Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel partitioning algorithm that utilizes competition between models to detect and separate these functional patterns. This competition is induced by multiple models iteratively submitting their predictions for the dataset, with the best prediction for each data point being rewarded with training on that data point. This reward mechanism amplifies each model's strengths and encourages specialization in different patterns. The specializations can then be translated into a partitioning scheme. We validate our concept with datasets with clearly distinct functional patterns, such as mechanical stress and strain data in a porous structure. Our partitioning algorithm produces valuable insights into the datasets' structure, which can serve various further applications. As a demonstration of one exemplary usage, we set up modular models consisting of multiple expert models, each learning a single partition, and compare their performance on more than twenty popular regression problems with single models learning all partitions simultaneously. Our results show significant improvements, with up to 56% loss reduction, confirming our algorithm's utility.

The explosive growth of molecular sequence data has made it possible to estimate species divergence times under relaxed-clock models using genome-scale datasets with many gene loci. In order both to improve model realism and to best extract information about relative divergence times in the sequence data, it is important to account for the heterogeneity in the evolutionary process across genes or genomic regions. Partitioning is a commonly used approach to achieve those goals. We group sites that have similar evolutionary characteristics into the same partition and those with different characteristics into different partitions, and then use different models or different values of model parameters for different partitions to account for the among-partition heterogeneity. However, how to partition data in practical phylogenetic analysis, and in particular in relaxed-clock dating analysis, is more art than science. Here, we use computer simulation and real data analysis to study the impact of the partition scheme on divergence time estimation. The partition schemes had relatively minor effects on the accuracy of posterior time estimates when the prior assumptions were correct and the clock was not seriously violated, but showed large differences when the clock was seriously violated, when the fossil calibrations were in conflict or incorrect, or when the rate prior was mis-specified. Concatenation produced the widest posterior intervals with the least precision. Use of many partitions increased the precision, as predicted by the infinite-sites theory, but the posterior intervals might fail to include the true ages because of the conflicting fossil calibrations or mis-specified rate priors. We analyzed a dataset of 78 plastid genes from 15 plant species with serious clock violation and showed that time estimates differed significantly among partition schemes, irrespective of the rate drift model used. Multiple and precise fossil calibrations reduced the differences among partition schemes and were important to improving the precision of divergence time estimates. While the use of many partitions is an important approach to reducing the uncertainty in posterior time estimates, we do not recommend its general use for the present, given the limitations of current models of rate drift for partitioned data and the challenges of interpreting the fossil evidence to construct accurate and informative calibrations.

This dataset was used to conduct the study with the title "Effect of Knowledge Differentiation and State Space Partitioning on Subjective Probability Estimation"

Disk Partitioning Software Market Outlook

According to our latest research, the global disk partitioning software market size reached USD 1.92 billion in 2024. The market is anticipated to expand at a robust CAGR of 11.1% during the forecast period from 2025 to 2033. By 2033, the disk partitioning software market is projected to attain a value of USD 5.04 billion. This growth is primarily driven by the increasing adoption of advanced storage management solutions across enterprises and individual users, the rising demand for efficient data management, and the proliferation of cloud computing environments globally.

One of the most significant growth factors propelling the disk partitioning software market is the exponential increase in data generation across various industries. Organizations are facing unprecedented volumes of data due to digital transformation initiatives, Internet of Things (IoT) deployments, and the growing reliance on big data analytics. As a result, there is a heightened need for effective storage management solutions that can optimize the use of storage resources, improve system performance, and ensure data integrity. Disk partitioning software enables organizations to create, modify, and manage disk partitions efficiently, thereby enhancing data accessibility and minimizing downtime. The integration of user-friendly interfaces and automation features in modern disk partitioning tools further accelerates their adoption among both technical and non-technical users.

