cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25318/9810024401-fra
title: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné et le type de ménage incluant la structure de la famille de recensement : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné pour le Canada, les provinces et les territoires, les régions métropolitaines de recensement et les agglomérations de recensement. Comprend l'âge du principal soutien du ménage et le type de ménage incluant la structure de la famille de recensement pour les ménages propriétaires et locataires.
DOI: 10.5281/zenodo.10067284
title: Productos multivitamínicos en stock de la tienda online HSN con sus descuentos, características y valoraciones

description: Los datos extraídos mediante web scraping de la web de HSN nos proporcionan información sobre los productos multivitamínicos que ofrece esta tienda online. En el dataset se recogen los diferentes productos que ofrecen y en sus diferentes tamaños, siempre y cuando alguno de los tamaños disponibles para el producto esté en stock. 

Código: Referencia del producto de HSN, diferentes formatos del mismo producto tienen el mismo código al pertenecer al mismo producto.
Nombre: Nombre del producto
Clasificación: Categoría a la que pertenece el producto
Grupo: Subcategoría a la que pertenece el producto
Dosificación: Formato o tamaño en el que se presenta el producto
Precio: Precio original del producto antes del descuento
Precio_descuento: Precio final del producto tras realizar el descuento
%_Descuento: Porcentaje de descuento sobre el precio original
Rating: Valoración sobre 5 del producto según los consumidores
Numero_valoraciones: Número de valoraciones realizadas sobre el producto
Serie: Serie a la que pertenece el producto
Descripción: Descripción del producto
Edulcorantes: Recoge si el producto incluye o no edulcorantes
Veganos: Recoge si el producto es apto para veganos
Vegetarianos: Recoge si el producto es apto para vegetarianos
OMG: Recoge si el producto contiene organismos modificados genéticamente
Gluten: Recoge si el producto contiene gluten
Lactosa: Recoge si el producto contiene lactosa
Lácteos: Recoge si el producto contiene lácteos
Soja: Recoge si el producto contiene soja
Huevo: Recoge si el producto contiene huevo
Conservantes: Recoge si el producto contiene conservantes
Colorantes: Recoge si el producto contiene colorantes artificiales
Frutos_cascara: Recoge si el producto contiene frutos de cáscara
Pescado: Recoge si el producto contiene pescado
En cuanto al periodo de tiempo al que pertenecen los datos (02-11-2023) hay que destacar que la página está constantemente publicando nuevas ofertas, por lo que se podría considerar que los datos son válidos en ese momento, y que posteriormente a la recogida de datos la página puede publicar nuevas ofertas.
DOI: 10.5281/zenodo.10067283
title: Productos multivitamínicos en stock de la tienda online HSN con sus descuentos, características y valoraciones

description: Los datos extraídos mediante web scraping de la web de HSN nos proporcionan información sobre los productos multivitamínicos que ofrece esta tienda online. En el dataset se recogen los diferentes productos que ofrecen y en sus diferentes tamaños, siempre y cuando alguno de los tamaños disponibles para el producto esté en stock. 

Código: Referencia del producto de HSN, diferentes formatos del mismo producto tienen el mismo código al pertenecer al mismo producto.
Nombre: Nombre del producto
Clasificación: Categoría a la que pertenece el producto
Grupo: Subcategoría a la que pertenece el producto
Dosificación: Formato o tamaño en el que se presenta el producto
Precio: Precio original del producto antes del descuento
Precio_descuento: Precio final del producto tras realizar el descuento
%_Descuento: Porcentaje de descuento sobre el precio original
Rating: Valoración sobre 5 del producto según los consumidores
Numero_valoraciones: Número de valoraciones realizadas sobre el producto
Serie: Serie a la que pertenece el producto
Descripción: Descripción del producto
Edulcorantes: Recoge si el producto incluye o no edulcorantes
Veganos: Recoge si el producto es apto para veganos
Vegetarianos: Recoge si el producto es apto para vegetarianos
OMG: Recoge si el producto contiene organismos modificados genéticamente
Gluten: Recoge si el producto contiene gluten
Lactosa: Recoge si el producto contiene lactosa
Lácteos: Recoge si el producto contiene lácteos
Soja: Recoge si el producto contiene soja
Huevo: Recoge si el producto contiene huevo
Conservantes: Recoge si el producto contiene conservantes
Colorantes: Recoge si el producto contiene colorantes artificiales
Frutos_cascara: Recoge si el producto contiene frutos de cáscara
Pescado: Recoge si el producto contiene pescado
En cuanto al periodo de tiempo al que pertenecen los datos (02-11-2023) hay que destacar que la página está constantemente publicando nuevas ofertas, por lo que se podría considerar que los datos son válidos en ese momento, y que posteriormente a la recogida de datos la página puede publicar nuevas ofertas.
DOI: 10.48366/r648213
title: Overview of AIS data mining in water transport using machine learning

