Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination.

This Hospital Management System project features a fully normalized relational database designed to manage hospital data including patients, doctors, appointments, diagnoses, medications, and billing. The schema applies database normalization (1NF, 2NF, 3NF) to reduce redundancy and maintain data integrity, providing an efficient, scalable structure for healthcare data management. Included are SQL scripts to create tables and insert sample data, making it a useful resource for learning practical database design and normalization in a healthcare context.

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (

Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.
Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.
Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best-performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor-performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2). Methods

Study Participants and Samples

The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women.

All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685.

Blood Collection and Processing

Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above.

Characterization of DNA Methylation using the EPIC array

Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA).

Processing and Analysis of DNA Methylation Data

The R/Bioconductor packages Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0) and wateRmelon (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number < 3. Probes with more than 5% of samples having low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the SeSAMe (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data.

Normalization Methods Evaluated

The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using minfi’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by minfi’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from minfi’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples.

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

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data and their impact on downstream differential expression analysis results. Based on this study, the TMM and RLE library size normalization methods give similar results for CESC dataset. In addition, the simulated datasets results show that the SVA (“BE”) method outperforms the other methods (SVA “Leek”, PCA) by correctly estimating the number of latent artifacts. Moreover, ignoring the loss of degrees of freedom due to normalization results in an inflated type I error rates. We recommend adjusting not only for library size differences but also the assessment of known and unknown technical artifacts in the data, and if needed, complete across sample normalization. In addition, we suggest that one includes the known and estimated latent artifacts in the design matrix to correctly account for the loss in degrees of freedom, as opposed to completing the analysis on the post-processed normalized data.

Dataset for human osteoarthritis (OA) — microarray gene expression (Affymetrix GPL570) PMC +1

Contains expression data for 7 healthy control (normal) tissue samples and 7 osteoarthritis patient tissue samples from synovial / joint tissue. PMC +1

Pre-processed for normalization (background correction, log-transformation, normalization) to remove technical variation.

Suitable for downstream analyses: differential gene expression (normal vs OA), subtype- or phenotype-based classification, machine learning.

Can act as a validation dataset when combining with other GEO datasets to increase sample size or test reproducibility. SpringerLink +1

Useful for biomarker discovery, pathway enrichment analysis (e.g., GO, KEGG), immune infiltration analysis, and subtype analysis in osteoarthritis research.

Tick Data Normalization Market Outlook

According to our latest research, the global Tick Data Normalization market size reached USD 1.02 billion in 2024, reflecting robust expansion driven by the increasing complexity and volume of financial market data. The market is expected to grow at a CAGR of 13.1% during the forecast period, reaching approximately USD 2.70 billion by 2033. This growth is fueled by the rising adoption of algorithmic trading, regulatory demands for accurate and consistent data, and the proliferation of advanced analytics across financial institutions. As per our analysis, the market’s trajectory underscores the critical role of data normalization in ensuring data integrity and operational efficiency in global financial markets.

The primary growth driver for the tick data normalization market is the exponential surge in financial data generated by modern trading platforms and electronic exchanges. With the proliferation of high-frequency trading and the integration of diverse market data feeds, financial institutions face the challenge of processing vast amounts of tick-by-tick data from multiple sources, each with unique formats and structures. Tick data normalization solutions address this complexity by transforming disparate data streams into consistent, standardized formats, enabling seamless downstream processing for analytics, trading algorithms, and compliance reporting. This standardization is particularly vital in the context of regulatory mandates such as MiFID II and Dodd-Frank, which require accurate data lineage and auditability, further propelling market growth.

Another significant factor contributing to market expansion is the growing reliance on advanced analytics and artificial intelligence within the financial sector. As firms seek to extract actionable insights from historical and real-time tick data, the need for high-quality, normalized datasets becomes paramount. Data normalization not only enhances the accuracy and reliability of predictive models but also facilitates the integration of machine learning algorithms for tasks such as anomaly detection, risk assessment, and portfolio optimization. The increasing sophistication of trading strategies, coupled with the demand for rapid, data-driven decision-making, is expected to sustain robust demand for tick data normalization solutions across asset classes and geographies.

