Title: Module 5: Research Data Management in Practice
Course: Teaching Open Science and Research Data Management for Undergraduates > Section: Module 5: Research Data Management in Practice

Planning data management and Data Management Plans

The importance of data management planning lies in ensuring that risks associated with data, such as data loss or potential complexities in ownership and user rights, can be effectively addressed. When dealing with sensitive data, particular emphasis should be placed on the essential task of strategically planning how the data will be securely managed at various stages of the project.^1

The planning process enables the anticipation of the necessary tools and resources required to maintain the safety and integrity of the data. By crafting a Data Management Plan (DMP), the data can be safeguarded beyond the project's duration, emphasizing a proactive approach to data security and continuity.^1 See image 1.

Image 1. Benefits of Research Data Management.^1 (CC BY 4.0)

Data management plan templates can vary depending on who it is aimed for, for example funders can have their own templates. However, according to Science Europe, regardless of the template a DMP usually consists of the following topics^2:

1. Data types and amount of data
2. Ethical and legal issues
3. Data documentation and metadata
4. Storing data during the project and information security
5. Making data available for reuse and preserving data
6. Data management responsibilities and resources^2

See this video for a short introduction to data management planning^3:
The what, why and how of data management planning

There are tools available to help researchers create a DMP.

Machine actionable DMP or maDMP

Many data management planning tools enable making the DMP in a machine-actionable format, meaning that the information is understandable for computers. Machine-actionable DMPs (maDMPs) use persistent identifiers, which are globally unique pointers for digital objects, to make the information understandable for computers.^4

The goal of a machine-actionable DMP is to facilitate a research data management system that allows data and information about research to be communicated and shared across stakeholders, linking metadata, repositories, and institutions - or generally speaking, to embed DMPs in existing workflows.^4

Tools for making data management plans

(the tools have ready templates, which can be used)^4

- ARGOS by OpenAIRE
- Data Stewardship Wizard (used in life sciences)
- Easy DMP by EUDAT
- DMPonline
- DaMap

References

1. CSC. Data Management Planning. https://research.csc.fi/data-management-planning. Accessed 26 Mar. 2025.

2. Science Europe. Practical Guide to the International Alignment of Research Data Management. CC BY 4.0. https://doi.org/10.5281/zenodo.4915862 . Accessed 26 Mar. 2025.

3. The what, why and how of data management planning. (2014). YouTube, uploaded by Research Data Netherlands, 1 Apr 2014.

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

Collecting, discovering and reusing existing data

In this chapter, we will explore the processes of collecting, discovering, and reusing existing data.

Data collection

Data collection and management practices depend largely on the type and specifications of the data and can be discipline specific. Tools and well planned documentation methods help you during data collection and ensure good data quality. Remember to take care of data protection and security issues, if you work with sensitive or confidential data as well as permissions and consents needed if your study objects are humans. You can search for available data for your purposes from https://www.re3data.org with the search term “data”.

Tips for ensuring quality and consistency of data by University of Jyväskylä:^1

- Take a copy of the raw data or initial situation and work with the copy if possible.
- Check that the original data content is preserved when data is exported from one system, format or location to another.
  - E.g. from a survey program to a U-station or from an interview recording to a transcript.
- Keeping backups of different versions
  - It's a good idea to keep backups of different versions so that you can revert to a previous version if something has gone wrong if necessary.
- The transcriptions of recorded and/or filmed data are checked, for example, by someone else working on the same project.
  - Note: if the data contains personal data (such as speaking voice), the data cannot be checked by any third party. In master's theses done as pair work, reviewing transcriptions together is a good practice.
- Ensure that the interview framework and questions are as similar as possible for all interviewees.
- Check the calibrations of the measuring instruments.
- Checksums are used if the software offers one.
- Ensure that the digitised data corresponds sufficiently accurately to the original physical or analogue data.^1

The following video produced by the New York University Health Sciences Library,  'How to avoid a data management nightmare' demonstrates, in a fun way, the potentially disastrous consequences of quality assurance failures in data management.^2,3

https://youtu.be/nNBiCcBlwRA

References

1. University of Jyväskylä. Description and Quality of Data. https://www.jyu.fi/en/for-students/instructions-for-bachelors-and-masters-students/information-seeking-and-research-data-management/research-data-management/description-and-quality-of-data . Accessed 26 Mar. 2025.

