Hard Drive Partitioning Software Market Outlook

The global hard drive partitioning software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period from 2024 to 2032. The growth of this market is driven by the increasing demand for efficient data management solutions and the proliferation of digital data across various sectors.

One of the primary growth factors for the hard drive partitioning software market is the exponential increase in data generation. Businesses and individuals are generating data at an unprecedented rate due to the widespread use of digital technologies. The need for effective organization and management of this data has led to a rising demand for advanced partitioning software that can efficiently segment hard drives into multiple partitions. This segmentation allows for better data organization, enhances system performance, and facilitates easier backups and data recovery processes.

Another significant growth driver is the increasing adoption of cloud computing and virtualization technologies. As organizations move their IT infrastructure to the cloud and implement virtualized environments, the need for robust hard drive partitioning solutions becomes critical. These technologies allow for the creation of multiple virtual partitions within a single physical hard drive, optimizing storage utilization and improving system performance. The ability to easily allocate and manage storage resources in virtualized environments is a key factor contributing to the market's growth.

Additionally, the growing emphasis on data security and compliance is boosting the demand for hard drive partitioning software. With stringent data protection regulations being enforced across various industries, organizations are increasingly adopting partitioning solutions to ensure the secure and compliant management of their data. Partitioning software helps in isolating sensitive data, preventing unauthorized access, and enabling efficient encryption and decryption processes. The rising awareness about data privacy and security is expected to further propel the market's growth in the coming years.

From a regional perspective, North America holds a significant share in the hard drive partitioning software market, driven by the presence of major technology companies and a high adoption rate of advanced IT infrastructure. Europe follows closely, with increasing investments in digital transformation initiatives and stringent data protection regulations. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to rapid technological advancements, growing digitalization, and increasing adoption of cloud computing solutions in emerging economies like China and India. Latin America and the Middle East & Africa are also expected to contribute to the market's growth, driven by the expanding IT sector and increasing focus on data management solutions.

Component Analysis

The hard drive partitioning software market is segmented into software and services. Software components dominate this segment, driven by the demand for robust and efficient partitioning tools that can handle large volumes of data. These software solutions are designed to provide easy-to-use interfaces, advanced features, and seamless integration with existing IT infrastructure, making them essential for both individual and enterprise users. Software solutions come in various forms, including standalone applications, integrated tools within operating systems, and specialized software for specific tasks such as data recovery or data migration.

Services, on the other hand, encompass a range of offerings, including installation, configuration, training, and support services. As businesses seek to optimize their data management processes, the demand for professional services to assist in the implementation and maintenance of partitioning software is growing. Service providers offer expertise in customizing partitioning solutions to meet specific organizational needs, ensuring that the software functions optimally and provides maximum benefits. These services are particularly valuable for large enterprises with complex IT infrastructures and stringent data management requirements.

The integration of artificial intelligence (AI) and machine learning (ML) techniques into partitioning software is an emerging trend within the software segment. AI-driven solutions can

OPTIMAL PARTITIONS OF DATA IN HIGHER DIMENSIONS BRADLEY W. JACKSON, JEFFREY D. SCARGLE, AND CHRIS CUSANZA, DAVID BARNES, DENNIS KANYGIN, RUSSELL SARMIENTO, SOWMYA SUBRAMANIAM, TZU-WANG CHUANG** Abstract. Consider piece-wise constant approximations to a function of several parameters, and the problem of finding the best such approximation from measurements at a set of points in the parameter space. We find good approximate solutions to this problem in two steps: (1) partition the parameter space into cells, one for each of the N data points, and (2) collect these cells into blocks, such that within each block the function is constant to within measurement uncertainty. We describe a branch-and-bound algorithm for finding the optimal partition into connected blocks, as well as an O(N2) dynamic programming algorithm that finds the exact global optimum over this exponentially large search space, in a data space of any dimension. This second solution relaxes the connectivity constraint, and requires additivity and convexity conditions on the block fitness function, but in practice none of these items cause problems. From the wide variety of intelligent data understanding applications (including cluster analysis, classification, and anomaly detection) we demonstrate two: partitioning of the State of California (2D) and the Universe (3D).

