This deep learning model is used to transform incorrect and non-standard addresses into standardized addresses. Address standardization is a process of formatting and correcting addresses in accordance with global standards. It includes all the required address elements (i.e., street number, apartment number, street name, city, state, and postal) and is used by the standard postal service.

      An address can be termed as non-standard because of incomplete details (missing street name or zip code), invalid information (incorrect address), incorrect information (typos, misspellings, formatting of abbreviations), or inaccurate information (wrong house number or street name). These errors make it difficult to locate a destination. Although a standardized address does not guarantee the address validity, it simply converts an address into the correct format. This deep learning model is trained on address dataset provided by openaddresses.io and can be used to standardize addresses from 10 different countries.



  Using the model


      Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.



    Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input
    Text (non-standard address) on which address standardization will be performed.

    Output
    Text (standard address)

    Supported countries
    This model supports addresses from the following countries:

      AT – Austria
      AU – Australia
      CA – Canada
      CH – Switzerland
      DK – Denmark
      ES – Spain
      FR – France
      LU – Luxemburg
      SI – Slovenia
      US – United States

    Model architecture
    This model uses the T5-base architecture implemented in Hugging Face Transformers.
    Accuracy metrics
    This model has an accuracy of 90.18 percent.

    Training dataThe model has been trained on openly licensed data from openaddresses.io.Sample results
    Here are a few results from the model.

Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

including the test data of three versions of RSA timing attack program and workflow verification system respectively. The attributes of test data contain no.

Fisheries management is generally based on age structure models. Thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. The otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. It contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. The traditional human reading method to determine age is very time-consuming. Automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. Using the routinely measured otolith data of plaice (Pleuronectes platessa; Linnaeus, 1758) and striped red mullet (Mullus surmuletus; Linnaeus, 1758) in the eastern English Channel and north-east Arctic cod (Gadus morhua; Linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. Contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. To finalize this standardization process, all images were resized and binarized. Several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. For this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). This method was approved to these three species and could be applied for others species for age determination and stock identification.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

The documents contained in this dataset reflect NASA's comprehensive IT policy in compliance with Federal Government laws and regulations.

Software benchmarking study of finalists in NIST's lightweight cryptography standardization process. This data set includes the results on several microcontrollers, as well as the benchmarking framework used.

Data show measurements of total diameter, lumen diameter, and relative theoretical hydraulic conductivity, which were taken on vessel elements and wide-dband tracheids of two non-fibrous cacti species.

ABSTRACT Grain quality determination involves important stages such as collection of the representative sample, homogenization, and dilution. The interrelation among sampling, homogenization, and working sample size is essential to the reliability of the information generated. Therefore, this work aimed to analyse the performance of mechanical homogenizers used in the commercialization of grains in Brazil, as a function of the size of the working sample masses during grain classification. The samples were homogenized and diluted in Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter until reaching masses of 0.025, 0.050, 0.075, 0.100 and 0.125 kg to determine the level of damaged grains. A 3 x 4 x 5 factorial design was used, meaning three treatments relative to homogenizers (Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter), four dilutions (4, 8, 12 and 16% damaged grains), and five grain sample sizes (0.025, 0.050, 0.075, 0.100 and 0.125 kg) with nine repetitions. The means were compared by Tukey test and to the original means of prepared samples (4, 8, 12, and 16%) by Student’s t-test. Working samples can be utilized with masses between 0.025 and 0.125 kg to classify damaged soybeans grains. The devices Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter are similar in the reduction and homogenization of soybean samples for different levels of damaged grains and sample sizes.

This dataset provides processed and normalized/standardized indices for the management tool 'Business Process Reengineering' (BPR). 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 BPR 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 "business process reengineering" + "process reengineering" + "reengineering management". Processing: None. The dataset utilizes the original Google Trends index, which is base-100 normalized against the peak search interest for the specified terms and period. 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 Reengineering + Business Process Reengineering + Process Reengineering. Processing: The annual relative frequency series was normalized by setting the year with the maximum value to 100 and scaling all other values (years) proportionally. 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 BPR-related keywords [("business process reengineering" OR ...) AND ("management" OR ...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly publication counts in Crossref. Data deduplicated via DOIs. Processing: For each month, the relative share of BPR-related publications (BPR Count / Total Crossref Count for that month) was calculated. This monthly relative share series was then normalized by setting the month with the maximum relative share to 100 and scaling all other months proportionally. 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: Reengineering (1993, 1996, 2000, 2002); Business Process Reengineering (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Reengineering" and "Business Process Reengineering" were treated as a single conceptual series for BPR. Normalization: The combined series of original usability percentages was normalized relative to its own highest observed historical value across all included years (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: Reengineering (1993, 1996, 2000, 2002); Business Process Reengineering (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Reengineering" and "Business Process Reengineering" were treated as a single conceptual series for BPR. Standardization (Z-scores): Original scores (X) were standardized using Z = (X - ?) / ?, with a theoretically defined neutral mean ?=3.0 and an estimated pooled population standard deviation ??0.891609 (calculated across all tools/years relative to ?=3.0). Index Scale Transformation: Z-scores were transformed to an intuitive index via: Index = 50 + (Z * 22). This scale centers theoretical neutrality (original score: 3.0) at 50 and maps the approximate range [1, 5] to [?1, ?100]. 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 BPR dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Used process models in the study.

