cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4635286
title: The spatial landscape of lung pathology during COVID-19 progression - targeted spatial transcriptomics data

description: Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.
description: We make available 311 samples of targeted transcriptomes in a spatial context (GeoMx) from lung tissue of 14 donors. Metadata is also available. Files are provided as Parquet archives in order to preserve the data type (boolean/categorical, etc). Companion deposit to https://doi.org/10.5281/zenodo.4110560
DOI: 10.5281/zenodo.4635285
title: The spatial landscape of lung pathology during COVID-19 progression - targeted spatial transcriptomics data

description: Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.
description: We make available 311 samples of targeted transcriptomes in a spatial context (GeoMx) from lung tissue of 14 donors. Metadata is also available. Files are provided as Parquet archives in order to preserve the data type (boolean/categorical, etc). Companion deposit to https://doi.org/10.5281/zenodo.4110560
DOI: 10.5523/bris.1w6pbarlu82re2g8g5b5cgdwm3
title: Hippocampus QMRI

description: The Hippocampus QMRI dataset contains anonymised brain extracted imaging data (.nii) from heathy elderly volunteers, individuals with mild cognitive impairment and individuals with Alzheimer's Disease as well as performance scores on cognitive tests and blood biomarkers.
DOI: 10.5281/zenodo.4636417
title: data set related to article Narcissistic traits as predictors of emotional problems in children with oppositional defiant disorder: A longitudinal study

description: This record contains raw data related to article Narcissistic traits as predictors of emotional problems in children with oppositional defiant disorder: A longitudinal study
DOI: 10.5281/zenodo.4636416
title: data set related to article Narcissistic traits as predictors of emotional problems in children with oppositional defiant disorder: A longitudinal study

description: This record contains raw data related to article Narcissistic traits as predictors of emotional problems in children with oppositional defiant disorder: A longitudinal study
DOI: 10.5281/zenodo.4446043
title: Determinants of Airbnb prices in European cities: A spatial econometrics approach (Supplementary Material)

description: This repository contains supplementary materials for the article: <strong>Determinants of Airbnb prices in European cities: A spatial econometrics approach</strong> <strong>(</strong>DOI<strong>: </strong>https://doi.org/10.1016/j.tourman.2021.104319) The materials include the used datasets and Python scripts for spatial regression models. <strong>Datasets</strong> For each city two files are provided: data for weekday and weekend offers The columns are as following: realSum: the full price of accommodation for two people and two nights in EUR room_type: the type of the accommodation room_shared: dummy variable for shared rooms room_private: dummy variable for private rooms person_capacity: the maximum number of guests host_is_superhost: dummy variable for superhost status multi: dummy variable if the listing belongs to hosts with 2-4 offers biz: dummy variable if the listing belongs to hosts with more than 4 offers cleanliness_rating: cleanliness rating guest_satisfaction_overall: overall rating of the listing bedrooms: number of bedrooms (0 for studios) dist: distance from city centre in km metro_dist: distance from nearest metro station in km attr_index: attraction index of the listing location attr_index_norm: normalised attraction index (0-100) rest_index: restaurant index of the listing location attr_index_norm: normalised restaurant index (0-100) lng: longitude of the listing location lat: latitude of the listing location <strong>Programming Scripts</strong> In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py). The codes cover the following regression models: OLS SLX (lagged_x) SAR (lagged_y) SDM (lagged_x_y) SEM (lagged_e) SDEM (lagged_e_x) Main parameters: cities - list of cities from the dataset to be included in the analysis Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours Robust=True: calculate all regression models with different specifications of W direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y) Key functions: create_weights - defines the W specification write_stats - calculates's Moran's I and Geary's C direct - calculates the direct effect of the variable indirect - calculates the indirect effect coord - sets the coordinate refence system (CRS) appropriate to the analysed city total_results calculates the regressions the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon) all regressions are saved as formatted txt table the results can be also saved as csv table
description: This research was supported by National Science Centre, Poland: Project number 2017/27/N/HS4/00951
DOI: 10.5281/zenodo.4437019
title: Determinants of Airbnb prices in European cities: A spatial econometrics approach (Supplementary Material)

