cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17606522
title: Nicotine_stainV1: A Benchmark Dataset for Finger Nicotine Stain Segmentation

description: Dataset Description

Nicotine_stainV1 is a novel benchmark dataset developed to support research on automated nicotine-stain segmentation from human fingertips. Due to the lack of publicly available datasets in this domain, Nicotine_stainV1 was constructed entirely from scratch.

The initial collection phase gathered over 300 raw fingertip images from diverse online sources, including Google Image Search and various social media platforms. To further increase the heterogeneity and robustness of the dataset, synthetic samples were generated using state-of-the-art vision-language generative models (Sora-2 and Nano-Banana). This acquisition strategy ensures broad coverage of variations in hand pose, illumination, background complexity, and skin tone.

All images were manually annotated at the pixel level using the Roboflow platform to produce high-quality segmentation masks that precisely delineate nicotine-stained regions. To mitigate overfitting and enhance generalization, extensive data augmentation was applied, including rotations (90° and random angles between −15° and +15°), shearing (±10%), and photometric adjustments (±15% brightness).

The final dataset contains 743 image-mask pairs. The dataset is organized into training, validation, and testing splits following an 80/10/10 stratified sampling strategy to preserve class balance across all partitions.

Folder structure:

dataset/    train/        images/        masks/    valid/        images/        masks/ Nicotine_stainV1 represents the first publicly released dataset dedicated to nicotine-stain segmentation and aims to facilitate reproducible research in clinical imaging, digital dermatology, and computational health monitoring.
DOI: 10.4121/52802249-7f83-4936-ba07-c42325be6809
title: Supporting data for "2D FWI for subsoil density: building aliasing-free seismic data using seismic interferometry"-Chapter 3

description: This dataset contains the supporting materials for Chapter 3 of Yusuke Kawasaki’s PhD project on the field application of 2D FWI using aliasing-free seismic data obtained through seismic interferometry (SI). It includes preprocessed seismic data, including virtual data retrieved by SI, input files for the FWI workflow, inversion results (shear-wave velocity and density models), and the associated codes for FWI and result visualization. The raw seismic data and their description are available at 10.4121/a8553b7e-82ae-4e9b-bc54-2a6b9ca6063c. This dataset supports research aimed at demonstrating the benefits of using virtual seismic data from SI to mitigate the negative impact of spatial aliasing on FWI results.
DOI: 10.4121/52802249-7f83-4936-ba07-c42325be6809.v1
title: Supporting data for "2D FWI for subsoil density: building aliasing-free seismic data using seismic interferometry"-Chapter 3

description: This dataset contains the supporting materials for Chapter 3 of Yusuke Kawasaki’s PhD project on the field application of 2D FWI using aliasing-free seismic data obtained through seismic interferometry (SI). It includes preprocessed seismic data, including virtual data retrieved by SI, input files for the FWI workflow, inversion results (shear-wave velocity and density models), and the associated codes for FWI and result visualization. The raw seismic data and their description are available at 10.4121/a8553b7e-82ae-4e9b-bc54-2a6b9ca6063c. This dataset supports research aimed at demonstrating the benefits of using virtual seismic data from SI to mitigate the negative impact of spatial aliasing on FWI results.
DOI: 10.15468/dl.w6abgf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mayaheros beani",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032701-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17240581
title: Dataset for the responses of ammonium consumption to increased suspended particulate matter in a eutrophic estuary

description: This is a dataset for the responses of ammonium consumption to increased suspended particulate matter in the Jiulong River estuary in southern China. Using 15N-labeling experiments, we quantified ammonia oxidation (AO) and ammonium uptake (AU) in SPM addition experiments along the salinity gradient of the Jiulong River Estuary, a typically human-impacted eutrophic and turbid estuary in China.
DOI: 10.5281/zenodo.17240582
title: Dataset for the responses of ammonium consumption to increased suspended particulate matter in a eutrophic estuary

description: This is a dataset for the responses of ammonium consumption to increased suspended particulate matter in the Jiulong River estuary in southern China. Using 15N-labeling experiments, we quantified ammonia oxidation (AO) and ammonium uptake (AU) in SPM addition experiments along the salinity gradient of the Jiulong River Estuary, a typically human-impacted eutrophic and turbid estuary in China.
DOI: 10.6084/m9.figshare.30617066.v1
title: Source data for Jing et al., 2025

