cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26023/19j9-8s9n-s50y
title: DEM and DSM Data Based on Leaf-off Lidar for the Core Area in CHEESEHEAD Domain. Version 1.0

description: This dataset provides the digital elevation model (DEM) and digital surface model (DSM) for CHEESEHEAD core study area (10km ×10km). DEM and DSM are projected to WGS 84 / UTM zone 15N (EPSG:32615) at 1m spatial resolution. The unit for the height is foot. The DEM and DSM are mosaics from tiles for three counties: Ashland (2019), Iron (2019), and Price (2018). All the tiles are derived from leaf-off lidar point cloud collected by USGS and can be found at https://geodata.wisc.edu/?f%5Bdct_provenance_s%5D%5B%5D=WisconsinView . Tiles used in this dataset and quality for each tile are recorded in tile_lookup.csv The GeoData@Wisconsin is an online geoportal that provides discovery and access to Wisconsin geospatial data, imagery, and scanned maps. It is developed and maintained by the UW-Madison Geography Department's Robinson Map Library and State Cartographer's Office.
DOI: 10.15468/dl.7zw5rr
title: Occurrence Download

description: A dataset containing 4876 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Nuuk Basic"
 ]
}

 The dataset includes 4876 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058454-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4t642h
title: Occurrence Download

description: A dataset containing 7487 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Georgian Oribatida"
 ]
}

 The dataset includes 7487 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058456-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/bjsp-3pqq-930c
title: PERiLS_2023: ULM Mobile Doppler Wind Lidar (DWL) Data. Version 1.0

description: VAD (Velocity Azimuth Display) and vertical stare observations from the University of Louisiana Monroe (ULM) mobile Doppler Wind Lidar that was deployed at locations around the southeast United States for the PERiLS_2023 campaign. Includes vertical profiles of horizontal and vertical winds during five deployments.
DOI: 10.5281/zenodo.14986473
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.26

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 8.26 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 8.26.
DOI: 10.5281/zenodo.14986470
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.25

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 8.25 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 8.25.
DOI: 10.5281/zenodo.14986647
title: The Role of Fear of Missing Out (FoMO) in Shaping Digital Consumption Behavior Implications of IT Product Adoption and User Retention

description: Fear of Missing Out (FoMO) has emerged as a significant psychological factor influencing digital consumption behaviors, particularly in the context of IT product adoption and user retention. This study examines the role of FoMO in shaping consumer engagement with digital platforms and technology-based services. Drawing upon existing literature, we explore how FoMO affects user behavior, leading to impulsive digital purchases, increased social media engagement, and prolonged screen time. The study also investigates the mediating effects of psychological and social variables, such as stress perception, social support, and procrastination, on the relationship between FoMO and digital consumption. This study uses the statistical technique of Structural Equation Modeling (SEM) with SmartPLS as an instrument. Using Google Forms, data from 375 respondents were collected online, but only 353 responses were valid. Respondents are students and employees from Jakarta, Bogor, Depok, Tangerang, and other cities in Indonesia. This study uses the Purposive Sampling method; Data were collected in March 2025. This study contributes to providing deeper insights into the impact of fear of missing out (FOMO) on digital consumption behaviors, particularly in the context of IT product adoption and user retention. So that more users can consider the excessive fear of missing out habit.
DOI: 10.5281/zenodo.14986648
title: The Role of Fear of Missing Out (FoMO) in Shaping Digital Consumption Behavior Implications of IT Product Adoption and User Retention

description: Fear of Missing Out (FoMO) has emerged as a significant psychological factor influencing digital consumption behaviors, particularly in the context of IT product adoption and user retention. This study examines the role of FoMO in shaping consumer engagement with digital platforms and technology-based services. Drawing upon existing literature, we explore how FoMO affects user behavior, leading to impulsive digital purchases, increased social media engagement, and prolonged screen time. The study also investigates the mediating effects of psychological and social variables, such as stress perception, social support, and procrastination, on the relationship between FoMO and digital consumption. This study uses the statistical technique of Structural Equation Modeling (SEM) with SmartPLS as an instrument. Using Google Forms, data from 375 respondents were collected online, but only 353 responses were valid. Respondents are students and employees from Jakarta, Bogor, Depok, Tangerang, and other cities in Indonesia. This study uses the Purposive Sampling method; Data were collected in March 2025. This study contributes to providing deeper insights into the impact of fear of missing out (FOMO) on digital consumption behaviors, particularly in the context of IT product adoption and user retention. So that more users can consider the excessive fear of missing out habit.
DOI: 10.5281/zenodo.14986469
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.21

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 8.21 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 8.21.
DOI: 10.5281/zenodo.14986471
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.25

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 8.25 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 8.25.
DOI: 10.5281/zenodo.14986468
title: CMIP6 input data usage information for IPCC WGI AR6 figure 8.21

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 8.21 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 8.21.
DOI: 10.5281/zenodo.14895137
title: Anticipatory detection and characterization of urban stormwater system flood risk: a model-based fault diagnosis approach

description: File Descriptions



lab_system.inp - EPA SWMM model input file.

lab_system_config.yml - Configuration file used by UrbanSurge to set up the EPA SWMM model.

phy_healthy_impulse_database.csv - Simulated fault-free stormwater system depth and velocity measurements using survey impulse.

phy_impulse_2.5yr_fault_database.csv - Simulated stormwater system depth and velocity measurements using design storm impulse under fault conditions.