description: This repository contains supplementary materials for the article: <strong>Determinants of Airbnb prices in European cities: A spatial econometrics approach</strong> <strong>(</strong>DOI<strong>: </strong>https://doi.org/10.1016/j.tourman.2021.104319) The materials include the used datasets and Python scripts for spatial regression models. <strong>Datasets</strong> For each city two files are provided: data for weekday and weekend offers The columns are as following: realSum: the full price of accommodation for two people and two nights in EUR room_type: the type of the accommodation room_shared: dummy variable for shared rooms room_private: dummy variable for private rooms person_capacity: the maximum number of guests host_is_superhost: dummy variable for superhost status multi: dummy variable if the listing belongs to hosts with 2-4 offers biz: dummy variable if the listing belongs to hosts with more than 4 offers cleanliness_rating: cleanliness rating guest_satisfaction_overall: overall rating of the listing bedrooms: number of bedrooms (0 for studios) dist: distance from city centre in km metro_dist: distance from nearest metro station in km attr_index: attraction index of the listing location attr_index_norm: normalised attraction index (0-100) rest_index: restaurant index of the listing location attr_index_norm: normalised restaurant index (0-100) lng: longitude of the listing location lat: latitude of the listing location <strong>Programming Scripts</strong> In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py). The codes cover the following regression models: OLS SLX (lagged_x) SAR (lagged_y) SDM (lagged_x_y) SEM (lagged_e) SDEM (lagged_e_x) Main parameters: cities - list of cities from the dataset to be included in the analysis Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours Robust=True: calculate all regression models with different specifications of W direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y) Key functions: create_weights - defines the W specification write_stats - calculates's Moran's I and Geary's C direct - calculates the direct effect of the variable indirect - calculates the indirect effect coord - sets the coordinate refence system (CRS) appropriate to the analysed city total_results calculates the regressions the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon) all regressions are saved as formatted txt table the results can be also saved as csv table
description: This research was supported by National Science Centre, Poland: Project number 2017/27/N/HS4/00951
DOI: 10.25646/8214
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5519/qd.w8imnro1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4772609 records)
DOI: 10.5281/zenodo.4636718
title: Dataset from Ranucci M, Pavesi M, Pistuddi V, Baryshnikova E. Preoperative Anemia Correction in Cardiac Surgery: A Propensity-Matched Study. J Cardiothorac Vasc Anesth. 2021 Mar;35(3):874-881. doi: 10.1053/j.jvca.2020.07.015. Epub 2020 Jul 7. PMID: 32741609.