description: Dataset authored by Jing Yu, Han Yeong Kaw, Qi Yang, Cong Fan, Xuejing Ma, Lizhong Zhu, Wei Wang.Source data for the study entitled "Evolved extracellular polymeric substances act as a frontline shield against antibiotic tolerance and resistance in <i>Escherichia coli</i>".<br><br><br>
DOI: 10.15468/dl.t98r9v
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters is less than or equal to 1000.0",
 "GadmGid is ZAF",
 "OccurrenceStatus is Present",
 "TaxonKey is Agathosma crenulata"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032697-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/3808b1b4-7c14-47f7-aa12-3abdc0bd3f96
title: A COVID-19 dataset for academic investigation: Comparison of ML methods vs. Traditional statistics

description: A comprehensive COVID-19 dataset with comorbidites and laboratory variables are listed in the dataset. Dataset includes variables such as gender , Computerized Tomography (CT) results, vital signs, comorbidites, and coagulation variables (PT, aPTT, INR,Fibrinogen, Platelet and D-Dimer). The data have been prepared Anonymously. Both English and Turkish version of the dataset have been provided.
DOI: 10.4121/3808b1b4-7c14-47f7-aa12-3abdc0bd3f96.v1
title: A COVID-19 dataset for academic investigation: Comparison of ML methods vs. Traditional statistics

description: A comprehensive COVID-19 dataset with comorbidites and laboratory variables are listed in the dataset. Dataset includes variables such as gender , Computerized Tomography (CT) results, vital signs, comorbidites, and coagulation variables (PT, aPTT, INR,Fibrinogen, Platelet and D-Dimer). The data have been prepared Anonymously. Both English and Turkish version of the dataset have been provided.
DOI: 10.57760/sciencedb.27771
title: Kinematic and fMRI Data for Motor Strategy Adaptation Using Function-Specific rTMS
title: Kinematic and fMRI Data for Motor Strategy Adaptation Using Function-Specific rTMS

description: This dataset contains kinematic and neuroimaging data from 46 healthy right-handed adult participants who underwent a 7-day backward glide shot put training intervention. All participants were randomly assigned to one of three groups: a function-specific repetitive transcranial magnetic stimulation (rTMS) group (n = 16), a motor hotspot rTMS group (n = 15), or a control group without rTMS (n = 15). All groups received standardized daily training, including motor imagery and physical shot put practice.The kinematic data were obtained from high-speed video recordings of training sessions before and after the intervention. Ten key joint angles across five critical technical phases (backswing, glide, transition, buffering, and release) were extracted using Kinovea software, following a standardized anatomical labeling protocol. Neuroimaging data include resting-state functional magnetic resonance imaging (fMRI) acquired before and after the intervention using a 3T MRI scanner. Users are required to remove the first 10 time points before analyzing the resting-state fMRI data.The dataset includes the following files:rest-fMRI.zip: resting-state functional MRI data for each participant at baseline and post-intervention;Spatial Kinematic Characteristics.xls: joint angle data for each participant;Target.docx: rTMS targets for each participant.
description: This dataset contains kinematic and neuroimaging data from 46 healthy right-handed adult participants who underwent a 7-day backward glide shot put training intervention. All participants were randomly assigned to one of three groups: a function-specific repetitive transcranial magnetic stimulation (rTMS) group (n = 16), a motor hotspot rTMS group (n = 15), or a control group without rTMS (n = 15). All groups received standardized daily training, including motor imagery and physical shot put practice.The kinematic data were obtained from high-speed video recordings of training sessions before and after the intervention. Ten key joint angles across five critical technical phases (backswing, glide, transition, buffering, and release) were extracted using Kinovea software, following a standardized anatomical labeling protocol. Neuroimaging data include resting-state functional magnetic resonance imaging (fMRI) acquired before and after the intervention using a 3T MRI scanner. Users are required to remove the first 10 time points before analyzing the resting-state fMRI data.The dataset includes the following files:rest-fMRI.zip: resting-state functional MRI data for each participant at baseline and post-intervention;Spatial Kinematic Characteristics.xls: joint angle data for each participant;Target.docx: rTMS targets for each participant.
DOI: 10.5281/zenodo.17458486
title: Digital surface model of the watercourses Elbe and Lower Havel (Germany), DGM-W Elbe project, DOM Elbe 2022

description: The high-resolution digital surface model (DSM1, DOM1) of the watercourses Elbe and Lower Havel is based on the airborne laser scanning data, undertaken from 06 January 2022 to 18 March 2022 in the Elbe area and from 20 to 22 December 2021 in the Havel area. 

It was produced and published by Germany’s Federal Institute of Hydrology (BfG), on behalf of the River Basin Community Elbe (RBC Elbe, FGG Elbe). The work was supported by the German Federal Waterways and Shipping Administration (WSV) and the surveying offices and water management administrations of six German states - Saxony, Saxony-Anhalt, Brandenburg, Lower Saxony, Mecklenburg-Vorpommern and Schleswig-Holstein. Recent homogeneous geotopographic data of the Elbe watercourse and its historical floodplain are required for a number of applications in the fields of water management, hydraulic engineering and ecology. 

