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10,356 results for “severity”
Anaktuvuk River Burn Eddy Flux Measurements, Severe Site, North Slope Alaska, 2013-2019
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the post fire energy and mass exchange at the severe burn site for 2013-2019.
Tree regeneration after fire: Effects of burn severity, CPCRW soil analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains more detailed data on organic soil measurements for the site at CPCRW. Data are included from 3 measurement dates: 4 August 2000 (org. depth and pH) 23 July 2001 (org. depth, soil wet and dry weights, pH) 16 July 2002 (org. depth, sample volume, soil wet and dry weights) Dry weights were obtained following drying in an oven for 48 hourse at 40-60 degrees C.
Tree regeneration after fire: Effects of burn severity, Delta soil analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains more detailed data from organic and mineral soil measurements made in the Delta 99 burn. Note that the file format differs from the CPCRW soil datafile. Information on warming manipulation plots at the Delta site are included here. Measurements of soil organic layer depth, wet weight, dry weight, %moisture, and pH are included for 4 sample dates. Some data are available only for one or two sample dates. Samples were taken with a 5-cm hand corer, except in 2001, where a 2-cm diameter corer was used. The sample dates are: 1 August 2000 17 July 2001 11 July 2002 17 July 2002
Tree regeneration after fire: Effects of burn severity, CPCRW vegetative cover analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains visual cover estimates, by species, for 2000-2002 at the CPCRW site. Sample dates are: 4 August 2000 25 July 2001 16 July 2002. Values are percent cover in a 1x1m quadrat, where T=trace (less than 0.5%), out=outside quadrat but inside experimental plot. An additional line is included which give the full species names associated with the 6-letter codes used as column headers.
Tree regeneration after fire: Effects of burn severity, Delta vegetative cover analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains visual cover estimates, by species, for 2000-2002 at the Delta site. Data for severity and warming treatments are included. Sample dates are: 31 July 2000 18 July 2001 13 July 2002. Values are percent cover in a 1x1m quadrat, where T=trace (less than 0.5%), out=outside quadrat but inside experimental plot. An additional line is included which give the full species names associated with the 6-letter codes used as column headers.
Tree regeneration after fire: Effects of burn severity, seedling germination analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains data on seedlings that germinated in the severity plots, for seeded and un-seeded (control) subplots. Data from the control subplots has been standardized by area (seedlings/subplot, where the subplot area=0.28 m2), and could be used to estimate natural rates of seedling establishment. Control values are the same for black and white spruce, as these species could not be distinguished.
Tree regeneration after fire: Effects of burn severity, germinated seedlings analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains data on total aboveground dry biomass of germinated seedlings harvested in late August, 2002. Seedlings are separated into those found in seeded subplots (target), non-seeded control plots (ctl), or partially-seeded control plots (seed-ctl, where plots were seeded in Sept 2000 but not June 2000). Cohorts indicate the year in which the seedlings were first observed. All seedlings of a species*cohort within a subplot were weighed together, and the average weight/seedling calculated. Note that a plot entry is included in the file only where a seedling was present.
Tree regeneration after fire: Effects of burn severity, transplanted seedlings analysis
This study examines the effects of burn severity on patterns of post-fire tree establishment in the boreal forest. We collected data from 5 separate field experiments examining seedling establishment across different severity levels in 4 burns in central Yukon Territory, Canada, and interior Alaska, USA. The experimental studies focus on the germination, survival, and growth responses of four common tree species, trembling aspen (Populus tremuloides), lodgepole pine (Pinus contorta), white spruce (Picea glauca) and black spruce (Picea mariana). Data on the soil organic layer (depth, moisture, bulk density, pH) were also collected at each site. This file contains summary biomass data for seedlings transplanted into field plots in June 2001, and harvested in late August 2002. Data represent current year biomass from 2002 (dry biomass, leaves and stems pooled). Values are average weights/seedling, where most plots had 3 seedlings, but some had fewer due to mortality or problems with planting. Note that there are no data for white spruce at the Carmacks site.
Fire-severity effects on plant-fungal interactions after a novel tundra wildfire disturbance: implications for arctic shrub and tree migration
Background-Vegetation change in high latitude tundra ecosystems is expected to accelerate due to increased wildfire activity. High-severity fires increase the availability of mineral soil seedbeds, which facilitates recruitment, yet fire also alters soil microbial composition, which could significantly impact seedling establishment. Results - We investigated the effects of fire severity on soil biota and associated effects on plant performance for two plant species predicted to expand into Arctic tundra. We inoculated seedlings in a growth chamber experiment with soils collected from the largest tundra fire recorded in the Arctic and used molecular tools to characterize root-associated fungal communities. Seedling biomass was significantly related to the composition of fungal inoculum. Biomass decreased as fire severity increased and the proportion of pathogenic fungi increased. Conclusions - Our results suggest that effects of fire severity on soil biota reduces seedling performance and thus we hypothesize that in certain ecological contexts fire-severity effects on plant-fungal interactions may dampen the expected increases in tree and shrub establishment after tundra fire.
