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.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2021 estimated population) for selected grouped causes, by sex, 2000 to most recent year.

The table displays weekly age standardized mortality rates for every province in Canada (excluding territories), by sex, since 2019. The standardization is done using the 2011 Canadian population.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class contains the Special Surveys which are non-rectangular components of the PLSS from BLM survey records. These special survey areas are generated from combinations of special survey codes, designators, notes and suffix information in the PLSS Intersected feature class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2011 population) for selected grouped causes, by sex, 2000 to most recent year.

Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data.

The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. These are the corners of the PLSS. This feature class contains summary information about the coordinate location and reliability of corner coordinate information. alternate names or aliases for corners are also inlcuded in this feature class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. The fully intersected data is the atomic level of the PLSS that is similar to the Esri coverage or the smallest pieces used to build the PLSS. Polygons may overlap in this feature class. This feature class will also contain retired or replaced areas of the PLSS.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. These are the corners of the PLSS. This feature class contains summary information about the coordinate _location and reliability of corner coordinate information. alternate names or aliases for corners are also inlcuded in this feature class.

This table provides information on the degree of standardization of data definitions at large companies, by industry. This concerns the extent to which agreements exist about the data format (the form and size in which the data is stored), the meaning of data within the company and agreements about this with third parties outside the company. In this table, large companies are understood to mean companies with 250 or more employed persons. The data is broken down by industry. Data available for the period 2006-2008. Status of the figures: The figures are final. Changes as of April 6, 2011 This table has been discontinued. When will new numbers come out? The subject in this table was only requested in 2006, 2007 and 2008. The data in this table is no longer being updated.

Rank, number of deaths, percentage of deaths and age standardized mortality rates (based on 2021 estimated population) for leading causes of death, by sex, 2000 to most recent year.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This statistic shows how organizations use standardized project management practices worldwide in 2018. During the survey, 37 percent of respondents said standardized practices are used by some departments.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. The fully intersected data is the atomic level of the PLSS that is similar to the Esri coverage or the smallest pieces used to build the PLSS. Polygons may overlap in this feature class. This feature class will also contain retired or replaced areas of the PLSS.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. The fully intersected data is the atomic level of the PLSS that is similar to the Esri coverage or the smallest pieces used to build the PLSS. Polygons may overlap in this feature class. This feature class will also contain retired or replaced areas of the PLSS.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. These are the corners of the PLSS. This feature class contains summary information about the coordinate location and reliability of corner coordinate information. alternate names or aliases for corners are also inlcuded in this feature class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.