cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17559694
title: Interview Participants Demographic and Contextual Data Table

description: This file contains a detailed table of interview participants involved in the qualitative study. It includes demographic and contextual information such as gender, age, occupation, geographic location, and professional experience, along with contextual notes about each participant’s background.The file is used to describe the sample characteristics and ensure representativeness of the research context, supporting analysis of patterns and relationships between demographic factors and qualitative findings
DOI: 10.15468/dl.vgtfuc
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trichopsis vittata, Ctenops vittatus)",
 "Country is Singapore",
 "OccurrenceStatus is Present",
 "EventDate is 1800,2026"
 ]
}

 The dataset includes 34 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023171-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f399hs
title: Occurrence Download

description: A dataset containing 4998 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Ohio State University Tetrapod Division - Amphibian Collection (OSUM)"
 ]
}

 The dataset includes 4998 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023172-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fs6kjq
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Impatiens chinensis"
 ]
}

 The dataset includes 153 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023173-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p1vu2iyb
title: Preliminary Landslide Hazard Estimate Maps—2025 Hurricane Melissa Landslide Emergency Response

description: This data release includes relative landslide hazard estimate maps for areas of Jamaica and Haiti that were affected by 2025 Hurricane Melissa (Figure 1, Figure 2). The hurricane made landfall in southwestern Jamaica on October 28, 2025. Versions of these preliminary landslide hazard maps were generated in early November 2025. The landslide hazard maps were used to provide rapid event-specific situational awareness of the possible extent and severity of landslides and debris flows to support the U.S. Government’s response and safety of operations planning until the true effects of the storm could be assessed. These maps do not represent real mapped landslide occurrences.
DOI: 10.5281/zenodo.17559699
title: Underwater images collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07. 

            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!
DOI: 10.5066/p1jmkwmf
title: Rapid Imagery Assessment for Hazardous Landslides in Jamaica—2025 Hurricane Melissa Emergency Response
DOI: 10.21227/4nrp-kn51
title: "Hybrid Renewable Energy System (HRES) Dataset for Smart Grid Stability under Variable Renewable Fluctuations Using Machine Learning"

description: "The current dataset paper presents a comprehensive collection of simulation results generated using HOMER software to support advanced research in grid-integrated hybrid renewable systems. The present dataset captures the operational dynamics of a smart grid framework that integrates solar photovoltaic (PV), wind turbines, and the main grid through a bidirectional converter. The base configuration represents an HRES system supplying a daily electrical demand of 3950 kWh with a peak load of 479.38 kW, as illustrated in the schematic diagram. To study the effect of renewable intermittency on grid stability and optimization performance, two distinct random variability scenarios were modeled: a low variability case (5%) and a high variability case (20%) for both day-to-day and timestep fluctuations. Each variability condition was simulated under multiple operational settings, resulting in a total of 2000 simulation cases per scenario, thereby providing 4000 complete data instances. Each case records key parameters such as power generation, grid interaction, energy conversion efficiency, renewable penetration, and system cost metrics. The present dataset aims to serve as a benchmark resource for researchers focusing on machine learning-based stability analysis, forecasting models, and optimization algorithm validation in renewable energy networks. By incorporating nature-inspired optimization frameworks such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Adaptive Genetic Algorithms the dataset can be utilized to test dynamic adaptation mechanisms under fluctuating renewable inputs.Current research work contributes to the ongoing efforts in smart grid stability enhancement, renewable energy integration, and data-driven optimization. It enables users to explore how random variability impacts grid resilience, system losses, and reliability indices, providing a foundation for developing adaptive control strategies and prediction models. The dataset supports both academic and industrial applications related to energy system optimization, hybrid microgrid planning, and renewable resource management, offering a real-world simulation environment that bridges the gap between theoretical modeling and practical implementation."
DOI: 10.6084/m9.figshare.30408484
title: Application of real data

description: fitting data for IDNAI distribution compared with some existing distributions
DOI: 10.6084/m9.figshare.30408484.v2
title: Application of real data

