This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.2. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.

A single regression model is unlikely to hold throughout a large and complex spatial domain. A finite mixture of regression models can address this issue by clustering the data and assigning a regression model to explain each homogenous group. However, a typical finite mixture of regressions does not account for spatial dependencies. Furthermore, the number of components selected can be too high in the presence of skewed data and/or heavy tails. Here, we propose a mixture of regression models on a Markov random field with skewed distributions. The proposed model identifies the locations wherein the relationship between the predictors and the response is similar and estimates the model within each group as well as the number of groups. Overfitting is addressed by using skewed distributions, such as the skew-t or normal inverse Gaussian, in the error term of each regression model. Model estimation is carried out using an EM algorithm, and the performance of the estimators and model selection are illustrated through an extensive simulation study and two case studies.

1. Structured population models are among the most widely used tools in ecology and evolution. Integral projection models (IPMs) use continuous representations of how survival, reproduction, and growth change as functions of state variables such as size, requiring fewer parameters to be estimated than projection matrix models (PPMs). Yet almost all published IPMs make an important assumption: that size-dependent growth transitions are or can be transformed to be normally distributed. In fact, many organisms exhibit highly skewed size transitions. Small individuals can grow more than they can shrink, and large individuals may often shrink more dramatically than they can grow. Yet the implications of such skew for inference from IPMs has not been explored, nor have general methods been developed to incorporate skewed size transitions into IPMs, or deal with other aspects of real growth rates, including bounds on possible growth or shrinkage. 2. Here we develop a flexible approach to mode...

Asap7772/skewed-exp-multi-passcode-sft dataset hosted on Hugging Face and contributed by the HF Datasets community

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

This dataset includes original data reported in Mortazavi et al., Under review, 2025. 75 right-handed adults were scanned with Functional Magnetic Resonance Imaging while performing the Skewed Gambling Task. Please refer to the methods described in the paper for more details on task and design, or to https://osf.io/fvd69/?view_only=d84508a56bcb42468f80a720fdd68192

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW13, RW14, RW15 and RW16) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 5A. This type of discharging profile is referred to here as random walk (RW) discharging. A customized probability distribution is used in this experiment to select a new load setpoint every 1 minute during RW discharging operation. The custom probability distribution was designed to be skewed towards selecting lower currents.

1. The performance of ectotherms integrated over time depends in part on the position and shape of the distribution of body temperatures (Tb) experienced during activity. For several complementary reasons, physiological ecologists have long expected that Tb distributions during activity should have a long left tail (left-skewed); but only infrequently have they quantified the magnitude and direction of Tb skewness in nature.
2. To evaluate whether left-skewed Tb distributions are general for diurnal desert lizards, we compiled and analyzed Tb (∑ = 9,023 temperatures) from our own prior studies of active desert lizards on three continents (25 species in Western Australia, 10 in the Kalahari Desert of Africa, and 10 species in western North America). We gathered these data over several decades, using standardized techniques.
3. Many species showed significantly left-skewed Tb distributions, even when records were restricted to summer months. However, magnitudes of skewness were always small, such that mean Tb were never more than 1°C lower than median Tb. The significance of Tb skewness was sensitive to sample size, and power tests reinforced this sensitivity.
4. The magnitude of skewness was not obviously related to phylogeny, desert, body size, or median body temperature. Moreover, formal phylogenetic analysis is inappropriate because geography and phylogeny are confounded (that is, are highly collinear).
5. Skewness might be limited if lizards pre-warm inside retreats before emerging in the morning, emerge only when operative temperatures are high enough to speed warming to activity Tb, or if cold lizards are especially wary and difficult to spot or catch. Telemetry studies may help evaluate these possibilities.

