cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7669874
title: data set related to article Sensory Profiles in School-Aged Children with Autism Spectrum Disorder: A Descriptive Study Using the Sensory Processing Measure-2 (SPM-2)

description: This record contains raw data related to article Sensory Profiles in School-Aged Children with Autism Spectrum Disorder: A Descriptive Study Using the Sensory Processing Measure-2 (SPM-2) Per la richiesta di accesso al file contattare il Dr. Antonio Narzisi all'indirizzo email antonio.narzisi@fsm.unipi.it
DOI: 10.5281/zenodo.7643007
title: Characterization of Continuous Experimentation in Software Engineering: Expressions, Models and Strategies

description: Systematic Mapping Study's Dataset
DOI: 10.5281/zenodo.7643008
title: Characterization of Continuous Experimentation in Software Engineering: Expressions, Models and Strategies

description: Systematic Mapping Study's Dataset
DOI: 10.5281/zenodo.7669902
title: Characterization of Continuous Experimentation in Software Engineering: Expressions, Models and Strategies

description: Systematic Mapping Study's Dataset
DOI: 10.5281/zenodo.7669896
title: Datset related to article "NEURONAVIGATION APPLIED TO PIEZOSURGERY: TOWARD ROBOTIC IMAGE-AIDED CRANIOTOMY? A TECHNICAL NOTE"

description: <strong>clinical data </strong>
DOI: 10.5281/zenodo.7669895
title: Datset related to article "NEURONAVIGATION APPLIED TO PIEZOSURGERY: TOWARD ROBOTIC IMAGE-AIDED CRANIOTOMY? A TECHNICAL NOTE"

description: <strong>clinical data </strong>
DOI: 10.5281/zenodo.7669904
title: Dataset for the research about the relationship between attitudes towards STEM and climate change.

description: Dataset for the research about the relationship between attitudes towards STEM and climate change; an exploration of students’ profiles with a gender perspective
DOI: 10.5281/zenodo.7669903
title: Dataset for the research about the relationship between attitudes towards STEM and climate change.

description: Dataset for the research about the relationship between attitudes towards STEM and climate change; an exploration of students’ profiles with a gender perspective
DOI: 10.5519/qd.bxqrdnnz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (223 records)
DOI: 10.6084/m9.figshare.16595220
title: Sensitivity analysis and retrieval of optimum SLEUTH model parameters

description: The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of <i>self-modifying</i> constants, <i>Monte Carlo</i> runs and <i>critical slope</i> to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban <i>area</i>, <i>clusters</i>, <i>edges</i>, <i>mean cluster size</i>, and <i>cluster radius</i>, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for <i>self-modifying parameters</i> as {1.3, 0.10, 0.90, and 1.25} for <i>boom</i>, <i>bust</i>, <i>critical low</i> and <i>critical high</i> respectively; <i>Monte Carlo</i> runs as sixty (60) and <i>critical slope</i> as 15 to simulate the urban growth of the study area.
DOI: 10.6084/m9.figshare.16595220.v1
title: Sensitivity analysis and retrieval of optimum SLEUTH model parameters

description: The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of <i>self-modifying</i> constants, <i>Monte Carlo</i> runs and <i>critical slope</i> to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban <i>area</i>, <i>clusters</i>, <i>edges</i>, <i>mean cluster size</i>, and <i>cluster radius</i>, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for <i>self-modifying parameters</i> as {1.3, 0.10, 0.90, and 1.25} for <i>boom</i>, <i>bust</i>, <i>critical low</i> and <i>critical high</i> respectively; <i>Monte Carlo</i> runs as sixty (60) and <i>critical slope</i> as 15 to simulate the urban growth of the study area.
DOI: 10.6084/m9.figshare.16659624
title: Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

description: Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R<sup>2</sup> = 0.66, vs 0.50), however, the combination provided the best fit (R<sup>2</sup> = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.
DOI: 10.6084/m9.figshare.16659624.v1
title: Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

description: Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R<sup>2</sup> = 0.66, vs 0.50), however, the combination provided the best fit (R<sup>2</sup> = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.
DOI: 10.6084/m9.figshare.16918102
title: Semi-automated mapping of glacial lakes – a study in Sikkim Himalayas, India

description: In the Himalayas, rising number of glacial lakes with increase in area of existing lakes is a matter of serious concern and correct mapping is an important step towards regular monitoring of these glacial lakes. This study used Double Window Flexible Pace Search method to identify an optimal threshold of Normalized Difference Water Index value on multi-spectral Sentinel-2 image of 31<sup>st</sup> October 2020. A threshold value of 0.30 is obtained that resulted in identification of 419 glacial lakes with an estimated total area of about 29.86 ± 6.85 km<sup>2</sup>. The results are found to be in close agreement with previously published research article on Sikkim Himalayas and boundaries of classified lakes are validated by overlaying on high resolution PlanetScope dataset for the same date. Hence, this study presents an effective and simple way for mapping of glacial lakes that may further be extended for regular monitoring of glacial lakes in the region.
DOI: 10.6084/m9.figshare.16918102.v2
title: Semi-automated mapping of glacial lakes – a study in Sikkim Himalayas, India

