Normalize data

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

Corporate Registry Data Normalization Market Outlook

According to our latest research, the global corporate registry data normalization market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by digital transformation and regulatory compliance demands across industries. The market is forecasted to grow at a CAGR of 13.6% from 2025 to 2033, reaching a projected value of USD 4.23 billion by 2033. This impressive growth is primarily attributed to the increasing need for accurate, standardized, and accessible corporate data to support compliance, risk management, and digital business processes in a rapidly evolving regulatory landscape.

One of the primary growth factors fueling the corporate registry data normalization market is the escalating global regulatory pressure on organizations to maintain clean, consistent, and up-to-date business entity data. With the proliferation of anti-money laundering (AML), know-your-customer (KYC), and data privacy regulations, companies are under immense scrutiny to ensure that their corporate records are accurate and accessible for audits and compliance checks. This regulatory environment has led to a surge in adoption of data normalization solutions, especially in sectors such as banking, financial services, insurance (BFSI), and government agencies. As organizations strive to minimize compliance risks and avoid hefty penalties, the demand for advanced software and services that can seamlessly normalize and harmonize disparate registry data sources continues to rise.

Another significant driver is the exponential growth in data volumes, fueled by digitalization, mergers and acquisitions, and global expansion of enterprises. As organizations integrate data from multiple jurisdictions, subsidiaries, and business units, they face massive challenges in consolidating and reconciling heterogeneous registry data formats. Data normalization solutions play a critical role in enabling seamless data integration, providing a single source of truth for corporate identity, and powering advanced analytics and automation initiatives. The rise of cloud-based platforms and AI-powered data normalization tools is further accelerating market growth by making these solutions more scalable, accessible, and cost-effective for organizations of all sizes.

Technological advancements are also shaping the trajectory of the corporate registry data normalization market. The integration of artificial intelligence, machine learning, and natural language processing into normalization tools is revolutionizing the way organizations cleanse, match, and enrich corporate data. These technologies enhance the accuracy, speed, and scalability of data normalization processes, enabling real-time updates and proactive risk management. Furthermore, the proliferation of API-driven architectures and interoperability standards is facilitating seamless connectivity between corporate registry databases and downstream business applications, fueling broader adoption across industries such as legal, healthcare, and IT & telecom.

From a regional perspective, North America continues to dominate the corporate registry data normalization market, driven by stringent regulatory frameworks, early adoption of advanced technologies, and a high concentration of multinational corporations. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, increasing cross-border business activities, and evolving regulatory requirements. Europe remains a key market due to GDPR and other data-centric regulations, while Latin America and the Middle East & Africa are witnessing steady growth as local governments and enterprises invest in digital infrastructure and compliance modernization.

Component Analysis

The corporate registry data normalization market is segmented by component into software and services, each playing a pivotal role in the ecosystem. Software solutions are designed to automate and streamline the normalization process, offering functionalities such as data cleansing, deduplication, matching, and enrichment. These platforms often leverage advanced algorithms and machine learning to handle large volumes of complex, unstructured, and multilingual data, making them indispensable for organizations with global operations. The software segment is witnessing substantial investment in research and development, with vendors focusing on enhancing

Reference genes used in normalizing qRT-PCR data are critical for the accuracy of gene expression analysis. However, many traditional reference genes used in zebrafish early development are not appropriate because of their variable expression levels during embryogenesis. In the present study, we used our previous RNA-Seq dataset to identify novel reference genes suitable for gene expression analysis during zebrafish early developmental stages. We first selected 197 most stably expressed genes from an RNA-Seq dataset (29,291 genes in total), according to the ratio of their maximum to minimum RPKM values. Among the 197 genes, 4 genes with moderate expression levels and the least variation throughout 9 developmental stages were identified as candidate reference genes. Using four independent statistical algorithms (delta-CT, geNorm, BestKeeper and NormFinder), the stability of qRT-PCR expression of these candidates was then evaluated and compared to that of actb1 and actb2, two commonly used zebrafish reference genes. Stability rankings showed that two genes, namely mobk13 (mob4) and lsm12b, were more stable than actb1 and actb2 in most cases. To further test the suitability of mobk13 and lsm12b as novel reference genes, they were used to normalize three well-studied target genes. The results showed that mobk13 and lsm12b were more suitable than actb1 and actb2 with respect to zebrafish early development. We recommend mobk13 and lsm12b as new optimal reference genes for zebrafish qRT-PCR analysis during embryogenesis and early larval stages.

