cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.cjd2p9
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nothobranchius venustus Nagy, Watters & Bellstedt, 2020"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0314558-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60712/si-id41355.1
title: StrainInfo SI-ID 41355.1

description: StrainInfo dataset 41355 about a strain of Herbaspirillum seropedicae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id41356.1
title: StrainInfo SI-ID 41356.1

description: StrainInfo dataset 41356 about a strain of Filifactor alocis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id41421.1
title: StrainInfo SI-ID 41421.1

description: StrainInfo dataset 41421 about a strain of Vibrio cholerae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id41276.1
title: StrainInfo SI-ID 41276.1

description: StrainInfo dataset 41276 about a strain of Mycobacterium intracellulare. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id41277.1
title: StrainInfo SI-ID 41277.1

description: StrainInfo dataset 41277 about a strain of Mycobacterium intracellulare. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.15468/dl.kpdvhq
title: Occurrence Download

description: A dataset containing 17629062 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Colombia"
 ]
}

 The dataset includes 17629062 records from 3562 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0316576-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18269706
title: Non-stationary multivariate bias-corrected CMIP6 climate projections of daily precipitation and temperature over India [Part3: Maximum Temperature Data]

description: We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation (P) and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs).

The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction.

The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset.

The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment.

The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. Part-3 includes the bias-corrected maximum temperature, provided in NetCDF (.nc) format. This part consists of five compressed files to reduce storage requirements: one historical dataset (Pr_historical.zip) and four future scenario datasets (Pr_SSP126.zip, Pr_SSP245.zip, Pr_SSP370.zip, and Pr_SSP585.zip).

Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.
DOI: 10.5281/zenodo.18269707
title: Non-stationary multivariate bias-corrected CMIP6 climate projections of daily precipitation and temperature over India [Part3: Maximum Temperature Data]

description: We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation (P) and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs).

The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction.

The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset.

The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment.

The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. Part-3 includes the bias-corrected maximum temperature, provided in NetCDF (.nc) format. This part consists of five compressed files to reduce storage requirements: one historical dataset (Pr_historical.zip) and four future scenario datasets (Pr_SSP126.zip, Pr_SSP245.zip, Pr_SSP370.zip, and Pr_SSP585.zip).

Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.
DOI: 10.15468/dl.72src3
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Epinephelus sexfasciatus"
 ]
}

 The dataset includes 50 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002487-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18231864
title: Non-stationary multivariate bias-corrected CMIP6 climate projections of daily precipitation and temperature over India [Part1: Precipitation Data]

description: We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation (P) and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs).

The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction.

The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset.

The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment.

The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. Part-1 includes the bias-corrected precipitation dataset, provided in NetCDF (.nc) format. This part consists of five compressed files to reduce storage requirements: one historical dataset (Pr_historical.zip) and four future scenario datasets (Pr_SSP126.zip, Pr_SSP245.zip, Pr_SSP370.zip, and Pr_SSP585.zip).

Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.
DOI: 10.5281/zenodo.18231865
title: Non-stationary multivariate bias-corrected CMIP6 climate projections of daily precipitation and temperature over India [Part1: Precipitation Data]

description: We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation (P) and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs).

The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction.

The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset.

The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment.

The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. Part-1 includes the bias-corrected precipitation dataset, provided in NetCDF (.nc) format. This part consists of five compressed files to reduce storage requirements: one historical dataset (Pr_historical.zip) and four future scenario datasets (Pr_SSP126.zip, Pr_SSP245.zip, Pr_SSP370.zip, and Pr_SSP585.zip).

Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.
DOI: 10.15468/dl.kueq4v
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ramphotyphlops marxi"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002488-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.daeche
title: Occurrence Download

description: A dataset containing 4213 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carpodacus thura"
 ]
}

 The dataset includes 4213 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002489-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.85ntzs
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ramphotyphlops cumingii"
 ]
}

 The dataset includes 75 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002490-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1411878.10fu
title: SH1411878.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/e243838
title: NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome

description: NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome - 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note:
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published.
NC: Not calculated — There is insufficient data available to support the calculation of this statistic.
Cum: Cumulative year-to-date counts.
Max: Maximum — Maximum case count during the previous 52 weeks.
* Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf.
†Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).
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description: (Downloaded 1/12/2026)
DOI: 10.15156/bio/sh1411879.10fu
title: SH1411879.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/e243838v1
title: NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome

description: NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome - 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note:
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published.
NC: Not calculated — There is insufficient data available to support the calculation of this statistic.
Cum: Cumulative year-to-date counts.
Max: Maximum — Maximum case count during the previous 52 weeks.
* Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf.
†Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).
Read less
description: (Downloaded 1/12/2026)
DOI: 10.25954/gytk-n232
title: Lipoceramic Technologies: a Solution to Low and Variable Bioavailability of Poorly Soluble Anti-Inflammatory Drugs [IWEMC-282]
DOI: 10.15468/dl.322qxk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Certhiaxis cinnamomeus)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078383-250717081556266/datasets/export for details.
DOI: 10.7910/dvn/umu2le
title: Replication Data for: When Repressed Civil Society Attracts Foreign Investors: The Dual Dimensions of Chinese FDI in Southeast Asia

description: The replication file contains the dataset (in CSV format) and a STATA do-file for the paper titled "When Repressed Civil Society Attracts Foreign Investors: The Dual Dimensions of Chinese FDI in Southeast Asia."
DOI: 10.15468/dl.pv5u4r
title: Occurrence Download

description: A dataset containing 121 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Centropus steerii)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 121 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086156-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.