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Experimental data used in the article entitled "Exploring microstructure refinement and deformation mechanisms in severely deformed LPBF AlSi10Mg alloy"
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Gridded Palmer Drought Severity Idex (PDSI) for Rondônia - Brazil, from 1981 to 2019
<p>Monthly gridded Palmer Drought Severity Index (PDSI) over Rondônia using available water holding capacity</p>
Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures
<p>The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: <br>"Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures".</p> <p>0. DATA</p> <p>FOLDER: 0_Data<br>This folder contains the raw phenotypic and genotypic data.</p> <p>I. FILE PREPARATION</p> <p>FOLDER: 1_FilePrep_Design_Fig<br>The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes.</p> <p>II. PHENOTYPIC ANALYSIS </p> <p>FOLDER: 2_GeneticEffects_PhenoCorr_Residus<br>The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models.</p> <p>III. A. DGE-BASED GWAS</p> <p>FOLDER: 3_DGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P).<br>For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype.</p> <p>III. B. Analysis of GWAS Results for DGE</p> <p>FOLDER: 3_DGE_GWAS<br>Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>IV. A. IGE-BASED GWAS</p> <p>FOLDER: 4_IGE_GWAS<br>This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P).<br>For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype.</p> <p>IV. B. Analysis of GWAS Results for IGE</p> <p>FOLDER: 4_IGE_GWAS<br>Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs.</p> <p>V. PREPARATION OF FILES FOR CO-GWAS</p> <p>FOLDER: 5_FilePrep_coGWAS<br>The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning.<br>The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning.<br>The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning.</p> <p>VI. CO-GWAS</p> <p>FOLDER: 6_coGWAS<br>This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P). <br>For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype.<br>The other scripts in this folder combine the result files.</p> <p>VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS</p> <p>FOLDER: 7_Heatmaps_qqplots_coGWAS<br>This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P).<br>For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot.</p> <p>VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS</p> <p>FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS<br>This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P).<br>For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions. </p> <p>IX. CIRCULAR PLOTS </p> <p>FOLDER: 9_CircularPlots_coGWAS<br>This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P).<br>For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot. <br>The script circos.conf creates the circular plot. </p> <p>X. GO ENRICHMENT ANALYSIS </p> <p>FOLDER: 10_GOterms_coGWAS<br>This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P).<br>For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.</p>
Restigmatization in severe persistent mental illness: a conceptual proposal
<p>Dataset of the article: "Restigmatization in severe persistent mental illness: a conceptual proposal"</p>
Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS using Rapid Update 4DVar Assimilation of C-band Phased Array Weather Radar Data
<div> <p>These are data supporting the conclusions of the paper. The observation dataset including sounding data, automatic weather station data and radar data. And Vdras outputs are also provided here. The corresponding scripts are based on python(version 3.9) and NCL (version 6.6.2).</p> <p> </p> </div> <h2>Files</h2>
Data from: A year in hypoxia: epibenthic community responses to severe oxygen deficit at a subsea observatory in a coastal inlet
Changes in ocean ventilation driven by climate change result in loss of oxygen in the open ocean that, in turn, affects coastal areas in upwelling zones such as the northeast Pacific. Saanich Inlet, on the west coast of Canada, is a natural seasonally hypoxic fjord where certain continental shelf species occur in extreme hypoxia. One study site on the VENUS cabled subsea network is located in the hypoxic zone at 104 m depth. Photographs of the same 5 m2 area were taken with a remotely-controlled still camera every 2/3 days between October 6th 2009 and October 18th 2010 and examined for community composition, species behaviour and microbial mat features. Instruments located on a near-by platform provided high-resolution measurements of environmental variables. We applied multivariate ordination methods and a principal coordinate analysis of neighbour matrices to determine temporal structures in our dataset. Responses to seasonal hypoxia (0.1–1.27 ml/l) and its high variability on short time-scale (hours) varied among species, and their life stages. During extreme hypoxia, microbial mats developed then disappeared as a hippolytid shrimp, Spirontocaris sica, appeared in high densities (200 m22) despite oxygen below 0.2 ml/l. The slender sole Lyopsetta exilis was abundant in severe hypoxia and diminished as oxygen increased in the summer. This planktivore may be responding to changes in the depth of the diurnal migration of zooplankton. While the squat lobster Munida quadrispina was common at all times, juveniles disappeared in fluctuating conditions. Despite low oxygen conditions, animal densities were high indicating that the risk from hypoxia is balanced by factors such as food availability and escape from less tolerant predators. As hypoxia increases on the continental shelf, we expect benthic communities to become dominated by low diversity, hypoxia-tolerant species of low commercial significance.
