cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr03454
title: Current Population Survey, August 1995: Veterans Supplement
title: Archival Version

description: Data are provided in this collection on labor force
 activity for the week prior to the survey. Comprehensive data are
 available on the employment status, occupation, and industry of
 persons 15 years old and older. Also shown are personal
 characteristics such as age, sex, race, marital status, veteran
 status, household relationship, educational background, and Hispanic
 origin. The veterans supplement provides estimates on the demographic
 and labor force characteristics of veterans with service-connected
 disabilities in comparison to all other veterans. The data also
 identify Vietnam-theater veterans: those who actually served in
 Vietnam, Laos, or Cambodia. Respondents were also queried on whether
 they served in the Persian Gulf War theater after August 2, 1990.
 Estimates of the number of veterans with service-connected
 disabilities along with their labor force participation rate,
 occupation, unemployment rate, and use of certain educational and job
training programs are also provided.
description: The probability sample selected to represent the universe
consists of approximately 57,000 households.
description: Datasets: 
DS1: Current Population Survey, August 1995: Veterans Supplement
description: All persons in the civilian noninstitutional population of
the United States living in households.
description: personal interview
DOI: 10.35080/data.c.2021.p08
title: 中国地质调查局: 晋冀交界怀安镇幅(K50E022002)1∶50 000地质图数据库
title: 1:50,000 geological map database of Huai'an Map Sheet(K50E022002) in the Shanxi-Hebei border area, China

description: 怀安镇幅（K50E022002）位于晋冀交界地区的恒山—桑干高压麻粒岩带内，属华北克拉通典型的早前寒武纪高级变质岩区。怀安镇幅1∶50 000地质图数据库是按照中国地质调查局新颁布的《1∶50 000区域地质调查技术要求》（DD 2019—01）和行业其他统一标准及要求，采用现代变质岩区填图技术方法和数字填图系统编制完成。通过区域地质调查对测区新太古代变质深成岩体进行了详细解体，划分出5个不同构造层次的变质深成岩体填图单元；新厘定出新太古界桑干岩群和古元古界集宁岩群2期不同时代、不同性质的变质表壳岩单元，为晋冀蒙交界地区早前寒武纪变质地层单元的划分与对比提供了新的依据；系统总结出古元古代3期变质事件，重塑了古元古代造山构造演化过程。该图幅采用特殊线段直观地表达了古老造山带深部地壳岩石塑性流变构造变形样式，丰富了图面表达形式；建立了高级变质岩区变质深成岩和变质表壳岩的识别标志。该数据库为MapGIS格式，数据内容包括1∶50 000地质图和图饰部分，并包含11个锆石U–Pb年龄数据，数据量为137 MB。本地质图数据库充分反映了晋冀交界地区最新的区域地质调查成果，为后续地质矿产调查和科学研究提供了基础地质图库。
DOI: 10.15468/dl.z66ryv
title: Occurrence Download

description: A dataset containing 456 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diplothrix legata (Thomas, 1906)"
 ]
}

 The dataset includes 456 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007763-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.35080/report.a.2020.nga52144.z1.1.v1
title: 海口幅F-49-33、海尾幅E-49-1、占县幅E-49-2、文昌幅E-49-3、东方幅E-49-7、琼中幅E-49-8、琼海幅E-49-9、乐东幅E-49-13、崖县幅E-49-14、陵水幅E-49-15 1:200 000区域地质测量报告书

description: 测区包括海南岛及其邻近小岛，测区面积33560平方公里。本次调查共完成以下工作量：观测线长24442.06公里；观测点27704个；实测剖面251.56公里；砂钻1320米；取薄片样5170块；岩石光谱样3336个；人工重砂样718个；绝对年龄样12个；硅酸盐样88个；化石178处。本次调查主要成果是：建立了区内崖县、东方--琼中两个地层小区和基本的地层层序；查明了区内岩浆岩、区域混合岩的类型、分布范围；初步分析了岩浆岩的侵入期次、岩性特征和区域混合岩的岩性特征、成因及与矿产的关系；划分了区内大地构造单元、构造阶段及各阶段的构造形态特征；划分区内地貌的成因类型和地下水类型；并在综合资料基础上初步总结了区内的成矿规律，圈出找矿远景区。
DOI: 10.5281/zenodo.14986562
title: CMIP6 input data usage information for IPCC WGI AR6 figure CCB10.4.1

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 CCB10.4.1 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 CCB10.4.1.
DOI: 10.5281/zenodo.14986555
title: CMIP6 input data usage information for IPCC WGI AR6 figure Atlas.26

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.26 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.26.
DOI: 10.5281/zenodo.14986535
title: CMIP6 input data usage information for IPCC WGI AR6 figure 9.4

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 9.4 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 9.4.
DOI: 10.5281/zenodo.14986536
title: CMIP6 input data usage information for IPCC WGI AR6 figure 9.4

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 9.4 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 9.4.
DOI: 10.5281/zenodo.11177493
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 

            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.11177653
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 

            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.11178052
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 

            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.q3628n
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Odorrana junlianensis Huang, Fei & Ye, 2001"
 ]
}

 The dataset includes 12 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020546-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xpgs9b
title: Occurrence Download

description: A dataset containing 1920 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversidad de moluscos opistobranquios (Mollusca: Opisthobranchiata), del Pacífico mexicano: Isla Cedros-Vizcaíno"
 ]
}

 The dataset includes 1920 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132683-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xsi22e
title: Occurrence Download

description: A dataset containing 282224 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trombidiformes"
 ]
}

 The dataset includes 282224 records from 321 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009438-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.805146
title: Charcoal records of ODP Hole 175-1078C

description: Depth, sediment = Depth, mcd
DOI: 10.15468/dl.8phqjs
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Akania lucens (F.Muell.) Airy Shaw",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0141533-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25909717
title: How Green Finance Instruments Impact Green Total Factor Energy Efficiency: Evidence from Fuzzy-Set Qualitative Comparative Analysis

description: Data and command files
DOI: 10.1594/pangaea.801824
title: (Supplementary table 1) Summary table of sample ages, 230Th concentrations and the calculated vertical flux
DOI: 10.1594/pangaea.801825
title: (Supplementary table 2) Summary table of sample depths, corresponding age, and total pollen counts as well as pollen concentrations and accumulation rates and freshwater algae concentrations and accumulation rates
DOI: 10.15468/dl.sua2ap
title: Occurrence Download

description: A dataset containing 54531 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)",
 "Year 1970-2024"
 ]
}

 The dataset includes 54531 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066908-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.805153
title: Charcoal records of sediment core GeoB1023-5