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10,356 results for “severity”
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Dysglycemias in patients admitted to ICUs with severe acute respiratory syndrome due to COVID-19 versus other causes – A cohort study - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p>Importance: Dysglycemias have been associated with worse prognosis in critically ill patients with or without diabetes, but data on their association with severe COVID-19 and outcomes are lacking. Objectives: To analyze the relationship of dysglycemias with COVID-19 in hospitalized patients with severe acute respiratory syndrome (SARS) and assess the influence of dysglycemias on mortality. Design, Setting and Participants: Cohort of consecutive patients with SARS and suspected COVID-19 hospitalized in intensive care units (ICUs) across eight hospitals in Curitiba-Brazil. Main Outcomes and Measures: The primary outcome was the influence of COVID-19 on the variation of the following parameters of dysglycemia: highest glucose level at admission, mean and highest glucose levels during ICU stay, mean glucose variation, and percentage of days with hyperglycemia. The secondary outcome was the influence of COVID-19 and each of the five parameters of dysglycemia on hospital mortality within 30 days from ICU admission. Results: We compared 703 patients with COVID-19 and 138 without COVID-19 admitted to the ICUs due to SARS. Compared with patients without COVID-19, those with COVID-19 had significantly higher glucose peaks at admission (198.1mg/dL vs. 167.8mg/dL, respectively) and during ICU stay (285.9mg/Dl vs. 230.9md/dL), higher mean daily glucose values (167.9mg/dL vs. 149.8mg/dL), higher percentage of days with hyperglycemia during ICU stay (vs. 45.0 vs. 31.5), and greater mean daily glucose variations (85.3mg/dL vs. 63.5mg/dL). However, these associations were lost after adjustment for APACHE II scores, SOFA scores, CRP level, corticosteroid use and nosocomial infection. Dysglycemia and COVID-19 were each independent risk factors for mortality. Conclusions and Relevance: Patients with SARS due to COVID-19 had higher mortality and more frequent dysglycemia than patients with SARS due to other causes. This association seemed to be related to disease severity and inflammation and was independent of corticosteroid use, suggesting no specific relationship with the SARS-CoV-2 infection.</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients
<p>Code and data for the manuscript "Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients".</p> <p>Contains all code located at <a href="https://github.com/lasalletj/COVID_Neutrophils">https://github.com/lasalletj/COVID_Neutrophils</a> as well as additional data files needed to run the code.</p> <p>Three additional publicly available data objects are required to run the code from start to finish. The first, covid.combined_final.Robj, from the Sinha et al. Nature Medicine 2022 paper (<a href="https://doi.org/10.1038/s41591-021-01576-3">https://doi.org/10.1038/s41591-021-01576-3</a>), is downloadable from the following link: <a href="https://figshare.com/ndownloader/files/31562957">https://figshare.com/ndownloader/files/31562957</a>. The other two required objects, seurat_COVID19_Neutrophils_cohort2_rhapsody_jonas_FG_2020-08-18.rds and seurat_COVID19_freshWB-PBMC_cohort2_rhapsody_jonas_FG_2020-08-18.rds, are from the Schulte-Schrepping et al. Cell 2020 paper (<a href="https://doi.org/10.1016/j.cell.2020.08.001">https://doi.org/10.1016/j.cell.2020.08.001</a>), and can be downloaded from <a href="https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files">https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files</a> and <a href="https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files">https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files</a>, respectively.</p> <p>Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact, Moshe Sade-Feldman (msade-feldman@mgh.harvard.edu) upon request.</p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Acunex intraocular lens Zernike coefficients measured with a NIMO device for several optical zone diameters
<p>Zernike coefficient values obtained in vitro with a NIMO device for the Acunex family of intraocular lenses, both monofocal and multifocal, for three nominal powers (+10, +20, +30) for several optical zone diameters</p>
Gene-level counts according to their poly(A) length and additional uridine modifications in several stages of zebrafish, Xenopus, and mouse embryos
<p>This HDF5 file contains the processed data of primary poly(A) tail length analyses for the TAIL-seq runs used for Chang and Yeo et al. (2018; doi:10.1016/j.molcel.2018.03.004). The read count tables are stored under the two-level group structure of the run identifier as the first level and the sample identifier as the second level. A dataset at a leaf node is an unsigned integer array of the read count numbers by the length of poly(A) in rows and the length of U tails following after poly(A) in columns.</p> <p>Please refer to the <a href="https://data.mendeley.com/datasets/tzc5wwczyg/1">supplementary data page</a> of the original paper for more information about the experimental design.</p> <p> </p>
Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden – a prospective population-based study.