Data from: Regional and local patterns of genetic variation and structure in yellow-necked mice − the roles of geographic distance, population abundance and winter severity
The goal of this study, conducted in seven large woodlands and three areas with small woodlots in north-eastern Poland in 2004-2008, was to infer genetic structure in yellow-necked mouse Apodemus flavicollis population and to evaluate the roles of environmental and population ecology variables in shaping the spatial pattern of genetic variation using 768 samples genotyped at 13 microsatellite loci. Genetic variation was very high in all studied regions. The primal genetic subdivision was observed between the northern and the southern parts of the study area, which harboured two major clusters and the intermediate area of highly admixed individuals. The probability of assignment of individual mice to the northern cluster increased significantly with lower temperatures of January and July and declined in regions with higher proportion of deciduous and mixed forests. Despite the detected structure, genetic differentiation among regions was very low. Fine-scale structure was shaped by the population density, whereas higher level structure was mainly shaped by geographic distance. Genetic similarity indices were highly influenced by mouse abundance (which positively correlated with the share of deciduous forests in the studied regions) and exhibited the greatest change between 0 and 1 km in the forests, 0 and 5 km in small woodlots. Isolation by distance pattern, calculated among regions, was highly significant but such relationship between genetic and geographic distance was much weaker, and held the linearity at very fine scale (~1.5 km), when analyses were conducted at individual level.
Data from: Top–down limits on prey populations may be more severe in larger prey species, despite having fewer predators
Variation in the vulnerability of herbivore prey to predation is linked to body size, yet whether this relationship is size‐nested or size‐partitioned remains debated. If size‐partitioned, predators would be focused on prey within their preferred prey size range. If size‐nested, smaller prey species should become increasingly more vulnerable because increasingly more predators are capable of catching them. Yet, whether either of these strategies manifests in top‐down prey population limitation would depend both on the number of potential predator species as well as the total mortality imposed. Here we use a rare ecosystem scale "natural experiment" comparing prey population dynamics between a period of intense predator persecution and hence low predator densities and a period of active predator protection and population recovery. We use three decades of data on herbivore abundance and distribution to test the role of predation as a mechanism of population limitation among prey species that vary widely in body size. Notably, we test this within one of the few remaining systems where a near‐full suite of megaherbivores occur in high density and are thus able to include a thirtyfold range in herbivore body size gradient. We test whether top‐down limitation on prey species of particular body size leads to compositional shifts in the mammalian herbivore community. Our results support both size‐nested and size‐partitioning predation but suggest that the relative top‐down limiting impact on prey populations may be more severe for intermediate sized species, despite having fewer predators than small species. In addition we show that the gradual recovery of predator populations shifted the herbivore community assemblage towards large‐bodied species and has led to a community that is strongly dominated by large herbivore biomass.
Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape
Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.
Data from: Association between the severity of influenza A(H7N9) virus infections and length of the incubation period
Background: In early 2013, a novel avian-origin influenza A(H7N9) virus emerged in China, and has caused sporadic human infections. The incubation period is the delay from infection until onset of symptoms, and varies from person to person. Few previous studies have examined whether the duration of the incubation period correlates with subsequent disease severity. Methods and Findings: We analyzed data of period of exposure on 395 human cases of laboratory-confirmed influenza A(H7N9) virus infection in China in a Bayesian framework using a Weibull distribution. We found a longer incubation period for the 173 fatal cases with a mean of 3.7 days (95% credibility interval, CrI: 3.4–4.1), compared to a mean of 3.3 days (95% CrI: 2.9–3.6) for the 222 non-fatal cases, and the difference in means was marginally significant at 0.47 days (95% CrI: -0.04, 0.99). There was a statistically significant correlation between a longer incubation period and an increased risk of death after adjustment for age, sex, geographical location and underlying medical conditions (adjusted odds ratio 1.70 per day increase in incubation period; 95% credibility interval 1.47–1.97). Conclusions: We found a significant association between a longer incubation period and a greater risk of death among human H7N9 cases. The underlying biological mechanisms leading to this association deserve further exploration.