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.

In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC by comparing amongst mixture models and amongst partition models. We find that under non-standard conditions (i.e. when some edges have a small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under non-standard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e. grouping sites that have not evolved under the same process) affects both the performance of partition models compared to mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers be cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping should be explored, but may suffer similar limitations. Methods This document records the pipeline used in data analyses in ``Performance of Akaike Information Criterion and Bayesian Information Criterion in selecting partition models and mixture models''. The main processes included generating alignments, fitting four different partition and mixture models, and analysing results. The data were generated under Seq-Gen-1.3.4 (Rambaut and Grass 1997). The model fitting was performed IQ-TREE2 (Minh et al. 2020) on a Linux system. The results were analysed using the R package phangorn in R (version 3.6.2) (Schliep 2011, R Core Team 2019). We wrote custom bash scripts to extract relevant parts of the results from IQ-TREE2, and these results were processed in R. The zip files contain four folders: "bash-scripts", "data", "R-codes", and "results-IQTREE2". The bash-scripts folder contains all the bash scripts for simulating alignments and performing model fitting. The "data" folder contains two child folders: "sequence-data" and "Rdata". The child folder "sequence-data" contains the alignments created for the simulations. The other child folder, "Rdata", contains the files created by R to store the results extracted from "IQTREE2" and the results calculated in R. The "R-codes" folder includes the R codes for analysing the results from "IQTREE2". The folder "results-IQTREE2" stores all the results from the fitted models. The three simulations we performed were essentially the same. We used the same parameters of the evolutionary models, and the trees with the same topologies but different edge lengths to generate the sequences. The steps we used were: simulating alignments, model fitting and extracting results, and processing the extracted results. The first two steps were performed on a Linux system using bash scripts, and the last step was performed in R. Simulating Alignment To simulate heterogeneous data we created two multiple sequence alignments (MSAs) under simple homogeneous models with each model comprising a substitution model and an edge-weighted phylogenetic tree (the tree topology was fixed). Each MSA contained eight taxa and 1000 sites. This was performed using the bash script “step1_seqgen_data.sh” in Linux. These two MSAs were then concatenated together giving a MSA with 2000 sites. This was equivalent to generating the concatenated MSA under a two-block unlinked edge lengths partition model (P-UEL). This was performed using the bash script “step2_concat_data.sh”. This created the 0% group of MSAs. In order to simulate a situation where the initial choice of blocks does not properly account for the heterogeneity in the concatenated MSA (i.e., mispartitioning), we randomly selected a proportion of 0%, 5%, 10%, 15%, …, up to 50% of sites from each block and swapped them. That is, the sites drawn from the first block were placed in the second block, and the sites drawn from the second block were placed in the first block. This process was repeated 100 times for each proportion of mispartitioned sites giving a total of 1100 MSAs. This process involved two steps. The first step was to generate ten sets of different amounts of numbers without duplicates from each of the two intervals [1,1000] and [1001,2000]. The amounts of numbers were based on the proportions of incorrectly partitioning sites. For example, the first set has 50 numbers on each interval, and the second set has 100 numbers on each interval, etc. The first step was performed in R, and the R code was not provided but the random number text files were included. The second step was to select sites from the concatenated MSAs from the locations based on the numbers created in the first step. This created 5%, 10%, 15%, …, 50% groups of MSAs. The second step used the following bash scripts: “step3_1_mixmatch_pre_data.sh” and “step3_2_mixmatch_data.sh”. The MSAs used in the simulations were created and stored in the “data” folder. Model Fitting and Extracting Results The next steps were to fit four different partition and mixture models to the data in IQ-TREE2 and extract the results. The models used were P-LEL partition model, P-UEL partition model, M-UGP mixture model, and M-LGP mixture model. For the partition models, the partitioning schemes were the same: the first 1000 sites as a block and the second 1000 sites as another. For the groups of MSAs with different proportions of mispartitioned sites, this was equivalent to fitting the partition models with an incorrect partitioning scheme. The partitioning scheme was called “parscheme.nex”. The bash scripts for model fitting were stored in the “bash-scripts” folder. To run the bash scripts, users can follow the order which was shown in the names of these bash scripts. The inferred trees, estimated base frequencies, estimated rate matrices, estimated weight factors and AIC values, and BIC values were extracted from the IQTREE2 results. These extracted results were stored in the “results-IQTREE2” folder and used to evaluate the performance of AIC, BIC, and models in R. Processing Extracted Results in R To evaluate the performance of AIC, BIC, and the performance of fitted partition models and mixture models, we calculated the following measures: the rEKL values, the bias of AIC in estimating the rEKL, BIC values, and the branch scores (bs). We also compared the distribution of the estimated model parameters (i.e. base frequencies and rate matrices) to the generating model parameters. These processes were performed in R. The first step was to read in the inferred trees, estimated base frequencies, estimated rate matrices, estimated weight factors, AIC values, and BIC values that were extracted from IQTREE2 results. These R scripts were stored in the “R-codes” folder, and the names of these scripts started with “readpara_...” (e.g. “readpara_MLGP_standard”). After reading in all the parameters for each model, we estimated the measures mentioned above using the corresponding R scripts that were also in the “R-codes” folder. The functions used in these R scripts were stored in the “R_functions_simulation”. It is worth noting that the directories need to be changed if users want to run these R scripts on their computers.