Furthermore, the transition to cloud-based infrastructure has transformed the operational landscape for banks, hedge funds, and asset managers. Cloud deployment offers scalability, flexibility, and cost-efficiency, enabling firms to manage large-scale tick data normalization workloads without the constraints of on-premises hardware. This shift is particularly relevant for smaller institutions and emerging markets, where cloud adoption lowers entry barriers and accelerates the deployment of advanced data management capabilities. At the same time, the availability of managed services and API-driven platforms is fostering innovation and expanding the addressable market, as organizations seek to outsource complex data normalization tasks to specialized vendors.

Regionally, North America continues to dominate the tick data normalization market, accounting for the largest share in terms of revenue and technology adoption. The presence of leading financial centers, advanced IT infrastructure, and a strong regulatory framework underpin the region’s leadership. Meanwhile, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitalization of financial services, burgeoning capital markets, and increasing participation of retail and institutional investors. Europe also maintains a significant market presence, supported by stringent compliance requirements and a mature financial ecosystem. Latin America and the Middle East & Africa are witnessing steady growth, albeit from a lower base, as financial modernization initiatives gain momentum.

Component Analysis

The tick data normalizati

Multi-OEM VRF Data Normalization Market Outlook

According to our latest research, the global Multi-OEM VRF Data Normalization market size reached USD 1.14 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 12.6% during the forecast period, reaching a projected value of USD 3.38 billion by 2033. This impressive growth is primarily fueled by the increasing adoption of Variable Refrigerant Flow (VRF) systems across multiple sectors, the proliferation of multi-OEM environments, and the rising demand for seamless data integration and analytics within building management systems. The market’s expansion is further supported by advancements in IoT, AI-driven analytics, and the urgent need for energy-efficient HVAC solutions worldwide.

One of the primary growth drivers for the Multi-OEM VRF Data Normalization market is the rapid digital transformation in the HVAC industry. Organizations are increasingly deploying VRF systems from multiple original equipment manufacturers (OEMs) to optimize performance, reduce costs, and future-proof their infrastructure. However, the lack of standardization in data formats across different OEMs presents significant integration challenges. Data normalization solutions bridge this gap by ensuring interoperability, enabling seamless aggregation, and facilitating advanced analytics for predictive maintenance and energy optimization. As facilities managers and building operators seek to harness actionable insights from disparate VRF systems, the demand for sophisticated data normalization platforms continues to rise, driving sustained market growth.

Another significant factor propelling market expansion is the growing emphasis on energy efficiency and sustainability. Regulatory mandates and green building certifications are pushing commercial, industrial, and residential end-users to adopt smart HVAC solutions that minimize energy consumption and carbon emissions. Multi-OEM VRF Data Normalization platforms play a pivotal role in this transition by enabling real-time monitoring, granular energy management, and automated system optimization across heterogeneous VRF networks. The ability to consolidate and analyze operational data from multiple sources not only enhances system reliability and occupant comfort but also helps organizations achieve compliance with stringent environmental standards, further fueling market adoption.

The proliferation of cloud computing, IoT connectivity, and AI-powered analytics is also transforming the Multi-OEM VRF Data Normalization landscape. Cloud-based deployment models offer unparalleled scalability, remote accessibility, and cost-efficiency, making advanced data normalization solutions accessible to a broader spectrum of users. Meanwhile, the integration of AI and machine learning algorithms enables predictive maintenance, anomaly detection, and automated fault diagnosis, reducing downtime and optimizing lifecycle costs. As more organizations recognize the strategic value of unified, normalized VRF data, investments in next-generation data normalization platforms are expected to accelerate, driving innovation and competitive differentiation in the market.

Regionally, the Asia Pacific market dominates the Multi-OEM VRF Data Normalization sector, accounting for the largest share in 2024, driven by rapid urbanization, robust construction activity, and widespread adoption of VRF technology in commercial and residential buildings. North America and Europe follow closely, fueled by stringent energy efficiency standards, a mature building automation ecosystem, and strong investments in smart infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, underpinned by rising demand for modern HVAC solutions and increasing awareness about the benefits of data-driven facility management. The regional outlook remains highly positive, with each geography contributing uniquely to the global market’s upward trajectory.

