Guide to Project Data Management

Briefly describe what types of data you are collecting or producing. In addition, explain what kinds of already existing data you will (re)use. List, for example, the types of texts, images, photographs, measurements, statistics, physical samples or codes.

Categorise your data in a table or with a clear list, for example:
A) previously collected existing data which is being reused in this project,
B) data collected for this project,
C) data produced as an outcome of the research process.
The categorisation can form a general structure for the rest of the DMP.

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 after the project. List both. The file format is a primary factor in the accessibility and reusability of your data in the future.

In the DMP, what is important is to describe the required disk space, not how many informants participated in the project. A rough estimation of the size of the data is sufficient, for example, less than 100 GB, approx. 1 TB or several petabytes.

Tips for best practices

• Use a table or bullet points for a concise way to present data types, file formats, the software used and the size of the data.
• Examples of file formats are .csv, .txt, .docx, .xslx and .tif.
• Make sure to describe any special or uncommon software necessary to view or use the data, especially if the software is coded in your project.
• You can also estimate the increase in data production or collection during the project for a specific time period: "The project is producing/collecting approximately 100 GB of data per week."

AVOID OVERLAPS WITH THE RESEARCH PLAN! Data analysis and methodological issues related to data and materials should be described in your research plan.

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

Describe how you will agree upon the rights of use related to your research data – including the collected, produced and (re)used data of your project. Here, you can employ your categorisation in the first question. Each of these categories involves different rights and licenses. Describe the transfer of rights procedures relevant to your project. Describe confidentiality issues if applicable in your project.

Tips for best practices

• Agreements on rights of use should be made as early as possible in the project life cycle.
• Have you gained consent for data preservation and sharing?
• Follow the funder's or publisher's policies.

It is recommended to make all of the research data, code and software created within a research project available for reuse, e.g., under a Creative Commons (https://creativecommons.org/choose/Opens in a new window), GNU (https://www.gnu.org/licenses/gpl-3.0.en.htmlOpens in a new window) or MIT license (https://opensource.org/licenses/MITOpens in a new window), or under another relevant license.

Data documentation enables data sets and files to be discovered, used and properly cited by other users (human or computer). Without sufficient documentation the data cannot be reused.

Documentation includes essential information regarding the data, for example a) core metadata (for discovery and identification) where, when, why and how the data were collected as well as b) descriptive information how the data is interpreted correctly using metadata standards, vocabularies and e.g. readme-files.

Tips for best practices

• Describe all the types of documentation (README files, metadata standards, vocabularies etc.) you will provide to help secondary users to understand and reuse your data. Repositories often require the use of a specific metadata standard. Check whether a discipline-specific metadata schema or standard exists that can be adopted.
• Consider how the data will be organised during the project. Describe, for example, your file-naming conventions, version control and folder structure.
• Use research instruments, which create standardised metadata formats automatically.

Identify the types of information that should be captured to enable other researchers to discover, access, interpret, use and cite your data. See for example Qvain requirements (https://www.fairdata.fi/en/user-guides/qvain-user-guide/#QvainDataset)

Describe where you will store and back up your data during your research project. Consider who will be responsible for backup and recovery. If there are several researchers involved, create a plan with your collaborators and ensure safe transfer between participants.

Show that you are aware of the storing solutions provided by Laurea or your organisation. Do not merely refer to IT services. In the end, you are responsible for your data, not the IT department or the organisation.

Explain the methods for preserving and sharing your data after your research project has ended in more detail in Section 5.

Tips for best practices

• The use of a safe and secure storage provided and maintained by Laurea’s IT support or other reliable IT provider such as CSC is preferable. Read the Laurea Guide for Processing information materials.
• Do NOT USE external hard drives as the main storing option.
• Follow Laurea's data security requirements

It is essential to consider data security issues, especially if your data include sensitive data, personal data, politically sensitive information or trade secrets. Describe who has access to your data, what they are authorised to do with the data, or how you will ensure the safe transfer of data to your collaborators.

Tips for best practices

• Access controls should always be in line with the level of confidentiality involved.

Briefly describe what part of your data you will preserve, where it is preserved, and for how long. Long term preservation means that data is preserved for as long as necessary, for several decades or even centuries.

You can categorise your data sets according to the anticipated preservation period:

A) Data to be destroyed upon the ending of the project
B) Data to be archived for a verification period, which varies across disciplines, e.g., 5–15 years
C) Data to be archived for potential re-use, e.g., for 25 years
D) Data with long-term value to be archived by a curated facility for future generations for tens or hundreds of years

You will need to decide which of your research data to preserve and dispose of. Data that is unique or difficult to replicate might have long-term value and be fit for preservation. Special long term data repositories should be used for digital preservation. 

Tips for best practices

• Decisions about preserving data should begin during the data management planning stage, and should take into account e.g. institutional guidance and requirements.
• Use data repositories with a commitment to long-term curation, e.g. Fairdata Digital Preservation ServiceOpens in a new window is dedicated for research datasets that have significant value to the organization or on a national level currently and especially also in the future. Contact your home organisation for further information.

Summarise here all the roles and responsibilities described in the previous answers.

Tips for best practices

• Outline the roles and responsibilities for data management/stewardship activities, for example, data capture, metadata production, data quality, storage and backup, data archiving, and data sharing. Name the responsible individual(s) where possible.
• For collaborative projects, explain the co-ordination of data management responsibilities across partners.
• Indicate who is responsible for implementing the DMP and for ensuring that it is reviewed and, if necessary, revised.
• Consider scheduling regular updates of the DMP.

Finally, consider who will be responsible for the data resulting from your project after your project has ended.

6.2 What resources will be required for your data management procedures to ensure that the data can be opened and preserved according to FAIR principles (Findable, Accessible, Interoperable, Re-usable)?