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DMPTuuli guide: 1. General description of data

What kinds of data will be collected or produced

1.1 What kinds of data is your research based on? What data will be collected, produced or reused? What file formats will the data be in?

The DMP is part of your research plan. To avoid overlap between your DMP and the research plan, you can refer from one document to the other. Introduce your data analysis and other methods in your research plan.

Describe in this section, what kind of data you are going to collect or produce. Also discuss also what already existing data you are going to reuse (e.g. interviews, texts, images, photographs, audio or video recordings). If you are going to collect and process sensitive data, read more about data protection and the Uniarts Helsinki data protection policy.

Your answers to this section form a general structure for the rest of the plan. Categorize your data e.g. in the following way, and use the same categorization in all phases of your plan:

  1. data collected for this project
  2. data produced as an outcome of the process
  3. previously collected existing data reused in this project

List the file formats for each data set. In some cases, the file formats used during the research project may differ from those used in archiving the data. List both.

Tips for best practices

  • Use a table or bullet points to present  
    • data types
    • file formats (e.g. .csv, .xslx, .txt, .docx,  .mp3, .mp4 and .tiff)
    • other information
  • Separate previously produced or collected data from new data.

For consideration while gathering data

There are several issues to consider while gathering data, such as legal and ethical issues, contextual factors taking place during the research (political climate etc.) and also information about the the process of collecting data. All relevant information is attached to the archived data. Finally all data is described adequately (metadata) so that it will be easy to find by others.

Quality control

1.2 How will the consistency and quality of data be controlled?

Data quality control ensures that no data is accidentally changed and that the accuracy of data is maintained over its entire life cycle. Quality problems can emerge due to the technical handling, converting or transferring of data, or during its contextual processing and analysis.

Discuss how data integrity and quality are controlled, as well as how they are managed. Quality control can mean, for example, making sure that the original quality of image and audio-visual files is maintained in compressed format, standardized observation methods and practices, validating data input, peer-reviewing data, or describing data using vocabularies.

Tips for best practices

  • Transcriptions of audio or video interviews should be checked by someone other than the transcriber.
  • Analog material should be digitized in as high resolution as possible for accuracy.
  • In all conversions, maintaining the original information content should be ensured.
  • Software-producing checksums should be used.

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