description: The table shows the application of AIS data mining for different purposes within water transport. Data mining is the process of discovering patterns and extracting useful information from large datasets. In the context of water transport, data mining can be used to analyze Automatic Identification System (AIS) data to identify patterns in vessel movement and behavior. The aim of data mining is to construct a standardized AIS database that will be usable for research. This can be useful for clustering and extraction of ship routes, ship classification, maritime anomaly detection, ship route prediction to detect illegal activities, illegal fishing activity, optimize shipping routes, improve vessel safety and other applications.
DOI: 10.48366/r648215
title: Comparing the multi-perspective categorization of diffusion models in computer vision

description: Categorizing diffusion models in computer vision involves the use of various techniques to spread information through an image or video data. These models aim to capture information from different perspectives to enhance various computer vision tasks. Here's a comparison of multi-perspective categorization of diffusion models applied in computer vision
DOI: 10.26030/161z-1j19
title: Toward countering muscle and bone loss with spaceflight: GSK3 as a potential target (Extensor Digitorum Longus, BION-M1, Western blot)

description: We examined the effects of ~30 days of spaceflight on glycogen synthase kinase 3 (GSK3) content and inhibitory serine phosphorylation in murine muscle and bone samples from four separate missions (BION-M1, Rodent Research missions RR1, RR9, and RR18). Spaceflight reduced GSK3b content across all missions, whereas its serine phosphorylation was elevated with RR18 and BION-M1. The reduction in GSK3b was linked to the reduction in type IIA fibers commonly observed with spaceflight as these fibers are particularly enriched with GSK3. We then tested the effects of inhibiting GSK3 before this fiber type shift, and we demonstrate that muscle-specific Gsk3 knockdown increased muscle mass, preserved muscle strength, and promoted the oxidative fiber type with Earth-based hindlimb unloading. In bone, GSK3 activation was enhanced after spaceflight; and strikingly, muscle-specific Gsk3 deletion increased bone mineral density in response to hindlimb unloading. Thus, future studies should test the effects of GSK3 inhibition during spaceflight. This study derives results from the Western Blot assay using EDL tissue from the Bion-M1 mission. The Extensor Digitorum Longus data in this study are related to other studies using data from the same experiment; OSD-654 (Tibia), OSD-660 (Tibialis Anterior), OSD-661 (Lumbar Spine), OSD-662 (Soleus), and OSD-663 (Femur).
DOI: 10.25934/pr00009284
title: Associative Analysis of Comorbidities and Clinical Variables with Concomitant Presence of Congestive Heart Failure (CHF) in Patients Undergoing Cardiac Catheterization

description: Background
Congestive heart failure (CHF) remains a considerable health challenge, affecting millions globally. While research has helped identify several risk factors associated with CHF, there is a paucity of information regarding associative clinical variables among specific cohorts, such as patients undergoing cardiac catheterization.

Research Objective
The central purpose of this study is to utilize the Duke Cardiac Catheterization Research Dataset to comprehensively explore clinical variables that are strongly associated with the presence of CHF in patients undergoing cardiac catheterization. Recognizing the constraints of the data, our aim is not to predict future diagnoses of CHF. Instead, we focus on establishing a detailed clinical profile of this particular cohort and understanding the interplay of various factors with CHF.

