Title: Module 4: Introduction to Research Data Management
Course: Teaching Open Science and Research Data Management for Undergraduates > Section: Module 4: Introduction to Research Data Management

What is research data management?

Research data means all the material used in the research, on which the research is based. The research results are based on the analysis of the research material. The data can be self-collected, for example surveys, interviews, measurement results, notes or field diaries. The research can also use "ready-made" materials obtained elsewhere, for example archival sources, audio recordings, videos, photographs, literature as a research object, films, websites, message threads on discussion boards, medical imaging, simulations, etc. In this course we focus on digital research data, i.e. data that typically includes files. Descriptive and technical information is needed to accompany the data.

Research data management (RDM) means the managing and organisation of data during and after the active phase of a research project. It is the basis of successful research and ensures the sustainability and accessibility of data in the long-term and therefore re-use for future science.

Motivation: Why manage research data? What are the gains from good data management?

1. It is good scientific practice. Taking care of research data is essential as the results and conclusions are based on the data. Good data management makes the study reliable, reproducible and reusable: there will be a detailed record of the procedure and description of the data available if someone wants to re-use the data or there will be any questions about the data in the future.
2. There are new ways to gain merit in science, because open research outputs and open research methods give scientific credit to researchers. Data reuse and citation increase the impact and visibility of the research, the researcher and their home institution. Therefore, good research data management can bring added value to the whole community as well as create new opportunities for collaboration and funding.^1

Watch the video: Knowledge clip: What is Research Data Management^ (Ghent University Open Science, 2020)^2

Knowlegde clip: What is Research Data Management (RDM)?

References

1. CSC. Research Data Management (RDM) online course. CC BY. 4.0. https://e-learn.csc.fi/course/view.php?id=63. Accessed 26 Mar. 2025.

2. Ghent University Open Science. (2020). Knowledge clip: What is Research Data Management (RDM)? YouTube, uploaded by UGent Open Science, 11 May 2020.

Research data life cycle

Good research data management (RDM) is the basis of successful research. Its purpose is to ensure the reproducibility of research, which is the fundamental prerequisite of all scientific research. Good data management throughout the data life-cycle is crucial for facilitating data sharing and ensuring the sustainability and accessibility of data in the long-term and therefore their re-use for future science and for society at large.^1

There are different ways of describing the life cycle of research data and naming the stages. The following video by Ghent University Data Stewards presents the data management activities that can take place at different stages of the research process.

Watch the video: Knowledge clip: The research data lifecycle^ (Ghent University Open Science, 2020)^2

Knowledge clip: The research data lifecycle

Research data management (RDM) includes many aspects, e.g. the planning of data collection or generation, organising data, documentation and description, storage, version control, and after the conclusion of the research project, such as what data should be discarded, what should be kept and what can be shared. It is beneficial to plan how the data should be managed at the very start of a research project.^3 See image 1.

Image 1. Research data life cycle^4  (RDMkit, CC BY 4.0).

During the planning phase, not only the design of the research should be planned but also the management of the data. By preparing a data management plan, it is important to consider aspects of data management before the actual work starts. In the planning phase the researcher gets an impression of what kind of datasets will be generated, has a look into useful existing data sources and agrees on how to share data during and after the project, documents data collection and processing, names files, etc.^4

In the collecting phase, new datasets will be generated and existing ones found that can be re-used for the research´s purpose. At best, third-party data that is free for reuse (free license). In other cases, payment may be required for the utilization of existing data. It is always to be remembered to check and document the source of the data and ensure the capture of all other relevant metadata (metadata means detailed information that describes and provides context for research data). ^4

During the processing phase, data is worked on, checked, validated, and cleaned, with anonymization carried out whenever required. It is important to have the data described clearly and all data processing steps documented. Throughout this period, the safe storage of data is necessary, and it is often shared with project partners. Subsequently, the data is analyzed and interpreted, and written research outputs referring to the data sources are produced.^4

When the actual research work is completed, all relevant data needs to be preserved, and data selected for publication is to be published. The publication of data necessitates the use of a suitable repository (a specialized online platform where researchers can deposit/share/publish and retrieve data), the selection of appropriate access conditions and licenses, clear descriptions, and the inclusion of all relevant metadata. A preservation- and reuse-friendly file format should be chosen for both preservation and publication.^4 An overview of suitable formats can be found in the Library of Congress Recommended Formats Statement.