This archive contains affymetrix files necessary to normalize microarrays data and modified annotations files required in GIANT APT-Normalize tool for annotation of normalized data.

The CSV dataset contains sentence pairs for a text-to-text transformation task: given a sentence that contains 0..n abbreviations, rewrite (normalize) the sentence in full words (word forms).

Training dataset: 64,665 sentence pairs Validation dataset: 7,185 sentence pairs. Testing dataset: 7,984 sentence pairs.

All sentences are extracted from a public web corpus (https://korpuss.lv/id/Tīmeklis2020) and contain at least one medical term.

A data set used to normalize the detector response of the ARCS instrument see ARCS_226797.md in the data set for more details.

The values in this raster are unit-less scores ranging from 0 to 1 that represent normalized dollars per acre damage claims from antelope on Wyoming lands. This raster is one of 9 inputs used to calculate the "Normalized Importance Index."

As economic conditions in the United States continue to improve, the FOMC may consider normalizing monetary policy. Whether the FOMC reduces the balance sheet before raising the federal funds rate (or vice versa) may affect the shape of the yield curve, with consequences for financial institutions. Drawing lessons from the previous normalization in 2015–19, we conclude that normalizing the balance sheet before raising the funds rate might forestall yield curve inversion and, in turn, support economic stability.

Security Data Normalization Platform Market Outlook

According to our latest research, the global Security Data Normalization Platform market size reached USD 1.48 billion in 2024, reflecting robust demand across industries for advanced security data management solutions. The market is registering a compound annual growth rate (CAGR) of 18.7% and is projected to achieve a value of USD 7.18 billion by 2033. The ongoing surge in sophisticated cyber threats and the increasing complexity of enterprise IT environments are among the primary growth factors driving the adoption of security data normalization platforms worldwide.

The growth of the Security Data Normalization Platform market is primarily fuelled by the exponential rise in cyberattacks and the proliferation of digital transformation initiatives across various sectors. As organizations accumulate vast amounts of security data from disparate sources, the need for platforms that can aggregate, normalize, and analyze this data has become critical. Enterprises are increasingly recognizing that traditional security information and event management (SIEM) systems fall short in handling the volume, velocity, and variety of data generated by modern IT infrastructures. Security data normalization platforms address this challenge by transforming heterogeneous data into a standardized format, enabling more effective threat detection, investigation, and response. This capability is particularly vital as organizations move toward zero trust architectures and require real-time insights to secure their digital assets.

Another significant growth driver for the Security Data Normalization Platform market is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stringent data protection and cybersecurity regulations, compelling organizations to enhance their security postures. Compliance requirements such as GDPR, HIPAA, and CCPA demand that organizations not only secure their data but also maintain comprehensive audit trails and reporting mechanisms. Security data normalization platforms facilitate compliance by providing unified, normalized logs and reports that simplify audit processes and ensure regulatory adherence. The market is also witnessing increased adoption in sectors such as BFSI, healthcare, and government, where data integrity and compliance are paramount.

Technological advancements are further accelerating the adoption of security data normalization platforms. The integration of artificial intelligence (AI) and machine learning (ML) capabilities into these platforms is enabling automated threat detection, anomaly identification, and predictive analytics. Cloud-based deployment models are gaining traction, offering scalability, flexibility, and cost-effectiveness to organizations of all sizes. As the threat landscape becomes more dynamic and sophisticated, organizations are prioritizing investments in advanced security data normalization solutions that can adapt to evolving risks and support proactive security strategies. The growing ecosystem of managed security service providers (MSSPs) is also contributing to market expansion by delivering normalization as a service to organizations with limited in-house expertise.

From a regional perspective, North America continues to dominate the Security Data Normalization Platform market, accounting for the largest share in 2024 due to the presence of major technology vendors, high cybersecurity awareness, and significant investments in digital infrastructure. Europe follows closely, driven by strict regulatory mandates and increasing cyber threats targeting critical sectors. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitization, expanding IT ecosystems, and rising cybercrime incidents. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions accelerate their cybersecurity modernization efforts. The global outlook for the Security Data Normalization Platform market remains positive, with sustained demand expected across all major regions through 2033.

