What type of data will you create, collect, link to or record?

Types of Data

Qualitative
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Quantitative
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Nominal
Ordinal
Interval
Ratio

Have you defined each element of your dataset (e.g., definitions of abbreviations used, units of measurement)?

Consider creating a data dictionary. A data dictionary is a file that describes each element of your dataset. If your dataset includes tabular (spreadsheet) data, the data dictionary would include a list of the fields in the table and what they mean, including units and precision.

If your data included R or Python code or scripts, the dictionary would provide a brief overview of the purpose of the code (if not already contained in comments); and information about the code relates to the dataset. [From Smithsonian Data Management Best Practices. Describing Your Data: Data Dictionaries (pdf)].

Data dictionaries have several benefits:

  1. Keeping things consistent across a project. The dictionary can define data names, labels, units, constraints such as acceptable range of values, and other characteristics.
  2. Enabling software to process a data file, by providing details to the software about the file. This information might include the type of data in each column (integer, character, date, etc); the name of the column; the physical units, if relevant; whether nulls are included; etc.
  3. Increasing interoperability and reuse of the data that you want to share and publish.
  4. Providing “human-readable” details to support discovery, interpretation and analysis.

For more details on what might be in a data dictionary, how to make one, and examples, see:

 

Documenting Data


Metadata


ReadMe

Data Dictionary information created by The University of Iowa Libraries and used with permission, Creative Commons Attribution license (CC BY) .

 

 

Ethics and Legal Compliance
◉ Does your data include sensitive information or research involving humans?
◉ Are there person-related ethical considerations with your data?
◉ What legal or intellectual property issues might you encounter?
◉ Does your University have a data ownership policy?