cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc78wmz
title: CCDC 217081: Experimental Crystal Structure Determination

description: Related Article: D.Papoutsakis, A.S.Ichimura, V.G.Young Junior, J.E.Jackson, D.G.Nocera|2004|Dalton Trans.||224|doi:10.1039/b309432d
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: 10.15468/dl.r5xq5c
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Casuariiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((140 -50,150 -50,150 -40,140 -40,140 -50))"
 ]
}

 The dataset includes 23 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018132-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cc75s38
title: CCDC 214089: Experimental Crystal Structure Determination

description: Related Article: S.Konar, E.Zangrando, M.G.B.Drew, J.Ribas, N.R.Chaudhuri|2004|Dalton Trans.||260|doi:10.1039/b311988b
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: 10.15468/dl.zntgan
title: Occurrence Download

description: A dataset containing 1887 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Geometry POLYGON((65.05768 45.28118,68.38957 -15.48614,98.53524 2.75993,107.42027 -0.88928,114.56004 9.58237,166.12497 9.58237,156.44666 59.87804,65.05768 45.28118))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sporobolus fertilis"
 ]
}

 The dataset includes 1887 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018133-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrbytg
title: Occurrence Download

description: A dataset listing the 56 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Paraguay",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Culicidae"
 ]
}

 The dataset's 56 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022966-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17551836
title: animales_zoomadrid
title: animals_zoomadrid

description: Dataset Zoo Madrid

Este conjunto de datos ha sido obtenido mediante técnicas de web scraping a partir del portal oficial del Zoo Aquarium de Madrid (https://www.zoomadrid.com/descubre-el-zoo/animales-y-continentes/animales).

El dataset contiene información estructurada sobre las especies animales que se muestran en la web, incluyendo campos como nombre común, nombre científico, clase, continente, hábitat, dieta, peso, tamaño y estado de conservación.

Además, se incluyen las imágenes asociadas a cada especie, descargadas directamente desde el sitio web, con nombres de archivo que coinciden con los registros del fichero CSV.

El objetivo de la recolección de estos datos es educativo y demostrativo, en el marco de una práctica académica sobre técnicas de web scraping con Selenium y Python, desarrollada por dos estudiantes de análisis de datos.

Campos principales:





nombre




nombre_cientifico




clase




continente




habitat




dieta




peso




tamano




estado_conservacion




image_url




image_file




url



Nota: Los datos son públicos y se han extraído con fines exclusivamente educativos, respetando los términos de uso del portal. No se persigue ningún uso comercial de la información ni de las imágenes.
DOI: 10.5281/zenodo.17551835
title: animales_zoomadrid
title: animals_zoomadrid

description: Dataset Zoo Madrid

Este conjunto de datos ha sido obtenido mediante técnicas de web scraping a partir del portal oficial del Zoo Aquarium de Madrid (https://www.zoomadrid.com/descubre-el-zoo/animales-y-continentes/animales).

El dataset contiene información estructurada sobre las especies animales que se muestran en la web, incluyendo campos como nombre común, nombre científico, clase, continente, hábitat, dieta, peso, tamaño y estado de conservación.

Además, se incluyen las imágenes asociadas a cada especie, descargadas directamente desde el sitio web, con nombres de archivo que coinciden con los registros del fichero CSV.

El objetivo de la recolección de estos datos es educativo y demostrativo, en el marco de una práctica académica sobre técnicas de web scraping con Selenium y Python, desarrollada por dos estudiantes de análisis de datos.

Campos principales:





nombre




nombre_cientifico




clase




continente




habitat




dieta




peso




tamano




estado_conservacion




image_url




image_file




url



Nota: Los datos son públicos y se han extraído con fines exclusivamente educativos, respetando los términos de uso del portal. No se persigue ningún uso comercial de la información ni de las imágenes.
DOI: 10.5281/zenodo.17557992
title: Quantum-Coherent Bio-Control and Hybrid Conscious Systems (QCHCS)}
DOI: 10.15468/dl.ekcykv
title: Occurrence Download

description: A dataset containing 3495 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Spodoptera frugiperda)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 3495 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022968-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3y9ukx
title: Occurrence Download

description: A dataset containing 83480 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oxalis barrelieri, Pilea nummulariifolia, Brucea javanica, Decalobanthus peltatus, Ipomoea batatas, Monomorium floricola, Channa striata, Ramphotyphlops braminus, Phellinus noxius, Sagittaria sagittifolia, Phoenix dactylifera, Arundina graminifolia, Vanilla planifolia, Monochoria vaginalis, Cyperus aromaticus, Cyperus cyperinus, Cyperus distans, Cyperus imbricatus, Cyperus pilosus, Eleocharis geniculata)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 83480 records from 574 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022965-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eemybg
title: Occurrence Download

description: A dataset containing 66473 species occurrences available in GBIF matching the query:
{
 "PublishingCountry" : [
 "is Timor-Leste"
 ]
}

 The dataset includes 66473 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022964-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/6922
title: OpenTextVault
DOI: 10.15468/dl.hrrxuj
title: Occurrence Download

description: A dataset containing 2480 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Germany",
 "TaxonKey is Cypripedium calceolus",
 "Year 2000-2025"
 ]
}

 The dataset includes 2480 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022969-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17557758
title: Underwater images collected by a Kite surf in Le-Morne-One-Eye-Manawa, Mauritius - 2019-05-30

description: This dataset was collected by a Kite surf in Le-Morne-One-Eye-Manawa, Mauritius - 2019-05-30. 