Component Analysis

The Security Data Normalization Platform market is segmented by component into software and services. Software solutions form the core of this market, providing the essential functionalities for data aggregation, normalization, enrichment, and integration with downs

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

Neutron scattering Data from a Vanadium cylinder. Acquired on the ARCS spectrometer in white beam mode to normalize the detector efficiencies. During Cycle 2022B

Security Data Normalization Platform Market Outlook

According to our latest research, the global Security Data Normalization Platform market size reached USD 1.87 billion in 2024, driven by the rapid escalation of cyber threats and the growing complexity of enterprise security infrastructures. The market is expected to grow at a robust CAGR of 12.5% during the forecast period, reaching an estimated USD 5.42 billion by 2033. Growth is primarily fueled by the increasing adoption of advanced threat intelligence solutions, regulatory compliance demands, and the proliferation of connected devices across various industries.

The primary growth factor for the Security Data Normalization Platform market is the exponential rise in cyberattacks and security breaches across all sectors. Organizations are increasingly realizing the importance of normalizing diverse security data sources to enable efficient threat detection, incident response, and compliance management. As security environments become more complex with the integration of cloud, IoT, and hybrid infrastructures, the need for platforms that can aggregate, standardize, and correlate data from disparate sources has become paramount. This trend is particularly pronounced in sectors such as BFSI, healthcare, and government, where data sensitivity and regulatory requirements are highest. The growing sophistication of cyber threats has compelled organizations to invest in robust security data normalization platforms to ensure comprehensive visibility and proactive risk mitigation.

Another significant driver is the evolving regulatory landscape, which mandates stringent data protection and reporting standards. Regulations such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and various national cybersecurity frameworks have compelled organizations to enhance their security postures. Security data normalization platforms play a crucial role in facilitating compliance by providing unified and actionable insights from heterogeneous data sources. These platforms enable organizations to automate compliance reporting, streamline audit processes, and reduce the risk of penalties associated with non-compliance. The increasing focus on regulatory alignment is pushing both large enterprises and SMEs to adopt advanced normalization solutions as part of their broader security strategies.

The proliferation of digital transformation initiatives and the accelerated adoption of cloud-based solutions are further propelling market growth. As organizations migrate critical workloads to the cloud and embrace remote work models, the volume and variety of security data have surged dramatically. This shift has created new challenges in terms of data integration, normalization, and real-time analysis. Security data normalization platforms equipped with advanced analytics and machine learning capabilities are becoming indispensable for managing the scale and complexity of modern security environments. Vendors are responding to this demand by offering scalable, cloud-native solutions that can seamlessly integrate with existing security information and event management (SIEM) systems, threat intelligence platforms, and incident response tools.

From a regional perspective, North America continues to dominate the Security Data Normalization Platform market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the high concentration of technology-driven enterprises, robust cybersecurity regulations, and significant investments in advanced security infrastructure. Europe and Asia Pacific are also witnessing strong growth, driven by increasing digitalization, rising threat landscapes, and the adoption of stringent data protection laws. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness of cybersecurity challenges and the need for standardized security data management solutions.

Component Analysis

Dataset

This dataset was created by Hemanth S

Contents

Flight Data Normalization Platform Market Outlook

According to our latest research, the global flight data normalization platform market size reached USD 1.12 billion in 2024, exhibiting robust industry momentum. The market is projected to grow at a CAGR of 10.3% from 2025 to 2033, reaching an estimated value of USD 2.74 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics in aviation, the rising need for operational efficiency, and the growing emphasis on regulatory compliance and safety enhancements across the aviation sector.

A key growth factor for the flight data normalization platform market is the rapid digital transformation within the aviation industry. Airlines, airports, and maintenance organizations are increasingly relying on digital platforms to aggregate, process, and normalize vast volumes of flight data generated by modern aircraft systems. The transition from legacy systems to integrated digital solutions is enabling real-time data analysis, predictive maintenance, and enhanced situational awareness. This shift is not only improving operational efficiency but also reducing downtime and maintenance costs, making it an essential strategy for airlines and operators aiming to remain competitive in a highly regulated environment.