Another crucial factor contributing to the expansion of the disk partitioning software market is the rapid shift towards cloud-based infrastructure and virtualization. As businesses migrate their critical workloads to cloud environments, the demand for software capable of managing virtual disks and partitions has surged. Cloud-based disk partitioning solutions offer scalability, flexibility, and centralized control, allowing enterprises to manage storage resources across geographically dispersed data centers with ease. Additionally, the increasing adoption of hybrid and multi-cloud strategies among large enterprises is fueling the need for advanced partitioning tools that can seamlessly operate across heterogeneous IT environments. This trend is expected to remain a key driver for market growth throughout the forecast period.

The proliferation of digital devices and the growing number of individual users seeking efficient data management solutions are also driving the disk partitioning software market. With the rise in remote work, personal computing, and digital content creation, individual users require reliable tools to manage storage on their devices, recover lost data, and optimize disk usage. Disk partitioning software caters to these needs by providing functionalities such as partition resizing, disk cloning, and data migration. Furthermore, the increasing frequency of cyber threats and data breaches has heightened awareness about the importance of data backup and recovery, further boosting the adoption of advanced partitioning solutions among both enterprises and individual users.

From a regional perspective, North America currently dominates the disk partitioning software market, owing to the presence of major technology companies, high IT spending, and early adoption of advanced storage management solutions. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud technologies. Europe also holds a significant share of the market, supported by stringent data protection regulations and the widespread adoption of enterprise storage solutions. The Middle East & Africa and Latin America are emerging markets with substantial growth potential as organizations in these regions increasingly recognize the benefits of efficient disk partitioning and data management.

Component Analysis

The component segment of the disk partitioning software market is bifurcated into software and services. The software segment currently accounts for the largest market share, driven by the widespread adoption of standalone disk partitioning applications across diverse platforms and operating systems. These software solutions are designed to provide users with comprehensive tools for creating, resizing, merging, and deleting disk partitions, catering to both individual and enterprise requirements. The c

causing frequent parameter fluctuations. Therefore

Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes, led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.

Hycal Energy Research Laboratories Ltd. (Hycal) conducted a series of fluid phase behavior measurements to ascertain the preferential retention effect of H2S that is commonly present as a secondary component in typical acid gas injection streams. The study is part of multi-faceted research conducted under the Plains CO2 Reduction Partnership program at the Zama Acid Gas Enhanced Oil Recovery operation in northwestern Alberta.

The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.

Discover the booming partition management software market! This in-depth analysis reveals a $2.5B market in 2025, projected to grow at a 12% CAGR through 2033. Explore key drivers, trends, restraints, and leading companies shaping this dynamic sector. Learn more about cloud-based solutions, enterprise adoption, and regional market shares.

Computer models are used as replacements for physical experiments in a large variety of applications. Nevertheless, direct use of the computer model for the ultimate scientific objective is often limited by the complexity and cost of the model. Gaussian process regression has been the almost ubiquitous choice for a fast statistical emulator for such a computer model, due to its flexible form and analytical expressions for measures of predictive uncertainty. However, even this statistical emulator can be computationally intractable for large designs, due to computing time increasing with the cube of the design size. Multiple methods have been proposed for addressing this problem. We discuss several of them, and compare their predictive and computational performance in several scenarios. We propose solving this problem using a new method, adaptive design and analysis via partitioning trees (ADAPT). The new approach is motivated by the idea that most computer models are only complex in particular regions of the input space. By taking a data-adaptive approach to the development of a design, and choosing to partition the space in the regions of highest variability, we obtain a higher density of points in these regions and hence accurate prediction. Supplemental files for this article are available online.

Ranges or subsets are presented in parentheses. Abbreviations: dist. = distance(s); no. = number; prop. = proportion; seq. = sequence; sp. = species; tot. = total; var. = variation. “Combined” refers to data generated in this study combined with collected GenBank/Bold data.

