The data described here result from benthic coral demographic surveys for two life stages (juveniles, adults) across the Mariana archipelago since 2014. Juvenile colony surveys include morphology and size. Adult colony surveys record morphology, colony size, partial mortality in two categories - old dead and recent dead, cause of recent dead partial mortality, and non-lesion forming condition including bleaching and disease). In 2023 some segment observations were repeated for internal quality control; to filter for non-repeated data, be certain filter for TRANSECTNUM = 1. A two-stage stratified random sampling (StRS) design was employed to survey the coral reef ecosystems throughout the U.S. Pacific regions. The survey domain encompassed the majority of the mapped area of reef and hard bottom habitats in the 0-30 m depth range. The stratification scheme included island, reef zone, and depth in all regions, as well as habitat structure type in the Mariana Archipelago. Sampling effort was allocated based on strata area and sites were randomly located within strata. Sites were surveyed using belt transects to collect juvenile and adult coral colony metrics. These data provide information on juvenile and adult coral abundance (density, proportion occurrence, and total colony abundance), size distribution, partial mortality, prevalence and abundance of recent mortality and cause, prevalence of disease and bleaching, and diversity. The StRS design effectively reduces estimate variance through stratification using environmental covariates and by sampling more sites rather than sampling more transects at a site. Therefore, site-level estimates and site to site comparisons should be used with caution. The data from the coral demographic surveys can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.

Supplementary data for: Suanda, S. H., & Smith, R. O. (2024). Stratification and internal temperature oscillations near a coastal inlet. New Zealand Journal of Marine and Freshwater Research. https://doi.org/10.1080/00288330.2024.2439088 A vertical array of fast-response temperature sensors was deployed to investigate interior ocean temperature and thermal stratification variability during Spring, 2018 near an inlet entrance on the Otago inner continental shelf (15 m water depth). Over the 24-day observation period, the background water column structure evolved from supporting a near-surface pycnocline, to being characterised by a deep surface mixed layer. Periods of mostly low stratification due to atmospheric- and surface wave-driven mixing are also observed. Time-varying normal-mode projection demonstrates that the vertical structure of semi-diurnal temperature oscillations evolves in response to this background stratification in a manner consistent with the interpretation of internal waves. At higher frequencies (supratidal) the projection is less consistent as vertically coherent oscillations from non-linear internal solitons co-exist with non-coherent oscillations, a potential signature of stratified mixing. Although both processes are likely important contributors to the transport of coastal tracers and inlet-ocean exchange, further measurements are needed to determine the origin and fate of internal waves on the Otago continental shelf.

ABSTRACT The aims of the present study were to test the hypothesis that data stratification by cluster analysis and the use of other variables, in addition to DBH, can improve the precision of the estimates in diametric increment modeling for Mixed Ombrophilous Forest species. The study was carried out in the Irati National Forest. Data from 25 permanent sample plots of 1 ha each were used with all individuals presenting DBH equal to or greater than 10 cm being identified and measured. The increment modeling was performed for the whole forest (non-stratified data), ecological groups and species subgroups (stratified data) defined by cluster analysis. DBH presented a low correlation with the diametric increment and the use of other independent variables had a positive effect on the fitting, reducing the standard error of estimate and increasing the coefficient of determination. The data stratification did not make the models suitable to estimate the diametric increment; however, it provided improvements by reducing the standard error of estimate, suggesting that this technique can be better applied in the search for improvements to diametric modeling in natural forests.

Datasets used for the paper "Stratification in a reservoir mixed by bubble plumes under future climate scenarios". This includes models results from Blagdon Lake with both observed weather data and the downscaled future climate data. These runs cover five year intervals from 2030 to 2080 as well as 2017. These weather datasets are also given along with the scripts required to downscale the future data. Observations from Blagdon Lake from Late May to Early September 2017, including a heatwave from 2017.

A reduced dynamical model describing temperature stratification effects driven by natural convection in a liquid hydrogen cryogenic fuel tank has been developed. It accounts for cryogenic propellant loading, storage, and unloading in the conditions of normal, increased, and micro- gravity. The model involves multiple horizontal control volumes in both liquid and ullage spaces. Temperature and velocity boundary layers at the tank walls are taken into account by using correlation relations. Heat exchange involving the tank wall is considered by means of the lumped-parameter method. By employing basic conservation laws, the model takes into consideration the major multi-phase mass and energy exchange processes involved, such as condensation-evaporation of the hydrogen, as well as flows of hydrogen liquid and vapor in the presence of pressurizing helium gas. The model involves a liquid hydrogen feed line and a tank ullage vent valve for pressure control. The temperature stratification effects are investigated, including in the presence of vent valve oscillations. A simulation of temperature stratification effects in a generic cryogenic tank has been implemented in Matlab and results are presented for various tank conditions.

Predictive analytics involves the use of statistical models to make predictions; however, the power of these techniques is hindered by ever-increasing quantities of data. The richness and sheer volume of big data can have a profound effect on computation time and/or numerical stability. In the current study, we develop a novel approach to subsampling with the aim of overcoming this issue when dealing with regression problems in a supervised learning framework. The proposed method integrates stratified sampling and is model-independent. We assess the theoretical underpinnings of the proposed subsampling scheme, and demonstrate its efficacy in yielding reliable predictions with desirable robustness when applied to different statistical models. Supplementary materials for this article are available online.

