cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mkxr5wgbs5.1
title: Cave Dripwater Magnetic Minerals Record Seasonal Rainfall Variability: New Insights from a five-years Field Monitoring in Central China

description: Datasets in Fig. 6 for G-Cubed manuscript#2025GC012427 entiled "Cave Dripwater Magnetic Minerals Record Seasonal Rainfall Variability: New Insights from a five-years Field Monitoring in Central China" authored by Taiheng Shi, Zongmin Zhu, Yuhang Jia, Lingtao Yan, Hongbin Zhang, Shuyu Xue, Qian Luo, Jianyu Ding.
DOI: 10.15468/dl.29bzjk
title: Occurrence Download

description: A dataset containing 2334 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio angulatus",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2334 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039992-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wk79yyhh2h
title: Purchase Intention Dataset Based on AI Social Commerce, Influencer Marketing and FoMO

description: This dataset contains responses collected for a study examining the influence of Artificial Intelligence (AI) Social commerce, influencer marketing, and Fear of Missing Out (FoMO) on fashion purchase intention. The data were gathered from [insert number] participants using a structured online questionnaire with Likert scale items.

Two analytical tools were used: SmartPLS 4.0 for Partial Least Squares Structural Equation Modeling (PLS-SEM), and RapidMiner for decision tree classification. Variables include AI-driven commerce factors, influencer credibility, FoMO dimensions (emotional response, peer influence, etc.), and consumer purchase intention.

This dataset can be valuable for researchers studying digital marketing, AI in Social commerce, Marketing strategy like Influencer marketing, consumer behavior, and psychological factors like FoMO.
DOI: 10.17632/wk79yyhh2h.1
title: Purchase Intention Dataset Based on AI Social Commerce, Influencer Marketing and FoMO

description: This dataset contains responses collected for a study examining the influence of Artificial Intelligence (AI) Social commerce, influencer marketing, and Fear of Missing Out (FoMO) on fashion purchase intention. The data were gathered from [insert number] participants using a structured online questionnaire with Likert scale items.

Two analytical tools were used: SmartPLS 4.0 for Partial Least Squares Structural Equation Modeling (PLS-SEM), and RapidMiner for decision tree classification. Variables include AI-driven commerce factors, influencer credibility, FoMO dimensions (emotional response, peer influence, etc.), and consumer purchase intention.

This dataset can be valuable for researchers studying digital marketing, AI in Social commerce, Marketing strategy like Influencer marketing, consumer behavior, and psychological factors like FoMO.
DOI: 10.17632/rz8h4p7fkt
title: LINF_230022900

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/rz8h4p7fkt.1
title: LINF_230022900

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/3xs32pggnx
title: 1D vHS in Kagome metal

description: STM/STS data acquired on RbV3Sb5. 
Matlab script for calculating the LDOS of Kagome bands modified by a 1D potential.
DOI: 10.17632/3xs32pggnx.1
title: 1D vHS in Kagome metal

description: STM/STS data acquired on RbV3Sb5. 
Matlab script for calculating the LDOS of Kagome bands modified by a 1D potential.
DOI: 10.21227/rxb8-m396
title: "Low-loss terahertz negative curvature suspended-core fiber"

description: "Inspired by the design concept of negative curvature hollow-core fibers, this paper presents an innovative negative curvature suspended-core THz fiber. Compared to traditional suspended-core fibers, all structural units of this fiber are designed with circular boundaries, effectively avoiding the issues of insufficient mechanical strength and manufacturing difficulties associated with the wide and thin rectangular support arms in traditional structures. The numerical simulation using the full-vector finite element method shows that the optical fiber loss is as low as 0.0868dB\/cm (0.02cm-1) in 0.66-1.09THz, and the low loss bandwidth is 0.43THz. In addition,  near-zero flat dispersion of 0.33\u00b10.41 ps\/THz\/cm can be achieved. The fiber exhibits excellent characteristics of low loss, wide bandwidth, and low dispersion, theoretically opening a new research path for the design of low-loss THz fibers. "
DOI: 10.15468/dl.94tpys
title: Occurrence Download

description: A dataset containing 1435 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio angulatus",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1435 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039993-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5gssxpsmw9
title: LauWK_Cars_Msia_2012-25

