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    • DS Methods Overview
      • ¶ Data Visualization
        • Basic Charts
        • Timeline
        • Treemap
        • Network
      • ¶ Mapping
        • GIS
        • Story Maps
        • Maps as Interface
      • ¶ 3D & Immersive Technologies
        • Augmented Reality & Virtual Reality
        • 3D Modeling & Laser Scanning
        • Immersive Games
        • 360 Degree Capturing
      • ¶ Digital Exhibits
        • Example Exhibits
      • ¶ Hypertext
        • Publishing & Presenting
        • Multimedia
        • Narratives & Games
      • ¶ Textual Encoding Initiative
        • What Does TEI Markup Look Like?
        • Facsimiles & Critical Editions
      • ¶ Text Analysis
        • Out of the Box vs Coding and Scripting
        • Text Analysis Examples
    • Introduction to Data
      • ¶ What is Data?
        • Structured & Unstructured Data
        • Quantitative & Qualitative Data
        • Humanities & Data
      • ¶ What is Data Visualization?
      • ¶ DS Data Projects
        • Getting Started Questions
        • Project Examples
        • Visualization Tools
      • ¶ Research Data Lifecycle
        • Data Management Best Practices
      • ¶ Glossary
    • Introduction to Mapping
      • ¶ What is Spatial Data?
      • ¶ Vector and Raster Data
        • Vector and Raster Data Examples
        • File Format Examples
      • ¶ Starting a Mapping Project
        • Getting Started Questions
        • Project Examples
        • Mapping Tools and Platforms
    • Introduction to Digital Exhibits
      • ¶ What is a Digital Exhibit?
        • Related Concepts
      • ¶ Starting a Digital Exhibit
      • ¶ Exhibit Examples
      • ¶ Platforms
  • Digital Pedagogy
    • ¶ What is Digital Pedagogy?
    • ¶ Considerations
    • ¶ Recommendations
    • ¶ Assignment Design
      • Learning Outcomes
      • Mode/Method/Tool Process
      • Assignment Examples
    • ¶ Evaluation
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    • ¶ Maintenance & Archiving
      • Recommended File Formats
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  1. Digital Scholarship
  2. Introduction to Data

¶ Glossary

PreviousData Management Best PracticesNextIntroduction to Mapping

Last updated 4 years ago

Attributes are the describing characteristics or properties that define all items pertaining to a certain category applied to all cells of a column.

Data is a collection of facts, statistics, measurements, and the like that are recorded (or should be recorded) using standardized methods.

Data collection is a systematic process of gathering observations or measurements.

Data Visualization is a graphical representation of data.

Metadata is often simply defined as "data about data" or "information about information".

Data points are single units of data or single observations, e.g., a single measurement or a single geolocation point.

A Database is a systematic collection of data.

Dataset (or data set) is a collection of data. Typically, it is structured and housed in a tabular form (e.g., a spreadsheet).

The data life cycle represents all of the stages of data throughout its life from its creation for a study to its distribution and reuse. The data lifecycle begins with a researcher(s) developing a concept for a study; once a study concept is developed, data is then collected for that study.

Data Literacy is the ability to read, understand, create, and communicate data as information.

Geospatial data is defined in the series of standards as data and information having an implicit or explicit association with a location relative to Earth.

Quantitative data relates to the quantity of something, and typical examples of quantitative data are numbers.

Qualitative data is used to characterize objects or observations, which can be collected in a non-numerical and non-binary way, such as languages.

Structure data refers to data that resides in a fixed field within a file or record, e.g., spreadsheet.

Unstructured data refers to a bucket of content or data points that are not organized and categorized, e.g., PDF files, image files.

ISO/TC 211