cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4j7737443j.1
title: Damage Transfer Python Script for Abaqus

description: This dataset is supplementary material for the PhD thesis titled “Virtual Testing of Aerospace Composite Laminates: Impact and Fatigue” by Peyman Shabani, submitted to Carleton University in 2025. 

This Python script can be used to read the Abaqus output file (.odb), identify elements that experienced various failure modes, and create corresponding element sets for each composite failure mode and their combinations in the Abaqus/CAE file (.cae). In the thesis, the script was used to transfer damage from the low-velocity impact (LVI) model to the compression-after-impact (CAI) model, as well as to transfer damage between LVI models in the repeated impact simulations.
DOI: 10.17632/mk7958m3z6
title: Switchable One-/Two-Dimensional Nanopatterning on Si-Ti Hybrid Films Using a Single Linearly Polarized Femtosecond Laser

description: Microscopic images of the Si-Ti films with different Si thicknesses (20 nm, 30 nm, 45 nm, 60 nm, and 75 nm) and laser parameters， corresponding to Figures 2g and 3.
DOI: 10.17632/mk7958m3z6.1
title: Switchable One-/Two-Dimensional Nanopatterning on Si-Ti Hybrid Films Using a Single Linearly Polarized Femtosecond Laser

description: Microscopic images of the Si-Ti films with different Si thicknesses (20 nm, 30 nm, 45 nm, 60 nm, and 75 nm) and laser parameters， corresponding to Figures 2g and 3.
DOI: 10.17632/phmt7f3tp6
title: MATLAB code for the analytical solution of the low-velocity impact damage

description: This dataset is supplementary material for the PhD thesis titled “Virtual Testing of Aerospace Composite Laminates: Impact and Fatigue” by Peyman Shabani, submitted to Carleton University in 2025. 

This MATLAB code was developed using the analytical solution proposed by Esrail and Kassapoglou [1] to predict stresses and damage areas in composite laminates subjected to low-velocity impact (LVI) events.

[1] Esrail, F., and Kassapoglou, C., “An Efficient Approach for Damage Quantification in Quasi-Isotropic Composite Laminates under Low Speed Impact,” Composites Part B: Engineering, Vol. 61, 2014, pp. 116–126.
DOI: 10.17632/phmt7f3tp6.1
title: MATLAB code for the analytical solution of the low-velocity impact damage

description: This dataset is supplementary material for the PhD thesis titled “Virtual Testing of Aerospace Composite Laminates: Impact and Fatigue” by Peyman Shabani, submitted to Carleton University in 2025. 

This MATLAB code was developed using the analytical solution proposed by Esrail and Kassapoglou [1] to predict stresses and damage areas in composite laminates subjected to low-velocity impact (LVI) events.

[1] Esrail, F., and Kassapoglou, C., “An Efficient Approach for Damage Quantification in Quasi-Isotropic Composite Laminates under Low Speed Impact,” Composites Part B: Engineering, Vol. 61, 2014, pp. 116–126.
DOI: 10.15468/dl.w58qkz
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is India",
 "TaxonKey is Isachne confusa"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017610-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2b2kjxm5p4
title: Responses of particulate and mineral-associated organic carbon to improved agronomic management: A global perspective at profile scale

description: Disaggregating soil organic carbon (SOC) into labile particulate organic carbon (POC) and stable mineral-associated organic carbon (MAOC) fractions offers a more mechanistic understanding of soil carbon dynamics. Responses of profile-scale POC and MAOC to strategic agronomic management practices (e.g., cover cropping, fertilization, straw return) remain unclear. This dataset was used to quantifies the impact of improved management practices on POC and MAOC in deep soil profiles (&gt; 30 cm).
DOI: 10.17632/2b2kjxm5p4.1
title: Responses of particulate and mineral-associated organic carbon to improved agronomic management: A global perspective at profile scale

description: Disaggregating soil organic carbon (SOC) into labile particulate organic carbon (POC) and stable mineral-associated organic carbon (MAOC) fractions offers a more mechanistic understanding of soil carbon dynamics. Responses of profile-scale POC and MAOC to strategic agronomic management practices (e.g., cover cropping, fertilization, straw return) remain unclear. This dataset was used to quantifies the impact of improved management practices on POC and MAOC in deep soil profiles (&gt; 30 cm).
DOI: 10.17632/j7txy8dzmw
title: Effectiveness of Learning Interventions for Skin of Color Dermatology in Medical Education: A Systematic Review

description: Supplementary Materials for Effectiveness of Learning Interventions for Skin of Color Dermatology in Medical Education: A Systematic Review
DOI: 10.17632/j7txy8dzmw.4
title: Effectiveness of Learning Interventions for Skin of Color Dermatology in Medical Education: A Systematic Review

description: Supplementary Materials for Effectiveness of Learning Interventions for Skin of Color Dermatology in Medical Education: A Systematic Review
DOI: 10.17632/vxhx934tbn
title: Movement Sensor Dataset for Dog Behavior Classification

description: The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz. 