description: The principal investigator collected primary data across different states in Malaysia for new cars registered over the years from 2012 to 2015. The data set consists of quantitative data and qualitative data. Quantitative data are proxies for social economic factors, car prices and car characteristics, while qualitative data which are categorical data reflects factors or car features which car buyers took into account in their decision-making process.
DOI: 10.17632/5gssxpsmw9.1
title: LauWK_Cars_Msia_2012-25

description: The principal investigator collected primary data across different states in Malaysia for new cars registered over the years from 2012 to 2015. The data set consists of quantitative data and qualitative data. Quantitative data are proxies for social economic factors, car prices and car characteristics, while qualitative data which are categorical data reflects factors or car features which car buyers took into account in their decision-making process.
DOI: 10.15468/dl.msuq7n
title: Occurrence Download

description: A dataset containing 335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 335 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039995-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16365948
title: Screencasts of Vietnamese First-Year EFL Learners with ChatGPT for Argumentative Writing (n=100)

description: This dataset contains 100 anonymized screencasts of Vietnamese first-year university students interacting with ChatGPT in an autonomous learning environment to learn how to write English argumentative essays. Each screencast documents the on-screen activity of an individual learner as he or she worked on writing a specific component of an argumentative essay (e.g., introduction, argument, counter-argument, or conclusion). The recordings are part of a larger corpus comprising 104 screencasts in total; due to Zenodo’s file upload limit, only 100 files are shared here. Researchers interested in accessing the full dataset, including the remaining four recordings, may contact the dataset creators directly.
DOI: 10.5281/zenodo.16365949
title: Screencasts of Vietnamese First-Year EFL Learners with ChatGPT for Argumentative Writing (n=100)

description: This dataset contains 100 anonymized screencasts of Vietnamese first-year university students interacting with ChatGPT in an autonomous learning environment to learn how to write English argumentative essays. Each screencast documents the on-screen activity of an individual learner as he or she worked on writing a specific component of an argumentative essay (e.g., introduction, argument, counter-argument, or conclusion). The recordings are part of a larger corpus comprising 104 screencasts in total; due to Zenodo’s file upload limit, only 100 files are shared here. Researchers interested in accessing the full dataset, including the remaining four recordings, may contact the dataset creators directly.
DOI: 10.15468/dl.42h6vg
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Ficus oreodryadum",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 47 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039996-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18738/t8/vnatvn
title: Replication Data for "Dynamics and Couplings of Terrestrial Water Storage Extremes from GRACE and GRACE-FO Missions During 2002–2024"

description: Hydroclimatic extremes such as droughts and floods severely impact global livelihoods, economies, and ecosystems, yet their detection remains challenging. This study evaluates global Terrestrial Water Storage (TWS) extremeness and climate linkages using GRACE and GRACE-FO data from 2002 to 2024. By examining upper and lower deciles of TWS anomalies representing wet and dry extremes and assessing spatial dependencies, we identify key patterns, trends, and driving factors through dimensional reduction and probabilistic modeling. Results show global TWS extremes are governed by a 2–3-year oscillatory cycle linked to El Niño–Southern Oscillation, which synchronizes drought and pluvial conditions across continents. Drought extremes show broader spatial coherence than pluvial events, indicating moisture deficits propagate more uniformly through the land–atmosphere system. A weaker quasi-decadal cycle (6 to 10 years) modulates these responses and underlies a shift around 2011–2012: before 2011, wet extremes intensified, while after 2012, dry extremes became dominant, particularly in interior Asia, western United States, and southern Africa. Neither pluvial nor drought extremes show significant global trends in intensity; however, they remain phase-locked, with wet events twice as intense as dry ones, reflecting asymmetric hydrologic response to moisture surpluses versus deficits. We probabilistically reconstruct TWS extremeness during satellite data gaps using leading spatio-temporal patterns. The current record, spanning less than one multidecadal cycle, remains insufficient for robust attribution. Extending satellite gravimetry is essential to refine uncertainty in attributing global pluvial and drought extremes under climate change.
DOI: 10.15468/dl.h4ukuf
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus virginiana",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039997-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rwdr29jvz8
title: Replication Folder

description: Process of Calculating the Results Presented in the Paper
DOI: 10.17632/rwdr29jvz8.1
title: Replication Folder

description: Process of Calculating the Results Presented in the Paper
DOI: 10.5281/zenodo.16411299
title: Remote Sensing Bio‑Digester Dataset

description: Remote Sensing Bio‑Digester Dataset

Overview

To the best of our knowledge, this is the first large-scale satellite dataset of bio‑digesters, with facility‑level and part‑level annotations. It comprises high‑resolution aerial and satellite imagery of bio‑digester sites across France’s Grand Est region, drawn from multiple sources. Ground‑truth labels are geolocated segmentation masks for three classes:  



Whole installation (entire bio‑digester site)  

Anaerobic digestion tanks (internal tanks)  

Biomass piles (feedstock storage areas)


Code available On Github

Composition



Training & validation sets include SPOT, Sentinel, and aerial modalities. Training is made easy using MMRotate as annotations are also available in DOTA format.

