cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/gp6jir
title: Increasing accuracy motivations does not reduce Republicans’ belief in false news

description: Data for Harvard Kennedy School Misinformation Review submission
DOI: 10.7910/dvn/gpi3is
title: Per-emb-54, image data

description: Imaged continuum and spectral line cubes for (Per54, NGC 1333 IRAS6, NGC1333 IRAS6)
DOI: 10.7910/dvn/iihdtv
title: Male Immorality: An Evolutionary Account of Sex Differences in Unethical Negotiation Behavior

description: The dataset pertains to the paper "Male Immorality: An Evolutionary Account of Sex Differences in Unethical Negotiation Behavior" (https://journals.aom.org/doi/abs/10.5465/amj.2015.0461), and contains materials, data, and analysis codes for all the studies.
description: The associated Open Science Framework (OSF) webpage (https://osf.io/z2ipw/) also contains materials, data, and analysis codes for all the studies.
DOI: 10.7910/dvn/lv0ysq
title: Harvard graduate school tuition

description: Annual tuition at Harvard's arts, business, design, divinity, education, government, law, medical, dental, and public health schools for every year since 1985.
description: http://oir.harvard.edu/fact-book/tuition_graduate
DOI: 10.7910/dvn/qibjbu
title: Replication Data for: Do Elections Improve Constituency Responsiveness? Evidence from U.S. Cities

description: Do elections motivate incumbent politicians to serve their voters? In this paper we use millions of service requests placed by residents in U.S. cities to measure constituency responsiveness. We then test whether an unusual policy change in New York City, which enabled city councilors to run for three rather than two terms in office, improved constituency responsiveness in previously term-limited councilors' districts. Using difference-in-differences, we find robust evidence for this. Taking advantage of differential timing of local election races in New York City and San Francisco, we also find late-term improvements to responsiveness in districts represented by reelection seeking incumbents. Elections improve municipal services, but also create cycles in constituency responsiveness. These findings have implications for theories of representative democracy.
DOI: 10.7910/dvn/whhft9
title: Sociological Study of Korean Immigrants' Mental Health, 1986-1988

description: The aim of this cross-sectional epidemiological survey of Korean immigrants' mental health was "to examine the structural and situational contexts of Korean immigrants' uprooting and adjustment processes (pre- and post-immigration socioeconomic conditions and sociocultural adaptation patterns in the U.S.)"; "identify the high-risk situations (the critical stages in adaptation process) in which Korean immigrants are generally vulnerable to mental and behavioral disorders"; and "analyze precipitating factors associated with the high-risk situations and the immigrants' mental health."
&lt;br /&gt;&lt;br /&gt;

The sample included 622 Korean-American immigrants, 288 women, 334 men, 20-82 years, and averaging 41.6 years. Participants resided in Metropolitan Chicago (which included 70 suburban communities). The study was conducted from April, 1986 to March, 1988.&lt;br /&gt;&lt;br /&gt;

Specific areas of focus in the study included personal background; acculturation; social interaction & religious participation; social and psychological well-being; attitudes toward family members and Korean immigrants; attitudes toward the American culture and society; expectations in family, occupational, and social relations; family life
& satisfaction; and occupational experience.&lt;br /&gt;&lt;br /&gt;

Follow-up of study participants is not possible.
DOI: 10.7910/dvn/d2ehad
title: California Poll: 6005 -- September 23-28, 1960

description: The Field Poll, established in 1945 as The California Poll by Mervin Field, has operated continuously as an independent, non-partisan, media-sponsored public opinion news service. Each year the Field Polls cover a wide range of political and social topics examining California public opinion. Continuing measures are made of voter support for leading political figures vying for major state and federal offices, job ratings of important political figures and reactions to significant political events. Voter awareness, understanding and predispositions of major campaign issues and salient statewide ballot propositions are also tracked over time. For poll 6005 N=1347.
description: Subject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: The UCLA Social Science Data Archive (“the Archive”) gives permission and any required licenses to Data-PASS to make the Content available for archiving, preservation and access (with permission of the Archive), within the Data Preservation Alliance for the Social Sciences ("Data-PASS") in accordance with the Data-PASS terms (the "Terms") of use (available from: http://www.icpsr.org/Data-PASS). Including permission to: (a) redisseminate copies of the Data Collection in a variety of distribution formats only according to the standard terms of use of the Archive; (b) promote and advertise the Data Collection in any publicity (in any form) for Data-PASS and the Archive; (c) describe, catalog, validate and document the Content; (d) store, translate, copy or re-format the Data Collection in any way to ensure its future preservation and accessibility, improve usability and/or protect respondent confidentiality; (e) incorporate metadata (cataloging information) or documentation regarding this study into public access catalogues. The Archive represents and warrants that the Content conforms to all Terms and that the Archive is lawfully entitled and has full authority to license Data-Pass to use the Materials in the ways described in the Terms.;
DOI: 10.7910/dvn/fphev1
title: Stiffness, Noise Level 0

description: Stiffness, Noise Level 0
DOI: 10.15468/dl.x5v4cx
title: Occurrence Download

description: A dataset containing 81757 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is Deilephila porcellus"
 ]
}

 The dataset includes 81757 records from 452 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006524-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15229774
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14. 

            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.15230177
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14. 

            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.15230304
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-14. 

            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.yy4cbu
title: Occurrence Download

description: A dataset containing 101629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "DatasetKey is one of (Fauna silvestre reubicada en el marco del Proyecto Hidroeléctrico Ituango, Colección de Herpetología del Instituto de Ciencias Naturales (ICN-MHN-Her), Colección de Anfibios del Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-Am), Colección de anfibios - Museo de Herpetología de la Universidad de Antioquia, Colección de anfibios del Museo de Historia Natural de la Pontificia Universidad Javeriana, Colección Anfibios Instituto Sinchi, Colección Herpetológica (Anfibios) del Museo de Historia Natural de la Universidad Industrial de Santander, Colección de anfibios y reptiles, Universidad del Valle (Colección UV-C), Monitoreo de seguimiento de fauna pozo Lisama 158, séptimo informe, iNaturalist Research-grade Observations)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anura"
 ]
}

 The dataset includes 101629 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017022-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15291053
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-22

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-22. 

            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.15264763
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-22

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2025-04-22. 

            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