By publishing data in a proper way, it can be re-used by others, such as for follow-up research, research reviews, or training and teaching purposes.^4

References

1. Docs CSC. User guides and tutorials. Tips for Data Management. https://docs.csc.fi/data/datasets/datamanagement/. Accessed 26 Mar. 2025.

2. Ghent University Open Science. (2020). Knowledge clip: The research data lifecycle. YouTube, uploaded by UGent Open Science, 11 May 2020.

3. Fairdata. Data Management Checklist. https://www.fairdata.fi/en/data-management-checklist/. Accessed 26 Mar. 2025.

4. ELIXIR. (2021). Research Data Management Kit. A deliverable from the EU-funded ELIXIR-CONVERGE project (grant agreement 871075). CC BY 4.0. https://rdmkit.elixir-europe.org. Accessed 26 Mar. 2025.

FAIR vs. Open Data

In this chapter, we will explore the FAIR principles and clarify the differences between FAIR data and open data.

What is Open Data?

Open data is as defined by the Open Definition ^1: "Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike."

Open data is machine-actionable (or: machine-readable) data with an open license. Thanks to the license, the data is freely available to everyone. Open data can be, for example, population data, map data or, for example, real-time data on bus locations.^1

The Open Data Handbook ^2^ summarizes the most important:

- Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.
- Re-use and Redistribution: the data must be provided under terms that permit re-use and redistribution including the intermixing with other datasets.
- Universal Participation: everyone must be able to use, re-use and redistribute - there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed.^2

References

1. The Open Definition. Defining Open in Open Data, Open Content and Open Knowledge. CC BY 4.0. https://opendefinition.org/. Accessed 26 Mar. 2025.

2. Open Data Handbook. What Is Open Data? CC BY 4.0. https://opendatahandbook.org/guide/en/what-is-open-data/ . Accessed 26 Mar. 2025.

What is FAIR data?

When we talk about FAIR data, we mean data that complies with the FAIR Principles, whereas the concept of open data is based on the democratic idea to provide data to anybody interested - without any barrier, restriction, or payment.

FAIR principles have been developed to enhance the quality of research data. The acronym FAIR comes from the words Findable, Accessible, Interoperable, and Reusable. The principles emphasize machine-actionability; this means that research data can be found, accessed, it is interoperable and reusable using computational systems, with none or minimal human intervention.^1

In the context of FAIR, data should be understood as a broad concept that can refer to data, metadata, software, code, models and other documentation that makes research reproducible.^1

FAIR does not mean that all data should be made openly available; only the discovery metadata needs to be openly available. The data can be either open or available with restrictions if necessary. This should be stated in the terms of use in the administrative discovery metadata and include a machine actionable license.^1

Discovery metadata contains core information such as a persistent identifier, the title of the dataset, creators as well as administrative information about rights e.g. a license, possible access restriction, rights owners, and how to apply for use permit and access if these are needed.^1

Making research data FAIR requires well planned data management and following domain specific practices when possible. Using trusted services for data publishing will help in providing discovery metadata in a machine actionable format.^1

Familiarise yourself with FAIR-principles (video in Finnish with English subtitles).^2,3

https://www.youtube.com/watch?v=PTxB5Ont3aI

References

1. The Elements of FAIR video series. Video CSC, uploaded by Siiri Fuchs, 20 Dec. 2022. https://video.csc.fi/playlist/dedicated/0_723o7br1/0_q2jf8r0y. Accessed 26 Mar. 2025.

