cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/evg-xst-maw
title: Cervidae Goldfuss, 1820

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jhs-fnb-5xw
title: Elephas Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2tq-jts-nr3
title: Bovidae Gray, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ny2-y1v-60b
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mft-szk-ns8
title: Pongo pygmaeus (Linnaeus, 1760)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2ln-x9v-8y2
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a9k-2ca-e8r
title: Elephas Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g6y-9br-1xs
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/tsm-wnk-qp2
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8yr-3cj-cr1
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mjy-6mt-man
title: Cervus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pn6-k6l-d01
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/asv-3c0-h2j
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hgv-fjy-a85
title: Pongo pygmaeus (Linnaeus, 1760)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ds5-am8-3ag
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gba-zvq-sdd
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/b20-db9-jrv
title: Muntiacus muntjak (Zimmermann, 1780)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rrd-x81-5n5
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qlv-f0r-gkx
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wsx-qn6-1xa
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.pp5ffy
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-9.53696 41.75805,-6.48382 41.75805,-6.48382 43.98184,-9.53696 43.98184,-9.53696 41.75805))",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetragonia tetragonoides"
 ]
}

 The dataset includes 40 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022922-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13655433
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.15468/dl.nfvaqx
title: Occurrence Download

description: A dataset containing 1082 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sagra femorata"
 ]
}

 The dataset includes 1082 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022923-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13655465
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.5281/zenodo.17555520
title: Strain-Vacancy Engineering Model (SVEM) for Enhanced Superconductivity in La₃Ni₂O₇ Thin Films: A Pathway to 80K Critical Temperature

description: We propose a phenomenological Strain-Vacancy Engineering Model (SVEM) to predict and control the critical temperature (Tc) in epitaxial La₃Ni₂O₇ thin films through coupled tuning of biaxial strain and oxygen stoichiometry. Building upon recent experimental observations of Tc ≈ 80K under high pressure and Tc ≈ 26-42K in strained thin films at ambient pressure, we develop a physically-motivated framework that unifies these phenomena into a single predictive equation:

Tc(ε, δ) = Tc₀ (1 − α ε + β ε²) exp(−kvac δ)

where Tc₀ = 48K represents the baseline critical temperature, α ≈ 9.5 K/% captures linear strain coupling, β ≈ −15 K/%² accounts for strain saturation at large deformations, and kvac ≈ 15 quantifies exponential suppression by oxygen vacancies. Validation against literature data yields R² = 0.951 and RMSE = 2.5K.

For optimized synthesis conditions—compressive strain ε = −10% and ultra-low oxygen vacancy concentration δ < 0.005—the model predicts Tc ≈ 80K, representing a realistic and experimentally achievable enhancement of superconductivity in nickelates at ambient pressure. We provide detailed synthesis protocols (molecular beam epitaxy, substrate selection, oxygen annealing) and characterization roadmaps (X-ray diffraction, electron microscopy, transport measurements) to validate these predictions.

This work establishes SVEM as a quantitative tool for rational design of next-generation nickelate superconductors, bridging the gap between high-pressure discoveries and technologically relevant thin-film implementations.

Keywords: Nickelate superconductors, La₃Ni₂O₇, strain engineering, oxygen vacancies, epitaxial thin films, critical temperature enhancement, phenomenological modeling.
DOI: 10.5281/zenodo.17399186
title: Replication package for "Wealth and its Distribution in Germany, 1895-2021"

description: This package contains the data, programs and instructions to replicate manuscript "Wealth and its Distribution in Germany, 1895-2021" by Albers, Bartels and Schularick forthcoming at JEEA.
DOI: 10.5281/zenodo.17399187
title: Replication package for "Wealth and its Distribution in Germany, 1895-2021"

description: This package contains the data, programs and instructions to replicate manuscript "Wealth and its Distribution in Germany, 1895-2021" by Albers, Bartels and Schularick forthcoming at JEEA.
DOI: 10.15468/dl.wzddxa
title: Occurrence Download

description: A dataset containing 1082 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sagra femorata"
 ]
}

 The dataset includes 1082 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022925-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17557618
title: Evolutionary and ecological determinants of the phenology of births in wild terrestrial ungulates, a systematic review

description: This dataset contains the descriptive information related to each article retained in the analyses as well as quantitative information about the tests and conclusions associated with the investigation of the drivers of the phenology of births in terrestrial ungulates (n = 124 articles).
DOI: 10.15468/dl.d3vjy3
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Glehnia littoralis"
 ]
}

 The dataset includes 108 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022927-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17557500
title: Underwater images collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-25

description: This dataset was collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-25. 

