cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/hj0mmx
title: Single atoms agglomeration and redispersion dynamics in heterogeneous catalysis targeting co2 conversion and valorization

description: The data is part of the PhD thesis defended at The University of Campinas (Universidade Estadual de Campinas, UNICAMP) by Leonardo da Silva Sousa on May 14th 2025 This dataset includes a combination of advanced spectroscopic and chemometric techniques to investigate the formation, agglomeration, and redispersion dynamics of supported metallic phases in heterogeneous catalysis, specifically targeting CO and CO2 conversion. Key techniques include in situ X-ray Absorption Fine Structure (XAFS), used to track changes in oxidation state and local coordination environment of active metal species under reaction conditions; Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), applied to monitor surface intermediates and adsorbates; and Modulation Excitation with Phase-Sensitive Detection (ME-PSD-DRIFTS), which enhances sensitivity and selectivity in detecting dynamic species. Additionally, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is employed to deconvolute overlapping spectral signals and extract chemically meaningful components. Together, these methods aim to provide a detailed, time-resolved understanding of catalyst structure–performance relationships and the dynamic behavior of metal-support interactions. Within the dataset there is a large scope of complementary characterization techniques, such as, in situ Diffuse Reflectance UV-Vis, CO-DRIFTS, X-ray Photoelectron Spectroscopy, Scanning Transmission Electron Microscopy, Thermogravimetric analysis, and X-ray Pair Distribution Function analysis.
DOI: 10.15468/dl.garmfx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Eudocimus albus)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027814-250717081556266/datasets/export for details.
DOI: 10.15468/dl.kcne43
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phasianus colchicus",
 "Country is Ecuador",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027813-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w5r5az
title: Occurrence Download

description: A dataset containing 202 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rubus phoenicolasius",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 202 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027815-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hb3mps
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rubus phoenicolasius",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027816-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/03vd-k773
title: "sce1"

description: "Files for STK"
DOI: 10.34740/kaggle/dsv/12543584
title: Underwater Sensor Dataset
DOI: 10.6084/m9.figshare.29617640
title: ICC8001 oxford nanopore data

description: ONT dataset
DOI: 10.6084/m9.figshare.29617640.v1
title: ICC8001 oxford nanopore data

description: ONT dataset
DOI: 10.15468/dl.ddv5ye
title: Occurrence Download

description: A dataset containing 31991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rubus phoenicolasius",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 31991 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027817-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j2797t
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helichrysum meyeri-johannis",
 "HasCoordinate is true",
 "Geometry POLYGON ((-17.66889 -35.11691, 51.34702 -35.11691, 51.34702 37.62, -17.66889 37.62, -17.66889 -35.11691))"
 ]
}

 The dataset includes 55 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027819-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gb4be5
title: Occurrence Download

description: A dataset containing 203266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Eudocimus albus)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 203266 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027796-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18197625
title: Machine Learning-Based Power Quality Prediction in a Microgrid for Community Energy Systems

description: Abstract:

To mitigate environmental impact, specifically the CO2 emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimisation and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include LSTM, GRU, DNN, CNN1D-LSTM, BiLSTM, attention mechanisms, DT, SVM, and XGBoost. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion THDu, Current Total Harmonic Distortion THDi, and short-term flicker severity Pst. Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilised Root Mean Square Error (MSE as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing.

 

Funding:

This article has been supported by EU funds under the project “Increasing the resilience of power grids in the context of decarbonisation, decentralisation and sustainable socioeconomic development” (CZ.02.01.01/00/23_021/0008759), through the Operational Programme Johannes Amos Comenius.
DOI: 10.5281/zenodo.18197624
title: Machine Learning-Based Power Quality Prediction in a Microgrid for Community Energy Systems

description: Abstract:

To mitigate environmental impact, specifically the CO2 emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimisation and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include LSTM, GRU, DNN, CNN1D-LSTM, BiLSTM, attention mechanisms, DT, SVM, and XGBoost. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion THDu, Current Total Harmonic Distortion THDi, and short-term flicker severity Pst. Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilised Root Mean Square Error (MSE as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing.

