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Responsible Thesis-Writing Process

The University of Vaasa's guidelines for research ethics and research data management.

Research data management and the FAIR principles

Research data refers to any data with which the analysis and results of a study can be repeated and validated. The data may have been collected by the researcher, generated during the study or consist of pre-existing archival data, and include various measurement results, survey and interview data, notes, research diaries, software or source codes.

Data management refers to the systematic collection, processing, storing and description of research data. Students are encouraged to learn about data management early in their studies, because good data management skills are beneficial to study progress and to adopting suitable data management practices during the thesis-writing process.

Data management practices should seek to comply with the FAIR principles, ensuring that the data is

  • Findable,
  • Accessible,
  • Interoperable and
  • Re-usable.

This is achieved, for example, through the use of open file formats, clear definition of ownership, terms and conditions, as well as licenses, versatile description of the content, structure and rights of use of the data (rich metadata), and the use of persistent identifiers (e.g., DOI, URN, ORCID). Learn more about the FAIR principles and the policy component for open access to research data.

FAIR: Findable, Accessible, Interoperable, reusable

Stages of data management

1. Planning

Before you collect any data, record the most suitable data practices in a Data Management Plan (DMP) that can be supplemented as the work progresses and plans become more accurate. Formulating a plan will help you identify potential data protection risks, as well as solutions suitable for storing and describing your data. Careful data management also allows you to make the data accessible for potential reuse and thus improve the reliability of your research and the repeatability of the results.

Planning can be done using the DMPTuuli tool that is accessible with your HAKA credentials. DMPTuuli contains templates and instructions that can be applied to the data management plan of a thesis.

DMPTuuli logo.

 

2. Storing and Organising Data

Choose a secure storage solution for your data, based on the demand for the data and its confidentiality level. Secure storage, version control and backup help prevent any unintentional deletion of data. Open file formats, logical file naming and folder structure, as well as rich content descriptions facilitate the findability, intelligibility and sharing of data. Consider the following questions when choosing the storage solution:

  • Should the data be remotely accessible or shareable with others?
    • Ensure secure access control so that only authorised parties have access to the data.
    • Utilise the university’s cloud storage solutions (e.g., Owncloud, SharePoint) suitable for data sharing.
  • What is the confidentiality level of the data?
    • Protect confidential data with a password or encryption.
    • Confidential data should not be stored in commercial cloud services, such as Dropbox or Google Drive.
  • Are you utilising storage space provided by the university or your personal devices?
    • If you store data on your personal devices, ensure backups, device password protection and anti-virus protection.
    • If you use services provided by the university, using automatic backup solutions is recommended.

NB: External storage media, such as flash drives, are not recommended as primary storage solutions, because data stored on them is susceptible to becoming lost, deleted and unintentionally shared with outsiders.

Backup

Backing up helps you decrease the risk of irreparable damage to or deletion of data. Always keep separate working and backup copies of the research data. Choose storage solutions that include automatic backup. Backing up should be based on the 3-2-1 Rule, meaning that data is stored in the following way:

  • in at least three copies
  • on two different types of storage media
  • one of which is kept physically separate from the others.

File formats

To ensure the usability of your data on a variety of devices and software, using open, non-commercial file formats is recommended. Most software supports the following common file formats:

  • Text: txt, .odt., .rtf, .csv, PDF/A, .html,.xml
  • Images: jpeg, tiff, png, dng
  • Video: MPEG-4 (.mp4), dpx
  • Sound:  FLAC, aif, aac.

File naming and Folder structure

Systematic file naming practices and folder structures ensure the identifiability and findability of your data, even when there are time lapses in processing it. Clear file naming also simplifies file sharing. When you name a file:

  • Choose a descriptive name
  • Avoid names that are too long or short
  • Avoid special characters and spaces
  • To separate parts of the name, use the underscore (_), hyphen (-) or initial capital letters
  • Add dates, version numbers and/or modifier initials to distinguish between different file versions
  • Avoid overlap in folder and file names

NB: If you use abbreviations, remember to define them in writing so that they can be understood.

Documenting

Document the basics of your data during the thesis writing process to ensure the findability and usability of your data. Documenting makes it easy to check the contents of your data, how it has been processed and where it is stored. The simplest option is to record the descriptive data (or metadata) related to your data in a text file (a.k.a. README file) that you save as a separate file along with your data. Metadata may also be published according to the description guidelines of the particular publishing service. Record at least the following information in the file:

  • Data name, size and file format
  • Data content and descriptions of variables (abbreviations, measuring scales, coding)
  • Data collection (who, where, when, how)
  • Data processing (who, how, when)
  • Storing data and terms and conditions

Read more about storing, file naming, recommended file formats and documenting in the Data Management Guidelines of the Finnish Social Science Data Archive.

3. Publishing, archiving or deleting

Take care of your research data even after the completion of the thesis. Electronic data requires further measures to stay up-to-date, and not all data needs to be archived for long periods of time. Based on the reuse value of your data, choose appropriate measures, such as data archiving, publishing or deletion. Keep in mind that your right to use the University of Vaasa IT services expires after graduation, unless you continue in another university role, such as a position of doctoral researcher or employee. If the data is stored in the University of Vaasa systems, remember to transfer or delete it before your access rights expire.

If your data contains personal details, it is usually deleted after the thesis has been accepted. Keep in mind that moving a file to the recycle bin does not sufficiently delete the data. More thorough measures, such as overwriting a drive or mechanically destroying a flash drive, are needed. Further information on deleting the data: Office of the Data Protection Ombudsman or Data Management Guidelines of the Finnish Social Science Data Archive.

If the data has reuse value and you have permission to reuse or publish the data, you may publish or archive your data in a chosen data archive. Keep in mind that you may need permission for data reuse or publishing from your research subjects or potential customer, and that data anonymisation may be a condition for publishing. For example, the Finnish Social Science Data Archive, The Language Bank of Finland, and Fairdata’s IDA and Qvain offer domestic solutions for publishing data and related metadata, while Zenodo or EUDAT B2Share are some of the international service provider options.

Data Protection (GDPR)

Data protection refers to the safeguarding of personal data. The notion of personal data is broad, and what qualifies as personal data is any information that either directly or indirectly enables the identification of a person, for example by connecting an individual piece of information to another piece of information. More information on personal data: Office of the Data Protection Ombudsman. Personal data processing related to studies must adhere to the principles of the University of Vaasa data protection policy: University of Vaasa Information Security Policy.

Before collecting and processing personal data:

  • Discuss the issue with your thesis supervisor or the teacher in charge of the course
  • Familiarise yourself with the University of Vaasa personal data processing instructions: Data Processing Instructions (NB: The instructions can only be accessed by university personnel) and University of Vaasa Data Protection Statement (NB: currently available only in Finnish).
  • Be sure to provide the research participants with a privacy notice. Further reading available in this guide under Privacy Notice.

While collecting and processing personal data:

  • Limit the processing of personal data to what is necessary to achieve the aims of the thesis. Do not collect personal data “just in case”!
  • Store personal data in a secure way and ensure that third parties do not accidentally or intentionally gain access to personal data.

The student collecting personal data acts as the data controller.

Useful links

Saavutettavuusseloste Tillgänglighetsutlåtande