description: fitting data for IDNAI distribution compared with some existing distributions
DOI: 10.6084/m9.figshare.30408484.v3
title: Application of real data

description: fitting data for IDNAI distribution compared with some existing distributions
DOI: 10.5281/zenodo.17545031
title: PRROMenade functional index (KEGG EC-labeled), snapshot 2020-06-06 — mirror of IBM IFGP dataset used by Haiminen et al. 2021

description: This is the PRROMenade pre-computed index for the IBM Functional Genomics Platform (IFGP), previously known as OMXWare and previously available on https://developer.ibm.com/exchanges/data/, as described in https://github.com/ComputationalGenomics/PRROMenade. 

This index was described and used in:

Niina Haiminen, Filippo Utro, Ed Seabolt and Laxmi Parida: Functional profiling of COVID-19 respiratory tract microbiomes. Scientific Reports, 2021. https://www.nature.com/articles/s41598-021-85750-0It counts (21.2 million bacterial; 53,000 viral domains), and it’s labelled with the KEGG EC hierarchy.

However, as the link https://developer.ibm.com/exchanges/data/ does not provide such an index anymore, this is a new accessible link, so the functional profiling pipeline available in the HERMES-WIRE suite (https://github.com/Baud-lab/hermes-wire) can be performed.
DOI: 10.5281/zenodo.17545032
title: PRROMenade functional index (KEGG EC-labeled), snapshot 2020-06-06 — mirror of IBM IFGP dataset used by Haiminen et al. 2021

description: This is the PRROMenade pre-computed index for the IBM Functional Genomics Platform (IFGP), previously known as OMXWare and previously available on https://developer.ibm.com/exchanges/data/, as described in https://github.com/ComputationalGenomics/PRROMenade. 

This index was described and used in:

Niina Haiminen, Filippo Utro, Ed Seabolt and Laxmi Parida: Functional profiling of COVID-19 respiratory tract microbiomes. Scientific Reports, 2021. https://www.nature.com/articles/s41598-021-85750-0It counts (21.2 million bacterial; 53,000 viral domains), and it’s labelled with the KEGG EC hierarchy.

However, as the link https://developer.ibm.com/exchanges/data/ does not provide such an index anymore, this is a new accessible link, so the functional profiling pipeline available in the HERMES-WIRE suite (https://github.com/Baud-lab/hermes-wire) can be performed.
DOI: 10.5281/zenodo.17546687
title: HDF5 file containing the Genetic Correlation Matrix (GRM) and Genotypes of HS rats

description: An HDF5 file containing the Genetic Correlation Matrix (GRM) and Genotypes (4,880,609 autosomal SNPs) of HS rats (6,258 animals), used on the experiments described in:Chitre, A. S. et al. Genome‐Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. Obesity 28, 1964–1973 (2020).

Tonnele, H. et al. Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats. Preprint at https://doi.org/10.1101/2025.03.20.644349 (2025).
DOI: 10.5281/zenodo.17546688
title: HDF5 file containing the Genetic Correlation Matrix (GRM) and Genotypes of HS rats

description: An HDF5 file containing the Genetic Correlation Matrix (GRM) and Genotypes (4,880,609 autosomal SNPs) of HS rats (6,258 animals), used on the experiments described in:Chitre, A. S. et al. Genome‐Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. Obesity 28, 1964–1973 (2020).