Most components of an organism’s phenotype can be viewed as the expression of multiple traits. Many of these traits operate as complexes, where multiple subsidiary parts function and evolve together. As trait complexity increases, so does the challenge of describing complexity in intuitive, biologically meaningful ways. Traditional multivariate analyses ignore the phenomenon of individual complexity and provide relatively abstract representations of variation among individuals. We suggest adopting well-known diversity indices from community ecology to describe phenotypic complexity as the diversity of distinct subsidiary components of a trait. Using a hierarchical framework, we illustrate how total trait diversity can be partitioned into within-individual complexity (α diversity) and between-individual components (β diversity). This approach complements traditional multivariate analyses. The key innovations are (i) addition of individual complexity within the same framework as between-individual variation and (ii) a group-wise partitioning approach that complements traditional level-wise partitioning of diversity. The complexity-as-diversity approach has potential application in many fields, including physiological ecology, ecological and community genomics, and transcriptomics. We demonstrate the utility of this complexity-as-diversity approach with examples from chemical and microbial ecology. The examples illustrate biologically significant differences in complexity and diversity that standard analyses would not reveal.

Abstract: This contribution provides MATLAB scripts to assist users in factor analysis, constrained least squares regression, and total inversion techniques. These scripts respond to the increased availability of large datasets generated by modern instrumentation, for example, the SedDB database. The download (.zip) includes one descriptive paper (.pdf) and one file of the scripts and example output (.doc). Other Description: Pisias, N. G., R. W. Murray, and R. P. Scudder (2013), Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts, Geochem. Geophys. Geosyst., 14, 4015–4020, doi:10.1002/ggge.20247.

Bayesian computation of high-dimensional linear regression models using Markov chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high-dimensional linear regressions with large or streaming data. DFP constructs a pseudo posterior density of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high-dimensional linear regression models with Gaussian scale mixture priors and spike-and-slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudoposterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the supplementary material. Supplementary material also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in https://github.com/Rene-Gutierrez/DynParRegReg. The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg_Implementation.