Another significant driver fueling the expansion of the flight data normalization platform market is the stringent regulatory landscape governing aviation safety and compliance. Aviation authorities worldwide, such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA), are mandating the adoption of advanced flight data monitoring and normalization solutions to ensure adherence to safety protocols and to facilitate incident investigation. These regulatory requirements are compelling aviation stakeholders to invest in platforms that can seamlessly normalize and analyze data from diverse sources, thereby supporting proactive risk management and compliance reporting.

Additionally, the growing complexity of aircraft systems and the proliferation of connected devices in aviation have led to an exponential increase in the volume and variety of flight data. The need to harmonize disparate data formats and sources into a unified, actionable format is driving demand for sophisticated flight data normalization platforms. These platforms enable stakeholders to extract actionable insights from raw flight data, optimize flight operations, and support advanced analytics use cases such as fuel efficiency optimization, fleet management, and predictive maintenance. As the aviation industry continues to embrace data-driven decision-making, the demand for robust normalization solutions is expected to intensify.

Regionally, North America continues to dominate the flight data normalization platform market owing to the presence of major airlines, advanced aviation infrastructure, and early adoption of digital technologies. Europe is also witnessing significant growth, driven by stringent safety regulations and increasing investments in aviation digitization. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rapid growth in air travel, expanding airline fleets, and government initiatives to modernize aviation infrastructure. Latin America and the Middle East & Africa are gradually embracing these platforms, supported by ongoing efforts to enhance aviation safety and operational efficiency.

Component Analysis

The component segment of the flight data normalization platform market is broadly categorized into software, hardware, and services. The software segment accounts for the largest share, driven by the increasing adoption of advanced analytics, machine learning, and artificial intelligence technologies for data processing and normalization. Software solutions are essential for aggregating raw flight data from multiple sources, standardizing formats, and providing actionable insights for decision-makers. With the rise of clou

Raw data used to normalize detector performance on the ARCS instrument For the run cycle starting in June 2023. This is a V cylinder and the T0 chopper is set to 150 Hz and phased for 300 meV. All Fermi Choppers are out of the Beam.

The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:

--uniformize=1

--predictor_norm_type_string="z_score"

--vector_target_norm_type_string=""

--scalar_target_norm_type_string=""

Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.

Cloud EHR Data Normalization Platforms Market Outlook

According to our latest research, the global Cloud EHR Data Normalization Platforms market size in 2024 reached USD 1.2 billion, reflecting robust adoption across healthcare sectors worldwide. The market is experiencing a strong growth trajectory, with a compound annual growth rate (CAGR) of 16.5% projected from 2025 to 2033. By the end of 2033, the market is expected to attain a value of approximately USD 4.3 billion. This expansion is primarily fueled by the rising demand for integrated healthcare data systems, the proliferation of electronic health records (EHRs), and the critical need for seamless interoperability between disparate healthcare IT systems.

One of the principal growth factors driving the Cloud EHR Data Normalization Platforms market is the global healthcare sector's increasing focus on digitization and interoperability. As healthcare organizations strive to improve patient outcomes and operational efficiencies, the adoption of cloud-based EHR data normalization solutions has become essential. These platforms enable the harmonization of heterogeneous data sources, ensuring that clinical, administrative, and financial data are standardized across multiple systems. This standardization is critical for supporting advanced analytics, clinical decision support, and population health management initiatives. Moreover, the growing adoption of value-based care models is compelling healthcare providers to invest in technologies that facilitate accurate data aggregation and reporting, further propelling market growth.

Another significant growth catalyst is the rapid advancement in cloud computing technologies and the increasing availability of scalable, secure cloud infrastructure. Cloud EHR data normalization platforms leverage these technological advancements to offer healthcare organizations flexible deployment options, robust data security, and real-time access to normalized datasets. The scalability of cloud platforms allows healthcare providers to efficiently manage large volumes of data generated from diverse sources, including EHRs, laboratory systems, imaging centers, and wearable devices. Additionally, the integration of artificial intelligence and machine learning algorithms into these platforms enhances their ability to map, clean, and standardize data with greater accuracy and speed, resulting in improved clinical and operational insights.