The dataset includes 30 minutes values of partitioned evaporation (E) and transpiration (T), T:ET ratios, and other ancillary datasets for three ET partitioning methods viz. Flux Variance Similarity (FVS) method, Transpiration Estimation Algorithm (TEA), and Underlying Water Use Efficiency (uWUE) method for three wheat sites. Three wheat sites had different grazing treatments. For example, Site 1 was Grain-only and Graze-grain wheat for the 2016-17 and 2017-18 growing seasons, respectively. Site 2 was Grain-only wheat for the 2017-18 growing season. Site 3 was Graze-grain and Graze-out wheat for the 2016-17 and 2017-18 growing seasons, respectively. The grain-only wheat system is a single purpose to produce wheat grains only. Graze-grain wheat system has a dual purpose as it serves as a pasture for grazing cattle from November to February and is used to produce wheat grains later. Graze-out wheat system is also a single purpose crop that is grazed by the cattle for the entire season to solely serve as a pasture. FVS method performed ET partitioning using the high frequency (10 Hz) data collected from Eddy Covariance Flux stations, located near the middle of each field. The high-frequency data were also processed using the EddyPro software to get good quality estimates of different fluxes at 30-minute intervals. The processed 30-min data were used by TEA and uWUE methods for ET partitioning. Ancillary hydro-meteorological variables including net radiation, air temperature, soil water content, relative humidity, and others, also have been included in this dataset. The study sites were located at the United States Department of Agriculture, Agricultural Research Service (USDA-ARS), Grazinglands Research Laboratory, El Reno, Oklahoma. All sites were rainfed. Resources in this dataset:Resource Title: FVS output and other met data and site info. File Name: FVS_output_and_other_met_data_and_site_info.xlsxResource Description: Output of FVS model along with corresponding meteorological data and site metadata.Resource Title: TEA output. File Name: TEA_output.xlsxResource Description: Out from TEA model along with site metadata.Resource Title: WUE output. File Name: uWUE_output.xlsxResource Description: Output of WUE model run along with site metadata.

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called “chromosome partitioning” where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.

Although it is widely agreed that data from multiple sources are necessary to confidently resolve phylogenetic relationships, procedures for accommodating and incorporating heterogeneity in such data remain underdeveloped. We explored the use of partitioned, model-based analyses of heterogeneous molecular data in the context of a phylogenetic study of swallowtail butterflies (Lepidoptera: Papilionidae). Despite substantial basic and applied study, phylogenetic relationships among the major lineages of this prominent group remain contentious. We sequenced 3.3 kb of mitochondrial (COI/COII α 2.3 kb) and nuclear (EF-1α 1.0 kb) DNA for 22 swallowtails, including representatives of Baroniinae, Parnassiinae, and Papilioninae, and several moth and butterfly outgroups. Parsimony encountered considerable difficulty in resolving the deepest splits among these taxa. We therefore chose two outgroups whose relationships to each other and to Papilionidae were undisputed and undertook detailed likelihood analyses of alternative topologies. Following previous studies that have demonstrated substantial heterogeneity in the evolutionary dynamics among process partitions of these genes, we estimated evolutionary parameters separately for gene-based and codon-based partitions. These values were then used as the basis for examining the likelihoods of possible resolutions and rootings under several partitioned and unpartitioned likelihood models. Partitioned models gave significantly better fits to the data than did unpartitioned models, and supported different topologies. However, the most likely topology varied from model to model. The most likely ingroup topology under the best-fitting, six partition GTR + Γ model favors a paraphyletic Parnassiinae. However, when examining the likelihoods of alternative rootings of this tree relative to rootings of the classical hypothesis two rootings of the latter emerge as most likely. Of these two, the most likely rooting is within the Papilioninae, although a rooting between Baronia and the remaining Papilionidae is only nonsignificantly less likely.

Corresponding manuscript

This dataset corresponds to all input and output files that were used in the study reported in:

• Collins, E.L., Altenau, E., Pavelsky, T., David, C.H., Getirana, A., and Johnson, E.. (Submitted), River Network Routing and Discharge Partitioning on a Multichannel River Network.

