cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mhhkgc
title: Occurrence Download

description: A dataset containing 2210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-76.66809 1.88931,-77.51404 0.88986,-77.41516 0.38452,-76.27258 0.2417,-75.76721 0.65917,-75.67932 1.30726,-76.19568 1.95519,-76.66809 1.88931))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 2210 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025857-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52fbh7
title: Occurrence Download

description: A dataset listing the 73 species recorded in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rubus idaeus L."
 ]
}

 The dataset's 73 records were derived from 696 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085507-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.swzj63
title: Occurrence Download

description: A dataset containing 65218 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is FLOR - Herbário do Departamento de Botânica da Universidade Federal de Santa Catarina"
 ]
}

 The dataset includes 65218 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031165-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tv495w
title: Occurrence Download

description: A dataset listing the 5 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lanius collurio Linnaeus, 1758"
 ]
}

 The dataset's 5 records were derived from 692 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085518-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18129686
title: Multi Social Network Graph Datasets

description: This repository contains multiple social network graph datasets in .data format, generated for frequent subgraph mining experiments (gSpan, CMiner).
DOI: 10.5281/zenodo.18129687
title: Multi Social Network Graph Datasets

description: This repository contains multiple social network graph datasets in .data format, generated for frequent subgraph mining experiments (gSpan, CMiner).
DOI: 10.15468/dl.4snc9g
title: Occurrence Download

description: A dataset containing 47032 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.26.19_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 47032 records from 166 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070912-251120083545085/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/14370545
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.3886/e242226
title: University of Nigeria, Nsukka 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: University of Nigeria, Nsukka 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.15468/dl.85de5n
title: Occurrence Download

description: A dataset containing 1152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.26.1_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 1152 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070913-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uvxh45
title: Occurrence Download

description: A dataset containing 1748 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.26.22_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 1748 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070915-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242227
title: University of Port-Harcourt 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: University of Port-Harcourt 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242228
title: University of Uyo 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: University of Uyo 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.15468/dl.j8k2yv
title: Occurrence Download

description: A dataset containing 41995 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polypedates megacephalus"
 ]
}

 The dataset includes 41995 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070916-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w5zhz8
title: Occurrence Download

description: A dataset containing 75697 species occurrences available in GBIF matching the query:
{
 "and" : [
 "IucnRedListCategory is EX",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 75697 records from 1113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070909-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242229
title: Usumanu Danfodiyo University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Usumanu Danfodiyo University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.5281/zenodo.17417004
title: Locations and outlines of collapse sinkholes formed from 1992 to 2024 in evaporite karst at the eastern Dead Sea shore (Jordan) as defined in high resolution optical remote-sensing data

description: This archive contains preliminary remote sensing–based vector datasets produced as part of the study “Scaling and exceedance probability of sinkhole collapse in evaporite karst (Dead Sea, Jordan)”, which has been submitted for possible publication in Natural Hazards and Earth System Sciences.

Included datasets:





Sinkhole_points - Point shapefile marking the locations of individual sinkholes (1992–2024).




Coalescent_sinkhole_area - Polygon shapefile in which each polygon represents a merged (coalescent) sinkhole cluster. All coalescent sinkholes visible for a given timestamp have been annotated (1992–2024).




Singular_sinkhole_area - Polygon shapefile in which each polygon delineates the area of a single sinkhole that first appeared in a given year (1992–2024).


Python_notebooks - 3 Python scripts that contain the code for the plots and graphs in the manuscript. We worked with Jupyter Notebook and Google Colab.


Vector data is given as shapefiles with CRS code EPSG:32636.
DOI: 10.5281/zenodo.18130123
title: Locations and outlines of collapse sinkholes formed from 1992 to 2024 in evaporite karst at the eastern Dead Sea shore (Jordan) as defined in high resolution optical remote-sensing data

description: This archive contains preliminary remote sensing–based vector datasets produced as part of the study “Scaling and exceedance probability of sinkhole collapse in evaporite karst (Dead Sea, Jordan)”, which has been submitted for possible publication in Natural Hazards and Earth System Sciences.

Included datasets:





Sinkhole_points - Point shapefile marking the locations of individual sinkholes (1992–2024).