2. The Open University. Data Quality. https://university.open.ac.uk/library-research-support/research-data-management/data-quality. Accessed 26 Mar. 2025.

3. NYU Health Sciences Library. (2014). How to avoid a data management nightmare. YouTube, uploaded by NYU Health Sciences Library, 16 Mar 2014. A video created by NYU Health Sciences Library's Karen Hanson, Kevin Read, and Alisa Surkis.

Data discovery

Before data can be reused, it must be findable and accessible. In fact, the reusability of research data is linked to how well others can find the data.

Also for student research projects, it's important to explore existing data resources related to your study topic. This is crucial for projects that use data from another project and also valuable for projects planning to collect their own original data.

For data discovery, you can either search discipline-specific repositories, which are, for example, listed in re3data.org or use search engines. Examples of useful search engines are Google’s Dataset Search, BASE, B2FIND, or the DataCite Metadata Search.

Advice by Tampere University Library's Student's guide to responsible and open science:^2 ^

- Any copyright or terms of use associated with material you receive from elsewhere.
- Not all material available online may be available for use in your own work. Please check the terms of use of the service you are using to see if you are allowed to use the material for research purposes.
- In particular, if you upload images or texts to your own computer, you should ensure that you have the right to do so.
- Sometimes the terms of permission for images and texts are specified in the licenses ^1.
- For example, if you are using social media posts as your material, check the terms of use of the service to ensure that you can download the posts to your computer if necessary.
- Read the advice of the Copyright Lawyer on how students can use images in their thesis.^2

Read the advice of ImagOA - Open Science and Use of Images guide on how stidents can use images in their thesis.^3

Read more:
The process of data discovery - Data Management Expert Guide

References

1. Creative Commons. About CC Licenses. https://creativecommons.org/share-your-work/cclicenses/. CC BY 4.0. Accessed 26 Mar. 2025.

2. 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.

3. Mikkonen, J., Pesola, M., Rehbinder, M., & Sarvilahti, M. ImagOA: Open science and use of images. Metropolia; Valovirta Design; Hatzikelis, J.; Timonen, U.; Multanen, M. http://libguides.aalto.fi/imagoa. CC BY 4.0. Accessed 26 Mar. 2025.

Access, re-use and cite data

When utilizing and re-using data collected or produced by others the origin, content, location, license, restrictions of use, and other information are needed. Search services include descriptive information (metadata) on research datasets. The better the description of the dataset is, the easier it is to find and use it. Existing research datasets may be available for reuse.^1

The quality of the data depends very much on its intended use and discipline. Data should be sufficiently well described to allow an assessment of its fitness for purpose. It is very common in the research world for the user of the data to contact the data producer to confirm details of the data before using it.

Finding a seemingly suitable dataset is the first step. Assessing the usefulness of the found dataset for the intended research purposes is an important second step. In the video by Utrecht University "How useful is a dataset" you will find a number of helpful criteria.^2 How useful is a dataset?

Once you find suitable data for your purpose and you've checked data quality how can you get it? The steps below emphasize a number of aspects that you may encounter along the way:

Check the terms and conditions of access and use

Different access arrangements may be in place for any data collection, especially those containing more detailed, sensitive or confidential data. Generally, the following access options exist:

- Open Data
Anyone can freely access, use, modify, and share for any purpose.
- Open after registration
The user must register and provide the required information. The information often includes personal data, institutional affiliation, and purpose of use. No specific conditions of access are required.
- Open under specific terms and conditions
For example:
  - Access to a data collection requires permission from the data depositors or data owners;
  - 'Scientific use files' are available only for academic research and education;
  - Data use is limited to 'non-commercial use only';
  - Sensitive and confidential data are available only under strict conditions of use and security measures.^1
- Access to metadata only
Many data files are inaccessible for different reasons. Even if data files are inaccessible, relevant metadata may still be available in repositories and information obtained from it may be also helpful for you.^1
- Embargo
Some repositories contain data under embargo, i.e. after a specified time period (e.g., 6 or 12 months) the data is released for public use.^1

Watch the video^3: Aalto University. (2019). Legal Aspects of Data (Maria Rehbinder)
Legal Aspects of Data (Maria Rehbinder)

Citing data

When you use research data in your work, it's important to cite it properly—just like you would with any other research source. Research data may be protected by intellectual property rights, and citing it is often part of the terms of use. But beyond legal requirements, giving credit is also a matter of research ethics.^1

Minimal data citation:

The minimal data citation recommended by DataCite is:^4

Creator (PublicationYear). Title. Publisher. Identifier

References

1. Docs CSC. User guides and tutorials. Dataset sources: Gathering datasets. https://docs.csc.fi/data/datasets/sourcing-datasets. Accessed 26 Mar. 2025.

