cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26023/4jzz-11xe-208
title: Cayenne French Falcon Flight Track Data. Version 1.0

description: This dataset contains flight track data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Cayenne, French Guiana. The data is from the French Falcon, operated by SAFIRE, for the 18 flights flown during the project and are in kml format.
DOI: 10.15468/dl.42bz94
title: Occurrence Download

description: A dataset containing 6596 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Silphium terebinthinaceum)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6596 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040955-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sbjkxa
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-3.69249 40.34012,-3.68383 40.34012,-3.68383 40.34504,-3.69249 40.34504,-3.69249 40.34012))",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040956-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/4btw-xzd0-n10e
title: Cayenne NRC Convair Flight Track Data. Version 1.0

description: This dataset contains flight track data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Cayenne, French Guiana. The data is from the NRC Convair 580, operated by the National Research Council Canada, for the 12 flights flown during the project and are in kml format.
DOI: 10.15468/dl.5chujg
title: Occurrence Download

description: A dataset containing 4775 species occurrences available in GBIF matching the query:
{
 "or" : [
 "Geometry POLYGON((-122.68557 58.55872,-122.65699 58.55812,-122.63258 58.57658,-122.62573 58.59682,-122.63198 58.62392,-122.64448 58.63821,-122.66949 58.64684,-122.69182 58.64773,-122.70432 58.64654,-122.7204 58.63791,-122.72606 58.62957,-122.72933 58.62034,-122.72903 58.60665,-122.72427 58.59355,-122.71951 58.58224,-122.7076 58.57003,-122.68557 58.55872))",
 "Geometry POLYGON((-122.69649 58.66765,-122.68191 58.67014,-122.67374 58.6729,-122.66039 58.67897,-122.64592 58.68396,-122.62768 58.69356,-122.61775 58.69851,-122.60774 58.71079,-122.59929 58.74317,-122.59492 58.76266,-122.59582 58.7754,-122.60962 58.79844,-122.62569 58.81147,-122.64788 58.81982,-122.6727 58.82219,-122.69165 58.82007,-122.7065 58.81317,-122.71949 58.80626,-122.73218 58.79139,-122.74722 58.77414,-122.75148 58.7588,-122.75544 58.74573,-122.75786 58.7191,-122.73077 58.68069,-122.69649 58.66765))"
 ]
}

 The dataset includes 4775 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040957-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/cz6q-4ync-8z08
title: Cayenne Honeywell Flight Track Data. Version 1.0

description: This dataset contains flight track data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Cayenne, French Guiana. The data is from the Honeywell B757, operated by Honeywell Aerospace, for the 10 flights flown during the project and are in kml format.
DOI: 10.15468/dl.7xruyz
title: Occurrence Download

description: A dataset containing 434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vespidae",
 "Year is 1954",
 "Continent is Europe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 434 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040961-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bd9brg
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Orobanche hederae",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040962-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15139/s3/yn7aqe
title: Data Management and Sharing Plan for: 5/6 HBCD Prenatal Experiences and Longitudinal Development (PRELUDE) Consortium

description: The Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data.
description: Data Management and Sharing Plan
DOI: 10.15468/dl.yaj9xe
title: Occurrence Download

description: A dataset containing 7817 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Allium tricoccum"
 ]
}

 The dataset includes 7817 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040960-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.23b8a9
title: Occurrence Download

description: A dataset containing 7817 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Allium tricoccum"
 ]
}

 The dataset includes 7817 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040959-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6v3tyu
title: Occurrence Download

description: A dataset containing 250 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Orobanche hederae",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 250 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040963-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/1py6-s9g0-f404
title: Cayenne Ground Weather Radar Raw Data. Version 1.0

description: This dataset contains ground weather radar raw data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Cayenne, French Guiana. The data is from the CNES radar at Grand Leblond and the images were produced by Meteo-France Nowcasting Department. The files are ascii files contained in tar files by day. All use of radar data and images should cite the following legal mention: Image produced by Meteo-France Nowcasting Department. Origin of reflectivity data: CNES Radar at Grand Leblond.
DOI: 10.6084/m9.figshare.30267488
title: Implementation of the ICH E9 (R1) addendum in vaccine efficacy studies: the hypothetical and principal stratum strategies

description: Over the past decades, the primary interest in vaccine efficacy evaluation has mostly been on the effect observed in trial participants complying with the protocol requirements (per protocol analysis). The ICH E9 (R1) addendum provides a structured framework to formulate the clinical questions of interest and formalize them as estimands. In this paper, the estimand framework is retrospectively implemented in a human papillomavirus (HPV) phase 3 trial, where the vaccine efficacy was originally estimated on the per protocol set. We focus on two strategies for dealing with the presence of intercurrent events: the hypothetical and the principal stratum strategies. We address the interpretation of these two estimands, their estimation as well as articulation of the underlying identifiability assumptions. Finally, we leverage the results of the HPV application to formulate general considerations regarding the implementation of the ICH E9 (R1) addendum in vaccine efficacy studies.
DOI: 10.6084/m9.figshare.30267488.v1
title: Implementation of the ICH E9 (R1) addendum in vaccine efficacy studies: the hypothetical and principal stratum strategies