This dataset includes the following files:

1. A pdf file containing the method naming standards survey questions we used in Qualtrics for surveying professional developers. The file contains the Likert scale questions and source code examples used in the survey.

2. A CSV file containing professional developers responses to the Likert scale questions and their feedback about each method naming standard, as well as their answers to the demographic questions.

3. A pdf copy of the survey paper (Preprint).

Survey Paper Citation: Alsuhaibani, R., Newman, C., Decker, M., Collard, M.L., Maletic, J.I., "On the Naming of Methods: A Survey of Professional Developers", in the Proceedings of the 43rd International Conference on Software Engineering (ICSE), Madrid Spain, May 25 - 28, 2021, 12 pages

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Data standardization of BP neural network input layer.

Snapshot img

Analytical Standards Market Size 2025-2029

The analytical standards market size is forecast to increase by USD 734.1 million, at a CAGR of 7.1% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the burgeoning life sciences industry. The increasing demand for precise and accurate analytical results in this sector is fueling the market's expansion. Another key trend is the rising adoption of customized analytical standards, catering to the unique requirements of various industries and applications. However, this market is not without challenges. The limited shelf life of analytical standards poses a significant hurdle, necessitating continuous production and supply to maintain consistency and accuracy.
Companies must address this issue by investing in advanced technologies and supply chain management strategies to ensure a steady flow of fresh standards to their customers. Navigating these dynamics requires strategic planning and a deep understanding of market demands and challenges to capitalize on opportunities and mitigate risks. This robust market performance is attributed to the increasing importance of accurate and reliable analytical data in various industries, including pharmaceuticals, food and beverage, and environmental testing.

What will be the Size of the Analytical Standards Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant quest for improved quality control and regulatory compliance across various sectors. Analytical techniques, such as statistical analysis and measurement uncertainty assessment, play a pivotal role in ensuring accuracy and precision in laboratory testing. Instrument qualification and system suitability testing are essential components of quality management systems, ensuring the reliability and performance of analytical instruments. Error analysis and traceability standards enable the identification and resolution of issues, while sample preparation and standard operating procedures ensure consistent results. Reproducibility studies and reference materials are crucial for method validation and performance indicators, which are essential for laboratory accreditation and method validation.

For instance, a pharmaceutical company successfully increased its sales by 15% by implementing a robust quality assurance program, focusing on data integrity, instrument calibration, and method validation. The industry growth in analytical standards is expected to reach 5% annually, driven by the increasing demand for data-driven decision-making and regulatory requirements. The market's dynamism is reflected in the ongoing development of testing methodologies, calibration procedures, and testing protocols, which aim to enhance method performance and data acquisition systems. The limit of detection and limit of quantification continue to be critical performance indicators, ensuring the reliable and accurate measurement of analytes in various matrices.

How is this Analytical Standards Industry segmented?

The analytical standards industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Chromatography
  Spectroscopy
  Titrimetry
  Physical properties testing


Application

  Food and beverages
  Pharmaceuticals and life sciences
  Environmental
  Others


Methodology

  Bioanalytical testing
  Raw material testing
  Stability testing
  Dissolution testing
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The Chromatography segment is estimated to witness significant growth during the forecast period. The market is driven by the increasing demand for accurate and reliable results in various industries, particularly in quality control and regulatory compliance. Analytical techniques, such as chromatography, play a pivotal role in this market due to their ability to provide precise measurement and identification of components in complex samples. Chromatography technology, including liquid chromatography (LC) and gas chromatography (GC), is widely used for separating and identifying impurities in diverse sample types. The integration of chromatography with advanced analytical tools like mass spectrometry (MS) further enhances its analytical power, ensuring more accurate and comprehensive profiling of complex mixtures.