Resolutions: 0.5 m, 1.5 m, and 5 m per pixel.  

Modalities: Aerial, SPOT, and Sentinel imagery—to study transferability and resolution impact.  

Labels remain geolocated; due to modality mismatches, some labels transfer poorly, so aerial annotations serve as the definitive reference. Coordinates provided in EPSG:2154.


Directory Structure

```├── README.md├── res_0.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   └── vpv│       ├── train│       └── val├── res_1.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   ├── test│   │   ├── image│   │   ├── label│   │   └── meta│   └── vpv│       ├── train│       └── val└── res_5    ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val    ├── label    │   ├── train    │   └── val    └── vpv        ├── train        └── val

```

Testing

Test Split

Covers the entire French department of Marne, pre‑tiled at 1.5 m resolution in 1000×1000 px tiles (~5000 tiles).

Environment & Modalities

Available only in aerial and SPOT modalities at 1.5 m resolution.A precomputed tiling facilitates large‑scale evaluation without engineering overhead.

Objective & Metrics

- Detect all bio‑digester sites within 200 m accuracy (AP@200 m as introduced in the paper).  - Augmented with newly discovered Marne‑region digesters to better estimate precision at scale:    - SPOT: 29 sites    - Aerial: 27 sites (including 4 under construction)  

Visualization

Optionally, use vpv to explore the dataset:```vpv ac aw nw ./res_1.5/image/BDORTHO/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/PLEIADES/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SPOT/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SENTINEL/train/ svg:./res_1.5/vpv/train/```
DOI: 10.5281/zenodo.16411300
title: Remote Sensing Bio‑Digester Dataset

description: Remote Sensing Bio‑Digester Dataset

Overview

To the best of our knowledge, this is the first large-scale satellite dataset of bio‑digesters, with facility‑level and part‑level annotations. It comprises high‑resolution aerial and satellite imagery of bio‑digester sites across France’s Grand Est region, drawn from multiple sources. Ground‑truth labels are geolocated segmentation masks for three classes:  



Whole installation (entire bio‑digester site)  

Anaerobic digestion tanks (internal tanks)  

Biomass piles (feedstock storage areas)


Code available On Github

Composition



Training & validation sets include SPOT, Sentinel, and aerial modalities. Training is made easy using MMRotate as annotations are also available in DOTA format.

Resolutions: 0.5 m, 1.5 m, and 5 m per pixel.  

Modalities: Aerial, SPOT, and Sentinel imagery—to study transferability and resolution impact.  

Labels remain geolocated; due to modality mismatches, some labels transfer poorly, so aerial annotations serve as the definitive reference. Coordinates provided in EPSG:2154.


Directory Structure

```├── README.md├── res_0.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   └── vpv│       ├── train│       └── val├── res_1.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   ├── test│   │   ├── image│   │   ├── label│   │   └── meta│   └── vpv│       ├── train│       └── val└── res_5    ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val    ├── label    │   ├── train    │   └── val    └── vpv        ├── train        └── val

```

Testing

Test Split

Covers the entire French department of Marne, pre‑tiled at 1.5 m resolution in 1000×1000 px tiles (~5000 tiles).

Environment & Modalities

Available only in aerial and SPOT modalities at 1.5 m resolution.A precomputed tiling facilitates large‑scale evaluation without engineering overhead.

Objective & Metrics

- Detect all bio‑digester sites within 200 m accuracy (AP@200 m as introduced in the paper).  - Augmented with newly discovered Marne‑region digesters to better estimate precision at scale:    - SPOT: 29 sites    - Aerial: 27 sites (including 4 under construction)  

Visualization

Optionally, use vpv to explore the dataset:```vpv ac aw nw ./res_1.5/image/BDORTHO/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/PLEIADES/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SPOT/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SENTINEL/train/ svg:./res_1.5/vpv/train/```
DOI: 10.15468/dl.9p4bet
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Campylomormyrus curvirostris)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039994-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g7r8ax
title: Occurrence Download

description: A dataset containing 2524 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phytophthora cinnamomi",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2524 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039998-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kms75dhc2c
title: Moroccan Seismic Activity Dataset for Deep Learning Analysis (1909-2024)

description: This dataset presents historical earthquake occurrence data for the Morocco region, covering the period from 1909 to 2024. It includes detailed seismic attributes and geospatial parameters compiled from reliable seismological database, intended for use in scientific research, especially in the field of deep learning-based earthquake prediction and early warning systems. The data is curated from the official source, United States geological Survey. 