FIGURE 1. Macaranga esseriana W.N.Takeuchi. Severed top from a in Floristic discoveries from the LNG Pipeline in Papua New Guinea: Macaranga esseriana sp. nov. (Euphorbiaceae), and noteworthy records for twelve taxa from the southern provinces
FIGURE 1. Macaranga esseriana W.N.Takeuchi. Severed top from a pistillate plant (Takeuchi et al. 23531).
Data from: Association of serum neurofilament light (sNfL) and disease severity in patients with spinocerebellar ataxia type 3
<p><a name="_Hlk22462511"><b>Objective:</b></a> To investigate serum neurofilament light protein (sNfL) levels in patients with spinocerebellar ataxia type 3 (SCA3) and to determine whether they are associated with disease severity.</p> <p><b>Methods: </b>This cross-sectional study enrolled 185 healthy controls and 235 <i>ATXN3</i> mutation carriers (17 asymptomatic stage, 20 preclinical stage, and 198 ataxic stage)<span class="fontstyle01"><span>. We measured </span></span>sNfL levels with the single molecule array (Simoa) platform. Clinical disease severity was assessed using the scale of assessment and rating of ataxia (SARA) and the inventory of non-ataxia signs (INAS). In a subgroup of 50 ataxic stage patients, we further evaluated the gray matter volume and the integrity of white matter fibers by magnetic resonance imaging (MRI).</p> <p><b>Results:</b> sNfL concentrations were elevated in asymptomatic, preclinical,<i> </i>and<i> </i>ataxic<i> ATXN3</i> mutation carriers compared to controls (12.18 [10.20-13.92], 21.84 [18.37-23.45], 36.06 [30.04-45.90] and 8.24 [5.92-10.84] pg/mL, median [interquartile range], respectively, <i>P </i>< 0·001). sNfL correlated with SARA (r=0.406, 95% confidence interval 0.284-0.515, <span class="fontstyle01"><span><i>P </i></span></span><span class="fontstyle01"><span>< 0.0001</span></span>) and INAS (r=0.375, 95% confidence interval 0.250-0.487, <span class="fontstyle01"><span><i>P </i></span></span><span class="fontstyle01"><span>< 0.0001</span></span>), and remained significant after adjustment for age and CAG repeats. In addition, we observed negative correlations of the sNfL with gray matter volume in the left precentral gyrus and the left paracentral lobule as well as with the mean diffusivity in widespread white matter tracts.</p> <p><b>Conclusion: </b>Our results demonstrate that sNfL levels are increased in <span class="fontstyle01"><span>SCA3</span></span> and are associated with clinical disease severity, which supports sNfL as a biomarker for disease severity in SCA3.</p>
FIGURE 1. A–B. Cyathea corcovadensis. A in A proposal to distinguish several taxa in the Brazilian tree fern Cyathea corcovadensis (Cyatheaceae)
FIGURE 1. A–B. Cyathea corcovadensis. A. Pinna, silhouette (D.B.G. Bussmann 90, JOI); B. Detail of fertile pinnule, abaxially (N.P. Smith 330, FLOR). C–D. Cyathea feeana. C. Pinna, silhouette (A.F. Regnell 479, S); D. Detail of fertile pinnule, abaxially (G. Hatschbach 23226, MBM). E. Cyathea miersii. Pinna, silhouette (J. Miers 149, B). Same scale for all pinnae and details, respectively.