Definition: The skewness "Sk1" is a measure of the symmetry of the cumulative curve, which indicates the ratio of coarse to fine parts in the particle size distribution. Folk & Ward (1957) quantify this symmetry in a value range from -1 to 1. Positive values ​​greater than 0 to 1 indicate a "left skewing" for metric cumulative curves, i.e. fine grain fractions predominate in comparison to coarse fractions. Negative values ​​of less than 0 to -1 indicate a "right-skewing" for metric cumulative curves, which correspondingly indicates a predominance of coarse compared to fine fractions. Sk1 = 0 indicates a perfectly symmetrical cumulative curve. Conclusions about the deposition environment can be drawn from the skewness. Data generation: The basis for sedimentological evaluations are surface sediment samples, which were interpolated within the framework of the EasyGSH project using anisotropic interpolation methods and taking into account hydrodynamic factors and erosion and sedimentation processes from individual samples from different years to a grid valid for one year. The sediment distribution is therefore available as a cumulative curve at each of these grid nodes. For the German Bight, this basic product is available for the years 1996, 2006 and 2016 in a 100 m grid, for the exclusive economic zone of Germany for the year 1996 in a 250 m grid. The parts for ϕ5, ϕ16, ϕ50, ϕ84 and ϕ95 required for the calculation rule for the skewness according to Folk & Ward (1957) can be determined directly from these cumulative curves and the skewness parameter Sk1 can be calculated. Product: 100 m grid of the German Bight (1996, 2006, 2016) or 250 m grid of the Exclusive Economic Zone (1996), on which the skewness Sk1 according to Folk & Ward (1957) is stored at each grid node. The product is provided in GeoTiff format. Literature: Folk, R.L., & Ward, W.C. (1957). A study in the significance of grain size parameters. Journal of Petrology, 37, 327-354. For further information, please refer to the information portal (http://easygsh.wb.tu-harburg.de/) and the download portal (https://mdi-de.baw.de/easygsh/). English Download: The data for download can be found under References ("further references"), where the data can be downloaded directly or via the web page redirection to the EasyGSH-DB portal. For further information, please refer to the download portal (https://mdi-de.baw.de/easygsh/EasyEN_index.html).

We generate 10 model trees for a given number of genomes (). The number of false positives (FP), the number of false negatives (FN), and the execution time (time) in a cell are the average of the finished computations (finished: the number of finished computations within 24 hours) out of 10 trials using 10 different model trees. , , and in the tables are hours, minutes, and seconds, respectively. is the number of genes in a genome, which is 100 in our experiments.

Skewed XCI plays an important role in the phenotypic heterogeneities of many X-linked disorders, even involving in diseases caused by XCI-escaping genes. DDX3X-related intellectual disability is more common in females and less common in males, who usually inherit from unaffected heterozygous mothers. As an X inactivation (XCI) escaping gene, the role of skewed XCI in the phenotype of DDX3X mutant female is unknown. Here we reported a DDX3X: c.694_711dup18 de novo heterozygous mutation in a female with intellectual disability on the maternal X chromosome on the basis of SNPs detected by PCR-sanger sequencing. AR assay revealed that the maternal mutant X chromosome was extremely inactivated in the proband. Using RNA sequencing and whole-exome sequencing, we quantified allelic read counts and allele-specific expression, and confirmed that the mutant X chromosome was inactive. Further, we verified that the mutant DDX3X allele had a lower expression level by RNA sequencing and RT-PCR, and the normal and mutated DDX3X expression accounted for respectively 70% and 30% of total. In conclusion, we found a symptomatic female with extreme skewing XCI in the DDX3X mutant allele. It was discovered that XCI in the mutant allele was insufficient to reverse the phenotype of DDX3X-related neurodevelopmental disorder. It contributed to a better understanding of the role of skewed XCI in phenotypic differences, which can aid in the genetic counseling and prenatal diagnosis of disorders in females with DDX3X defects.

Asap7772/skewed-random-num-gen-sft dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is about: (Table T9) Median diameter, mode, sorting, and skewness of ODP Site 178-1101. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.735544 for more information.

This dataset contains upper air Skew-T Log-P charts taken at Denver during the HIPPO-3 project. The imagery are in GIF format. The imagery cover the time span from 2010-03-17 00:00:00 to 2010-04-19 12:00:00.

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW17, RW18, RW19 and RW20) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 5A. This type of discharging profile is referred to here as random walk (RW) discharging. A customized probability distribution is used in this experiment to select a new load setpoint every 1 minute during RW discharging operation. The custom probability distribution was designed to be skewed towards selecting higher currents.

Observed phenotypic responses to selection in the wild often differ from predictions based on measurements of selection and genetic variance. An overlooked hypothesis to explain this paradox of stasis is that a skewed phenotypic distribution affects natural selection and evolution. We show through mathematical modelling that, when a trait selected for an optimum phenotype has a skewed distribution, directional selection is detected even at evolutionary equilibrium, where it causes no change in the mean phenotype. When environmental effects are skewed, Lande and Arnold’s (1983) directional gradient is in the direction opposite to the skew. In contrast, skewed breeding values can displace the mean phenotype from the optimum, causing directional selection in the direction of the skew. These effects can be partitioned out using alternative selection estimates based on average derivatives of individual relative fitness, or additive genetic covariances between relative fitness and trait (Robe...

Asap7772/skewed-exp-n-1000-multi-passcode-staggered-sft dataset hosted on Hugging Face and contributed by the HF Datasets community

This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.6. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.

This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.

ayushchakravarthy/skewed-skexp-n-1000-multi-passcode-lang-sft dataset hosted on Hugging Face and contributed by the HF Datasets community