description: In the Himalayas, rising number of glacial lakes with increase in area of existing lakes is a matter of serious concern and correct mapping is an important step towards regular monitoring of these glacial lakes. This study used Double Window Flexible Pace Search method to identify an optimal threshold of Normalized Difference Water Index value on multi-spectral Sentinel-2 image of 31<sup>st</sup> October 2020. A threshold value of 0.30 is obtained that resulted in identification of 419 glacial lakes with an estimated total area of about 29.86 ± 6.85 km<sup>2</sup>. The results are found to be in close agreement with previously published research article on Sikkim Himalayas and boundaries of classified lakes are validated by overlaying on high resolution PlanetScope dataset for the same date. Hence, this study presents an effective and simple way for mapping of glacial lakes that may further be extended for regular monitoring of glacial lakes in the region.
DOI: 10.6084/m9.figshare.21959987
title: PSI and GNSS derived ground subsidence detection in the UNESCO Heritage City of Ahmedabad, Western India

description: Presently, ground subsidence in urban areas is becoming a peril to the infrastructure and society. In this study, we conducted a detailed analysis of the results, based on the Persistent Scatterer Interferometry (PSI), Global Navigation Satellite System (GNSS) and groundwater in the ‘World Heritage City’ of Ahmedabad, a major metro city in western India. The PSI technique is applied to measure the ground subsidence using the Sentinel 1 A dataset for the period 2017 to 2020. The results reveal a subsidence, maximum up to the level of 25 mm/yr in the areas of the southeast and west parts of the city. Analysis derived from GNSS results indicates a significant amount of ground subsidence based on the 2009–2020 dataset. Groundwater data (1995–2019) were analyzed to identify factors likely to cause ground subsidence and the results are in close agreement with the PSI and GNSS results.
DOI: 10.6084/m9.figshare.16918102.v1
title: Semi-automated mapping of glacial lakes – a study in Sikkim Himalayas, India

description: In the Himalayas, rising number of glacial lakes with increase in area of existing lakes is a matter of serious concern and correct mapping is an important step towards regular monitoring of these glacial lakes. This study used Double Window Flexible Pace Search method to identify an optimal threshold of Normalized Difference Water Index value on multi-spectral Sentinel-2 image of 31<sup>st</sup> October 2020. A threshold value of 0.30 is obtained that resulted in identification of 419 glacial lakes with an estimated total area of about 29.86 ± 6.85 km<sup>2</sup>. The results are found to be in close agreement with previously published research article on Sikkim Himalayas and boundaries of classified lakes are validated by overlaying on high resolution PlanetScope dataset for the same date. Hence, this study presents an effective and simple way for mapping of glacial lakes that may further be extended for regular monitoring of glacial lakes in the region.
DOI: 10.6084/m9.figshare.21959987.v1
title: PSI and GNSS derived ground subsidence detection in the UNESCO Heritage City of Ahmedabad, Western India

description: Presently, ground subsidence in urban areas is becoming a peril to the infrastructure and society. In this study, we conducted a detailed analysis of the results, based on the Persistent Scatterer Interferometry (PSI), Global Navigation Satellite System (GNSS) and groundwater in the ‘World Heritage City’ of Ahmedabad, a major metro city in western India. The PSI technique is applied to measure the ground subsidence using the Sentinel 1 A dataset for the period 2017 to 2020. The results reveal a subsidence, maximum up to the level of 25 mm/yr in the areas of the southeast and west parts of the city. Analysis derived from GNSS results indicates a significant amount of ground subsidence based on the 2009–2020 dataset. Groundwater data (1995–2019) were analyzed to identify factors likely to cause ground subsidence and the results are in close agreement with the PSI and GNSS results.
DOI: 10.6084/m9.figshare.18026999
title: JCMS: Data for Triggers and Hierarchies of Social Identities

description: Contains the original raw datasets, truthtable, and simpliyfing assumptions for the Qualitative Comparative Analysis (QCA) conducted in the article "Triggers and Hierarchies of Social Identities in the European Parliament", published in the Journal of Common Market Studies. DOI: 101111.jcms/13474
DOI: 10.7488/700791ae-ad2f-4f06-b5e1-43372a6622d9
title: DataVault: Experimental data and EndMT bulk RNA-seq related to Monteiro et al, Circ.Res 2021