The data cover both the area around the inland water stretches of the Elbe from the Czech-German border to the village of Zollenspieker (part of the city of Hamburg) and the Lower Havel waterway from the town of Rathenow to its confluence with the Elbe. Since the dataset has a large coverage of 4,043 km², it is split into 62 sections. They were either labelled *HW in case of flood relevant areas (in German: “hochwasser-relevante Gebiete”) or *AU in case of historical floodplains (in German: “Altauengebiete”). Financing was divided according to these categories: In the HW areas, the project was co-funded by BfG, the WSV and the federal states, while in the AU areas, BfG covered all project costs.

For each section we provide hillshade (*HS) and height maps (*NHN).

The data are available as GeoTIFF-files (resolution 1 meter);

Coordinate reference frame: ETRS89.DREF91.R16;

Coordinate projection: UTM Zone 33N;

EPSG-Code: 25833;

Height reference system: DHHN2016, national vertical reference frame in Germany (2022).

 

As of now, only the southern part of the data is available (Elbe-km 0-411 km; Czech-German border to the confluence of Elbe and Lower Havel). The northern part (Elbe-km 411-600 and Lower Havel) will be added as soon as possible.

For further information please contact us.
DOI: 10.5281/zenodo.17458487
title: Digital surface model of the watercourses Elbe and Lower Havel (Germany), DGM-W Elbe project, DOM Elbe 2022

description: The high-resolution digital surface model (DSM1, DOM1) of the watercourses Elbe and Lower Havel is based on the airborne laser scanning data, undertaken from 06 January 2022 to 18 March 2022 in the Elbe area and from 20 to 22 December 2021 in the Havel area. 

It was produced and published by Germany’s Federal Institute of Hydrology (BfG), on behalf of the River Basin Community Elbe (RBC Elbe, FGG Elbe). The work was supported by the German Federal Waterways and Shipping Administration (WSV) and the surveying offices and water management administrations of six German states - Saxony, Saxony-Anhalt, Brandenburg, Lower Saxony, Mecklenburg-Vorpommern and Schleswig-Holstein. Recent homogeneous geotopographic data of the Elbe watercourse and its historical floodplain are required for a number of applications in the fields of water management, hydraulic engineering and ecology. 

The data cover both the area around the inland water stretches of the Elbe from the Czech-German border to the village of Zollenspieker (part of the city of Hamburg) and the Lower Havel waterway from the town of Rathenow to its confluence with the Elbe. Since the dataset has a large coverage of 4,043 km², it is split into 62 sections. They were either labelled *HW in case of flood relevant areas (in German: “hochwasser-relevante Gebiete”) or *AU in case of historical floodplains (in German: “Altauengebiete”). Financing was divided according to these categories: In the HW areas, the project was co-funded by BfG, the WSV and the federal states, while in the AU areas, BfG covered all project costs.

For each section we provide hillshade (*HS) and height maps (*NHN).

The data are available as GeoTIFF-files (resolution 1 meter);

Coordinate reference frame: ETRS89.DREF91.R16;

Coordinate projection: UTM Zone 33N;

EPSG-Code: 25833;

Height reference system: DHHN2016, national vertical reference frame in Germany (2022).

 

As of now, only the southern part of the data is available (Elbe-km 0-411 km; Czech-German border to the confluence of Elbe and Lower Havel). The northern part (Elbe-km 411-600 and Lower Havel) will be added as soon as possible.

For further information please contact us.
DOI: 10.34740/kaggle/dsv/13728427
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.mjm2dv
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cichlasoma bimaculatum",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032703-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.unh55r
title: Occurrence Download

description: A dataset containing 392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-0.79942 41.8069,-0.78331 41.8017,-0.76944 41.79979,-0.75073 41.80499,-0.7353 41.81417,-0.71173 41.81833,-0.70562 41.83811,-0.71106 41.85445,-0.71601 41.86634,-0.70735 41.88218,-0.70215 41.89753,-0.67197 41.92711,-0.67027 41.93467,-0.68856 41.9576,-0.71368 41.97369,-0.74562 41.96467,-0.76538 41.93199,-0.77483 41.86735,-0.79218 41.85956,-0.79827 41.84514,-0.79942 41.8069))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 392 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032704-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17606652
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114h Initial

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114h Initial. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.17606651
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114h Initial

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114h Initial. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.15468/dl.nr9mv8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cichlasoma dimerus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032706-251025141854904/datasets/export for details.
DOI: 10.15468/dl.enhdmp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nymphaea francae"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032707-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17582347
title: Dynamical development of strength and stability of asteroid material under 440 GeV proton beam irradiation

description: LDV displacement data are included in tdms files (each comprising an index file). The acquisition of LDV data was done as follows:



 40 ms of pretrigger at 4 kHz

 After the trigger: 4 MHz for first 5 ms and for 1 ms at 5, 10, 20, and 30 s.

