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This document provides comprehensive details on the data collection methodology, quality assurance, processing, and analysis related to a computer-assisted personal interview (CAPI) system used in
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How to fill out DATA DOCUMENTATION

01
Start with the title of the document at the top.
02
Include a brief introduction explaining the purpose of the data documentation.
03
List all the data sources used and describe each source in detail.
04
Define all data fields and their formats, including types and possible values.
05
Include examples of raw data and indicate how it is structured and organized.
06
Describe data processing and any transformations applied to the data.
07
Include information on data storage including formats and locations.
08
Add any relevant metadata that provides context to the data.
09
Address data quality, including validation methods and error handling.
10
Conclude with a summary and any pertinent references or additional resources.

Who needs DATA DOCUMENTATION?

01
Data analysts who work with the data.
02
Researchers who need to understand the data context.
03
Developers who are integrating the data into applications.
04
Regulatory bodies requiring documentation for compliance.
05
Project managers overseeing data-related projects.
06
New team members needing to familiarize themselves with data sources.
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A few examples of data documentation are: Laboratory notebooks and experimental procedures. Questionnaires, codebooks, data dictionaries.
What is data documentation? Data documentation provides the contextual information needed to discover, understand, access and reuse research data. Without this information it may be impossible for future users, including yourself, to understand your data.
A few examples of data documentation are: Laboratory notebooks and experimental procedures. Questionnaires, codebooks, data dictionaries.
Five Ways to Document Your Data Cite the source of your data. Define your data using a data dictionary. Describe the entire data package using a data specification or readme file. Track data lineage to visualize your data processing pipeline. Capture the reproducible environment and workflow of all changes made to your data.
Five Ways to Document Your Data Cite the source of your data. Define your data using a data dictionary. Describe the entire data package using a data specification or readme file. Track data lineage to visualize your data processing pipeline. Capture the reproducible environment and workflow of all changes made to your data.
Data documentation provides the contextual information needed to discover, understand, access and reuse research data. Without this information it may be impossible for future users, including yourself, to understand your data.
documentation | Intermediate English documentation. noun [ U ] /ˌdɑk·jə·menˈteɪ·ʃən/ official papers, or written material that provides proof of something. writing.

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DATA DOCUMENTATION refers to a collection of metadata and details that describe the data assets, their context, structure, and significance. It provides vital information regarding data management practices and data quality.
Typically, researchers, data stewards, and organizations that manage data are required to file DATA DOCUMENTATION to ensure proper data governance and compliance with regulations.
Filling out DATA DOCUMENTATION involves providing detailed descriptions of the data set, including variable definitions, data sources, methodology, and any relevant standards. It may require using specific templates or software tools designed for this purpose.
The purpose of DATA DOCUMENTATION is to enhance data transparency, facilitate data sharing, ensure compliance with legal and ethical standards, and help users understand the data better for analysis and decision-making.
Important information that must be reported includes the dataset title, authorship, date of creation, description of the data, format, access and usage rights, and any limitations or ethical considerations related to the data.
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