The partition management software market is experiencing robust growth, driven by the increasing adoption of cloud computing and the expanding need for efficient data management across various devices. The market, estimated at $2.5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value exceeding $7 billion by 2033. This growth is fueled by several key factors. The rise of large enterprises and SMEs adopting virtualization and cloud-based infrastructure necessitates sophisticated partition management tools. Furthermore, the increasing complexity of data storage and the need for optimized disk space utilization drive demand for advanced features like data migration, disk cloning, and advanced partitioning techniques offered by these software solutions. The segment dominated by cloud-based solutions reflects the ongoing shift toward cloud infrastructure and the benefits of accessibility and scalability. While the market faces some restraints, such as the availability of free, open-source alternatives and the potential for user error leading to data loss, the overall positive trajectory is supported by the consistent need for efficient data organization and management across diverse operating systems and storage devices. The competitive landscape is characterized by both established players and emerging companies offering a range of solutions from basic partitioning tools to comprehensive data management suites. EaseUS, AOMEI, MiniTool, and Acronis are notable players, offering feature-rich software catering to both individual users and businesses. The geographical distribution of the market shows strong growth potential across various regions, with North America and Europe currently leading in adoption, followed by the Asia-Pacific region. However, rising technological awareness and increasing digitalization in developing economies present considerable opportunities for expansion in the coming years. Continued innovation in areas such as AI-powered data management and improved user interfaces will further drive market expansion and influence the competitive dynamics.

When inferring phylogenies, one important decision is whether and how nucleotide substitution parameters should be shared across different subsets or partitions of the data. One sort of partitioning error occurs when heterogeneous subsets are mistakenly lumped together and treated as if they share parameter values. The opposite kind of error is mistakenly treating homogeneous subsets as if they result from distinct sets of parameters. Lumping and splitting errors are not equally bad. Lumping errors can yield parameter estimates that do not accurately reject any of the subsets that were combined whereas splitting errors yield estimates that did not benefit from sharing information across partitions. Phylogenetic partitioning decisions are often made by applying information criteria such as the Akaike Information Criterion (AIC). As with other information criteria, the AIC evaluates a model or partition scheme by combining the maximum log-likelihood value with a penalty that depends on the number of parameters being estimated. For the purpose of selecting an optimal partitioning scheme, we derive an adjustment to the AIC that we refer to as the AICP and that is motivated by the idea that splitting errors are less serious than lumping errors. We also introduce a similar adjustment to the Bayesian Information Criterion (BIC) that we refer to as the BICP. Via simulation and empirical data analysis, we contrast AIC and BIC behavior to our suggested adjustments. We discuss these results and also emphasize why we expect the probability of lumping errors with the AICP and the BICP to be relatively robust to model parameterization.

In this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non-empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between data sets. The RRP method is honest in that it does not match observations at any cost: if data sets are separated, the method clearly states it. The match obtained with RRP is invariant under monotonic transformation of the data. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used estimators. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable, in the presence of missing data or when the estimated propensity score is too sensitive to model specifications.

A simplified dataset for the "Lorenz fixed point partition" from

https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/representing-turbulent-statistics-with-partitions-of-state-space-part-1-theory-and-methodology/996103DAC408A34B0D15A465EB1F6B50

It contains a symbol sequence from the lorenz equations in the hdf5 format under "sequence". The time-step spacing for the recorded spacing is every 0.1 timesteps corresponding to the Lorenz equations with the original parameters.

We present here annotated MATLAB scripts (and specific guidelines for their use) for Q-mode factor analysis, a constrained least squares multiple linear regression technique, and a total inversion protocol, that are based on the well-known approaches taken by Dymond (1981), Leinen and Pisias (1984), Kyte et al. (1993), and their predecessors. Although these techniques have been used by investigators for the past decades, their application has been neither consistent nor transparent, as their code has remained in-house or in formats not commonly used by many of today's researchers (e.g., FORTRAN). In addition to providing the annotated scripts and instructions for use, we include a sample data set for the user to test their own manipulation of the scripts.

Data set accompanying the publication "Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms"

The data set contains:

- Input script for PyADF (v0.97) for calculating (a) all two body terms to use as graph weights and (b) fragmentation error for all k and nmax (aspf)

- PDB files of proteins and the "regions of interest" (RoI) used in this work.