description: Dataset from the article Ranucci M, Pavesi M, Pistuddi V, Baryshnikova E. Preoperative Anemia Correction in Cardiac Surgery: A Propensity-Matched Study. J Cardiothorac Vasc Anesth. 2021 Mar;35(3):874-881. doi: 10.1053/j.jvca.2020.07.015. Epub 2020 Jul 7. PMID: 32741609. ANEMIA IN CARDIAC surgery patients is a common condition. In a recent survey conducted in the United Kingdom, preoperative anemia was diagnosed in 23% to 45% of cardiac surgery patients, and it was more common in elderly patients, patients with diabetes, women, patients with low body mass index, and high-risk patients.1 Aside from hospital-acquired anemia (preoperative decrease of hemoglobin due to blood withdrawals, invasive procedures, and hemodilution), the most common cause of anemia is iron deficiency, followed by anemia of chronic disease.2 Preoperative anemia has been associated with worse outcomes in cardiac surgery,3-6 and the levels of hemoglobin (Hb) or hematocrit (HCT) before surgery are now included in a mortality risk score, with a relative risk increase of 18% per each percent point of HCT below 36%.7 In a recent randomized controlled trial, an ultrashort-term treatment with a combination of ferric carboxymaltose, erythropoietin, vitamin B12, and folates in patients with iron deficiency or anemia of any kind significantly reduced red blood cell (RBC) and total allogeneic blood product transfusions in patients undergoing cardiac surgery.8 However, this approach was not based on a specific treatment of the different kinds of anemia, and its ultra-short nature may have reduced the clinical impact of the drugs used to correct anemia. Therefore, there is presently a gap of knowledge with respect to the efficacy and clinical usefulness of a specific treatment aimed at starting days or weeks before surgery and one aimed at correcting anemia. Since 2017 at the authors’ institution, they have implemented a program of preoperative anemia correction for cardiac and noncardiac surgery patients. Cardiac surgery patients who can be examined as outpatients before being admitted to the hospital enter this program, which provides a targeted treatment for all anemic (Hb &lt; 13 g/dL) patients. Due to organization issues, and the nature of the institution (a tertiary center admitting many patients from other Italian regions), a minority (about 20%) of the cardiac surgery patients have access to this facility. Therefore, a considerable number of anemic patients undergo cardiac surgery without anemia correction. The present study was a retrospective, propensity-matched analysis of the effects of preoperative anemia correction with a tailored strategy in cardiac surgery patients from 2017 to 2019.
DOI: 10.5281/zenodo.4636717
title: Dataset from Ranucci M, Pavesi M, Pistuddi V, Baryshnikova E. Preoperative Anemia Correction in Cardiac Surgery: A Propensity-Matched Study. J Cardiothorac Vasc Anesth. 2021 Mar;35(3):874-881. doi: 10.1053/j.jvca.2020.07.015. Epub 2020 Jul 7. PMID: 32741609.

description: Dataset from the article Ranucci M, Pavesi M, Pistuddi V, Baryshnikova E. Preoperative Anemia Correction in Cardiac Surgery: A Propensity-Matched Study. J Cardiothorac Vasc Anesth. 2021 Mar;35(3):874-881. doi: 10.1053/j.jvca.2020.07.015. Epub 2020 Jul 7. PMID: 32741609. ANEMIA IN CARDIAC surgery patients is a common condition. In a recent survey conducted in the United Kingdom, preoperative anemia was diagnosed in 23% to 45% of cardiac surgery patients, and it was more common in elderly patients, patients with diabetes, women, patients with low body mass index, and high-risk patients.1 Aside from hospital-acquired anemia (preoperative decrease of hemoglobin due to blood withdrawals, invasive procedures, and hemodilution), the most common cause of anemia is iron deficiency, followed by anemia of chronic disease.2 Preoperative anemia has been associated with worse outcomes in cardiac surgery,3-6 and the levels of hemoglobin (Hb) or hematocrit (HCT) before surgery are now included in a mortality risk score, with a relative risk increase of 18% per each percent point of HCT below 36%.7 In a recent randomized controlled trial, an ultrashort-term treatment with a combination of ferric carboxymaltose, erythropoietin, vitamin B12, and folates in patients with iron deficiency or anemia of any kind significantly reduced red blood cell (RBC) and total allogeneic blood product transfusions in patients undergoing cardiac surgery.8 However, this approach was not based on a specific treatment of the different kinds of anemia, and its ultra-short nature may have reduced the clinical impact of the drugs used to correct anemia. Therefore, there is presently a gap of knowledge with respect to the efficacy and clinical usefulness of a specific treatment aimed at starting days or weeks before surgery and one aimed at correcting anemia. Since 2017 at the authors’ institution, they have implemented a program of preoperative anemia correction for cardiac and noncardiac surgery patients. Cardiac surgery patients who can be examined as outpatients before being admitted to the hospital enter this program, which provides a targeted treatment for all anemic (Hb &lt; 13 g/dL) patients. Due to organization issues, and the nature of the institution (a tertiary center admitting many patients from other Italian regions), a minority (about 20%) of the cardiac surgery patients have access to this facility. Therefore, a considerable number of anemic patients undergo cardiac surgery without anemia correction. The present study was a retrospective, propensity-matched analysis of the effects of preoperative anemia correction with a tailored strategy in cardiac surgery patients from 2017 to 2019.
DOI: 10.5281/zenodo.4636667
title: BAM and BAI files for single cell RNAseq example, astrocyte and neuron: genome mm10, chr13 data only