Source data and figures (png and pdf format) for Figures 1-4, Extended Data Figures 6 and 10. All of the source data including ocean stratification (squared buoyancy frequency, N2), potential density, temperature and salinity gridded products, N2 timeseries and trends in most of the figures are available in our website: http://152.226.119.60/cheng/. For further data and codes used to generate other figures in the scripts, please contact chenglijing@mail.iap.ac.cn or liguancheng15@mails.ucas.ac.cn.

Most animal traps are constructed from self-secreted silk, so antlions are rare among trap builders because they use only materials found in the environment. We show how antlions exploit the properties of the substrate to produce very effective structures in the minimum amount of time. Our modelling demonstrates how antlions (1) exploit self-stratification in granular media differentially to expose deleterious large grains at the bottom of the construction trench where they can be ejected preferentially and (2) minimize completion time by spiral rather than central digging. Both phenomena are confirmed by our experiments. Spiral digging saves time because it enables the antlion to eject material initially from the periphery of the pit where it is less likely to topple back into the centre. As a result, antlions can produce their pits — lined almost exclusively with small slippery grains to maximize powerful avalanches and hence prey capture — much more quickly than if they simply dig at the pit’s centre. Our demonstration, for the first time, of an animal utilizing self-stratification in granular media exemplifies the sophistication of extended phenotypes even if they are only formed from material found in the animal’s environment.

IntroductionNeuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. This inherent heterogeneity underscores the need for more precise prognostic markers at the time of diagnosis to enhance patient stratification, allowing for more personalized treatment strategies. In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient’s overall survival (OS) and improve their risk stratification.MethodsPRIMAGE database, including 513 patients (discovery cohort), was used for model training, validation, and testing. Additional 22 patients from different hospitals served as an external independent cohort. Primary tumor segmentation on T2-weighted MR images was semi-automatically edited by an experienced radiologist. From this area, 107 radiomics features were extracted. For the development of the prediction model, radiomics features were harmonized following the nested ComBat methodology and nested cross-validation approach was employed to determine the optimal preprocessing and model configuration.ResultsThe discovery cohort yielded a 78.8 ± 4.9 and 77.7 ± 6.1 of C index and time-dependent area under de curve (AUC), respectively, over the test set, with a random survival forest exhibiting the best performance. In the independent cohort, a C-index of 93.4 and a time-dependent AUC of 95.4 were achieved. Interpretability analysis identified lesion heterogeneity, size, and molecular variables as crucial factors in OS prediction. The model stratified neuroblastoma patients into low-, intermediate-, and high-risk categories, demonstrating a superior stratification compared to standard-of-care classification system in both cohorts.DiscussionOur results suggested that radiomics features improve current risk stratification systems in patients with neuroblastoma.

The data describes the spatio-temporal evolution of the magnetic field outside Earth's core and the velocity field at the core surface, as obtained from a numerical simulation of the geodynamo. See the README file for information.

This dataset is composed by the climatological seasonal field of the Ocean Salinity Stratification as defined from the Brunt-Vaisala frequency limited to the upper 300 m depth. The details are given in Maes, C., and T. J. O’Kane (2014), Seasonal variations of the upper ocean salinity stratification in the Tropics, J. Geophys. Res. Oceans, 119, 1706–1722, doi:10.1002/2013JC009366.

Occupation / job satisfaction / expectations regarding occupation / occupational and educational history / family and religious background / education and occupation of parents / parental attitude to school, schoolwork, achievements, contacts with teachers / contacts with family, friends etc / satisfaction with life in general / typical male and female behaviour / scales for progressiveness, neuroticism, perseverance. Background variables: basic characteristics/ residence/ housing situation/ household characteristics/ characteristics of parental family/household/ occupation/employment/ income/capital assets/ education/ social class/ politics/ religion/ organizational membership

Quantile regression (QR) GWAS summary statistics from the study "Quantile-specific confounding: correction for subtle population stratification via quantile regression". The preprint is available at https://doi.org/10.1101/2025.03.18.638253.

Summary statistics

The tab-delimited text files (gzipped) are QR GWAS summary statistics.

• Column "CHR": chromosome
• Column "POS": based pair position
• Column "ID": variant ID
• Column "A2": non-effect allele
• Column "A1": effect allele tested in GWAS
• Column "P_LR": p-value of the linear regression (LR) association statistic
• Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.
• Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).

Dataset

This dataset was created by Rajnish Singh

Released under Data files © Original Authors

Contents

The dataset and script allow users to replicate tables and figures in the article: Voters’ short-term responsiveness to coalition deals (see Related Publication).