Coalescent_sinkhole_area - Polygon shapefile in which each polygon represents a merged (coalescent) sinkhole cluster. All coalescent sinkholes visible for a given timestamp have been annotated (1992–2024).




Singular_sinkhole_area - Polygon shapefile in which each polygon delineates the area of a single sinkhole that first appeared in a given year (1992–2024).


Python_notebooks - 3 Python scripts that contain the code for the plots and graphs in the manuscript. We worked with Jupyter Notebook and Google Colab.


Vector data is given as shapefiles with CRS code EPSG:32636.
DOI: 10.17632/hkcyvhvmwp.3
title: Solar Energy Dataset

description: This dataset provides a monthly compilation of climate variables for an entire year crucial for evaluating the feasibility of solar power projects, including solar irradiance, surface albedo, cloud cover, and temperature—making it highly relevant for environmental and energy research communities.
The data can be used to train machine learning models that classify or rank geographic regions based on their suitability for solar energy harvesting, offering support to urban planners, energy developers, and policy-makers for data-driven decision-making.
Researchers working in climate science, renewable energy planning, or geospatial analytics can leverage this dataset to assess energy potential and analyze regional variability, helping optimize site selection strategies in different parts of the world.
The dataset facilitates the creation of interactive geospatial visualizations and maps, helping to democratize energy insights by making them accessible to a broader range of stakeholders including NGOs, startups, and governments focused on sustainable development.
This data can be used to benchmark and evaluate machine learning models aimed at site selection, climate prediction, or grid expansion, while also serving as a base for transfer learning in broader energy or environmental prediction tasks.
By offering publicly accessible, structured climate and solar energy data across a 10 -years period(January 2013 to December 2022) [1], this collection supports longitudinal studies and helps compare trends in solar resource availability across seasons and regions—an essential step toward scaling clean energy initiatives globally
DOI: 10.17632/wrxc9jxc2g
title: Bus_DCE_DIB_Data

description: This dataset comprises stated-preference data from a face-to-face Discrete Choice Experiment (DCE) conducted to quantify passenger willingness to pay (WTP) for public bus service improvements in Hanoi, Vietnam. The study evaluates trade-offs across six key attributes: access time, schedule punctuality, ride quality, app usability, emission reductions, and fare increments.

Structured as an encoded long-format panel, the dataset integrates respondent socio-demographics and environmental attitudes with binary choice tasks. It is optimized for discrete choice modeling (e.g., Conditional or Mixed Logit) to analyze preference heterogeneity and price sensitivity. This resource supports methodological benchmarking and policy analysis for sustainable urban transport planning.
DOI: 10.17632/wrxc9jxc2g.1
title: Bus_DCE_DIB_Data

description: This dataset comprises stated-preference data from a face-to-face Discrete Choice Experiment (DCE) conducted to quantify passenger willingness to pay (WTP) for public bus service improvements in Hanoi, Vietnam. The study evaluates trade-offs across six key attributes: access time, schedule punctuality, ride quality, app usability, emission reductions, and fare increments.

Structured as an encoded long-format panel, the dataset integrates respondent socio-demographics and environmental attitudes with binary choice tasks. It is optimized for discrete choice modeling (e.g., Conditional or Mixed Logit) to analyze preference heterogeneity and price sensitivity. This resource supports methodological benchmarking and policy analysis for sustainable urban transport planning.
DOI: 10.17632/pwbtxsdjdt
title: The Ensembles forecast data of the Stevens Flood Advisory System

description: The Stevens Flood Advisory System (http://Stevens.edu/SFAS) is an ensemble total water level forecast system run by the Stevens Institute of Technology since 2016. The Stevens model datasets used in the paper "Chen, et al (2026) Advantages and Challenges of Mid-latitude Super-Ensemble Coastal Water Level Forecasting".
DOI: 10.17632/pwbtxsdjdt.1
title: The Ensembles forecast data of the Stevens Flood Advisory System