 

Funding:

This article has been supported by EU funds under the project “Increasing the resilience of power grids in the context of decarbonisation, decentralisation and sustainable socioeconomic development” (CZ.02.01.01/00/23_021/0008759), through the Operational Programme Johannes Amos Comenius.
DOI: 10.15468/dl.24fqa9
title: Occurrence Download

description: A dataset containing 3222 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Ardisia crenata Sims"
 ]
}

 The dataset includes 3222 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015342-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/cpkwkn
title: Inventariserend Veldonderzoek, verkennende fase Velostrada, Voorschoten Gemeente Voorschoten

description: In opdracht van de provincie Zuid-Holland heeft IDDS Archeologie in mei 2024 een inventariserend veldonderzoek (IVO), verkennende fase, uitgevoerd in 8 deelgebieden langs het fietspad genaamd de Velostrada in Voorschoten, gemeente Voorschoten. De aanleiding voor dit onderzoek is de voorgenomen herontwikkeling van het huidige fietspad en wandelpad. De doel- en vraagstelling van het inventariserend veldonderzoek is het toetsen en zo nodig aanvullen van de gespecificeerde archeologische verwachting uit het bureauonderzoek van Kruithof / Hagens (2023a). 
Op basis van het veldonderzoek hebben de verschillende deelgebieden duidelijk afwijkende verwachtingen ten opzichte van het bureauonderzoek (alle verwachtingen gelden alleen voor het gebied van de geplande werkzaamheden):
 Op basis van de landschappelijke opbouw geldt in deelgebied 1 een lage archeologische verwachting.
 Op basis van de landschappelijke opbouw geldt in deelgebied 2 een lage archeologische verwachting.
 Op basis van de landschappelijke opbouw geldt in deelgebied 4 een hoge archeologische verwachting, met name voor het duinzand en het niveau direct onder de veenlaag in het duinzand.
 Op basis van de landschappelijke opbouw geldt in deelgebied 5 een lage archeologische verwachting.
 Op basis van de landschappelijke opbouw geldt in deelgebied 6 & F5 een lage archeologische verwachting in het zuidelijke deel waar het veenpakket ligt direct op het strandzand. Het noordelijke deel, bestaande uit duinzand (deels bedekt met een dunne veenlaag) heeft een hoge archeologische verwachting.
 Op basis van de landschappelijke opbouw geldt in deelgebied 8 een lage archeologische verwachting in het zuidelijke deel waar het veenpakket direct op het strandzand ligt. Het noordelijke deel, bestaande uit duinzand (bedekt met een dunne veenlaag), heeft een hoge archeologische verwachting.
 Op basis van de landschappelijke opbouw geldt in deelgebied F1 een hoge archeologische verwachting voor de top van het duinzand, direct onder het veenpakket op een diepte van gemiddeld 1,8 m -mv ofwel -1,5 m NAP. Voor het strandzand en het veen geldt een lage archeologische verwachting.
Tijdens het onderzoek is geconstateerd dat enkele deelgebieden van het plangebied een hoge archeologische verwachting hebben en de rest een lage verwachting, maar dat geen van de geplande ingrepen de natuurlijke afzettingen (waarvoor deze verwachtingen geldig zijn) zullen bereiken. IDDS Archeologie adviseert om het plangebied, voor wat betreft het aspect archeologie, vrij te geven voor de voorgenomen civieltechnische werkzaamheden. Indien in de toekomst in deelgebieden 4, 6 & F5, 8 en F1 dieper wordt gegraven dan de top van het duinzand (soms onder een dun veenpakket) of als bij de verschillende deelgebieden wordt gegraven buiten de zones waar de huidige werkzaamheden zijn gepland dan wordt geadviseerd wel aanvullend archeologisch onderzoek uit te voeren.
DOI: 10.5281/zenodo.18541368
title: ABOUT ONE OF THE STRUCTURAL FEATURES OF PROVERBS AND SAYINGS
DOI: 10.5281/zenodo.18541369
title: ABOUT ONE OF THE STRUCTURAL FEATURES OF PROVERBS AND SAYINGS
DOI: 10.5281/zenodo.18416671
title: Dataset for: Material Performance and Processing Behavior of Polyolefin Waste in Mechanical Recycling

