cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0025563.10fu
title: SH0025563.10FU

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: 10.3535/fpy-a5e-dtt
title: Hippuris tetraphylla L. fil.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/308-n9m-vb2
title: Hippuris tetraphylla L. fil.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/m1c-k26-3wc
title: Hippuris tetraphylla L. fil.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15468/dl.jj7uku
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Erodium cicutarium L)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 76 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007767-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14986466
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.18

description: The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 8.18 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 8.18.
DOI: 10.5281/zenodo.14986467
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.18

description: The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 8.18 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 8.18.
DOI: 10.5281/zenodo.14986458
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.15

description: The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 8.15 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 8.15.
DOI: 10.5281/zenodo.14986459
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.15

description: The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 8.15 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 8.15.
DOI: 10.5281/zenodo.11157458
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11157749
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11158542
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.r2xmrg
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Armeria euscadiensis Donad. & Vivant)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000147-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nyck8g
title: Occurrence Download

description: A dataset containing 2749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Malaysia",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhizophoraceae"
 ]
}

 The dataset includes 2749 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000150-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vqmxkc
title: Occurrence Download

description: A dataset containing 8627 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Tanzania, United Republic of",
 "TaxonKey is Combretaceae"
 ]
}

 The dataset includes 8627 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000151-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7h29g
title: Occurrence Download

description: A dataset containing 2307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abutilon indicum (L.) Sweet",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2307 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000152-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.shjcvj
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acaena splendens Hook. & Arn.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 128 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000153-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15470/qdtwdx
title: Herbario JA

description: El Herbario JA es un herbario especializado en hongos. La mayor parte de los registros corresponden a espec?menes recolectados en Andaluc?a, aunque tambi?n hay algunos del resto de Espa?a y de otros pa?ses.
DOI: 10.60656/fea475350f9f48df
title: Custom collection of weather station data from Hyytiälä between 30 Jan and 25 Feb 2024
DOI: 10.15468/dl.zx2r82
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Armeria sampaioi (Bernis) Nieto Fel.)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000159-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yn3mdu
title: Occurrence Download

description: A dataset containing 2117075 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Year 2019-2024"
 ]
}

 The dataset includes 2117075 records from 268 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000135-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.