description: The Stevens Flood Advisory System (http://Stevens.edu/SFAS) is an ensemble total water level forecast system run by the Stevens Institute of Technology since 2016. The Stevens model datasets used in the paper "Chen, et al (2026) Advantages and Challenges of Mid-latitude Super-Ensemble Coastal Water Level Forecasting".
DOI: 10.17632/sdp3s7c3yc
title: Building Information Modelling In Construction: Challenges of Facilities Management In Malaysia And Its Technological Conciliation

description: Kajian ini menumpukan kepada cabaran pelaksanaan Building Information Modelling (BIM) dalam Facilities Management (FM) di Malaysia. Penyelidikan ini mengenal pasti isu berkaitan integrasi BIM–FM, kekangan teknikal, kekurangan kemahiran serta jurang amalan industri.
DOI: 10.17632/sdp3s7c3yc.1
title: Building Information Modelling In Construction: Challenges of Facilities Management In Malaysia And Its Technological Conciliation

description: Kajian ini menumpukan kepada cabaran pelaksanaan Building Information Modelling (BIM) dalam Facilities Management (FM) di Malaysia. Penyelidikan ini mengenal pasti isu berkaitan integrasi BIM–FM, kekangan teknikal, kekurangan kemahiran serta jurang amalan industri.
DOI: 10.17632/65g9gm8zrd
title: ADFIR forensic dataset

description: An important aspect of digital forensics research is the ability to create datasets that meet specific requirements and reflect realistic scenarios. In general, there is no single dataset suitable for all research purposes in digital forensics, and researchers often face challenges related to dataset availability, documentation, and formal description of its construction. For our research, we aimed to develop a dataset representing real-world scenarios that may occur during security incidents, enabling the comparison of methods for analyzing digital evidence.

The dataset was created by Pavol Jozef Šafárik University in Košice (Slovakia) and IstroSec (Slovakia) within the project Automation of Digital Forensics and Incident Response (referred to as “ADFIR”), funded by the European Union – Next GenerationEU through the Recovery and Resilience Plan of the Slovak Republic under project No. 09-I05-03-V02-00079.

The dataset was primarily derived from CTF competition datasets and supplemented with realistically simulated attack data to enhance coverage and applicability. It consists of two complementary parts: (1) a dataset generated through simulation of various attacker techniques, collected in a controlled environment by IstroSec and processed using the Athena tool, and (2) a dataset created from disk images of devices (computers and laptops) obtained from CTF competitions and publicly available cases. Specifically, we used data from The Stolen Szechuan Sauce case from the DFIR Madness Portal, Magnet CTFs (2019, 2020, 2022), and the NIST Data Leakage Case. These datasets are widely used for training in digital forensics, incident response, and threat hunting.

The dataset includes tabular data (CSV files), a SQL database, and embedding data derived from multiple forensic artifacts of the Windows operating system, all based on the NTFS file system. Disk images include DC01 and Desktop Disk Image (E01) from The Stolen Szechuan Sauce, as well as Magnet CTF 2019 Windows Desktop, Magnet CTF 2022 Windows Laptop, and the NIST Data Leakage Case images. The simulated attack dataset provides a diverse set of attack techniques, whereas the CTF-based dataset captures realistic post-incident system states. Evaluated and processed outputs from forensic tools, along with embedded datasets, are included for data analysis and machine learning experiments.
DOI: 10.17632/65g9gm8zrd.1
title: ADFIR forensic dataset

description: An important aspect of digital forensics research is the ability to create datasets that meet specific requirements and reflect realistic scenarios. In general, there is no single dataset suitable for all research purposes in digital forensics, and researchers often face challenges related to dataset availability, documentation, and formal description of its construction. For our research, we aimed to develop a dataset representing real-world scenarios that may occur during security incidents, enabling the comparison of methods for analyzing digital evidence.

The dataset was created by Pavol Jozef Šafárik University in Košice (Slovakia) and IstroSec (Slovakia) within the project Automation of Digital Forensics and Incident Response (referred to as “ADFIR”), funded by the European Union – Next GenerationEU through the Recovery and Resilience Plan of the Slovak Republic under project No. 09-I05-03-V02-00079.