USGS Long-term daily streamflow data at several LEF locations
Vist the USGS water data center (https://waterdata.usgs.gov) for more information on these discharge and other data collected in Northeastern Puerto Rico. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef: Early life stage bottleneck determines rates of coral recovery following severe disturbance; Data for Speare et al., 2024, Ecology
The data included in this data package were collected on the north shore of Moorea, French Polynesia, from 2011-2018 to evaluate drivers of different recovery rates of corals at two depths (10m and 17m). Data on juvenile coral densities, growth, and mortality, were collected from annual time series photoquadrats. Data from two experiments on coral settlement tiles were used to evaluate how exclusion of fishes influences the density of coral recruits, and the survival of coral recruits at 10 and 17m. These data were used for analyses in the manuscript entitled "Early life stage bottleneck determines rates of coral recovery following severe disturbance". These data are in support of a publication Speare et al. (2024) Ecology. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024).
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
<p>This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA.</p> <p>Bres EE et al.<br> Lipoprotein receptor loss in forebrain radial glia results in<br> neurological deficits and severe seizures. Glia. 2020;1–33.</p>
Dataset for the paper "Prolonged prothrombin time as an early prognostic indicator of severe acute respiratory distress syndrome in patients with COVID-19 related pneumonia"
<p>The dataset contains the data on ICU-transferred (N=100) and Stable (N=131) patients with COVID-19 (N=156) and Non-COVID-19 viral pneumonia (N=75). Among COVID-19 patients of this study, 82 patients developed Refractory Respiratory Failure (RRF) or Severe Acute Respiratory Distress Syndrome (SARDS) and were transferred to Intensive Care Unit (ICU), 74 patients had a Stable course of disease and were not transferred to ICU. Collected data are presented as a table with columns:<br> - Gender;<br> - Age (years);<br> - SARS-CoV-2 RT-PCR testing results;<br> - Time between the disease onset and admission to the hospital (days);<br> - Time between admission to the hospital and transfer to ICU (days);<br> - Artificial lung ventilation in ICU needed;<br> - C-reactive protein (CRP) upon admission (mg/L);<br> - International Normalized Ratio (INR) upon admission;<br> - Prothrombin Time (PT) upon admission (sec.);<br> - Fibrinogen upon admission (mg/L);<br> - Chest Computed Tomography (CT) upon admission: lung tissue affected (%);<br> - Platelet count upon admission (10^9/L);<br> - Chest CT, 1 week after admission: lung tissue affected (%);<br> - CRP, 1 week after admission (mg/L);<br> - Platelet count, 1 week after admission (10^9/L).</p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Potential vorticity and wind from ERA5 at several isentropic surfaces
<p>This datasets collects winds (u and v components) and potential vorticity from ERA5 at four isentropic surfaces: 475, 600, 700 and 800 K. Data are available daily and monthly. Potential vorticity and modified potential vorticity are stored. </p>
FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas
<p>This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.</p><p>The data were created using python scripts provided at <a href="https://doi.org/10.5281/zenodo.10295920">https://doi.org/10.5281/zenodo.10295920</a>. </p><p>Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:</p><ul><li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li><li>simA, ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a></li><li>simA, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a></li><li>simA, ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a></li><li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li><li>simB: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC</a></li><li>simC, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC</a></li></ul><p> </p>
In search of a conditioned place preference test to assess the severity of experimental procedures
<p>This deposit contains the choice dataset belonging to the publication with the title "In search of a conditioned place preference test to assess the severity of experimental procedures". </p> <p>A preprint version of the paper will be published on bioRxiv.</p> <p>The data set contains:</p> <ul> <li>for the Conditioned Place Preference Tests 1-9: table of time stamps with the position of the mice during the habituation and the final test</li> <li>a txt-file containing a rough description of the columns and their meanings</li> <li>the R Script which was used to analyze the data tables, especially with regard to statistics</li> </ul> <p>For more details on methods and results please read the manuscript.</p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant Shigella spp.
<p><strong>dataset for The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant <em>Shigella</em> spp.</strong></p> <p>Elena Fernández Alvaro <sup>1*</sup>, Phat Voong Vinh <sup>2</sup>, Cristina de Cozar <sup>1</sup>, David Wille <sup>1</sup>, Beatriz Urones <sup>1</sup>,</p> <p>Alan Price <sup>1</sup>, Nhu Tran Do Hoang <sup>2</sup>, Tuyen Ha Thanh <sup>2</sup>, Molly McCloskey <sup>3</sup>, Shareef Shaheen<sup> 3</sup>, Denise Dayao<sup> 4</sup>, Jaime de Mercado <sup>1</sup>, Pablo Castañeda <sup>1</sup>, Adolfo García-Perez <sup>1</sup>, Benson Singa <sup>5</sup>, Patricia Pavlinac <sup>6</sup>,</p> <p>Judd Walson<sup>3</sup>, Maria Santos Martínez-Martínez <sup>1</sup>, Samuel L.M. Arnold <sup>3</sup>, Tzipori Saul <sup>4</sup>, Lluis Ballell <sup>1#</sup>,</p> <p>and Stephen Baker <sup>7,8*</sup></p> <p> </p>
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.