- Raw data: protein graph representations, resulting partitions, data underlying all figures shown in our article.

- Jupiter notebook to create all figures shown in the article and in the supporting information from data in the results folder.

- Images of protein structures and graph representations of ubiquitin.

Taxicab (SHL and Medallion) manufacturers and installers of partitions. Partitions are safety features of taxicabs that provide protection to the driver.

The data set is collected to evaluate if two parasitoids (Spathius galinae and Tetrastichus planipennisi), introduced for biocontrol of the invasive emerald ash borer (EAB), Agrilus planipennis, into North America have established niche-partitioning, co-existing populations following their sequential or simultaneous field releases to 12 hard-wood forests located in Midwest and Northeast regions of the United States. Ash trees of various sizes (large, pole-size and saplings) were debarked meter by meter in early spring of 2019 (Michigan sites) or fall of 2019 (Northeast states: Connecticut, Massachusetts and New York). Detailed data collection procedures can be found in the associated publication in Biological Control. Resources in this dataset:Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Michigan data. File Name: Michigan 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Michigan DatasetResource Software Recommended: JMP,url: https://www.JMP.com Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Northeast states data. File Name: NE Dataset 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Northeast States Data setResource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Data Dictionary. File Name: Data Dictionary for Parasitoid niche partitioning study from Biological Control.docxResource Description: Data dictionary

Data used in the figures and examples of molecular dynamics simulations outputs for calculating metal-silicate H2O partitioning

This updated dataset presents compiled experimental mineral/melt partitioning data for garnet from the literature (e.g. no unpublished collections). This dataset can help users to calculate differentiation scenarios (by sorting for compositions that match the system of interest), test models or create calibration datasets for models, and facilitate experimental design and data management plans. The format of the data is oriented vertically – each column represents the data from one published experiment – including experimental conditions, source publication, analytical techniques and the composition of the starting composition, liquid, mineral or fluid and the partition coefficient for the elements analyzed. This format is inclusive, and therefore complex, and care should be taken in processing the data from this raw form. Please see the “Readme" tab for further explanations and instructions. This data is presented in the traceDs template, a format for documenting experimental data that is available on the EarthChem website for investigator use. If you find errors in the data transcription or any omissions please contact Roger Nielsen or Gokce Ustunisik (roger.nielsen@sdsmt.edu; gokce.ustunisik@sdsmt.edu) with details of the error or a copy of the paper to be added.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset describes the correlation of the Ronlow beds to other geological units in the Galilee subregion. The Ronlow beds are stratigraphic equivalents of three formal geological units: the Hutton Sandstone, the hooray Sandstone, and the Injune Creek Group. For the preparation of potentiometric surface maps and other hydrogeological interpretation of data from the Galilee subregion, the Ronlow beds were partitioned into three sub-units, which were assigned to either the Hutton Sandstone, Hooray Sandstone, or Injune Creek Group. This partitioning was based on potentiometry of bores screened in the Ronlow beds.

Dataset History

Hydraulic head data for bores screened in the Ronlow beds from dataset 'JkrRonlow_beds_Partitioning.gdb' were compared to hydraulic head values in bores assigned to the Hutton Sandstone, Hooray Sandstone, and Injune Creek group. Bores screened in the Ronlow beds were then assigned to either the Hutton Sandstone aquifer, Hooray Sandstone aquifer, or Injune Creek Group aquitard based on similarities in hydraulic head.The polygons were created in an ArcMap editing session.

Dataset Citation

Bioregional Assessment Programme (2015) Ronlow beds partitioning. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/d2f60560-eda7-417d-86ca-1d29ce994edd.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From QDEX well completion reports (WCR) - Galilee v01

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

• Derived From Potentiometric head difference v01

• Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808

• Derived From Galilee subregion groundwater usage estimates dataset v01

• Derived From Galilee Water Accounts Table: volumes and purposes

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.

Approved statistical data on the entry of overseas Chinese and foreigners into Taiwan by month.