description: Over the past decades, the primary interest in vaccine efficacy evaluation has mostly been on the effect observed in trial participants complying with the protocol requirements (per protocol analysis). The ICH E9 (R1) addendum provides a structured framework to formulate the clinical questions of interest and formalize them as estimands. In this paper, the estimand framework is retrospectively implemented in a human papillomavirus (HPV) phase 3 trial, where the vaccine efficacy was originally estimated on the per protocol set. We focus on two strategies for dealing with the presence of intercurrent events: the hypothetical and the principal stratum strategies. We address the interpretation of these two estimands, their estimation as well as articulation of the underlying identifiability assumptions. Finally, we leverage the results of the HPV application to formulate general considerations regarding the implementation of the ICH E9 (R1) addendum in vaccine efficacy studies.
DOI: 10.6084/m9.figshare.30111234
title: Mapping rubber monoculture and jungle rubber across Kalimantan using multisource satellite data

description: Natural rubber is a strategic commodity and an important source of income for millions of smallholders in South-East Asia. In Indonesia, rubber systems vary widely, from high-yielding monocultures to complex agroforestry systems known as jungle rubber, making their accurate mapping challenging. The lack of reliable, high-resolution maps hampers efforts to monitor land-use change and comply with emerging deforestation-free commodity regulations. The objective of this study is to develop and evaluate a remote sensing-based methodology to map both rubber monocultures and jungle rubber across Kalimantan. We developed a method based on a Random Forest machine-learning classification algorithm calibrated on multi-source imagery from Sentinel-1 (radar) and Sentinel-2 (optical) satellites, applying a Principal Component Analysis for feature reduction. To distinguish jungle rubber from monocultures, we further integrated land-cover history obtained from the JRC Tropical Moist Forest (TMF) dataset, which showed good agreement with our training data: most jungle rubber points fell within TMF forest classes, while monocultures fell within non-forest classes. The final map, evaluated against extensive field data, gave high overall accuracy (87%) for total rubber area, estimated at 1.79 million hectares in 2020 for the three provinces of Central, South and West Kalimantan. The overall accuracy was 70% when including the distinction between monoculture and jungle rubber, with estimated areas of 0.59 Mha and 1.20 Mha, respectively. Spatial patterns revealed monocultures concentrated in accessible regions such as in South Kalimantan or along the Kapuas river, while jungle rubber dominated remote or less accessible zones, particularly in West Kalimantan. This classification approach is the first to produce reliable maps of monoculture and jungle rubber at large scale and provides a foundation for future monitoring and policy applications.
DOI: 10.6084/m9.figshare.30111234.v1
title: Mapping rubber monoculture and jungle rubber across Kalimantan using multisource satellite data

description: Natural rubber is a strategic commodity and an important source of income for millions of smallholders in South-East Asia. In Indonesia, rubber systems vary widely, from high-yielding monocultures to complex agroforestry systems known as jungle rubber, making their accurate mapping challenging. The lack of reliable, high-resolution maps hampers efforts to monitor land-use change and comply with emerging deforestation-free commodity regulations. The objective of this study is to develop and evaluate a remote sensing-based methodology to map both rubber monocultures and jungle rubber across Kalimantan. We developed a method based on a Random Forest machine-learning classification algorithm calibrated on multi-source imagery from Sentinel-1 (radar) and Sentinel-2 (optical) satellites, applying a Principal Component Analysis for feature reduction. To distinguish jungle rubber from monocultures, we further integrated land-cover history obtained from the JRC Tropical Moist Forest (TMF) dataset, which showed good agreement with our training data: most jungle rubber points fell within TMF forest classes, while monocultures fell within non-forest classes. The final map, evaluated against extensive field data, gave high overall accuracy (87%) for total rubber area, estimated at 1.79 million hectares in 2020 for the three provinces of Central, South and West Kalimantan. The overall accuracy was 70% when including the distinction between monoculture and jungle rubber, with estimated areas of 0.59 Mha and 1.20 Mha, respectively. Spatial patterns revealed monocultures concentrated in accessible regions such as in South Kalimantan or along the Kapuas river, while jungle rubber dominated remote or less accessible zones, particularly in West Kalimantan. This classification approach is the first to produce reliable maps of monoculture and jungle rubber at large scale and provides a foundation for future monitoring and policy applications.
DOI: 10.15468/dl.txgg6c
title: Occurrence Download

description: A dataset containing 12756838 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China"
 ]
}

 The dataset includes 12756838 records from 5451 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040824-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0201696.10fu
title: SH0201696.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201697.10fu
title: SH0201697.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201698.10fu
title: SH0201698.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201699.10fu
title: SH0201699.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201700.10fu
title: SH0201700.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201701.10fu
title: SH0201701.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201702.10fu
title: SH0201702.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0201703.10fu
title: SH0201703.10FU

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.