            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.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 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!
DOI: 10.5281/zenodo.17557444
title: Underwater images collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-25

description: This dataset was collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-25. 

            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.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 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!
DOI: 10.15468/dl.98b8qa
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cervus elaphus"
 ]
}

 The dataset includes 66 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022929-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13655506
title: Daily Market Prices of Commodity India (2001-2025)
DOI: 10.15468/dl.exnbhq
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (Japan, Korea, Republic of)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Thuja"
 ]
}

 The dataset includes 179 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022931-251025141854904/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.29670629
title: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas"

description: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas" Notably, the concentration of pNO3- in the original data was incorrectly uploaded; we have updated the correct concentration of pNO3- in the data-new file.<br>
DOI: 10.6084/m9.figshare.29670629.v2
title: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas"

description: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas" Notably, the concentration of pNO3- in the original data was incorrectly uploaded; we have updated the correct concentration of pNO3- in the data-new file.<br>
DOI: 10.6084/m9.figshare.29670629.v1
title: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas"

description: Data availability about the measurement report titled "Measurement report: High contribution of N<sub>2</sub>O<sub>5</sub> uptake to particulate nitrate formation in NO<sub>2</sub>-limited urban areas"
DOI: 10.15468/dl.sse5et
title: Occurrence Download

description: A dataset containing 23943 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Odontarrhena"
 ]
}

 The dataset includes 23943 records from 420 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022932-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17557440
title: Dataset for "Subdiffraction confinement and non-diffractive propagation of optical Stokes skyrmions enabled by a super-oscillatory metalens"

description: The following two folders contain the experimental data obtained in this study——:Subdiffraction confinement and non-diffractive propagation of optical Stokes skyrmions enabled by a super-oscillatory metalens

1.The folder named "Intensity" includes the YZ intensity distribution along the scanning optical needle and discrete cross-sectional intensity profiles. The numerical prefix in each .mat file corresponds to the distance (in micrometers) from the metasurface.

2.The folder named "Stokes Parameters" contains discrete cross-sectional measurements taken along the optical needle. The data were acquired using a Stokes polarimeter to capture the polarization components at various planes, which serve as the basis for calculating the Stokes parameters. The numerical prefix in each .tif file indicates the distance (in micrometers) from the metasurface.

3.The .m files in each folder are MATLAB scripts provided for data processing.
DOI: 10.5281/zenodo.17557441
title: Dataset for "Subdiffraction confinement and non-diffractive propagation of optical Stokes skyrmions enabled by a super-oscillatory metalens"

description: The following two folders contain the experimental data obtained in this study——:Subdiffraction confinement and non-diffractive propagation of optical Stokes skyrmions enabled by a super-oscillatory metalens

1.The folder named "Intensity" includes the YZ intensity distribution along the scanning optical needle and discrete cross-sectional intensity profiles. The numerical prefix in each .mat file corresponds to the distance (in micrometers) from the metasurface.

2.The folder named "Stokes Parameters" contains discrete cross-sectional measurements taken along the optical needle. The data were acquired using a Stokes polarimeter to capture the polarization components at various planes, which serve as the basis for calculating the Stokes parameters. The numerical prefix in each .tif file indicates the distance (in micrometers) from the metasurface.

3.The .m files in each folder are MATLAB scripts provided for data processing.
DOI: 10.15468/dl.g47pyn
title: Occurrence Download

description: A dataset containing 1197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Spodoptera frugiperda)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1975",
 "Year is less than or equal to 2015"
 ]
}

 The dataset includes 1197 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022933-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13655559
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.30571955.v1
title: 无题项目

description: 一种基于自适应分组代码建模的多层次情感驱动语音克隆方法 [数据集]
DOI: 10.5281/zenodo.17557642
title: Underwater images collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-26

description: This dataset was collected by  Scuba diving in Rodrigues-Saint-Francois, Mauritius - 2019-05-26. 

            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!