The dataset was primarily derived from CTF competition datasets and supplemented with realistically simulated attack data to enhance coverage and applicability. It consists of two complementary parts: (1) a dataset generated through simulation of various attacker techniques, collected in a controlled environment by IstroSec and processed using the Athena tool, and (2) a dataset created from disk images of devices (computers and laptops) obtained from CTF competitions and publicly available cases. Specifically, we used data from The Stolen Szechuan Sauce case from the DFIR Madness Portal, Magnet CTFs (2019, 2020, 2022), and the NIST Data Leakage Case. These datasets are widely used for training in digital forensics, incident response, and threat hunting.

The dataset includes tabular data (CSV files), a SQL database, and embedding data derived from multiple forensic artifacts of the Windows operating system, all based on the NTFS file system. Disk images include DC01 and Desktop Disk Image (E01) from The Stolen Szechuan Sauce, as well as Magnet CTF 2019 Windows Desktop, Magnet CTF 2022 Windows Laptop, and the NIST Data Leakage Case images. The simulated attack dataset provides a diverse set of attack techniques, whereas the CTF-based dataset captures realistic post-incident system states. Evaluated and processed outputs from forensic tools, along with embedded datasets, are included for data analysis and machine learning experiments.
DOI: 10.17632/b2v6scxdy8
title: Structural Heterogeneity Drives Low CO2 Mobility and Strong Hysteresis in Reservoir Carbonates

description: Data for "Structural Heterogeneity Drives Low CO$_2$ Mobility and Strong Hysteresis in Reservoir Carbonates".
DOI: 10.17632/b2v6scxdy8.1
title: Structural Heterogeneity Drives Low CO2 Mobility and Strong Hysteresis in Reservoir Carbonates

description: Data for "Structural Heterogeneity Drives Low CO$_2$ Mobility and Strong Hysteresis in Reservoir Carbonates".
DOI: 10.3886/e242230
title: Nigerian Maritime University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Nigerian Maritime University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.15468/dl.hubr6r
title: Occurrence Download

description: A dataset containing 34298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.26.5_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 34298 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070918-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242231
title: Air Force Institute of Technology, Kaduna 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Air Force Institute of Technology, Kaduna 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.5281/zenodo.18125844
title: Green Microfinance: Africa's Path to Resilience

description: This dataset compiles a balanced panel of annual macroeconomic, social, environmental, and financial indicators for 28 African countries over the period 2000–2023, designed to analyze the impact of green microfinance on vulnerable communities’ access to basic services, particularly drinking water. Data were collected from internationally recognized and publicly available sources, including the World Bank, the African Development Bank, and official national government publications. The selected period captures major structural transformations in Africa related to sustainable development, microfinance expansion, climate change, and economic shocks, including the emergence of green microfinance initiatives, the 2008 global financial crisis, and the post-2015 Paris Climate Agreement era. The dataset includes variables on access to clean water, green microfinance funding, economic development, inequality, poverty, energy transition, labor market conditions, and environmental pressure. To ensure consistency and comparability across countries and over time, all variables were harmonized in terms of measurement units and transformed into natural logarithms where appropriate. The dataset is intended to support empirical research on green finance, sustainable development, climate resilience, and social vulnerability in Africa.
DOI: 10.5281/zenodo.18125843
title: Green Microfinance: Africa's Path to Resilience

description: This dataset compiles a balanced panel of annual macroeconomic, social, environmental, and financial indicators for 28 African countries over the period 2000–2023, designed to analyze the impact of green microfinance on vulnerable communities’ access to basic services, particularly drinking water. Data were collected from internationally recognized and publicly available sources, including the World Bank, the African Development Bank, and official national government publications. The selected period captures major structural transformations in Africa related to sustainable development, microfinance expansion, climate change, and economic shocks, including the emergence of green microfinance initiatives, the 2008 global financial crisis, and the post-2015 Paris Climate Agreement era. The dataset includes variables on access to clean water, green microfinance funding, economic development, inequality, poverty, energy transition, labor market conditions, and environmental pressure. To ensure consistency and comparability across countries and over time, all variables were harmonized in terms of measurement units and transformed into natural logarithms where appropriate. The dataset is intended to support empirical research on green finance, sustainable development, climate resilience, and social vulnerability in Africa.