description: Este conjunto de datos contiene los resultados de caracterización que respaldan el estudio "Material Performance and Processing Behavior of Polyolefin Waste in Mechanical Recycling".

Se incluyen datos comparativos entre el residuo de poliolefinas inicial (tal como se recibió) y el material reciclado mecánicamente (tras su procesado).

Datos Incluidos:





DSC: Análisis térmico para identificar la composición de las muestras.




FTIR: Espectros infrarrojos para identificación química de la matriz y detección de contaminantes.




Ensayos de Tracción: Propiedades mecánicas (módulo, resistencia, elongación) del producto reciclado final.


MFI (Índice de Fluidez): Medidas de fluidez de las escamas del producto reciclado.


Consulte la publicación principal para detalles sobre las condiciones de procesado y análisis
DOI: 10.5281/zenodo.18416672
title: Dataset for: Material Performance and Processing Behavior of Polyolefin Waste in Mechanical Recycling

description: Este conjunto de datos contiene los resultados de caracterización que respaldan el estudio "Material Performance and Processing Behavior of Polyolefin Waste in Mechanical Recycling".

Se incluyen datos comparativos entre el residuo de poliolefinas inicial (tal como se recibió) y el material reciclado mecánicamente (tras su procesado).

Datos Incluidos:





DSC: Análisis térmico para identificar la composición de las muestras.




FTIR: Espectros infrarrojos para identificación química de la matriz y detección de contaminantes.




Ensayos de Tracción: Propiedades mecánicas (módulo, resistencia, elongación) del producto reciclado final.


MFI (Índice de Fluidez): Medidas de fluidez de las escamas del producto reciclado.


Consulte la publicación principal para detalles sobre las condiciones de procesado y análisis
DOI: 10.6084/m9.figshare.31293103
title: Application of the Branch-First Technique in Mesenteric Malperfusion Syndrome Prior to Delayed Open Aortic Repair for Acute Type A Aortic Dissection

description: This study evaluated delayed open aortic repair (OAR) following interventional radiology (IR) in patients with acute type A aortic dissection (ATAAD) and mesenteric malperfusion syndrome (MeMPS), and identified clinical predictors of intestinal ischemia-related organ failure. A retrospective analysis was conducted on 693 ATAAD patients (2018–2022). Thirty-seven hemodynamically stable MeMPS patients underwent upfront IR followed by delayed OAR. Controls were 656 ATAAD patients without malperfusion (Non-MPS) in any organ. The overall in-hospital mortality for MeMPS patients was 43.2% (16/37). 64.9% (24/37) of MeMPS patients survived initial IR and successfully underwent delayed OAR, achieving in-hospital mortality (12.5% vs. 8.2%, <i>p</i> = 0.714) and short-term survival comparable to those of Non-MPS patients. 35.1% (13/37) failed to undergo OAR following IR, primarily due to refractory organ failure (<i>n</i> = 10) or aortic rupture (<i>n</i> = 3). Reduced true lumen at diaphragm (Area Under Curve, AUC = 0.717), elevated fibrin degradation product (FDP) (AUC = 0.838), and D-dimer (AUC = 0.792) performed well in predicting intestinal ischemia-related organ failure. For high-risk MeMPS patients, salvage IR effectively bridges to central repair with comparable in-hospital mortality and short-term survival to Non-MPS patients. The reduced true lumen at diaphragm, increased D-dimer and FDP are first identified as independent predictors of organ failure. MeMPS patients who received interventional radiology followed by open aortic repair had better outcomes.
DOI: 10.6084/m9.figshare.31293103.v1
title: Application of the Branch-First Technique in Mesenteric Malperfusion Syndrome Prior to Delayed Open Aortic Repair for Acute Type A Aortic Dissection

