Normalize data

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.Â
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 b...

Weighted attributes normalization, scaling

Dataset

This dataset was created by Skylark4

Released under CC0: Public Domain

Contents

Calculations of precision on raw data and on normalized data.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Mission and Vision Statements', including related concepts like Purpose Statements. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Mission/Vision dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mission statement" + "vision statement" + "mission and vision corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mission Statements + Vision Statements + Purpose Statements + Mission and Vision. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Mission/Vision-related keywords [("mission statement" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Mission/Vision Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mission/Vision (1993); Mission Statements (1996); Mission and Vision Statements (1999-2017); Purpose, Mission, and Vision Statements (2022). Processing: Semantic Grouping: Data points across the different naming conventions were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Mission/Vision dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

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, driven by the increasing demand for standardized business information and regulatory compliance across industries. The market is experiencing robust expansion, with a Compound Annual Growth Rate (CAGR) of 13.8% anticipated over the forecast period. By 2033, the market is projected to attain a value of USD 4.24 billion, reflecting the growing importance of accurate, unified corporate registry data for operational efficiency, risk management, and digital transformation initiatives. This growth is primarily fueled by the rising complexity of business operations, stricter regulatory requirements, and the need for seamless data integration across diverse IT ecosystems.

The primary growth factor in the Corporate Registry Data Normalization market is the accelerating pace of digital transformation across both private and public sectors. Organizations are increasingly reliant on accurate and standardized corporate data to drive business intelligence, enhance customer experiences, and comply with evolving regulatory frameworks. As enterprises expand globally, the complexity of maintaining consistent and high-quality data across various jurisdictions has intensified, necessitating advanced data normalization solutions. Furthermore, the proliferation of mergers and acquisitions, cross-border partnerships, and multi-jurisdictional operations has made data normalization a critical component for ensuring data integrity, reducing operational risks, and supporting agile business decisions. The integration of artificial intelligence and machine learning technologies into data normalization platforms is further amplifying the market’s growth by automating complex data cleansing, enrichment, and integration processes.

Another significant driver for the Corporate Registry Data Normalization market is the increasing emphasis on regulatory compliance and risk mitigation. Industries such as BFSI, healthcare, and government are under mounting pressure to adhere to stringent data governance standards, anti-money laundering (AML) regulations, and Know Your Customer (KYC) requirements. Standardizing corporate registry data enables organizations to streamline compliance processes, conduct more effective due diligence, and reduce the risk of financial penalties or reputational damage. Additionally, the growing adoption of cloud-based solutions has made it easier for organizations to implement scalable, cost-effective data normalization tools, further propelling market growth. The shift towards cloud-native architectures is also enabling real-time data synchronization and collaboration, which are essential for organizations operating in dynamic, fast-paced environments.

The increasing volume and variety of corporate data generated from digital channels, third-party sources, and internal systems are also contributing to the expansion of the Corporate Registry Data Normalization market. Enterprises are recognizing the value of leveraging normalized data to unlock advanced analytics, improve data-driven decision-making, and gain a competitive edge. The demand for data normalization is particularly strong among multinational corporations, financial institutions, and legal firms that manage vast repositories of entity data across multiple regions and regulatory environments. As organizations continue to invest in data quality initiatives and master data management (MDM) strategies, the adoption of sophisticated data normalization solutions is expected to accelerate, driving sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the Corporate Registry Data Normalization market, accounting for the largest share in 2024, followed closely by Europe and the rapidly growing Asia Pacific region. The strong presence of major technology providers, early adoption of advanced data management solutions, and stringent regulatory landscape in North America are key factors contributing to its leadership position. Meanwhile, Asia Pacific is projected to exhibit the highest CAGR during the forecast period, driven by the digitalization of government and commercial registries, expanding financial services sector, and increasing cross-border business activities. Latin America and the Middle East & Africa are also witnessing steady growth, supporte

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Growth Strategies'. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Growth Strategies dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "growth strategies" + "growth strategy" + "growth strategies business". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Growth Strategies + Growth Strategy. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Growth Strategies-related keywords [("growth strategies" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Growth Strat. Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Growth Strategies (1996, 1999, 2000, 2002, 2004); Growth Strategy Tools (2006, 2008). Note: Not reported after 2008. Processing: Semantic Grouping: Data points for "Growth Strategies" and "Growth Strategy Tools" were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Growth Strategies (1996-2004); Growth Strategy Tools (2006, 2008). Note: Not reported after 2008. Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Growth Strategies dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

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."