Product Development
Virror Health is pioneering an innovative Al-based health monitoring solution. Our multi-functional smart mirror aims to offer daily monitoring of essential vital signs, delivering a seamless experience for users. The platform not only aims to enhance self-monitoring but also seeks to provide clinicians with a holistic view of patient health, augmented by data-driven clinical decision trees.

Patient Benefit
Our goal is to harness insights from the analysis of the Duke Cardiac Catheterization Research Dataset, specifically relating to patients undergoing cardiac catheterization, and incorporate these insights into Virror Health's diagnostic platform. This approach will optimize the relevancy and precision of the health alerts provided to users, rooted in a more nuanced understanding of this particular patient group. Ultimately, this research will drive improvements in patient care, enabling more timely interventions and fostering better patient-doctor interactions informed by data-driven insights.

Generalization of the Model
We acknowledge that the insights and associations derived from this study will be specifically pertinent to patients undergoing cardiac catheterization. Any models or conclusions drawn will be strictly contextualized for this cohort and will not be extrapolated to the general population. The primary intent is to offer a richer understanding of CHF among these patients, leading to more informed clinical decisions and patient interventions within this specific context.
DOI: 10.6084/m9.figshare.24487660
title: Lindisfarne water level data

description: Water level data collected using BaroTROLL and RuggedTROLL loggers from Lindisfarne ( 55.6775°N 1.8707°W) between 10/03/22 and 20/04/22.
DOI: 10.6084/m9.figshare.24487660.v1
title: Lindisfarne water level data

description: Water level data collected using BaroTROLL and RuggedTROLL loggers from Lindisfarne ( 55.6775°N 1.8707°W) between 10/03/22 and 20/04/22.
DOI: 10.6084/m9.figshare.24488410
title: Data for Sound of Success Experiments

description: This data consists of two parts 1) The acoustic properties of a series of recordings made of 3 types of lithic raw materials as they are being knapped. Consisting of individual strikes, analysed using "soundgen" (Anikin, 2019), and providing the 4 acoustic properties utilized in the accompanying paper.2) Responses of (87) listeners to a sample of those strikes provided as a choices on 5 point likert scale from strongly disagree to strongly agree.
DOI: 10.6084/m9.figshare.24488410.v1
title: Data for Sound of Success Experiments

description: This data consists of two parts 1) The acoustic properties of a series of recordings made of 3 types of lithic raw materials as they are being knapped. Consisting of individual strikes, analysed using "soundgen" (Anikin, 2019), and providing the 4 acoustic properties utilized in the accompanying paper.2) Responses of (87) listeners to a sample of those strikes provided as a choices on 5 point likert scale from strongly disagree to strongly agree.
DOI: 10.25422/azu.data.24455137.v1
title: Test dataset for Compressive video via IR-pulsed illumination

description: Compressive video via IR-pulsed illumination. Refer to publication for details.We propose and demonstrate a compressive temporal imaging system based on pulsed illumination to encode temporal dynamics into the imaging sensor during exposure time. Our approach enables &gt;10x increase in effective frame rate without increasing camera complexity. To mitigate the complexity of the inverse problem during reconstruction, we introduce two keyframes: one before and one after the coded frame. We also craft a novel deep learning architecture for improved reconstruction of the high-speed scenes. Simulation and experimental results clearly demonstrate the reconstruction of high-quality, high-speed videos from the compressed data.<br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.25422/azu.data.24455137
title: Test dataset for Compressive video via IR-pulsed illumination

description: Compressive video via IR-pulsed illumination. Refer to publication for details.We propose and demonstrate a compressive temporal imaging system based on pulsed illumination to encode temporal dynamics into the imaging sensor during exposure time. Our approach enables &gt;10x increase in effective frame rate without increasing camera complexity. To mitigate the complexity of the inverse problem during reconstruction, we introduce two keyframes: one before and one after the coded frame. We also craft a novel deep learning architecture for improved reconstruction of the high-speed scenes. Simulation and experimental results clearly demonstrate the reconstruction of high-quality, high-speed videos from the compressed data.<br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.5281/zenodo.7896698
title: SPRS Total Correlation clustering

