Research Data Management (the lifecycle of research data)

Well-organized and documented data is easy to use, share, open, preserve, and reuse. Documentation involves describing the methods, structure, and handling of the data. Keep documentation up to date throughout the research process. Post-research documentation can be significantly more difficult, if not impossible.

Good data documentation enables:

• Findability
• Accessibility
• Understandability
• Evaluability
• Long-term preservation
• Reusability

Different disciplines have various documentation practices, which should be followed. At its simplest, a readme file describing the overall content is created with the data.

Good documentation includes:

1. Data collection methods: sampling, data collection process, used devices, and software
2. Quality assurance methods
3. File and folder structure
4. Version control
5. Access and usage conditions or confidentiality information
6. Names, identifiers, and descriptions of variables, datasets, and values
7. Explanation or definition of codes and classification systems used
8. Definitions of technical terms and abbreviations used
9. Codes for missing values and their reasons

Source: Fuchs, S., Koivula, H., Korhonen, T., Lindholm, T., Rauste, P., & Siipilehto, L. (2023, May 17). Data Organisation ABC workshop - Datan Organisoinnin ABC työpaja. Zenodo. https://doi.org/10.5281/zenodo.7944449

Metadata and descriptive information should be collected during the research process. Describing data post-research is often more laborious.

High-quality metadata serves as a researcher's calling card for their study. Metadata includes information about the data's:

• Name
• Production date
• Producer
• Format
• Subject
• Usage rights

Different disciplines have established practices for describing data and marking metadata. However, it is important that the basics are described regardless of the field, promoting the findability, accessibility, interoperability, and reusability of research data in line with FAIR principles. Descriptions can be stored as text files or using an appropriate metadata format.

Guidelines for storing metadata by the Data Archive:

Each research dataset should have its own directory, containing both the data and its descriptive information. Some descriptive details are included within the main data file (e.g., variable explanations or unit information), but most are stored in separate descriptive files.

According to the Finnish Social Science Data Archive research data description should include these elements:

• Description of how the study was conducted
  • Crucial information include the original purpose of the data, creators/principal investigators, producers/funders, selection criteria of study population and units of observation, and information on how the data were collected. As well as information about possible publications related to the data, population/universe covered, unit of analysis, selection criteria, data collection and data source. 
• Instrument of data collection
  • Data collection instrument, such as an interview form, questions, etc., is stored in all languages used.
• Description of data files
  • All properties of a single file should be described. It is recommended that the following aspects are documented for each file:
    • name of the file
    • file location (file path)
    • file size
    • file format
    • software used to create the file
    • date of creation
    • file creator
    • file version
    • access rights set for the file
• Description of variables
  • The details of variables are described as accurately as possible. Additionally, information about data processing and any changes made are included. Some of the information may be directly in the data file, while some are in the descriptive metadata. The variables include:
    • number of variables and units of observation
    • muuttujalista, jossa luetellaan kunkin muuttujan nimi, selite ja sijainti tiedostossa sekä muuttujien saamat arvot ja niiden selitteet
    • list of variables with the name and label of each variable as well as its location in the file and its values and value labels
    • frequency distribution of each variable
    • information on the classifications used, e.g. "main categories of the ISCO-88 were used in the occupational classification" or "country codes: 3-digit ISO 3166"
    • meanings of abbreviations used
    • codings for missing data
    • information on constructed variables (e.g. how the weight variables and sum variables were calculated)
    • recoding and standardising of variables
    • data protection measures taken
• Availability information
  • In the descriptive metadata, provide information on how the data is accessible and where it is stored.
• Contextual information and paradata 
  • The descriptive metadata also includes information about the external conditions, as they are a prerequisite for the reuse and understanding of the research data.