Tonnele, H. et al. Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats. Preprint at https://doi.org/10.1101/2025.03.20.644349 (2025).
DOI: 10.5281/zenodo.17545482
title: Kaiju index of the GTDB R207 bacterial and archaeal full protein catalogue for representative species

description: A protein index containing all proteins from the 65,703 bacterial and archaeal species clusters of the GTDB R207 release (https://gtdb.ecogenomic.org/stats/r207, download link: https://data.ace.uq.edu.au/public/gtdb/data/releases/release207/207.0/genomic_files_reps/gtdb_proteins_aa_reps_r207.tar.gz), created to be used by Kaiju (https://bioinformatics-centre.github.io/kaiju/) in the HERMES-WIRE taxonomic profiling pipeline (https://github.com/Baud-lab/hermes-wire)
DOI: 10.5281/zenodo.17545483
title: Kaiju index of the GTDB R207 bacterial and archaeal full protein catalogue for representative species

description: A protein index containing all proteins from the 65,703 bacterial and archaeal species clusters of the GTDB R207 release (https://gtdb.ecogenomic.org/stats/r207, download link: https://data.ace.uq.edu.au/public/gtdb/data/releases/release207/207.0/genomic_files_reps/gtdb_proteins_aa_reps_r207.tar.gz), created to be used by Kaiju (https://bioinformatics-centre.github.io/kaiju/) in the HERMES-WIRE taxonomic profiling pipeline (https://github.com/Baud-lab/hermes-wire)
DOI: 10.5281/zenodo.17559717
title: Teaching by One's Own Example in Confucian and Buddhist Thought: An Empirical Study of Moral Formation in Higher Education
DOI: 10.5281/zenodo.17559718
title: Teaching by One's Own Example in Confucian and Buddhist Thought: An Empirical Study of Moral Formation in Higher Education
DOI: 10.15468/dl.9wmj2n
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trigonostigma heteromorpha, Rasbora heteromorpha)",
 "Country is Singapore",
 "OccurrenceStatus is Present",
 "EventDate is 1800,2026"
 ]
}

 The dataset includes 38 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023176-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j2j5bf
title: Occurrence Download

description: A dataset containing 354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chinese Taipei",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Prosopocoilus astacoides, Prosopocoilus astacoides blanchardi)",
 "Year 1985-2025"
 ]
}

 The dataset includes 354 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023177-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17559698
title: Underwater images collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07. 

            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.17559729
title: Underwater images collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07

description: This dataset was collected by  Scuba diving in Prairie-Bastien, Mauritius - 2019-07-07. 

            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: 10.34740/kaggle/dsv/13658801
title: Daily Market Prices of Commodity India (2001-2025)
DOI: 10.15468/dl.augbbw
title: Occurrence Download

description: A dataset containing 1019408 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bolboschoenus maritimus, Pennisetum clandestinum, Pennisetum villosum, Chamomilla chamomilla, Cota coelopoda, Chenopodium glaucum, Chenopodium murale, Polygonum orientale, Anagallis arvensis, Mentha aquatica, Oxalis pes-caprae, Papaver hybridum, Papaver rhoeas, Ranunculus arvensis, Ranunculus muricatus, Tetranychus urticae, Periplaneta americana, Blattella germanica, Araecerus fasciculatus, Lissorhoptrus oryzophilus)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 1019408 records from 3387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023170-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18671391
title: THE ROLE OF AN INDIVIDUAL AND DIFFERENTIATED APPROACH TO EACH STUDENT IN MODERN CONDITIONS

description: This article discusses the role of individualized and differentiated approaches to students. The types of student competencies are discussed.
DOI: 10.5281/zenodo.18671390
title: THE ROLE OF AN INDIVIDUAL AND DIFFERENTIATED APPROACH TO EACH STUDENT IN MODERN CONDITIONS

description: This article discusses the role of individualized and differentiated approaches to students. The types of student competencies are discussed.
DOI: 10.5517/cc747l5
title: CCDC 212616: Experimental Crystal Structure Determination

description: Related Article: P.M.T.Piggot, L.A.Hall, A.J.P.White, D.J.Williams|2004|Inorg.Chim.Acta|357|250|doi:10.1016/S0020-1693(03)00424-9
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc747m6
title: CCDC 212617: Experimental Crystal Structure Determination

description: Related Article: P.M.T.Piggot, L.A.Hall, A.J.P.White, D.J.Williams|2004|Inorg.Chim.Acta|357|250|doi:10.1016/S0020-1693(03)00424-9
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.