2. Utrecht University. (2018). How useful is a dataset? Follow this short tutorial. YouTube, uploaded by Utrecht University, 20 Jul 2018.

3. Aalto University. (2019). Legal Aspects of Data (Maria Rehbinder). YouTube, uploaded by Alexander Jung, 20 Nov 2019.

4. DataCite CC BY 4.0. https://support.datacite.org/docs/data-citation

Data documentation: Organizing and documenting

In this chapter, we will examine the importance of organizing and documenting data. We will explore best practices for structuring data, creating clear documentation, and ensuring data usability and transparency for future use.

Organizing

Important part of good data management is also taking care of data organization. This includes for example thoughtful file naming, clear folder structure, accessible file formats and explicit version control.

Files and file formats

It is good practice to make a clear file naming system from the start of the project. This helps to understand what files contain from the name. Different research fields might also have instructions and guidelines for organizing data.

All digital information is structured data. A file format is a standard way that information is encoded for storage in a computer file. An open format is a file format for storing digital data, defined by a published specification usually maintained by a standardization organization, that can be used and implemented by anyone. In contrast to open formats, closed (or proprietary) formats are considered trade secrets. For example, a good deal of commercial equipment or software produce data that can not be read or interpreted without other tools from the same provider. When organizing, storing and publishing data it is important to create coherent, intelligible and transparent entities that are easy to access and reuse. This is possible with open formats that can be opened and used also with commonly used open tools.^1

Organizing your data

- Sort and classify your data. For instance, don't mix different types of information in excel columns: it is usually easier to combine datasets than sort out ill structured data later.
- Decide on formats, units, codes etc. and be consistent.
- Use common file formats, preferably open.
- Write a code book, document. README files are often necessary. Good documentation ensures reproducibility.
- Think about intelligibility. Be careful when rearranging, reformatting, sorting or copy-pasting data.
- Have processes in place for checking the data quality and completeness Be clear about master copies and other copies.
- Be careful and plan well for sensitive data and anonymization.
- Think about security and access rights, give access only for those who need it.
- Plan and agree on which versions of a dataset will be archived and/or published.^1

Files and folders: structuring and naming

It is important to take some time to plan file and folder structures and naming.

- Create and agree on a system for naming files and folders and be consequent
- Try to organize files logically using folders and subfolders rather than including all files in a single folder
- Avoid very deep folder structures, since they can be difficult to handle
- If your data is time-sensitive, and logically organized by time periods, it could be useful to organize files by time-specific folders, such as YYYY-MM-DD
- Use meaningful, unique file and folder names
- Keep file and folder names as short as possible but relevant. 25 characters are usually considered maximum.
- Dates in YYYY-MM-DD format allows you to sort and search your files
- Avoid using special characters such as % & / \ : ; * . ? < > ^! " () and Scandinavian letters
- Indicate version number by using 'V' or 'version' and three digits (or 4 if you have a large number of files) i.e. 001, 002…….201, 202 (not 1, 2, 21).
- Use underscores (_) instead of spaces
- If using a personal name in the name give the surname first followed by the first name
- Though, be very careful with personal data when naming files and folders^1

References

1. Docs CSC. User guides and tutorials. Metadata and data documentation: Data organization. https://docs.csc.fi/data/datasets/metadata-and-documentation/#data-organization. Accessed 26 Mar. 2025.

Documenting

Metadata

Metadata is information regarding the data, for example, where, when, why, and how the data was collected, processed, and interpreted. Metadata may also contain details about experiments, analytical methods, and research context.^1

Metadata is an umbrella term that can mean both the discovery metadata, which is always open information as well as the data documentation documents. Documentation, metadata and how to interpret the data, can be open or restricted information (see image 1 below). You can read more about discovery metadata in the Publishing section.