Component Analysis

The Mul

Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes (e.g., ribosomal protein genes) or long genes (e.g., extracellular matrix genes) are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene–gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data.

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

Simulation script 1This R script will simulate two populations of microbiome samples and compare normalization methods.Simulation script 2This R script will simulate two populations of microbiome samples and compare normalization methods via PcOAs.Sample.OTU.distributionOTU distribution used in the paper: Methods for normalizing microbiome data: an ecological perspective

Cleaned Meta-Kaggle Dataset

The Original Dataset - Meta-Kaggle

Explore our public data on competitions, datasets, kernels (code / notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but we do think there's a lot to learn (and plenty of fun to be had) from this collection of rich data about Kaggle’s community and activity.

Strategizing to become a Competitions Grandmaster? Wondering who, where, and what goes into a winning team? Choosing evaluation metrics for your next data science project? The kernels published using this data can help. We also hope they'll spark some lively Kaggler conversations and be a useful resource for the larger data science community.

https://i.imgur.com/2Egeb8R.png" alt="" title="a title">

This dataset is made available as CSV files through Kaggle Kernels. It contains tables on public activity from Competitions, Datasets, Kernels, Discussions, and more. The tables are updated daily.

Please note: This data is not a complete dump of our database. Rows, columns, and tables have been filtered out and transformed.

August 2023 update

In August 2023, we released Meta Kaggle for Code, a companion to Meta Kaggle containing public, Apache 2.0 licensed notebook data. View the dataset and instructions for how to join it with Meta Kaggle here

We also updated the license on Meta Kaggle from CC-BY-NC-SA to Apache 2.0.

The Problems with the Original Dataset

• The original dataset is 32 CSV files, with 268 colums and 7GB of compressed data. Having so many tables and columns makes it hard to understand the data.
• The data is not normalized, so when you join tables you get a lot of errors.
• Some values refer to non-existing values in other tables. For example, the UserId column in the ForumMessages table has values that do not exist in the Users table.
• There are missing values.
• There are duplicate values.
• There are values that are not valid. For example, Ids that are not positive integers.
• The date and time columns are not in the right format.
• Some columns only have the same value for all rows, so they are not useful.
• The boolean columns have string values True or False.
• Incorrect values for the Total columns. For example, the DatasetCount is not the total number of datasets with the Tag according to the DatasetTags table.
• Users upvote their own messages.

The Solution

• To handle so many tables and columns I use a relational database. I use MySQL, but you can use any relational database.
• The steps to create the database are:
• Creating the database tables with the right data types and constraints. I do that by running the db_abd_create_tables.sql script.
• Downloading the CSV files from Kaggle using the Kaggle API.
• Cleaning the data using pandas. I do that by running the clean_data.py script. The script does the following steps for each table:
  • Drops the columns that are not needed.
  • Converts each column to the right data type.
  • Replaces foreign keys that do not exist with NULL.
  • Replaces some of the missing values with default values.
  • Removes rows where there are missing values in the primary key/not null columns.
  • Removes duplicate rows.
• Loading the data into the database using the LOAD DATA INFILE command.
• Checks that the number of rows in the database tables is the same as the number of rows in the CSV files.
• Adds foreign key constraints to the database tables. I do that by running the add_foreign_keys.sql script.
• Update the Total columns in the database tables. I do that by running the update_totals.sql script.
• Backup the database.

EV Charging Data Normalization Middleware Market Outlook

According to our latest research, the global EV Charging Data Normalization Middleware market size reached USD 1.12 billion in 2024, reflecting a strong surge in adoption across the electric vehicle ecosystem. The market is projected to expand at a robust CAGR of 18.7% from 2025 to 2033, reaching a forecasted size of USD 5.88 billion by 2033. This remarkable growth is primarily driven by the exponential increase in electric vehicle (EV) adoption, the proliferation of charging infrastructure, and the need for seamless interoperability and data integration across disparate charging networks and platforms.

