are used for the 'infilling' step
of simple climate model assessment.
This involves inferring emissions of one species (e.g. HFC32)
based on emissions of another species (e.g. CO2)
and relationships between these two species seen in other scenarios.
For more details about infilling, see
Lamboll et al., 2020.

The database is provided in three different formats.
The versions of the scenarios used to compile this database
is provided in the versions.json file.

The database was derived using the code in this repository:
https://github.com/iiasa/emissions_harmonization_historical.
The filenames are composed of identifiers related to the processing
of each of the different input data sources.
To identify the exact meaning of these identifiers,
please see the processing code in
https://github.com/iiasa/emissions_harmonization_historical.
DOI: 10.5281/zenodo.17514978
title: CMIP7 ScenarioMIP infilling database for simple climate model workflow

description: Infilling database for CMIP7 ScenarioMIP simple climate model assessment

The files here are infilling databases.
They are used for the 'infilling' step
of simple climate model assessment.
This involves inferring emissions of one species (e.g. HFC32)
based on emissions of another species (e.g. CO2)
and relationships between these two species seen in other scenarios.
For more details about infilling, see
Lamboll et al., 2020.

The database is provided in three different formats.
The versions of the scenarios used to compile this database
is provided in the versions.json file.

The database was derived using the code in this repository:
https://github.com/iiasa/emissions_harmonization_historical.
The filenames are composed of identifiers related to the processing
of each of the different input data sources.
To identify the exact meaning of these identifiers,
please see the processing code in
https://github.com/iiasa/emissions_harmonization_historical.
DOI: 10.15468/dl.8xp4ba
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cinnamomum glanduliferum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 87 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007958-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cyck2y
title: Occurrence Download

description: A dataset containing 528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((74.84774 12.47616,76.79047 7.76537,77.7029 7.88969,76.32569 12.74955,74.84774 12.47616))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Troides minos"
 ]
}

 The dataset includes 528 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007957-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vcr6z
title: Occurrence Download

description: A dataset containing 4084 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ehrharta calycina)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is not null",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 4084 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007956-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18184515
title: V2-111111111111111
DOI: 10.5281/zenodo.18184514
title: V2-111111111111111
DOI: 10.5281/zenodo.18607111
title: Zkouska vkladani dat do Zenodo

description: Zkouska vkladani datasetu na repozitar Zenodo.
DOI: 10.5281/zenodo.18607110
title: Zkouska vkladani dat do Zenodo

description: Zkouska vkladani datasetu na repozitar Zenodo.
DOI: 10.5281/zenodo.17845154
title: CMIP7 ScenarioMIP historical timeseries for harmonisation and simple climate model workflow

description: History for CMIP7 ScenarioMIP emissions harmonisation

The files here are compiled historical experiment timeseries.
They were compiled for use as part of the CMIP7 ScenarioMIP exercise
and are primarily used for supporting emissions harmonisation.
Here, 'harmonisation' means alignment of modelled emissions from IAMs
with the emissions used for the CMIP7 historical experiment.

As a result, they are a key input for the process of
'gridding' emissions (i.e. taking raw emissions from IAMs
and assigning them to a spatial grid, ready for use by Earth System Models (ESMs))
and for running the simple climate model based assessment of the scenarios
to derive a first-order estimate of the warming associated with these scenarios
(the ESMs will quantify the warming and other climate change associated with these scenarios
as part of ScenarioMIP, and this quantification is underpinned by a deeper,
more physically-based set of modelling assumptions.)

There are three different files,
provided in three different formats each.
The three files are:



gridding-history*: the history used for harmonisation at the 'gridding' level.
The gridding requires emissions with regional and sectoral detail.
It also has to support every IAMs' native regions.
As a result, there are lots (of order 30 000) timeseries.

country-history*: same as above, but at the country level
rather than in native IAM regions.

global-workflow-history*: the history used for harmonisation at the 'global' level.
This only has global total emissions,
except for CO2 which is split into fossil-based
and land-based (i.e. originating from the land carbon pool) emissions.
It includes a number of species that are not used in the gridding workflow
but are relevant for climate projections
e.g. all of the greenhouse gases covered by the Montreal Protocol.
As a result, there are only 52 timeseries.


The files were derived using the code in this repository:
https://github.com/iiasa/emissions_harmonization_historical.
The filenames are composed of identifiers related to the processing
of each of the different input data sources.
To identify the exact meaning of these identifiers,
please see the processing code in
https://github.com/iiasa/emissions_harmonization_historical.
DOI: 10.5281/zenodo.15357372
title: CMIP7 ScenarioMIP historical timeseries for harmonisation and simple climate model workflow

description: History for CMIP7 ScenarioMIP emissions harmonisation

The files here are compiled historical experiment timeseries.
They were compiled for use as part of the CMIP7 ScenarioMIP exercise
and are primarily used for supporting emissions harmonisation.
Here, 'harmonisation' means alignment of modelled emissions from IAMs
with the emissions used for the CMIP7 historical experiment.

As a result, they are a key input for the process of
'gridding' emissions (i.e. taking raw emissions from IAMs
and assigning them to a spatial grid, ready for use by Earth System Models (ESMs))
and for running the simple climate model based assessment of the scenarios
to derive a first-order estimate of the warming associated with these scenarios
(the ESMs will quantify the warming and other climate change associated with these scenarios
as part of ScenarioMIP, and this quantification is underpinned by a deeper,
more physically-based set of modelling assumptions.)

There are three different files,
provided in three different formats each.
The three files are:



gridding-history*: the history used for harmonisation at the 'gridding' level.
The gridding requires emissions with regional and sectoral detail.
It also has to support every IAMs' native regions.
As a result, there are lots (of order 30 000) timeseries.

country-history*: same as above, but at the country level
rather than in native IAM regions.

global-workflow-history*: the history used for harmonisation at the 'global' level.
This only has global total emissions,
except for CO2 which is split into fossil-based
and land-based (i.e. originating from the land carbon pool) emissions.
It includes a number of species that are not used in the gridding workflow
but are relevant for climate projections
e.g. all of the greenhouse gases covered by the Montreal Protocol.
As a result, there are only 52 timeseries.


The files were derived using the code in this repository:
https://github.com/iiasa/emissions_harmonization_historical.
The filenames are composed of identifiers related to the processing
of each of the different input data sources.
To identify the exact meaning of these identifiers,
please see the processing code in
https://github.com/iiasa/emissions_harmonization_historical.
DOI: 10.15468/dl.n9fu72
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aloe welwitschii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 20 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007960-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ej9w3r
title: Occurrence Download

description: A dataset containing 19250 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Microstegium vimineum)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is not null",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 19250 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007959-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.