2. Fairdata. FAIR Principles. https://www.fairdata.fi/en/about-fairdata/fair-principles/. Accessed 26 Mar. 2025.

3. CSC – Tieteen tietotekniikan keskus / CSC – IT Center for Science. (2023). Fair principles (English audio). YouTube, uploaded by CSC – Tieteen tietotekniikan keskus / CSC – IT Center for Science, 21 Apr 2023.

Data protection

"Data protection is the protection of personal data. The processing of personal data must always be based on law." Personal data is any information that can be used to identify a person, either directly or indirectly.^1

References

1. Tampere University Library. Student's guide to responsible and open science: Data management and data protection. https://libguides.tuni.fi/open-science-for-students/data-management-and-data-protection. Accessed 26 Mar. 2025.

Sensitive data

What is sensitive data?

Sensitive data is defined as any information that is protected against unwarranted disclosure. Protection of data may be required for legal or ethical reasons, for issues pertaining to personal privacy, or for proprietary considerations.

Sensitive data includes:

- Human data (e.g. health, genetic and personal information, data that may identify a person)
- Ecological data e.g. location of endangered species or other conservation efforts
- Confidential data (e.g. trade secrets)
- Data that is otherwise deemed sensitive^1

Sensitive personal data

There are a few simple guidelines for identifying human sensitive data, which are all derived from the national and EU legislation. The GDPR prohibits the processing of personal sensitive data unless there are valid lawful bases: consent, performance of a contract, a legitimate interest, a vital interest, a legal requirement, and a public interest.

The following categories are classified as special categories of personal data and processing is prohibited by the General Data Protection Regulation ^2 (GDPR, Art. 9 (1), Art. 10):

- Racial or ethnic origin
- Political opinions
- Religious or philosophical beliefs, or trade union membership
- Genetic data
- Biometric data for the purpose of uniquely identifying a natural person
- Data concerning health
- Data concerning a natural person's sex life or sexual orientation
- Data relating to criminal convictions and offences, or related security measures^2

Processing of personal human data is regulated by the GDPR and by national laws.^1

How to manage sensitive data?

The EU General Data Protection Regulation (GDPR) outlines the principles on sensitive data processing, without giving exact technical details on how to process sensitive data.

At this point only certain basic rules regarding sensitive personal data are highlighted.

1. Minimize the data. This means that you should only process the data that is absolutely needed.
2. Anonymize or pseudonymize the data whenever possible.
3. Encrypt the data.
4. Destroy the data you do not need.^3

When are data anonymous and when pseudonymous?

Finnish Social Science Data Archive's definition :

Anonymous data: An individual data unit (person) cannot be re-identified with reasonable effort based on the data provided or by combining the data with additional data points. Completely anonymous data do not exist, but with well-executed procedures one can achieve a result where individual persons cannot be identified with reasonable effort. Anonymisation refers to the various techniques and tools used to achieve anonymity. Anonymized data is no longer sensitive information, so its processing does not require GDPR-compliant actions.^4

Pseudonymous data: An individual data unit cannot be re-identified based on the pseudonymised data without additional, separate information. Pseudonymisation refers to the removal or replacement of identifiers with pseudonyms or codes, which are kept separately and protected by technical and organisational measures. The data remain pseudonymous as long as the additional identifying information exists.^4

References

1. CSC. Services for Research. Definition of Sensitive Data.https://research.csc.fi/sensitive-data/definition-of-sensitive-data/. Accessed 26 Mar. 2025.

2. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://data.europa.eu/eli/reg/2016/679/oj/eng. Accessed 26 Mar. 2025.

3. Docs CSC. User guides and tutorials. Tips for Data Management: How to manage sensitive data. https://docs.csc.fi/data/datasets/datamanagement/#how-to-manage-sensitive-data. Accessed 26 Mar. 2025.

4. Finnish Social Science Data Archive. Anonymisation and personal data. CC BY 4.0. https://www.fsd.tuni.fi/en/services/data-management-guidelines/anonymisation-and-identifiers/. Accessed 26 Mar. 2025.