phy_impuslse_fault_database.csv - Simulated stormwater system depth and velocity measurements using survey impulse under fault conditions.

pipeline_diagnose.ipynb - Python Jupyter Notebook used to perform fault detection and diagnosis and produce results figures found in the paper.
DOI: 10.5281/zenodo.14895138
title: Anticipatory detection and characterization of urban stormwater system flood risk: a model-based fault diagnosis approach

description: File Descriptions



lab_system.inp - EPA SWMM model input file.

lab_system_config.yml - Configuration file used by UrbanSurge to set up the EPA SWMM model.

phy_healthy_impulse_database.csv - Simulated fault-free stormwater system depth and velocity measurements using survey impulse.

phy_impulse_2.5yr_fault_database.csv - Simulated stormwater system depth and velocity measurements using design storm impulse under fault conditions.

phy_impuslse_fault_database.csv - Simulated stormwater system depth and velocity measurements using survey impulse under fault conditions.

pipeline_diagnose.ipynb - Python Jupyter Notebook used to perform fault detection and diagnosis and produce results figures found in the paper.
DOI: 10.15468/dl.5mzc6s
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vernonia nantcianensis (Pamp.) Hand.-Mazz."
 ]
}

 The dataset includes 31 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000136-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eke54r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Argyranthemum winteri (Svent.) Humphries)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000137-250226125047203/datasets/export for details.
DOI: 10.17028/rd.lboro.28497983
title: Supplementary information files for "Assessing the efficacy of hydro-ecological based wetland management approach for flood resilience of a large river catchment"

description: Supplementary files for article "Assessing the efficacy of hydro-ecological based wetland management approach for flood resilience of a large river catchment"Riverine flooding is a widespread global challenge and is predicted to worsen with climate change. Wetlands have been recognised as a potential nature-based solution (NbS) to riverine flooding, but are rapidly degrading globally, reducing their capacity to store water. Furthermore, in monsoon climates, natural wetlands can prove inadequate during peak flows, reaching their capacity early in the monsoon season. Management and rejuvenation of these wetlands provides a potential method to overcome the limitations and ensure the efficacy of wetlands as nature-based solutions for flood risk reduction. In this study, a hydro-ecological based management approach is adopted that formulates a storage release policy by considering threshold precipitation and wetland ecological requirements for assessing the flood resilience of both existing and rejuvenated wetlands. The approach is applied over a large river catchment, the Brahmaputra River, to explore the efficiency of the proposed release policy under different wetland rejuvenation scenarios and to assess the influence of wetland location on flood risk reduction. Additionally, the influence of rainfall distribution and the number of wetlands present in different zones of the catchment, and their operationality on streamflow response is analysed. The proposed wetland management approach showed peak streamflow reduction at the catchment outlet of 1–2 % for existing wetlands and up to 8 % for maximum rejuvenation of wetlands. At the sub-catchment scale, one of the major tributaries observed streamflow reductions of ∼ 6 % in the existing scenario and ∼ 30 % in the rejuvenation scenario. The occurrence of flood threat level events at prominent cities was reduced up to 60 % with wetland rejuvenation. The findings of this study demonstrate the potential impact of wetlands as NbS and provide guidance to help develop strategies for sustainable catchment-scale wetland management practices to enhance flood resilience.<br><br>© The Author(s) CC BY 4.0
DOI: 10.17028/rd.lboro.28497983.v1
title: Supplementary information files for "Assessing the efficacy of hydro-ecological based wetland management approach for flood resilience of a large river catchment"

description: Supplementary files for article "Assessing the efficacy of hydro-ecological based wetland management approach for flood resilience of a large river catchment"Riverine flooding is a widespread global challenge and is predicted to worsen with climate change. Wetlands have been recognised as a potential nature-based solution (NbS) to riverine flooding, but are rapidly degrading globally, reducing their capacity to store water. Furthermore, in monsoon climates, natural wetlands can prove inadequate during peak flows, reaching their capacity early in the monsoon season. Management and rejuvenation of these wetlands provides a potential method to overcome the limitations and ensure the efficacy of wetlands as nature-based solutions for flood risk reduction. In this study, a hydro-ecological based management approach is adopted that formulates a storage release policy by considering threshold precipitation and wetland ecological requirements for assessing the flood resilience of both existing and rejuvenated wetlands. The approach is applied over a large river catchment, the Brahmaputra River, to explore the efficiency of the proposed release policy under different wetland rejuvenation scenarios and to assess the influence of wetland location on flood risk reduction. Additionally, the influence of rainfall distribution and the number of wetlands present in different zones of the catchment, and their operationality on streamflow response is analysed. The proposed wetland management approach showed peak streamflow reduction at the catchment outlet of 1–2 % for existing wetlands and up to 8 % for maximum rejuvenation of wetlands. At the sub-catchment scale, one of the major tributaries observed streamflow reductions of ∼ 6 % in the existing scenario and ∼ 30 % in the rejuvenation scenario. The occurrence of flood threat level events at prominent cities was reduced up to 60 % with wetland rejuvenation. The findings of this study demonstrate the potential impact of wetlands as NbS and provide guidance to help develop strategies for sustainable catchment-scale wetland management practices to enhance flood resilience.<br><br>© The Author(s) CC BY 4.0
DOI: 10.15468/dl.adu5xy
title: Occurrence Download

description: A dataset containing 31168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Triumfetta L."
 ]
}

 The dataset includes 31168 records from 598 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000140-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.