Regulatory and compliance requirements are also playing a pivotal role in shaping the growth trajectory of the Cloud EHR Data Normalization Platforms market. Governments and regulatory bodies across major regions are mandating the adoption of interoperable health IT systems to improve patient safety, data privacy, and care coordination. Initiatives such as the 21st Century Cures Act in the United States and similar regulations in Europe and Asia Pacific are driving healthcare organizations to implement advanced data normalization solutions. These platforms help ensure compliance with data standards such as HL7, FHIR, and SNOMED CT, thereby reducing the risk of data silos and enhancing the continuity of care. As a result, the market is witnessing increased investments from both public and private stakeholders aiming to modernize healthcare IT infrastructure.

From a regional perspective, North America holds the largest share of the Cloud EHR Data Normalization Platforms market, driven by the presence of advanced healthcare infrastructure, high EHR adoption rates, and supportive regulatory frameworks. Europe follows closely, with significant investments in health IT modernization and interoperability initiatives. The Asia Pacific region is emerging as a high-growth market due to rising healthcare expenditures, expanding digital health initiatives, and increasing awareness about the benefits of data normalization. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by ongoing healthcare reforms and investments in digital health technologies. Collectively, these regional dynamics underscore the global momentum toward interoperable, cloud-based healthcare data ecosystems.

Component Analysis

The Cloud EHR Data Normalization Platforms market is segmented by component into software and services, each playing a distinct and critical role in driving the market's growth. Software solutions form the technological backbone of the market, enabling healthcare organizations to autom

Equipment Runtime Normalization Analytics Market Outlook

According to our latest research, the global Equipment Runtime Normalization Analytics market size reached USD 2.31 billion in 2024, demonstrating robust momentum across diverse industrial sectors. The market is expected to grow at a CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 6.88 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of industrial automation, the proliferation of IoT-enabled equipment, and the rising need for predictive maintenance and operational efficiency across manufacturing, energy, and other critical industries.

A key growth factor for the Equipment Runtime Normalization Analytics market is the accelerating pace of digital transformation within asset-intensive industries. As organizations strive to maximize the productivity and lifespan of their machinery, there is a growing emphasis on leveraging advanced analytics to normalize equipment runtime data across heterogeneous fleets and varying operational contexts. The integration of AI and machine learning algorithms enables enterprises to standardize runtime metrics, providing a unified view of equipment performance regardless of manufacturer, model, or deployment environment. This normalization is crucial for benchmarking, identifying inefficiencies, and implementing data-driven maintenance strategies that reduce unplanned downtime and optimize resource allocation.

Another significant driver is the rise of Industry 4.0 and the increasing connectivity of industrial assets through IoT sensors and cloud-based platforms. These technological advancements have generated an unprecedented volume of equipment performance data, necessitating sophisticated analytics solutions capable of normalizing and interpreting runtime information at scale. Equipment Runtime Normalization Analytics platforms facilitate seamless data aggregation from disparate sources, allowing organizations to derive actionable insights that enhance operational agility and competitiveness. Additionally, the shift towards outcome-based service models in sectors such as manufacturing, energy, and transportation is fueling demand for analytics that can accurately measure and compare equipment utilization, efficiency, and reliability across diverse operational scenarios.

The growing focus on sustainability and regulatory compliance is also propelling the adoption of Equipment Runtime Normalization Analytics. As governments and industry bodies impose stricter standards on energy consumption, emissions, and equipment maintenance, enterprises are increasingly turning to analytics tools that can provide standardized, auditable reports on equipment runtime and performance. These solutions not only help organizations meet compliance requirements but also support sustainability initiatives by identifying opportunities to reduce energy consumption, minimize waste, and extend equipment lifecycles. The convergence of these market forces is expected to sustain strong demand for Equipment Runtime Normalization Analytics solutions in the years ahead.

Regionally, North America currently leads the market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of industrial IoT, advanced analytics, and a mature manufacturing base. Europe’s strong emphasis on sustainability and regulatory compliance further drives adoption, while Asia Pacific is emerging as a high-growth region due to rapid industrialization, government initiatives to modernize manufacturing, and increasing investments in smart factory technologies. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding industrial infrastructure and the increasing penetration of digital technologies.

Component Analysis

The Component segment of the Equipment Runtime Normalization Analytics market is categorized into Software, Hardware, and Services. Software solutions form the backbone of this market, comprising advanced analytics platforms, AI-driven data processing engines, and visualization tools that enable users to normalize and interpret equipment runtime data. These software offerings are designed to aggregate data from multiple sources, apply normalization algorithms, and generate actionable insights for operational decision-making. The demand for robust

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.