cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11177117
title: Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2023-11-23

description: This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2023-11-23. 

            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.b9g787
title: Occurrence Download

description: A dataset containing 9801 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chenopodium berlandieri Moq."
 ]
}

 The dataset includes 9801 records from 287 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005343-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11177165
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23. 

            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.14986547
title: CMIP6 input data usage information for IPCC WGI AR6 figure Atlas.17

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 Atlas.17 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 Atlas.17.
DOI: 10.35080/data.c.2021.p14
title: 中国地质调查局: 江西省大桥幅(H50E023002)1∶50 000地质图数据库
title: 1:50,000 Geological Map Database of Daqiao Map Sheet (H50E023002), Jiangxi Province, China

description: 江西大桥幅（H50E023002）1∶50 000地质图数据库是按《1∶50 000区域地质调查技术要求 》（DD 2019—01）和地质行业的统一标准及要求，在充分搜集和利用1∶200 000、1∶250 000等区域地质调查、1∶50 000 矿产调查工作成果资料的基础上，采用数字填图系统 （DGSS）进行野外地质填图和数据库建设，结合 Spot、ETM等多种遥感影像对构造及岩性边界不断验证和完善，最终绘制成图。图件运用造山带填图理论详细填绘了测区新元古代地层单位，图面表达以构造建造为单位，对新元古代地层进行了重新划分，并对内部沉积序列进行了重新厘定，限定了宜丰岩组沉积时代和沉积环境。解体了九岭南缘新元古代花岗岩侵入体，查明了各侵入体岩石类型、矿物成分、结构构造、接触关系等，识别出测区新元古代、晚志留世、早白垩世3期岩浆事件，建立了构造–岩浆演化序列。图幅数据库的数据内容包含有11个沉积（火山）地层单位、7个变质岩岩层单位和3期岩浆事件，以及12 个样品的锆石 U–Pb 年龄数据，数据量为198 MB。该图幅充分反映了九岭南缘地区造山带结构和地质演化过程，是1∶50 000 区域地质调查最新示范性成果，对江南造山带构造演化有重要的科学研究价值，对后续该区域的地质调查工作也具有借鉴意义。
DOI: 10.15468/dl.mu57vy
title: Occurrence Download

description: A dataset containing 55292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Descurainia sophia (L.) Webb ex Prantl)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 55292 records from 696 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007761-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14986544
title: CMIP6 input data usage information for IPCC WGI AR6 figure Atlas.13

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 Atlas.13 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 Atlas.13.
DOI: 10.34740/kaggle/dsv/8535311
title: WallStreetBets 2022
DOI: 10.15468/dl.db68qw
title: Occurrence Download

description: A dataset containing 225670 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carcharhinus melanopterus (Quoy & Gaimard, 1824)"
 ]
}

 The dataset includes 225670 records from 159 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066892-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b54wbh
title: Occurrence Download

description: A dataset containing 2379 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is Lentinellus cochleatus (Pers.) P.Karst."
 ]
}

 The dataset includes 2379 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132682-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hjaqfp
title: Occurrence Download

description: A dataset containing 73345 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bombylius major Linnaeus, 1758"
 ]
}

 The dataset includes 73345 records from 517 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042125-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gtabxx
title: Occurrence Download

description: A dataset containing 4736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rodentia",
 "HasCoordinate is TRUE",
 "Geometry POLYGON((-77.14900208 5.45680094,-73.89620209 5.45680094,-73.89620209 9.00013924,-77.14900208 9.00013924,-77.14900208 5.45680094))"
 ]
}

 The dataset includes 4736 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013793-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5vdwr
title: Occurrence Download

description: A dataset containing 4712 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Leptodactylus fragilis (Brocchi, 1877))"
 ]
}

 The dataset includes 4712 records from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066895-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ckbbd
title: Occurrence Download

description: A dataset containing 696 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Protium amazonicum (Cuatrec.) Daly"
 ]
}

 The dataset includes 696 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013790-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kr5vky
title: Occurrence Download

description: A dataset containing 1747 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cerura erminea (Esper, 1783)"
 ]
}

 The dataset includes 1747 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0127570-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wkhgcm
title: Occurrence Download

description: A dataset containing 54600 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)",
 "Year 1924-2024"
 ]
}

 The dataset includes 54600 records from 156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066897-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.je9pqt
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "TaxonKey is Pinus merkusii Jungh. & de Vriese"
 ]
}

 The dataset includes 61 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013796-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vtfv46
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stenogammarus macrurus (G.O.Sars, 1894))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042137-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mpyjhd
title: Occurrence Download

description: A dataset containing 4164 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cornus controversa Hemsl."
 ]
}

 The dataset includes 4164 records from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0118384-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mhyeqa
title: Occurrence Download

description: A dataset containing 54641 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)",
 "Year 1876-2024"
 ]
}

 The dataset includes 54641 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066896-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1051845.10fu
title: SH1051845.10FU

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/sh1051849.10fu
title: SH1051849.10FU

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/sh1051850.10fu
title: SH1051850.10FU

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/sh1051853.10fu
title: SH1051853.10FU

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/sh1051858.10fu
title: SH1051858.10FU

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.