 4 kHz otherwise


Files are named



Number of beamshot_date_time
DOI: 10.5281/zenodo.17582346
title: Dynamical development of strength and stability of asteroid material under 440 GeV proton beam irradiation

description: LDV displacement data are included in tdms files (each comprising an index file). The acquisition of LDV data was done as follows:



 40 ms of pretrigger at 4 kHz

 After the trigger: 4 MHz for first 5 ms and for 1 ms at 5, 10, 20, and 30 s.

 4 kHz otherwise


Files are named



Number of beamshot_date_time
DOI: 10.15468/dl.vypyc4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trichromis salvini",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032708-251025141854904/datasets/export for details.
DOI: 10.6084/m9.figshare.30339022
title: <b>Artificial intelligence (AI) can’t feel me: </b><b> Lower perceived perspective</b><b>–</b><b>taking impedes human</b><b>–</b><b>AI cooperation</b>

description: This study found that people’s reluctance to cooperate with AI stems from their difficulty in perceiving perspective-taking from AI. Enhancing AI’s ability to accurately assess, or even slightly overestimate rather than underestimate, individuals’ emotional and cognitive experiences can make people more likely to perceive perspective-taking from AI, thereby increasing their willingness to cooperate with it.
DOI: 10.6084/m9.figshare.30339022.v1
title: <b>Artificial intelligence (AI) can’t feel me: </b><b> Lower perceived perspective</b><b>–</b><b>taking impedes human</b><b>–</b><b>AI cooperation</b>

description: This study found that people’s reluctance to cooperate with AI stems from their difficulty in perceiving perspective-taking from AI. Enhancing AI’s ability to accurately assess, or even slightly overestimate rather than underestimate, individuals’ emotional and cognitive experiences can make people more likely to perceive perspective-taking from AI, thereby increasing their willingness to cooperate with it.
DOI: 10.6084/m9.figshare.30294775
title: Dataset for “A Survey of Afghan Immigrants in the United States (2025)”

description: This dataset underpins the report <i>A Survey of Afghan Immigrants in the United States</i> (Kajishita et al., 2025), conducted between January and October 2024. It offers one of the most detailed empirical snapshots to date of Afghan immigrants’ experiences in the United States. The survey explores demographic characteristics, migration drivers, integration patterns, and perceptions of governance and community-based support.Findings highlight a predominantly young and family-oriented population navigating complex resettlement challenges. Migration decisions were shaped primarily by safety and livelihood concerns, while community-based organizations played a crucial role in facilitating access to healthcare, education, and language learning. Persistent barriers—including limited English proficiency, housing instability, and difficulties accessing essential services—underscore the need for targeted and inclusive policy interventions. Respondents also maintain strong transnational ties to Afghanistan, balancing adaptation to U.S. society with cross-border family obligations.This dataset enables further quantitative and comparative analyses on refugee integration, governance trust, and community resilience, offering valuable insights for policymakers, practitioners, and migration researchers. The complete report can be obtained from the Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University: https://nerps.org/wp-content/uploads/2025/11/Kajishita-et-al_2025_Afghan-Immigrants-Survey_Report.pdf.
DOI: 10.6084/m9.figshare.30294775.v1
title: Dataset for “A Survey of Afghan Immigrants in the United States (2025)”

description: This dataset underpins the report <i>A Survey of Afghan Immigrants in the United States</i> (Kajishita et al., 2025), conducted between January and October 2024. It offers one of the most detailed empirical snapshots to date of Afghan immigrants’ experiences in the United States. The survey explores demographic characteristics, migration drivers, integration patterns, and perceptions of governance and community-based support.Findings highlight a predominantly young and family-oriented population navigating complex resettlement challenges. Migration decisions were shaped primarily by safety and livelihood concerns, while community-based organizations played a crucial role in facilitating access to healthcare, education, and language learning. Persistent barriers—including limited English proficiency, housing instability, and difficulties accessing essential services—underscore the need for targeted and inclusive policy interventions. Respondents also maintain strong transnational ties to Afghanistan, balancing adaptation to U.S. society with cross-border family obligations.This dataset enables further quantitative and comparative analyses on refugee integration, governance trust, and community resilience, offering valuable insights for policymakers, practitioners, and migration researchers. The complete report can be obtained from the Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University: https://nerps.org/wp-content/uploads/2025/11/Kajishita-et-al_2025_Afghan-Immigrants-Survey_Report.pdf.