description: This study evaluated delayed open aortic repair (OAR) following interventional radiology (IR) in patients with acute type A aortic dissection (ATAAD) and mesenteric malperfusion syndrome (MeMPS), and identified clinical predictors of intestinal ischemia-related organ failure. A retrospective analysis was conducted on 693 ATAAD patients (2018–2022). Thirty-seven hemodynamically stable MeMPS patients underwent upfront IR followed by delayed OAR. Controls were 656 ATAAD patients without malperfusion (Non-MPS) in any organ. The overall in-hospital mortality for MeMPS patients was 43.2% (16/37). 64.9% (24/37) of MeMPS patients survived initial IR and successfully underwent delayed OAR, achieving in-hospital mortality (12.5% vs. 8.2%, <i>p</i> = 0.714) and short-term survival comparable to those of Non-MPS patients. 35.1% (13/37) failed to undergo OAR following IR, primarily due to refractory organ failure (<i>n</i> = 10) or aortic rupture (<i>n</i> = 3). Reduced true lumen at diaphragm (Area Under Curve, AUC = 0.717), elevated fibrin degradation product (FDP) (AUC = 0.838), and D-dimer (AUC = 0.792) performed well in predicting intestinal ischemia-related organ failure. For high-risk MeMPS patients, salvage IR effectively bridges to central repair with comparable in-hospital mortality and short-term survival to Non-MPS patients. The reduced true lumen at diaphragm, increased D-dimer and FDP are first identified as independent predictors of organ failure. MeMPS patients who received interventional radiology followed by open aortic repair had better outcomes.
DOI: 10.2908/cens_21urb11
title: Conventional dwellings by type of building, degree of urbanisation and occupancy status
title: Logements classiques par type de bâtiment, degré d’urbanisation et état d’occupation
title: Herkömmliche Wohnungen nach Gebäudeart, Verstädterungsgrad und Belegungsstatus

description: Die Daten kombinieren Zensus-2021-Daten mit dem Verstädterungsgrad und werden als zusätzlicher Datensatz über die Zensus-Basisdaten hinaus generiert. Bestimmte Zahlen können aufgrund von Unterschieden bei niedrigeren Geodatenebenen, die sich auf die Aggregation über den Verstädterungsgrad hinweg auswirken, leicht von anderen Tabellen abweichen. Gründe könnten beispielsweise Lücken auf detaillierter geografischer Ebene aufgrund von Datenschutzvorschriften sein. Liechtenstein hat nur einen VerstädterungsgradWeiterführende Informationen zur Klassifikation DEGURBA finden sich hier<br>
description: The data combine census 2021 data with degree of urbanisation and are generated as an additional dataset beyond the census base data. Certain numbers may deviate slightly from other tables, due to differences at lower geo data levels that impact on the aggregation across degree of urbanisation. Reasons could be for instance gaps at detailed geographical levels due to data privacy regulations. Liechtenstein has only one level of degree of urbanisationFurther information on the DEGURBA classification can be found here
description: Les données combinent les données du recensement 2021 avec le degré d’urbanisation et sont générées en tant qu’ensemble de données supplémentaire au-delà des données de base du recensement. Certains chiffres peuvent s’écarter légèrement d’autres tableaux, en raison de différences à des niveaux de données géographiques plus faibles qui ont une incidence sur l’agrégation entre les degrés d’urbanisation. Les raisons peuvent être, par exemple, des lacunes aux niveaux géographiques détaillés en raison de la réglementation en matière de confidentialité des données. Le Liechtenstein n’a qu’un seul degré d’urbanisationVous trouverez ici de plus amples informations sur la classification DEGURBA.<br>