Image 1. Discovery metadata^1 (CC BY4.0)

Data documentation means creating information, which enables the interpretations of the data correctly and independently. It is important to plan how to document the data and follow that plan throughout the project. It is wise to document how the data has been modified at different stages of the project - having to go back and fill in the gaps in documentation afterwards is laborious and complicated, and mostly even not possible!

Sufficient documentation of the data, changes made, resources, methods, abbreviations, codes and the processes used in the analyses is essential for both reproducibility and assessment of research quality. The provenance (record trail of the origin and changes made to the data) should be transparent. Reproducibility is an important part of providing evidence of the correctness of research results. Other researchers should be able to inspect the workflow and evaluate all of the steps that have been taken during the analysis and repeat them.

Data documentation includes a variety of documents, which describe all data used in a project, such as what the project data is, how it has been collected, what the abbreviations mean and how the data has been modified. ^2

Data documentation can be done for example by using the following ways:

- (Meta)data standards are discipline specific forms widely used.
- Electronic laboratory notebooks create metadata about the project, while keeping the notes up to date.
- Data dictionaries explain variables used in a data set.
- Codebooks are collections of codes and algorithms used in a project.
- Vocabularies are a standardized set of terms and provide a consistent way to describe data.
- Readme-files are text documents that provide information about data files to ensure they are interpreted correctly.^2

References

1. Docs CSC. User guides and tutorials. Metadata and Data Documentation. https://docs.csc.fi/data/datasets/metadata-and-documentation/ . Accessed 26 Mar. 2025.

2. 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.

Publishing data

Publishing data (or opening, both terms are used) is an important part of a good scientific process, it benefits you and the whole community as well as is the cornerstone of reproducible and replicable research. Nowadays funders also require it. When considering whether you should make your data available for reuse, the rule of thumb is to make the data "as open as possible, as closed as necessary". Publishing data does not automatically mean it is completely open and free to be downloaded by anyone. You can choose how openly available your data is, and it can have restricted access or be closed.^1

The minimum requirement is to publish discovery metadata, which is kind of like the label of your dataset. It consists of a persistent identifier, license as well as basic information about what the data is about, who collected it, when and how to gain access. By sharing descriptive information about the data in a machine actionable way it can be found through web based search engines.  Above all, metadata needs a persistent identifier that allows referring to the data. Published data must also have a license. (See image 1.)

Image 1. Tips for publishing research data^2 (CC BY 4.0)

What is important for students when publishing data?

Often, the research institution holds the rights to output that is generated by employed researchers and students. This can, for example, be stated in the working contract. But what about material that is generated by students without employment during their thesis?

Generally, the students hold the copyright and the utilisation rights on their material and, therefore, can decide on the publication, access conditions, and licenses. This applies at least to the research data; other rules may apply to the thesis itself.

Before publishing your research data, make sure that you are the exclusive rights holder. If others, e.g. a fellow student or your supervisor, were involved in generating the data, it is recommended to have a written and signed agreement on the publication that specifies the data and regulates important publication details, such as the license to be granted and the authors to be named. If external data ("third-party data") without a corresponding license was used for the thesis, written permission must be obtained from the "owner" before publication.

What are data papers and data journals?

A data paper is a peer-reviewed document describing a dataset published in a peer-reviewed journal. Data papers provide recognition for the effort to e.g. prepare, curate, and describe data by means of a scholarly article. The primary purpose of a data paper is to describe data and the circumstances of their collection, rather than to report hypotheses and conclusions.^3

Data journals consist of data articles that describe how, why and when a dataset was collected and how any derived data product produced.^4 Data journals provide strong incentives for data creators to verify, document and disseminate their data. They also bring data access and documentation into the mainstream of scholarly communication, rewarding data creators through existing mechanisms of peer-reviewed publication and citation tracking.^5

Persistent Identifiers (PIDs)

A PID is a globally unique, documented, and permanent identifier that provides a direct link to an online resource. PIDs should be machine actionable and for instance resolve to a web page that (re)presents the content. The assignment of a PID is a useful quality criterion for online publications. In the case of a dual book publication (open access and print), most publishers assign an ISBN for each edition.