description: BAM files downloaded from Allen Brain Atlas project https://www.ncbi.nlm.nih.gov/sra?term=SRP061902 for use in the Genome browser workshop available at the link below: https://www.melbournebioinformatics.org.au/tutorials/tutorials/Genome_browsers/GenomeBrowsers_Intro/
DOI: 10.5281/zenodo.4636666
title: BAM and BAI files for single cell RNAseq example, astrocyte and neuron: genome mm10, chr13 data only

description: BAM files downloaded from Allen Brain Atlas project https://www.ncbi.nlm.nih.gov/sra?term=SRP061902 for use in the Genome browser workshop available at the link below: https://www.melbournebioinformatics.org.au/tutorials/tutorials/Genome_browsers/GenomeBrowsers_Intro/
DOI: 10.21227/fjek-c464
title: Forecast for January 27

description: Forecast for January 27
DOI: 10.6084/m9.figshare.14307540.v1
title: Global geography of jokes

description: Abstract Abstract: Jokes between countries are useful to reveal the ethnotypes existing in each of them, to represent them in cartographic form allows to perceive their distribution and the spatial projection of mockery: who are we laughing at, who are the scapegoats for the inhabitants of each country? Based on the analysis of an ad hoc database covering more than 60% of the countries and territories of the world and 90% of its population, the text shows that these jokes are social constructions, have a temporality and are divided basically in two categories, from top to bottom and from bottom to top.
DOI: 10.6084/m9.figshare.14307539.v1
title: SOIL COMPACTION AND WATERPROOFING AND IMPLICATIONS IN URBAN FLUVIAL CHANNELS

description: Abstract Urban expansion and densification have resulted in phenomena related to the degradation of watersheds. This work evaluated the influence of increased anthropic pressure on the imbalances involving erosive processes and the consequent degradation of the river plain of a hydrographic basin in Aparecida de Goiânia - GO. The methodology analyses the spatial-temporal correlation between reduced concentration time, increased runoff estimates, and the consequent degradation of the plain in 1992, 2005, and 2016. The results indicate an increase in erosion in segments of channel convergence, with a low altimetric gradient and the occurrence of Fluvent Entisol, stemming from the increase in volume and flow peaks arising from areas with significant densification processes in the last twenty-four years. Thus, the importance of using indicators of increased anthropic pressure to understand the impacts related to urban density.
DOI: 10.6084/m9.figshare.14307537.v1
title: MEMORY AND CENTRALITY IN RESENDE

description: Abstract This article discusses the role of the Main Center in a medium-sized city undergoing urban restructuring at the interface between space and time and its different locational logics and dynamics. Our approach includes mapping the spatiality of the economic activities of Resende’s downtown and Campos Elíseos, especially bank branches and public administration buildings. The research materials were photographic records and their respective historical interpretations, reported memories given through interviews, and publications by memorialists, historians, and official sources, among others. The aim was to reconstitute the “present of the past”, following a chronological path from the time of the peak and decline of coffee production, at the beginning and mid-nineteenth century, respectively, to the formation of the Main Center of Campos Elíseos in the mid-twentieth century. The discussion has three parts: the formation of the Historical Center of Resende; the Center crossing the river, which debates the formation of the Main Center in Campos Elíseos, and finally, a reflection on the spatial struggle of urban centrality in a medium-sized city.
DOI: 10.6084/m9.figshare.14307536.v1
title: MONITORING OF BRAZILIAN SEASONALLY DRY TROPICAL FOREST BY REMOTE SENSING

description: Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.