description: This vault contains the experimental data related to Monteiro et al, Circ Res 2021 as well as additional data produced for this project by Alena Schmakova, Axelle Caudrillier and Joe Monteiro. It also contains the raw and processed data of the bulk EndMT RNA-seq (also published in Monteiro et al, Circ Res 2021). In this in-vitro model of Endothelial to mesenchymal transition, HUVEC and PAEC were treated with IL1a and/or TGFb2 for 7 days. Untreated cells were used as a Control.
DOI: 10.5281/zenodo.7669911
title: MCarreroPazos/ArqueologiaComputacionalTerritorio: Final release

description: First release
DOI: 10.7488/c99be5e6-15cd-4667-ac44-42adeb2950ca
title: DataVault: EndMT scRNAseq - related to Monteiro et al, Circ Res 2021

description: Single cell RNA-seq performed in an in-vitro model of Endothelial to Mesenchymal Transition. HUVEC were treated with IL1a+TGFb2 for 3 and 7 days. Untreated HUVEC were used as a Control. This vault includes the raw sequencing data, the output of CellRanger, some processed data obtained using downstream tools such as Seurat. Analysis done by Richard S. Taylor and Julie Rodor. Details of the dataset and some of the analysis can be found in Monteiro et al, Circ Res 2021.
DOI: 10.5281/zenodo.7669950
title: data set related to article Early Sensory Profile in Autism Spectrum Disorders Predicts Emotional and Behavioral Issues

description: This record contains raw data related to article Early Sensory Profile in Autism Spectrum Disorders Predicts Emotional and Behavioral Issues Per la richiesta di accesso al file contattare il Dr. Antonio Narzisi all'indirizzo antonio.narzisi@fsm.unipi.it
DOI: 10.5281/zenodo.7669949
title: data set related to article Early Sensory Profile in Autism Spectrum Disorders Predicts Emotional and Behavioral Issues

description: This record contains raw data related to article Early Sensory Profile in Autism Spectrum Disorders Predicts Emotional and Behavioral Issues Per la richiesta di accesso al file contattare il Dr. Antonio Narzisi all'indirizzo antonio.narzisi@fsm.unipi.it
DOI: 10.6084/m9.figshare.22045091.v7
title: The inhibition of emodin in human neuroblastoma SH-SY5Y cells

description: The experiment results of emodin in neuroblastoma SH-SY5Y cells.
DOI: 10.5281/zenodo.5172168
title: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.

description: Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers <strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy. <strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+ <strong>Affiliations</strong>: 1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy 2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden 3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden 4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden 5 Genevia technologies OY, Tampere, Finland # these authors equally contributed + Corresponding author <strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy. <strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.
DOI: 10.5281/zenodo.5172167
title: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.

description: Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers <strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy. <strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+ <strong>Affiliations</strong>: 1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy 2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden 3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden 4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden 5 Genevia technologies OY, Tampere, Finland # these authors equally contributed + Corresponding author <strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy. <strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.
DOI: 10.6084/m9.figshare.22147485
title: Late Jurassic – Early Cretaceous marine deoxygenation in Northeast Greenland

description: S3: Geochemistry raw data.
DOI: 10.6084/m9.figshare.22147485.v1
title: Late Jurassic – Early Cretaceous marine deoxygenation in Northeast Greenland

description: S3: Geochemistry raw data.
DOI: 10.18725/oparu-42997
title: A novel pulsatile blood pump design for cardiothoracic surgery - proof-of-concept in a mock circulation

description: Pulsatile perfusion during extracorporeal circulation is a promising concept to improve perfusion of critical organs. Clinical benefits are limited by the amount of pulsatile energy provided by standard pumps. We investigated the properties of a novel positive displacement blood pump in a mock circulation containing an aortic model with a human-like geometry and compliance as a pseudo patient. Using a regular oxygenator, maximum flow was 2.6 L/min at a pressure of 27 mmHg and a frequency (F) of 90 bpm. Pulse pressure (PP; 28.9 mmHg) and surplus hemodynamic energy (SHE; 26.1% of mean arterial pressure) were highest at F=40 bpm. Flow and pressure profiles appeared sinusoid. Using a low-resistance membrane ventilator to assess the impact of back pressure, maximum flow was 4.0 L/min at a pressure of 58.6 mmHg and F=40 bpm. At F=40 bpm, PP was 58.7 mmHg with an SHE of 33.4%. SHE decreased with increasing flow, heart rate, and systolic percentage but surpassed 10% with reasonable settings. The present prototype achieved sufficient flow and pressure ranges only in the presence of a low-resistance membrane ventilator. It delivered supraphysiologic levels of pulse pressure and SHE. Further modifications will help to establish this concept for adult pulsatile perfusion.
DOI: 10.5281/zenodo.7669967
title: data set related to article Reading Behaviors through Patterns of Finger-Tracking in Italian Children with Autism Spectrum Disorder

description: This record contains raw data related to article Reading Behaviors through Patterns of Finger-Tracking in Italian Children with Autism Spectrum Disorder Per la richiesta di accesso al file contattare il Dr. Antonio Narzisi all'indirizzo email antonio.narzisi@fsm.unipi.it