<p>Sepsis epidemiology study 2011-2012 Sweden</p> <p>Ljungström, Lars; Andersson, Rune; Jacobsson, Gunnar</p> <p> </p> <p>Data collected during the prospective "Sepsis Skaraborg study" performed 2011-2012 in the western region of Sweden. Adult patients admitted to the emergency department for suspicion of a community-onset sepsis were evaluated. The study was approved by the Regional Ethical Review Board of Gothenburg (376-11). The file includes data for patient characteristics, vital signs, biomarker measurements, cases of bacteremia, and patient classifications using Sepsis-2 and Sepsis-3 criteria.</p>
Characterization of Metabolism Associated with Outcomes in Severe Acute Pancreatitis: Insights from Serum Metabolomic Analysis
<p>1H NMR spectra data of SAP patients (Survivors/ Non-survivors). The spectra were binned as 0.02 ppm spectral buckets. The chemical shift regions corresponding to the water region and TSP were excluded to avoid spectral interference. This dataset was used for the metabolomics related study to highlight the dysregulation of metabolites in the study group.</p> <p> </p>
Is this bug severe? A text-cum-graph based model for bug severity prediction
<p>A snapshot of the dataset has been updated. For the time being, we are publishing a snapshot of the dataset where the bugs were reported after 2017.</p> <p>Paper link: <a href="https://arxiv.org/abs/2207.00623">https://arxiv.org/abs/2207.00623</a> (ECML-PKDD 2022)</p> <p>Cite our paper:</p> <p>@InProceedings{10.1007/978-3-031-26422-1_15,<br> author="Hazra, Rima<br> and Dwivedi, Arpit<br> and Mukherjee, Animesh",<br> editor="Amini, Massih-Reza<br> and Canu, St{\'e}phane<br> and Fischer, Asja<br> and Guns, Tias<br> and Kralj Novak, Petra<br> and Tsoumakas, Grigorios",<br> title="Is This Bug Severe? A Text-Cum-Graph Based Model for Bug Severity Prediction",<br> booktitle="Machine Learning and Knowledge Discovery in Databases",<br> year="2023",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="236--252",<br> isbn="978-3-031-26422-1"<br> }</p> <p><strong>*** Please see the new version. (10.5281/zenodo.5554978)</strong></p> <p>There is a total of six files.</p> <ul> <li><strong>bug_descriptions.csv:</strong> This file contains the bug id and its description.</li> <li><strong>bug_comments.csv:</strong> This file contains three columns. The columns are the bug ids, comments and timestamp of the comment.</li> <li><strong>bug_REPORTED_ON_details.csv:</strong> This file contains the bug id and the package name on which the bug is reported</li> <li><strong>affect_dataset.csv: </strong>This file contains the bug id and the affected packages along with the affect timestamp.</li> <li><strong>bug_heat_2019.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2019.</li> <li><strong>bug_heat_2020.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2020.</li> </ul>
Dataset: Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study
<p>Dataset underlying the publication "<strong>Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study</strong>" (Plos Medicine)</p> <p>Data originating from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021.</p> <p>Analysis of health workers' compliance with the treatment guidelines for severe malaria in the context of rolling out pre-referral rectal artesunate (RAS) in the Democratic Republic of the Congo, Nigeria and Uganda. Details provided in the publication.</p>
Data for "Shell microstructures (disturbance lines) of Arctica islandica (Bivalvia) – A potential proxy for severe oxygen depletion"
<p>All data used in the publication "Shell microstructures (disturbance lines) of <em>Arctica islandica</em> – A potential proxy for severe oxygen depletion" currently under review. This includes in situ environmental data from the Mecklenburg Bight, Baltic Sea (ODIN 2, Leibnitz Institute for Baltic Sea Research, https://odin2.io-warnemuende.de/), and biomineral unit (BMU) morphology measurements in scanning electron microscopy images of shells of <em>Arctica islandica</em>, as well as the BMU classifier used in Ilastik (Berg et al., 2019).</p> <p>Each BMU measurement represents summary statistics of the 15 % largest BMUs within one image (25, 50 and 75 % percentile of each BMU parameter). Environmental data were measured at 20 m water depth, ca. 5 m above the sediment surface.</p>
Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).
<p>This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis.</p> <p>CyTOF data:</p> <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> <p>scRNA-seq data:</p> <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.
Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu
Stem diameter of trees and shrubs in thinleaf alder sites along the Tanana River floodplains and severity of stem canker infection of alder in 2006.
This dataset includes the stem diameter of each woody plant >2m tall, by species (or genus), found in each of the thinleaf alder sites we established in the Tanana River floodplains in 2006, as well as an assessment of the severity of canker infection in each thinleaf alder stem. Species (or genus), diameter at breast height (dbh), and severity of canker infection are the variables included here. This dataset can be used in conjunction with a dataset of alder size/age relationships (DN_alder_stand_structure2.xls) to estimate the age structure of alder stands.
Age, size, and disease severity of thinleaf alder stems along the Tanana River floodplains, collected in 2006 and 2007.
Thinleaf alder stands on the Tanana River floodplains, sampled in 2006 and 2007: A subsample of thinleaf alder stems were aged using tree-ring analysis, and the age structures of alder populations were estimated from their size structures (DN_alder_stand_structure1), based on the size/age relationships derived from this dataset. Relationships between disease incidence/severity and size/age were also explored. Included in this dataset are: the stem diameter at breast height (dbh), age of stem at ground level, age at breast height, and disease severity.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.