For purely digital publications, DOIs (Digital Object Identifiers) are suitable identifiers. Often one DOI is assigned per publication. The additional allocation of DOIs for individual chapters, figures, and tables is sometimes considered desirable. DOIs are as well used for data publication. In addition to the PIDs for objects, there are also PIDs for persons, institutions, etc. For example, the ORCID iD is standard for the unique identification of scientists and the ROR IDs of the Research Organization Registry are used to identify institutions.

Watch the video^6: FAIRsFAIR EU. (2022). What are Persistent Identifiers and why to use them?
What are Persistent Identifiers and why to use them?

Discovery metadata (info box)

Discovery metadata is a set of standardized and structured descriptive, administrative and structural elements. These elements explain the data's origin, purpose, time reference, geographic location, author and terms of use. The discovery metadata includes a permanent identifier and a license. Discovery metadata can also be called public metadata.

It is important that both the metadata and the actual data comply with the FAIR principles so that the data is findable, accessible, interoperable and independently reusable. This is easier to do when using a reliable data publishing service, i.e. a data repository, which produces machine-actionable metadata from the given data. Data repositories also give the data a permanent identifier and a machine-actionable license.^1,7

Benefits of publishing data^1:

- Visibility for the research: opportunities for collaboration, merit, funding opportunities
- Discoverability of data by search engine (PIDs), citations, booking of data downloads
- Efficient use of research funds
- Complying with funders and publishers requirements
- Transparency of data: reproducible results and and new analysis to be conducted
- Access and make use of the project data even after the project^1

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

3. GBIF.org Data Papers. https://www.gbif.org/data-papers Accessed 19 Aug. 2025

4. King Abullah University of Science and Technology University Library. Research Data Management. https://library.kaust.edu.sa/data_sharing/data_journals Accessed 19 Aug. 2025

5. WALTERS, W. H. (2020). Data journals: incentivizing data access and documentation within the scholarly communication system. Insights: The UKSG Journal, 33(1), 1–20. CC BY 4.0 https://doi.org/10.1629/uksg.510

6. FAIRsFAIR EU. (2022). What are Persistent Identifiers and why to use them? YouTube, uploaded by FAIRsFAIR EU, 16 Feb 2022.

7. Docs CSC. User guides and tutorials. Metadata and Data Documentation: Discovery metadata. https://docs.csc.fi/data/datasets/metadata-and-documentation/#discovery-metadata. Accessed 26 Mar. 2025.

Data repositories

A good thumb rule is to always use disciplinary or data specific data repositories. These follow community standards regarding the data, and hence guide the user to publish data in a format that is consistent with other similar data. This enables data interoperability and combining datasets that are collected from various sources. You can search for repositories at re3data.org, which is a global registry of research data repositories.

Only if you cannot find a disciplinary repository for your needs, use either the repository of your institution (if available) or a general repository like Zenodo.

Data repositories should provide persistent identifiers for the uploaded datasets as well as sufficient metadata and clear license information. Licenses allow the uploader to specify the user rights for the data. Creative Commons licenses are widely used. The CC BY 4.0 license, for example, allows the use of the data but requires that the author is mentioned, whereas the CC0 license gives full rights to others for using the data.

How to choose what data to publish, preserve and delete?

When choosing what to archive and/or publish, it is good to switch viewpoints, and consider what the data (re)user sees. How far back can the reader of a thesis or scientific article be able to follow the process towards the raw data, before it is no longer possible to reproduce the original process? In many cases it is not possible to get back to the raw data, but if the data documentation, automation, saving of the logs and code, and their version management are practiced from the start it is possible to secure as much transparency as possible. If it is possible (this is highly discipline specific), it might be good to publish both raw data and processed data with the documentation.^1

Data deletion

Although it may be tempting to deposit all of your data just in case it proves useful in the future, this is not always possible, as archiving everything could prove to be costly and time-consuming and in some cases, unethical. Files that are not published, archived or preserved should be deleted when they have fulfilled their purpose.

Watch the video^2: CSC – IT Center for Science. (2024). Manage your research data - towards publishing data.  Manage your research data - towards publishing data

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. CSC – Tieteen tietotekniikan keskus / CSC – IT Center for Science. (2024). Manage your research data - towards publishing data. YouTube, uploaded by CSC – Tieteen tietotekniikan keskus / CSC – IT Center for Science, 25 Mar 2024.