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.

Challenge Description

This dataset and accompanying paper present a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. That is, a date written "31 May 2014" is spoken as "the thirty first of may twenty fourteen." We present a dataset of general text where the normalizations were generated using an existing text normalization component of a text-to-speech (TTS) system. This dataset was originally released open-source here and is reproduced on Kaggle for the community.

The Data

The data in this directory are the English language training, development and test data used in Sproat and Jaitly (2016).

The following divisions of data were used:

• Training: output_1 through output_21 (corresponding to output-000[0-8]?-of-00100 in the original dataset)

• Runtime eval: output_91 (corresponding to output-0009[0-4]-of-00100 in the original dataset)

• Test data: output_96 (corresponding to output-0009[5-9]-of-00100 in the original dataset)

In practice for the results reported in the paper only the first 100,002 lines of output-00099-of-00100 were used (for English).

Lines with "

Working Excel spreadsheet compilation of recently published GMarc normalized datasets mapped onto granular segments of canonical Luke and related statistical findings. There are now over 56400 word tokens mapped.

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.

Dataset

This dataset was created by Venugopal Adep

Contents

This dataset provides processed and normalized/standardized indices for the management tool 'Knowledge Management' (KM), including related concepts like Intellectual Capital Management and Knowledge Transfer. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding KM dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "knowledge management" + "knowledge management organizational". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Knowledge Management + Intellectual Capital Management + Knowledge Transfer. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching KM-related keywords [("knowledge management" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (KM Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Note: Not reported after 2010. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Knowledge Management (1999-2010). Note: Not reported after 2010. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding KM dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

United States MCT Inflation: Normalized data was reported at 1.190 % in Mar 2025. This records an increase from the previous number of 1.080 % for Feb 2025. United States MCT Inflation: Normalized data is updated monthly, averaging 0.600 % from Jan 1960 (Median) to Mar 2025, with 783 observations. The data reached an all-time high of 9.310 % in Jul 1974 and a record low of -1.050 % in Aug 1962. United States MCT Inflation: Normalized data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.I027: Multivariate Core Trend Inflation.

Simple normalization of the data provided by the CSSE daily reports on github. Preparations I made: - Normalizing the Timestamp (since they provide four different formats) - Pruning the column labels (Region/Country => Region_Country, etc) - Adding a country code column

Photo by CDC on Unsplash

Related Tables / Normalized VersionThis dataset provides demographic information related to arrests made by the Tempe Police Department. Demographic fields include race and ethnicity, age range at the time of arrest, and gender for each party. The data is sourced from the Police Department’s Records Management System (RMS) and supports analysis of patterns related to arrests, enforcement activity, and demographic trends over time. This information is a component of ongoing efforts to promote transparency and provide context for law enforcement within the community.For detailed guidance on interpreting arrest counts and demographic breakdowns, please refer to the User Guide: Understanding the Arrest Demographic Datasets - Related Tables.Why this Dataset is Organized this Way?The related tables such as persons, charges, and locations follow a normalized data model. This structure is often preferred by data professionals for more advanced analysis, filtering, or joining with external datasets.Providing this format supports a wide range of users, from casual data explorers to experienced analysts.Understanding the Arrests Data (as related tables)The related tables represent different parts of the arrest data. Each one focuses on a different type of information, like the officers, individuals arrested, charges, and arrest details.All of these tables connect back to the arrests table, which acts as the central record for each event. This structure is called a normalized model and is often used to manage data in a more efficient way. Visit the User Guide: Understanding the Arrest Demographic Datasets - Related Tables for more details outlining the relationships between the related tables.Data DictionaryAdditional InformationContact Email: PD_DataRequest@tempe.govContact Phone: N/ALink: N/AData Source: Versaterm RMSData Source Type: SQL ServerPreparation Method: Automated processPublish Frequency: DailyPublish Method: Automatic

Normalized 2020 and 2050 First Street flood risk data aggregated at the census-tract level. A lower number indicates less risk (0 is minimum) and a higher number indicates more risk (1 is maximum). The normalization process subtracts the mean from the local value and divides it by the standard deviation: ((tract_value - overall mean) / stand_dev). The overall mean is the national average of all census tracts.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

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.