When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.

Files included in this version

All 3-hourly discharge simulations (netCDF) for the SWORD Mirror (traditional, single channel) and SWORD (multichannel) river networks for the Amazon (b62) and Mackenzie (b82) river basins. Note that there are three files each for the SWORD river network for each basin. These correspond to each of the discharge partitioning approaches tested in the study: (1) width only ('wid'), (2) width and length ('wid_len'), and (3) width and sinuosity ('wid_sin').

• Qout_SWORDMirror_b62_20150101_20240531_GLDASv21.nc.zip

• Qout_SWORDMirror_b82_20150101_20240531_GLDASv21.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid_len.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid_sin.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid_len.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid_sin.nc.zip

The SWORD Mirror and SWORD river network shapefiles for each basin with a column containing reach IDs so that discharge simulations from the netCDFs can be attached and visualized.

• SWORDMirror_b62.zip

• SWORDMirror_b82.zip

• SWORD_b62.zip

• SWORD_b82.zip

Data from a laboratory study undertaken at the U.S. Geological Survey to investigate solid/water partitioning of per- and polyfluoroalkyl substances (PFAS) in New Hampshire soils and biosolids are presented here. Soils and biosolids used for the experiments were collected using PFAS-free sampling equipment, air dried, gently homogenized, and sieved (soils only). Soil samples were collected from locations with known PFAS contamination (n = 5) and nearby sites with similar soil characteristics but low expected PFAS concentrations (n = 4). Finished biosolids were collected directly from facilities at the final stage of processing and before distribution. Air-dried soils and biosolids were then used for a series of batch and column experiments to determine water/solid distribution coefficient (Kd) values. This study investigated the impact of pH, ionic strength, adsorption versus desorption, soil/biosolid type, experimental setup (batch versus column), and influence of sodium azide on ...

Hourly mean values of overall dynamic body acceleration alongside other data used to assess diel activity patterns of the six coastal shark species investigated in this study.

The global Disk Partition Manager Software market is poised for significant expansion, projected to reach an estimated $1,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 12% expected throughout the forecast period of 2025-2033. This growth is primarily fueled by the increasing proliferation of digital data across both enterprise and personal computing environments, necessitating efficient storage management and optimization solutions. Key drivers include the growing demand for SSD partitioning, advanced data recovery features, and the rising adoption of cloud-based solutions for flexibility and scalability. The market is also benefiting from the continuous need for disk space optimization, system performance enhancement, and effective data organization, especially as operating systems and applications become more resource-intensive. Businesses are increasingly investing in robust partition management tools to ensure data integrity, streamline IT operations, and improve overall system efficiency, while individual users seek simpler ways to manage their storage and enhance their computing experience. The market landscape is characterized by dynamic trends, including the shift towards more intuitive user interfaces, enhanced security features, and comprehensive disaster recovery capabilities. Advanced functionalities like AI-driven partition optimization and seamless migration to newer storage technologies are gaining traction. However, the market faces certain restraints, such as the availability of free, albeit less feature-rich, partition management tools and the potential for data loss or system corruption if partitioning is performed incorrectly. Nevertheless, the overall outlook remains strongly positive, driven by technological advancements and the escalating importance of data management. The market is segmented into Enterprise and Personal applications, with On-premises and Cloud-Based deployment types catering to diverse user needs. Key players like MiniTool, AOMEI Partition Assistant, and EaseUS Partition Master are actively innovating and expanding their offerings to capture a larger market share. This comprehensive report delves into the dynamic Disk Partition Manager Software market, offering in-depth analysis and strategic insights for stakeholders. Spanning a study period from 2019 to 2033, with a base and estimated year of 2025, this research provides a robust understanding of historical trends, current market conditions, and future projections within the forecast period of 2025-2033. The historical period of 2019-2024 has laid the groundwork for understanding evolving user needs and technological advancements.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and ten other commonly-studied organisms with a total of 131,972 genes.