The dataset is structured in CSV format and contains the following features such as date, time, latitude, depth, mag (Richter Scale), magnitude type (magType), Number of Stations (nst), gap, minimum distance to station (dmin), RMS, Network Code (net), Event Id (id), Last Updated Timestamp (updated), Location (place), type, horizontalError, depthError, magError, Number of Stations for Magnitude (magnst), status, locationSource and magSource . This dataset has been used for training and evaluating various deep learning algorithms such as Feedforward Neural Network (FNN) , Bi-directional LSTM and Long Short - Term Memory (LSTM) models, in order to identify spatiotemporal patterns and anomalies for predictive modeling of seismic activities and analyzing the performance of the models.

File Format: .csv
Region: Morocco
Time period: 1909-2024
Target Users: Researchers, data scientists, disaster management professionals and geophysicists.
DOI: 10.17632/kms75dhc2c.1
title: Moroccan Seismic Activity Dataset for Deep Learning Analysis (1909-2024)

description: This dataset presents historical earthquake occurrence data for the Morocco region, covering the period from 1909 to 2024. It includes detailed seismic attributes and geospatial parameters compiled from reliable seismological database, intended for use in scientific research, especially in the field of deep learning-based earthquake prediction and early warning systems. The data is curated from the official source, United States geological Survey. 

The dataset is structured in CSV format and contains the following features such as date, time, latitude, depth, mag (Richter Scale), magnitude type (magType), Number of Stations (nst), gap, minimum distance to station (dmin), RMS, Network Code (net), Event Id (id), Last Updated Timestamp (updated), Location (place), type, horizontalError, depthError, magError, Number of Stations for Magnitude (magnst), status, locationSource and magSource . This dataset has been used for training and evaluating various deep learning algorithms such as Feedforward Neural Network (FNN) , Bi-directional LSTM and Long Short - Term Memory (LSTM) models, in order to identify spatiotemporal patterns and anomalies for predictive modeling of seismic activities and analyzing the performance of the models.

File Format: .csv
Region: Morocco
Time period: 1909-2024
Target Users: Researchers, data scientists, disaster management professionals and geophysicists.
DOI: 10.17632/8hmr85pjw7
title: Chemical and Behavioral Fingerprinting Data for the Identification of Neuroactive Compounds in Crude Oil-Derived Water Accommodated Fractions

description: Data and Code Description
This dataset supports the identification and mechanistic evaluation of neuroactive chemicals present in crude oil-derived water accommodated fractions (WAF) using behavioral phenotyping in zebrafish larvae and analytical chemistry. 
It includes GC×GC-MS compound annotations, behavioral cluster assignments for reference neuroactive chemicals, and R scripts for generating standardized effect size (SSMD) heatmaps, cloud plots, and multidimensional scaling (MDS) analyses. 
Data processing was performed using KNIME, and all visualizations were created in RStudio.
DOI: 10.17632/8hmr85pjw7.3
title: Chemical and Behavioral Fingerprinting Data for the Identification of Neuroactive Compounds in Crude Oil-Derived Water Accommodated Fractions

description: Data and Code Description
This dataset supports the identification and mechanistic evaluation of neuroactive chemicals present in crude oil-derived water accommodated fractions (WAF) using behavioral phenotyping in zebrafish larvae and analytical chemistry. 
It includes GC×GC-MS compound annotations, behavioral cluster assignments for reference neuroactive chemicals, and R scripts for generating standardized effect size (SSMD) heatmaps, cloud plots, and multidimensional scaling (MDS) analyses. 
Data processing was performed using KNIME, and all visualizations were created in RStudio.
DOI: 10.15468/dl.a84x43
title: Occurrence Download

description: A dataset containing 319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio angulatus",
 "Country is Portugal",
 "HasCoordinate is true"
 ]
}

 The dataset includes 319 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039999-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.