The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2022.

The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 241 (2021): 105393. https://doi.org/10.1016/j.applanim.2021.105393

The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it.
DOI: 10.15468/dl.8xj9kv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerebratulus sinensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017611-251025141854904/datasets/export for details.
DOI: 10.17632/vxhx934tbn.4
title: Movement Sensor Dataset for Dog Behavior Classification

description: The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz. 

The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2022.

The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 241 (2021): 105393. https://doi.org/10.1016/j.applanim.2021.105393

The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it.
DOI: 10.5281/zenodo.17515043
title: Plate 8.5 Observation of Groundwater Level and Spring Discharge
title: Tafel 8.5 Beobachtung von Grundwasserstand und Quellschüttung
title: Planche 8.5 Observation du niveau des eaux souterraines et du débit des sources
title: Tavola 8.5 Monitoraggio del livello delle acque sotterranee e della portata delle sorgenti

description: Groundwater makes up over 80 % of potable water supplies in Switzerland. Responsibility for the observation and monitoring of this important resource is shared between the Confederation, the cantons, third level institutions and private organisations. The objective of these activities is to follow quantitative variations in groundwater resources in the short term and to appreciate long term changes sufficiently early, be they linked to natural or anthropogenic influences.This plate incorporates more than 900 measuring stations (wells, piezometers and springs) currently operating in Switzerland, as well as another 150 points which have been abandoned. 
description: In der Schweiz trägt Grundwasser zu mehr als 80 % zur Wasserversorgung bei. Die Beobachtung und Überwachung dieser lebenswichtigen Ressource wird von Bund, Kantonen, Hochschulen und privaten Büros ausgeführt. Die Aufgabe besteht darin, kurzfristige quantitative Schwankungen der Grundwasservorkommen zu dokumentieren und rechtzeitig hinsichtlich eines längerfristigen Wandels zu bewerten, sei es bezüglich natürlicher oder auch anthropogener Veränderungen.Die vorliegende Tafel stellt für die Schweiz mehr als 900 zur Zeit betriebene sowie 150 aufgehobene Messstationen dar (Brunnen, Piezometer und Quellen). 
description: En Suisse, les eaux souterraines contribuent à plus de 80 % à l’approvisionnement en eau. L’observation et la surveillance de cette ressource vitale sont partagées entre la Confédération, les cantons, les hautes écoles et les privés. Elles ont pour objectif de suivre les variations quantitatives des richesses en eau souterraine à court terme et d’apprécier suffisamment tôt les changements à long terme, soient-ils liés à des influences naturelles ou anthropiques.La présente planche figure plus de 900 stations de mesure (puits, piézomètres et sources) actuellement en fonction en Suisse, ainsi que 150 ayant été abandonnées.
description: In Svizzera le acque di falda contribuiscono all’approvvigionamento d’acqua per più dell’80 %. I compiti di monitoraggio di questa risorsa vitale sono ripartiti tra la confederazione, i cantoni, le università, i politecnici ed i privati. L’obbiettivo è quello di monitorare le variazioni quantitative delle risorse idriche sotterranee a breve termine e di valutare con un certo anticipo quelle a lungo termine, siano esse influenzate da fattori naturali o antropici.La seguente tavola rappresenta più di 900 stazioni di misura (pozzi, piezometri e sorgenti) attualmente in funzione sul territorio svizzero, come pure 150 punti d’osservazione ormai abbandonati.
DOI: 10.5281/zenodo.17515044
title: Plate 8.5 Observation of Groundwater Level and Spring Discharge
title: Tafel 8.5 Beobachtung von Grundwasserstand und Quellschüttung
title: Planche 8.5 Observation du niveau des eaux souterraines et du débit des sources
title: Tavola 8.5 Monitoraggio del livello delle acque sotterranee e della portata delle sorgenti

