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Guide to Project Data Management

Welcome!

This guide is designed to help you in the data management of your Laurea projects. It implements Laurea's RDI guidelines, Laurea Research Data Policy, and other recommendations for open RDI activities. This guide is suitable for use with Laurea's project partners.

When projects generate RDI data, the data itself, its legality, and ownership must be taken care of. Data management helps to do this. The guide also provides basic information on contracts, data protection, datasecurity, and research ethics.

What is data management?

Data management is the responsible handling of project data, taking into account good academic practice. Responsible data management is an important part of an RDI project.

Data management means that the data and the associated descriptive information (metadata) are created, stored and organized in such a way that the data remain usable and reliable and that data protection and security are ensured throughout the life cycle of the data. A key element of records management is planning and the most important tool is the records management plan.

Data management is part of research integrity practice. Proper handling, versioning and storage of data ensures the reliability of research. Good data management practices allow data to be re-used and open access published. For Laurea, the project and the researcher, opening and further use of data is a merit that will produce results for Laurea in the National Research.fi portal.

Laurea Research Data Policy

Laurea Research Data Policy describes the principles and guidelines related to the collection, use, and management of research, development, and innovation data (RDI data) generated in research, development, and teaching at Laurea University of Applied Sciences. The aim is the discoverability, accessibility, usability, reliability, and accountability of data and related descriptive information.

The data policy is based on Laurea's strategy and national and international data policies. The values guiding Laurea University of Applied Sciences' operations and work are openness, effectiveness, and accountability. Laurea's research data policy is based on these values and contributes to their implementation.

Laurea Roadmap for Openness describes the principles and objectives for openness in terms of policy, data management, learning and publishing.

 

What is RDI data?

Research, development and innovation data (RDI data) refers to the data produced or used during a project, on which the results of the project are based and which allow the analysis and results of the project's research to be verified and replicated.

Examples of RDI material collected for your project include:

  •     material generated from interviews (videos, audio files)
  •     transcriptions of the interviews
  •     surveys, questionnaires, responses, etc.
  •     working files of original material
  •     recordings and videos you have made
  •     material generated in a facilitated workshop, including photographs
  •     your research diaries, notes
  •     measurement results
  •     coding, cataloguing, structuring and annotating the material at different levels
  •     different versions of the analysed data
  •     physical and electronic laboratory notebooks
  •     source codes and software
  •     physical or electronic source material collected by you (e.g. biological samples, objects, text samples, collection

What is data and metadata?

RDI data is a resource used by a researcher or during a research, development, and innovation project (RDI project), i.e. the basic material of a research or development project in electronic or physical form. RDI data is collected, observed, measured, or created to confirm hypotheses and verify research results. Research data can usually be in digital form but can also exist in analog or physical form (e.g. laboratory notebooks).

Metadata, or descriptive information refers to information describing and summarizing the context, content, and structure of RTI data, its management and/or processing, and its compilation. Descriptive information about the data.

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