This geodatabase contains seasonal water stratification raster images for the Gulf of Maine. They were created by interpolating water density (sigma t) values at 0 meters and 50 meters binned by season, and subtracting the water density surface at 0 meters from the water density surface at 50 m. This subtraction (as denoted by the raster value) indicates the differences in water density throughout the Gulf of Maine during the year. The more positive or negative the raster value, the larger the difference between the water density raster at 0 m and the water density raster at 50 m and thus, the more stratified the water. Similarly, the closer the raster value is to 0, the smaller the difference between the water density raster at 0 m and the water density raster at 50 m and thus, the less stratified the water. We tried this density difference calculation with 0 and 10 meters as well as 0 and 30 meters. We chose to use 50 meters as the lower water density depth because water less than 10 meters may have been subject to wind and convective mixing, and water less than 30 meters may have been influenced by internal waves. Water at 50 meters, however, illustrated the greatest difference in stratification strength (buoyancy frequency squared) between layers in the water column (Brown and Irish, 1993). The naming convention of the raster includes the year (or years) included in the the interpolation, the season, and the depth (in meters). So for example, the name: ws_1997_2004_Fall would indicate that this water stratification raster was created by subtracting water density rasters that were interpolated using data from 1997-2004 for the fall. The seasons were defined using the same months as the remote sensing data--namely, fall = September, October, November; winter = December, January, February; Spring = March, April, May and Summer = June, July, August.

Replication data for: “Endogenous Stratification in Randomized Experiments”

The data and programs replicate tables and figures from "Status Goods: Experimental Evidence from Platinum Credit Cards", by Bursztyn, Ferman, Fiorin, Kanz, and Rao.

INSTANT: A New International Array to Measure the Indonesian Throughflow. The INSTANT field program (International Nusantara Stratification And Transport) began in August 2003 and consists of a 3-year deployment of an array of moorings and coastal pressure gauges that will directly measure sea level and full depth in situ velocity, temperature, and salinity of the ITF. For the first time, simultaneous, multipassage, multiyear measurements will be available, and allow the magnitude and properties of the interocean transport between the Pacific and Indian Oceans to be unambiguously known. The array will also provide an unprecedented data set revealing how this complex and fascinating region responds to local and remote forcing at many timescales never before well resolved. Moorings were located at the following locations: (115 45.48, 8 26.77) (115 53.77, 8 24.56) (122 58.36, 11 31.76) (122 57.40, 11 22.19) (122 51.5, 11 16.6) (122 46.8, 11 9.67) (125 32.26, 8 32.33) (125 2.26, 8 24.04)

Microhabitat differentiation of species communities such as vertical stratification in tropical forests contributes to species coexistence and thus biodiversity. However, little is known about how the extent of stratification changes during forest recovery and influences community reassembly. Environmental filtering determines community reassembly in time (succession) and in space (stratification), hence functional and phylogenetic composition of species communities are highly dynamic. It is poorly understood if and how these two concurrent filters – forest recovery and stratification – interact. In a tropical forest chronosequence in Ecuador spanning 34 years of natural recovery, we investigated the recovery trajectory of ant communities in three overlapping strata (ground, leaf litter, lower tree trunk) by quantifying 13 traits, as well as the functional and phylogenetic diversity of the ants. We expected that functional and phylogenetic diversity increase with recovery time and that each ant community within each stratum shows a distinct functional reassembly. We predicted that traits related to ant diet show divergent trajectories reflecting an increase in niche differentiation with recovery time. On the other hand, traits related to the abiotic environment were predicted to show convergent trajectories due to a more similar microclimate across strata with increasing recovery age. Most of the functional traits and the phylogenetic diversity of the ants were clearly stratified, confirming previous findings. However, neither functional nor phylogenetic diversity increased with recovery time. Community-weighted trait means had complex relationships to recovery time and the majority were shaped by a statistical interaction between recovery time and stratum, confirming our expectations. However, most trait trajectories converged among strata with increasing recovery time regardless of whether they were related to ant diet or environmental conditions. We confirm the hypothesized interaction among environmental filters during the functional reassembly in tropical forests. Communities in individual strata respond differently to recovery, and possible filter mechanisms likely arise from both abiotic (e.g., microclimate) and biotic (e.g., diet) conditions. Since vertical stratification is prevalent across animal and plant taxa, our results highlight the importance of stratum-specific analysis in dynamic ecosystems and may generalize beyond ants. Methods This data sets contains the raw data and R-script associated with our research article. It contains ant collection data in a chronosequence in Ecuador, as well as measurements of the ant traits. Note that a similar version of the ant and trait dataset has already been uploaded here for another article: https://doi.org/10.5061/dryad.83bk3j9sk We collected ants by leaf-litter extraction with Winkler, handsampling on the ground and handsampling on tree trunks on 61 plots. Ants are recorded as occurrences (=presence/absence) per method per plot (3 methods per plot). The methods represent different strata, and we analyse their traits and phylogeny across stratification and forest recovery. Our files include metadata on the plots, a list of the collected ant species and their occurence in each stratum, and a list of trait measurements on ant individuals. The ant traits were measured from individuals collected in our study site. A description of trait abbreviations can be found in our R-script. For more detailed descriptions of the sampling protocols, plot selection and statistical analysis can be found in the associated paper. The R-script is annotated and describes our statistical procedures and used R-packages, and contains also descriptive information on the csv.-files.