One of the primary growth factors fueling the EV Charging Data Normalization Middleware market is the rapid expansion of EV charging networks, both public and private, on a global scale. As governments and private entities accelerate investments in EV infrastructure to meet ambitious decarbonization and electrification goals, the resulting diversity of hardware, software, and communication protocols creates a fragmented ecosystem. Middleware solutions play a crucial role in standardizing and normalizing data from these heterogeneous sources, enabling unified management, real-time analytics, and efficient billing processes. The demand for robust data normalization is further amplified by the increasing complexity of charging scenarios, such as dynamic pricing, vehicle-to-grid (V2G) integration, and multi-operator roaming, all of which require seamless data interoperability.

Another significant driver is the rising emphasis on data-driven decision-making and predictive analytics within the EV charging sector. Stakeholders, including automotive OEMs, charging network operators, and energy providers, are leveraging normalized data to optimize charging station utilization, forecast energy demand, and enhance customer experiences. With the proliferation of IoT-enabled charging stations and smart grid initiatives, the volume and variety of data generated have grown exponentially. Middleware platforms equipped with advanced data normalization capabilities are essential for aggregating, cleansing, and harmonizing this data, thereby unlocking actionable insights and supporting the development of innovative value-added services. This trend is expected to further intensify as the industry moves towards integrated energy management and smart city initiatives.

The regulatory landscape is also playing a pivotal role in shaping the EV Charging Data Normalization Middleware market. Governments across regions are introducing mandates for open data standards, interoperability, and secure data exchange to foster competition, enhance consumer choice, and ensure grid stability. These regulatory requirements are compelling market participants to adopt middleware solutions that facilitate compliance and enable seamless integration with national and regional charging infrastructure registries. Furthermore, the emergence of industry consortia and standardization bodies is accelerating the development and adoption of common data models and APIs, further boosting the demand for middleware platforms that can adapt to evolving standards and regulatory frameworks.

Regionally, Europe and North America are at the forefront of market adoption, driven by mature EV markets, supportive policy frameworks, and advanced digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by aggressive electrification targets, large-scale urbanization, and significant investments in smart mobility solutions. Latin America and the Middle East & Africa, while currently at a nascent stage, are expected to witness accelerated growth as governments and private players ramp up efforts to expand EV charging networks and embrace digital transformation. The interplay of these regional dynamics is shaping a highly competitive and innovation-driven global market landscape.

Component Analysis

The Component segment of the EV C

Adventure Works 2022 Denormalized dataset

How this Dataset is created?

The CSV data was sourced from the existing Kaggle dataset titled "Adventure Works 2022" by Algorismus. This data was normalized and consisted of seven individual CSV files. The Sales table served as a fact table that connected to other dimensions. To consolidate all the data into a single table, it was loaded into a SQLite database and transformed accordingly. The final denormalized table was then exported as a single CSV file (delimited by | ), and the column names were updated to follow snake_case style.

DOI

doi.org/10.6084/m9.figshare.27899706

Data Dictionary

This repository contains a sample of the input data for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).

This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

This sample dataset also includes files relative to metadata, static data, normalization, and plotting.

To use the data, clone the corresponding repository and unzip this zip file in the data folder.

Analyzing big data is a task encountered across disciplines. Addressing the challenges inherent in dealing with big data necessitate solutions that cover its three defining properties: volume, variety, and velocity. However, what is less understood is the treatment of the data that must be completed even before any analysis can begin. Specifically, there is often a non-trivial amount of time and resources that are utilized to the end of retrieving and preprocessing big data. This problem, known collectively as data integration, is a term frequently used for the general problem of taking data in some initial form and transforming it into a desired form. Examples of this include the rearranging of fields, changing the form of expression of one or more fields, altering the boundary notation of records and/or fields, encrypting or decrypting records and/or fields, parsing non-record data and organizing it into a record-oriented form, etc. In this work, we present our progress in creating a benchmarking suite that characterizes a diverse set of data integration applications.

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the equality condition, an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data.

Example data of fusion features and growth indicators after Z-Score normalization.