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.

Normalize data

The global Normalizing Service market is experiencing robust growth, driven by increasing demand for [insert specific drivers, e.g., improved data quality, enhanced data security, rising adoption of cloud-based solutions]. The market size in 2025 is estimated at $5 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key trends, including the growing adoption of [insert specific trends, e.g., big data analytics, AI-powered normalization tools, increasing regulatory compliance requirements]. While challenges remain, such as [insert specific restraints, e.g., high implementation costs, data integration complexities, lack of skilled professionals], the market's positive trajectory is expected to continue. Segmentation reveals that the [insert dominant application segment, e.g., financial services] application segment holds the largest market share, with [insert dominant type segment, e.g., cloud-based] solutions demonstrating significant growth. Regional analysis shows a strong presence across North America and Europe, particularly in the United States, United Kingdom, and Germany, driven by early adoption of advanced technologies and robust digital infrastructure. However, emerging markets in Asia-Pacific, particularly in China and India, are exhibiting significant growth potential due to expanding digitalization and increasing data volumes. The competitive landscape is characterized by a mix of established players and emerging companies, leading to innovation and market consolidation. The forecast period (2025-2033) promises continued market expansion, underpinned by technological advancements, increased regulatory pressures, and evolving business needs across diverse industries. The long-term outlook is optimistic, indicating a substantial market opportunity for companies offering innovative and cost-effective Normalizing Services.

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.

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.

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

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."

Data and supplementary information for the paper entitled "Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages" to be published at EMNLP 2015: Conference on Empirical Methods in Natural Language Processing — September 17–21, 2015 — Lisboa, Portugal.

ABSTRACT: Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.

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

Discover the booming Normalizing Service market! Explore a detailed analysis revealing a $5 billion market in 2025 projected to reach $15 billion by 2033, driven by big data, cloud computing, and regulatory compliance. Learn about key trends, regional breakdowns, and leading companies.

Dataset

This dataset was created by Skylark4

Released under CC0: Public Domain

Contents

Background Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization. Results We first explore the conditions for validity of the 'constant mean assumption', the key assumption underlying current normalization methods. We introduce 'frequency normalization', a 'spike-in'-based normalization method which estimates array sensitivity, reduces background noise and allows comparison between array designs. This approach does not rely on the constant mean assumption and so can be effective in conditions where standard procedures fail. We also define 'scaled frequency', a hybrid normalization method relying on both spiked transcripts and the constant mean assumption while maintaining all other advantages of frequency normalization. We compare these two procedures to a standard global normalization method using experimental data. We also use simulated data to estimate accuracy and investigate the effects of noise. We find that scaled frequency is as reproducible and accurate as global normalization while offering several practical advantages. Conclusions Scaled frequency quantitation is a convenient, reproducible technique that performs as well as global normalization on serial experiments with the same array design, while offering several additional features. Specifically, the scaled-frequency method enables the comparison of expression measurements across different array designs, yields estimates of absolute message abundance in cRNA and determines the sensitivity of individual arrays.

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.

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.

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

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.

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.

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.

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 "

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