FIGURE 2 in A proposal to distinguish several taxa in the Brazilian tree fern Cyathea corcovadensis (Cyatheaceae)
FIGURE 2. Distributions of A. Cyathea atrovirens; B. Cyathea corcovadensis; C. Cyathea feeana; D. Cyathea miersii.
Data from: Severance of arbuscular mycorrhizal networks in restoration grasslands enhances seedling biomass
<p>1. Establishment and growth of grassland plant species is generally promoted by arbuscular mycorrhizal fungi (AMF) when grown in isolation. However, in grassland communities AMF form networks that may connect individual plants of different ages within and between species. Here, we use an in-growth core approach to examine how mycorrhizal networks influences performance of seedlings in grasslands.</p> <p>2. We selected 4 grass and 4 forb species with known negative or neutral-positive plant-soil feedback and grew them individually in steel mesh cores filled with living field soil. Cores were placed in six restored grasslands, three grasslands were of relatively young and three were of older successional age.</p> <p>3. Ingrowing mycorrhizal fungal hyphae were severed twice a week in half of all cores, which resulted into reduced AMF colonisation and increased seedling biomass, irrespective of the fields' succession stage, and the plants' grass/forb group, or plant-soil feedback type. In the control cores, root colonization by AMF was negatively correlated to seedling biomass, whereas there was no such relationships in the cores that had been lifted.</p> <p>4. We conclude that connections to arbuscular mycorrhizal networks of surrounding plants had a negative impact on biomass of establishing forb and grass seedlings.</p>
FTICR-MS Dataset for 'Playing with FiRE: A genome resolved view of the soil microbiome responses to high severity forest wildfire'
<p>This dataset is the raw data for FTICR-MS used in Nelson et al.: 'Playing with FiRE: A genome resolved view of the soil microbiome responses to high severity forest wildfire'. The files correspond to the raw spectral files for each sample and can be opened with Bruker data analysis software. The numbers correspond to sample number (See Supplementary Data 1 in publication for sample metadata) and the r1 and r2 identifiers indicate if there were replicates of that sample.</p>
text-fig. 37. Left manus of an undetermined ornithomimosaur (probably Ornithomimus edmontonicus', TMP 93.104.1), illustrating states for several manual characters; metacarpals in dorsal view, phalanges in lateral view. Scale bar represents 50 mm. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 37. Left manus of an undetermined ornithomimosaur (probably Ornithomimus edmontonicus', TMP 93.104.1), illustrating states for several manual characters; metacarpals in dorsal view, phalanges in lateral view. Scale bar represents 50 mm.
FIGURE 39 in Catalogue of the primary types of Cerylonidae, Endomychidae and Latridiidae (Coleoptera: Cucujoidea) deposited in the National Museum of Natural History, with additional notes and clarification of the status of several types 2229
FIGURE 39. Holotype of Stenotarsus solidus Casey, 1916 (=Stenotarsus hispidus (Herbst), 1799): A) Dorsal habitus; B) Ventral habitus; C) Lateral habitus; D) Specimen labels. Length = 4.2 mm.
FIGURE 20 in Catalogue of the primary types of Cerylonidae, Endomychidae and Latridiidae (Coleoptera: Cucujoidea) deposited in the National Museum of Natural History, with additional notes and clarification of the status of several types 2229
FIGURE 20. Neotype of Engonius angustefasciatus Pic, 1940 (=Sinocymbachus angustefasciatus (Pic), 1940): A) Dorsal habitus; B) Ventral habitus; C) Lateral habitus; D) Specimen labels. Length = 10.0 mm.
FIGURE 6 in Catalogue of the primary types of Cerylonidae, Endomychidae and Latridiidae (Coleoptera: Cucujoidea) deposited in the National Museum of Natural History, with additional notes and clarification of the status of several types 2229
FIGURE 6. Holotype of Lapethus discretus Casey, 1890 (=Mychocerus discretus (Casey), 1890): A) Dorsal habitus; B) Ventral habitus; C) Lateral habitus; D) Specimen labels. Length = 1.9 mm.
ScienceDex guides
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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.