            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.17557968
title: Underwater images collected by a Kite surf in Le-Morne-One-Eye-Manawa, Mauritius - 2019-05-30

description: This dataset was collected by a Kite surf in Le-Morne-One-Eye-Manawa, Mauritius - 2019-05-30. 

            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: 10.6084/m9.figshare.30572036
title: Dataset Meta-analysis self-compassion interventions with teachers

description: Educational psychology
DOI: 10.6084/m9.figshare.30572036.v1
title: Dataset Meta-analysis self-compassion interventions with teachers

description: Educational psychology
DOI: 10.5281/zenodo.17302232
title: MATLAB datasets for "Data-Driven Path-Following Control for USVs"

description: This dataset contains the input–state–output samples {X0,U0,X1} used for the data-driven controller design described in the accompanying manuscript entitled “Data-Driven Path-Following Control for Unmanned Surface Vehicles.”
DOI: 10.5281/zenodo.17558081
title: MATLAB datasets for "Data-Driven Path-Following Control for USVs"

description: This dataset contains the input–state–output samples {X0,U0,X1} used for the data-driven controller design described in the accompanying manuscript entitled “Data-Driven Path-Following Control for Unmanned Surface Vehicles.”
DOI: 10.15468/dl.8tzbze
title: Occurrence Download

description: A dataset containing 15130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Nothofagus solandri, Nothofagus cliffortioides, Nothofagus truncata)",
 "Year 1970-2025"
 ]
}

 The dataset includes 15130 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022971-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17558159
title: TEXNOLOGIK TA'LIM YO'NALISHI TALABALARIGA RAQAMLI MEDIA MATN VA TASVIR BILAN ISHLASH ALGORITIMINI O'RGATISHNING KELGUSI KASBIY FAOLIYATIDA TUTGAN O'RNI
DOI: 10.5281/zenodo.17558160
title: TEXNOLOGIK TA'LIM YO'NALISHI TALABALARIGA RAQAMLI MEDIA MATN VA TASVIR BILAN ISHLASH ALGORITIMINI O'RGATISHNING KELGUSI KASBIY FAOLIYATIDA TUTGAN O'RNI
DOI: 10.5281/zenodo.17554788
title: Galician Digital Media Database in 2024

description: Database of Galician digital media outlets in 2024 classified by province, locality, latitude, longitude and location.
DOI: 10.5281/zenodo.17554789
title: Galician Digital Media Database in 2024

description: Database of Galician digital media outlets in 2024 classified by province, locality, latitude, longitude and location.
DOI: 10.6084/m9.figshare.29191337
title: <b>Long-term evaluation of pollution</b><b>dynamics</b><b>in West Lake Taihu, China: Enhanced WQI and TLI models for improved accuracy and management relevance</b>

description: Accurately assessing water quality and eutrophication remains a critical challenge in global lake pollution management. Traditional models, such as the Water Quality Index (WQI) and Trophic Level Index (TLI), often rely on expert judgment and outdated guidelines, limiting their adaptability to evolving pollution conditions. This study improves overall model performance by integrating Principal Component Analysis (PCA) and Factor Analysis (FA) for parameter weighting, and using Random Forest (RF) to validate weight rankings. West Lake Taihu (WTH), the largest natural pretreatment reservoir of Lake Taihu, was selected as the case study for assessing long-term (2008–2023) pollution dynamics. Results showed that total phosphorus (TP) and total nitrogen (TN) were the dominant factors influencing both water quality and eutrophication, replacing traditional dissolved oxygen (DO) and chlorophyll-a (Chl-a). RF and exceedance rate analyses confirmed TP (24.79%) and TN (19.51%) as the primary contributors to WQI, with exceedance rates &gt; 80%, while DO showed little influence (&lt; 9%) and an exceedance rate below 5%. For TLI, TN and TP contributed 31.83% and 30.12%, while Chl-a exhibited lower contribution (23.76%) and a distinct temporal mismatch. From 2008 to 2023, WQI increased by 27.25% and TLI decreased by 22.70%, indicating substantial improvements in both water quality and eutrophication. However, persistent seasonal and spatial differences disrupted WQI-TLI consistency in spatiotemporal trends. The enhanced WQI model provided more realistic assessments by reducing the influence of stable parameters and emphasizing frequently exceeding ones. The modified TLI model improved the identification of eutrophic zones and periods, enhancing the model’s targeting ability and correlating more strongly with algal density. Thus, both models demonstrated their superior accuracy and relevance for contemporary lake pollution management, compared with traditional approaches. This study proposes a robust, data-driven framework for lake pollution evaluation, supporting adaptive management in dynamic freshwater ecosystems.
DOI: 10.6084/m9.figshare.29191337.v1
title: <b>Long-term evaluation of pollution</b><b>dynamics</b><b>in West Lake Taihu, China: Enhanced WQI and TLI models for improved accuracy and management relevance</b>