description: Total Correlation calculated using the Total Correlation Explanation algorithm (CorEx), which is an optimization technique that identifies latent variables to explain the dependence in the data.
DOI: 10.5281/zenodo.7896699
title: SPRS Total Correlation clustering

description: Total Correlation calculated using the Total Correlation Explanation algorithm (CorEx), which is an optimization technique that identifies latent variables to explain the dependence in the data.
DOI: 10.5281/zenodo.10067255
title: Breaker
DOI: 10.5281/zenodo.10067254
title: Breaker
DOI: 10.5281/zenodo.10059709
title: Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain

description: This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:

C2RCC (Brockmann et al. 2016)
SIAC (Yin et al. 2022)
ACOLITE (Vanhellemont & Ruddick, 2018)
6SV (Vermote et al. 2006)
Included Lakes and and their IDs:

Laguna de la Caldera (ID = P-2)
Laguna-embalse de las Yeguas (ID = D-6)
Laguna de Río Seco (ID = P-8)
Laguna Larga (ID = G-7)
Laguna de la Mosca (ID = G-11)
description: This research is part of the project "Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada" (LIFEWATCH-2019-10-UGR-4), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line. The project has also been co-financed by the Provincial Council of Granada.
description: This research is part of the project Lagos centinelas de cambio global en los Parques Nacionales: análisis multidisciplinar de los últimos 6000 años. Subproyecto LACEN-LIM, financed by the Organismo Autónomo de Parques Nacionales (OAPN), Ref. 2403S/2017.
DOI: 10.5281/zenodo.10059710
title: Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain

description: This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:

C2RCC (Brockmann et al. 2016)
SIAC (Yin et al. 2022)
ACOLITE (Vanhellemont & Ruddick, 2018)
6SV (Vermote et al. 2006)
Included Lakes and and their IDs:

Laguna de la Caldera (ID = P-2)
Laguna-embalse de las Yeguas (ID = D-6)
Laguna de Río Seco (ID = P-8)
Laguna Larga (ID = G-7)
Laguna de la Mosca (ID = G-11)
description: This research is part of the project "Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada" (LIFEWATCH-2019-10-UGR-4), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line. The project has also been co-financed by the Provincial Council of Granada.
description: This research is part of the project Lagos centinelas de cambio global en los Parques Nacionales: análisis multidisciplinar de los últimos 6000 años. Subproyecto LACEN-LIM, financed by the Organismo Autónomo de Parques Nacionales (OAPN), Ref. 2403S/2017.
DOI: 10.5281/zenodo.10059784
title: Sentinel-2 derived Chlorophyll-a prediction maps for high-altitude lakes in the Sierra Nevada, Spain

description: This dataset contains chlorophyll-a (ug/L) predictions for 4 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. Predictions were made using a simple linear regression model with field sample chlorophyll-a as the dependent variable, and the following Sentinel-2 derived spectral index as the independent variable:
B3 - (B4+((B2-B4)*((665-560)/(665-490)))
Prediction maps are included as GeoTiffs and NetCDF files. Sentinel-2 data were atmospherically corrected using the following algorithms: 

ACOLITE (Vanhellemont & Ruddick, 2018)
6SV (Vermote et al. 2006)
Included Lakes and and their IDs:

Laguna de la Caldera (ID = P-2)
Laguna-embalse de las Yeguas (ID = D-6)
Laguna de Río Seco (ID = P-8)
Laguna Larga (ID = G-7)
description: This research is part of the project "Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada" (LIFEWATCH-2019-10-UGR-4), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line. The project has also been co-financed by the Provincial Council of Granada.
description: This research is part of the project Lagos centinelas de cambio global en los Parques Nacionales: análisis multidisciplinar de los últimos 6000 años. Subproyecto LACEN-LIM, financed by the Organismo Autónomo de Parques Nacionales (OAPN), Ref. 2403S/2017.
DOI: 10.5281/zenodo.10059783
title: Sentinel-2 derived Chlorophyll-a prediction maps for high-altitude lakes in the Sierra Nevada, Spain

description: This dataset contains chlorophyll-a (ug/L) predictions for 4 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. Predictions were made using a simple linear regression model with field sample chlorophyll-a as the dependent variable, and the following Sentinel-2 derived spectral index as the independent variable:
B3 - (B4+((B2-B4)*((665-560)/(665-490)))
Prediction maps are included as GeoTiffs and NetCDF files. Sentinel-2 data were atmospherically corrected using the following algorithms: 

ACOLITE (Vanhellemont & Ruddick, 2018)
6SV (Vermote et al. 2006)
Included Lakes and and their IDs:

Laguna de la Caldera (ID = P-2)
Laguna-embalse de las Yeguas (ID = D-6)
Laguna de Río Seco (ID = P-8)
Laguna Larga (ID = G-7)
description: This research is part of the project "Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada" (LIFEWATCH-2019-10-UGR-4), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line. The project has also been co-financed by the Provincial Council of Granada.
description: This research is part of the project Lagos centinelas de cambio global en los Parques Nacionales: análisis multidisciplinar de los últimos 6000 años. Subproyecto LACEN-LIM, financed by the Organismo Autónomo de Parques Nacionales (OAPN), Ref. 2403S/2017.
DOI: 10.34740/kaggle/dsv/6866566
title: Infrared Solar Modules
DOI: 10.5281/zenodo.10067288
title: SST CCI Auxiliary Datasets

description: These are the auxiliary datasets used in the production of the ESA Climate Change Initiative (CCI) Sea Surface Temperature (SST) Climate Data Record (CDR) version 3.
Retrieval coefficients
Files starting 'ARCCoef' contain the retrieval coefficients used for the dual-view ATSR and SLSTR instruments.
Probability Density Functions
Files starting 'BayesPDF' contain the probability density function (PDF) lookup tables used for cloud detection.
Sea Surface Emissivity
ASCII tables of sea surface emissivity used for cloud detection (ATSR, SLSTR, and AVHRR sensors) and optimal estimation retrieval of SST (AVHRR sensors).
DOI: 10.5281/zenodo.10067287
title: SST CCI Auxiliary Datasets

description: These are the auxiliary datasets used in the production of the ESA Climate Change Initiative (CCI) Sea Surface Temperature (SST) Climate Data Record (CDR) version 3.
Retrieval coefficients
Files starting 'ARCCoef' contain the retrieval coefficients used for the dual-view ATSR and SLSTR instruments.
Probability Density Functions
Files starting 'BayesPDF' contain the probability density function (PDF) lookup tables used for cloud detection.
Sea Surface Emissivity
ASCII tables of sea surface emissivity used for cloud detection (ATSR, SLSTR, and AVHRR sensors) and optimal estimation retrieval of SST (AVHRR sensors).
DOI: 10.5281/zenodo.3822686
title: WTC1.0 (WikiTailor corpus v. 1.0)

description: <strong>Content:</strong> List of the 743 domains, their term vocabularies in 10 languages, and the Wikipedia articles associated to each domain extracted by the best model described in: <em> Cristina España-Bonet, Alberto Barrón-Cedeño and Lluís Màrquez. “Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction.” ArXiv abs/2005.01177 (2020)</em> <em> https://github.com/cristinae/WikiTailor</em> <strong>Files Description:</strong> commonCats2015.enesdefrcaareuelrooc.tsv Multilingual domains listed one per line, languages are separated by a tab in the order en, es, de, fr, ca, ar, eu, el, ro and oc. For each language we include the pair "ID categoryName" separated by a blank space. [LAN].0.tar.bz A folder per domain for language [LAN] containing the vocabulary and IDs of the extracted articles by the Wikitailor model 50-WT100.