description: Groundwater makes up over 80 % of potable water supplies in Switzerland. Responsibility for the observation and monitoring of this important resource is shared between the Confederation, the cantons, third level institutions and private organisations. The objective of these activities is to follow quantitative variations in groundwater resources in the short term and to appreciate long term changes sufficiently early, be they linked to natural or anthropogenic influences.This plate incorporates more than 900 measuring stations (wells, piezometers and springs) currently operating in Switzerland, as well as another 150 points which have been abandoned. 
description: In der Schweiz trägt Grundwasser zu mehr als 80 % zur Wasserversorgung bei. Die Beobachtung und Überwachung dieser lebenswichtigen Ressource wird von Bund, Kantonen, Hochschulen und privaten Büros ausgeführt. Die Aufgabe besteht darin, kurzfristige quantitative Schwankungen der Grundwasservorkommen zu dokumentieren und rechtzeitig hinsichtlich eines längerfristigen Wandels zu bewerten, sei es bezüglich natürlicher oder auch anthropogener Veränderungen.Die vorliegende Tafel stellt für die Schweiz mehr als 900 zur Zeit betriebene sowie 150 aufgehobene Messstationen dar (Brunnen, Piezometer und Quellen). 
description: En Suisse, les eaux souterraines contribuent à plus de 80 % à l’approvisionnement en eau. L’observation et la surveillance de cette ressource vitale sont partagées entre la Confédération, les cantons, les hautes écoles et les privés. Elles ont pour objectif de suivre les variations quantitatives des richesses en eau souterraine à court terme et d’apprécier suffisamment tôt les changements à long terme, soient-ils liés à des influences naturelles ou anthropiques.La présente planche figure plus de 900 stations de mesure (puits, piézomètres et sources) actuellement en fonction en Suisse, ainsi que 150 ayant été abandonnées.
description: In Svizzera le acque di falda contribuiscono all’approvvigionamento d’acqua per più dell’80 %. I compiti di monitoraggio di questa risorsa vitale sono ripartiti tra la confederazione, i cantoni, le università, i politecnici ed i privati. L’obbiettivo è quello di monitorare le variazioni quantitative delle risorse idriche sotterranee a breve termine e di valutare con un certo anticipo quelle a lungo termine, siano esse influenzate da fattori naturali o antropici.La seguente tavola rappresenta più di 900 stazioni di misura (pozzi, piezometri e sorgenti) attualmente in funzione sul territorio svizzero, come pure 150 punti d’osservazione ormai abbandonati.
DOI: 10.5281/zenodo.17529825
title: Underwater images collected by a Paddle in Black-Rocks, Mauritius - 2017-11-19

description: This dataset was collected by a Paddle in Black-Rocks, Mauritius - 2017-11-19. 

            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.17532022
title: Underwater images collected by a Paddle in Black-Rocks, Mauritius - 2017-11-19

description: This dataset was collected by a Paddle in Black-Rocks, Mauritius - 2017-11-19. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.y4yzhk
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isachne deccanensis"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017612-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17532008
title: Dataset of "Inductive and Capacitive Hysteresis of Current-Voltage Curves: Unified Structural Dynamics in Solar Energy Devices, Memristors, Ionic Transistors, and Bioelectronics"

description: This dataset contains the raw data supporting the article "Inductive and Capacitive Hysteresis of Current-Voltage Curves: Unified Structural Dynamics in Solar Energy Devices, Memristors, Ionic Transistors, and Bioelectronics".

For a detailed description of files and methods, see the accompanying Readme.txt.
DOI: 10.5281/zenodo.17532009
title: Dataset of "Inductive and Capacitive Hysteresis of Current-Voltage Curves: Unified Structural Dynamics in Solar Energy Devices, Memristors, Ionic Transistors, and Bioelectronics"

description: This dataset contains the raw data supporting the article "Inductive and Capacitive Hysteresis of Current-Voltage Curves: Unified Structural Dynamics in Solar Energy Devices, Memristors, Ionic Transistors, and Bioelectronics".

For a detailed description of files and methods, see the accompanying Readme.txt.
DOI: 10.15468/dl.gwawgk
title: Occurrence Download

description: A dataset containing 569 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence dataset from the waterbird survey of the middle and lower Huai He floodplain, China"
 ]
}

 The dataset includes 569 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017614-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jqgxew
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerebratulus sordidus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017615-251025141854904/datasets/export for details.