description: Accurately assessing water quality and eutrophication remains a critical challenge in global lake pollution management. Traditional models, such as the Water Quality Index (WQI) and Trophic Level Index (TLI), often rely on expert judgment and outdated guidelines, limiting their adaptability to evolving pollution conditions. This study improves overall model performance by integrating Principal Component Analysis (PCA) and Factor Analysis (FA) for parameter weighting, and using Random Forest (RF) to validate weight rankings. West Lake Taihu (WTH), the largest natural pretreatment reservoir of Lake Taihu, was selected as the case study for assessing long-term (2008–2023) pollution dynamics. Results showed that total phosphorus (TP) and total nitrogen (TN) were the dominant factors influencing both water quality and eutrophication, replacing traditional dissolved oxygen (DO) and chlorophyll-a (Chl-a). RF and exceedance rate analyses confirmed TP (24.79%) and TN (19.51%) as the primary contributors to WQI, with exceedance rates &gt; 80%, while DO showed little influence (&lt; 9%) and an exceedance rate below 5%. For TLI, TN and TP contributed 31.83% and 30.12%, while Chl-a exhibited lower contribution (23.76%) and a distinct temporal mismatch. From 2008 to 2023, WQI increased by 27.25% and TLI decreased by 22.70%, indicating substantial improvements in both water quality and eutrophication. However, persistent seasonal and spatial differences disrupted WQI-TLI consistency in spatiotemporal trends. The enhanced WQI model provided more realistic assessments by reducing the influence of stable parameters and emphasizing frequently exceeding ones. The modified TLI model improved the identification of eutrophic zones and periods, enhancing the model’s targeting ability and correlating more strongly with algal density. Thus, both models demonstrated their superior accuracy and relevance for contemporary lake pollution management, compared with traditional approaches. This study proposes a robust, data-driven framework for lake pollution evaluation, supporting adaptive management in dynamic freshwater ecosystems.
DOI: 10.6084/m9.figshare.29191337.v2
title: <b>Long-term evaluation of pollution</b><b>dynamics</b><b>in West Lake Taihu, China: Enhanced WQI and TLI models for improved accuracy and management relevance</b>

description: Accurately assessing water quality and eutrophication remains a critical challenge in global lake pollution management. Traditional models, such as the Water Quality Index (WQI) and Trophic Level Index (TLI), often rely on expert judgment and outdated guidelines, limiting their adaptability to evolving pollution conditions. This study improves overall model performance by integrating Principal Component Analysis (PCA) and Factor Analysis (FA) for parameter weighting, and using Random Forest (RF) to validate weight rankings. West Lake Taihu (WTH), the largest natural pretreatment reservoir of Lake Taihu, was selected as the case study for assessing long-term (2008–2023) pollution dynamics. Results showed that total phosphorus (TP) and total nitrogen (TN) were the dominant factors influencing both water quality and eutrophication, replacing traditional dissolved oxygen (DO) and chlorophyll-a (Chl-a). RF and exceedance rate analyses confirmed TP (24.79%) and TN (19.51%) as the primary contributors to WQI, with exceedance rates &gt; 80%, while DO showed little influence (&lt; 9%) and an exceedance rate below 5%. For TLI, TN and TP contributed 31.83% and 30.12%, while Chl-a exhibited lower contribution (23.76%) and a distinct temporal mismatch. From 2008 to 2023, WQI increased by 27.25% and TLI decreased by 22.70%, indicating substantial improvements in both water quality and eutrophication. However, persistent seasonal and spatial differences disrupted WQI-TLI consistency in spatiotemporal trends. The enhanced WQI model provided more realistic assessments by reducing the influence of stable parameters and emphasizing frequently exceeding ones. The modified TLI model improved the identification of eutrophic zones and periods, enhancing the model’s targeting ability and correlating more strongly with algal density. Thus, both models demonstrated their superior accuracy and relevance for contemporary lake pollution management, compared with traditional approaches. This study proposes a robust, data-driven framework for lake pollution evaluation, supporting adaptive management in dynamic freshwater ecosystems.
DOI: 10.5281/zenodo.17558186
title: DynaKG-NER- Python Code