Statistical analysis and measurement uncertainty are crucial aspects of the market, ensuring data integrity and reproduc

This dataset provides processed and normalized/standardized indices for the management tool 'Benchmarking'. 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 Benchmarking 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 "benchmarking" + "benchmarking management". 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 Benchmarking. 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 Benchmarking-related keywords ["benchmarking" 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 (Benchmarking 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: Benchmarking (1993, 1996, 1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported in 2022 survey data. 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: Benchmarking (1993-2017). Note: Not reported in 2022 survey data. 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 Benchmarking dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Lake County’s Ethics & Oversight Committee will be responsible for administering the complaint review process and making a recommendation to the County Board on what actions, if any, should be taken. Learn more about the complaint handling procedures.

Dataset Description

This dataset is a collection of customer, product, sales, and location data extracted from a CRM and ERP system for a retail company. It has been cleaned and transformed through various ETL (Extract, Transform, Load) processes to ensure data consistency, accuracy, and completeness. Below is a breakdown of the dataset components: 1. Customer Information (s_crm_cust_info)

This table contains information about customers, including their unique identifiers and demographic details.

Columns:

  cst_id: Customer ID (Primary Key)

  cst_gndr: Gender

  cst_marital_status: Marital status

  cst_create_date: Customer account creation date

Cleaning Steps:

  Removed duplicates and handled missing or null cst_id values.

  Trimmed leading and trailing spaces in cst_gndr and cst_marital_status.

  Standardized gender values and identified inconsistencies in marital status.

1. Product Information (s_crm_prd_info / b_crm_prd_info)

This table contains information about products, including product identifiers, names, costs, and lifecycle dates.

Columns:

  prd_id: Product ID

  prd_key: Product key

  prd_nm: Product name

  prd_cost: Product cost

  prd_start_dt: Product start date

  prd_end_dt: Product end date

Cleaning Steps:

  Checked for duplicates and null values in the prd_key column.

  Validated product dates to ensure prd_start_dt is earlier than prd_end_dt.

  Corrected product costs to remove invalid entries (e.g., negative values).

1. Sales Details (s_crm_sales_details / b_crm_sales_details)

This table contains information about sales transactions, including order dates, quantities, prices, and sales amounts.

Columns:

  sls_order_dt: Sales order date

  sls_due_dt: Sales due date

  sls_sales: Total sales amount

  sls_quantity: Number of products sold

  sls_price: Product unit price

Cleaning Steps:

  Validated sales order dates and corrected invalid entries.

  Checked for discrepancies where sls_sales did not match sls_price * sls_quantity and corrected them.

  Removed null and negative values from sls_sales, sls_quantity, and sls_price.

1. ERP Customer Data (b_erp_cust_az12, s_erp_cust_az12)

This table contains additional customer demographic data, including gender and birthdate.

Columns:

  cid: Customer ID

  gen: Gender

  bdate: Birthdate

Cleaning Steps:

  Checked for missing or null gender values and standardized inconsistent entries.

  Removed leading/trailing spaces from gen and bdate.

  Validated birthdates to ensure they were within a realistic range.

1. Location Information (b_erp_loc_a101)

This table contains country information related to the customers' locations.

Columns:

  cntry: Country

Cleaning Steps:

  Standardized country names (e.g., "US" and "USA" were mapped to "United States").

  Removed special characters (e.g., carriage returns) and trimmed whitespace.

1. Product Category (b_erp_px_cat_g1v2)

This table contains product category information.

Columns:

  Product category data (no significant cleaning required).

Key Features:

Customer demographics, including gender and marital status

Product details such as cost, start date, and end date

Sales data with order dates, quantities, and sales amounts

ERP-specific customer and location data

Data Cleaning Process:

This dataset underwent extensive cleaning and validation, including:

Null and Duplicate Removal: Ensuring no duplicate or missing critical data (e.g., customer IDs, product keys).

Date Validations: Ensuring correct date ranges and chronological consistency.

Data Standardization: Standardizing categorical fields (e.g., gender, country names) and fixing inconsistent values.

Sales Integrity Checks: Ensuring sales amounts match the expected product of price and quantity.

This dataset is now ready for analysis and modeling, with clean, consistent, and validated data for retail analytics, customer segmentation, product analysis, and sales forecasting.

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

This dataset contains all Chinese standard information data up to March 2024, including national standards 20000+, local standards 60000+, industry standards 20000+, and group standards 20000+, covering standard statuses such as "In force", "about to be implemented", and "abolished". Each type of standard has an independent. xlsx statistical table, which contains standard information fields such as standard name, standard number, release date, implementation date, status, and standard attributes.