“A lot can be fixed retrospectively,” believes Klára Kosová from the Institute of International Studies at the Faculty of Social Sciences, Charles University in Prague. According to her, however, some decisions made at the very beginning of a research project can determine whether the data can later be found, linked with other sources and reused. With qualitative and sensitive data, data management also goes beyond practical and technical considerations and becomes an ethical issue. Where does desirable openness end, and where does the need to protect respondents’ privacy and safety begin? Drawing on her experience as both a researcher and a data steward, Klára also highlights the practical side of data management and shows how it can make everyday work easier across a research team.
Being a data steward is essentially about guiding researchers through the world of research data management. It is not only about planning data collection at the beginning or publishing data at the end, but rather about providing ongoing support to researchers as they work with data.
I first became involved with the research teams during the planning phase, when we needed to consider what data could be collected, how they should be stored and documented, who should have access to them, and how they should be handled in the future. The main outcomes were a Data Management Plan and an Open Science handbook for the entire INTERFER team. As the project progresses, I help address specific situations, ranging from informed consent and documenting changes to the data to setting access permissions or adapting data collection procedures. Towards the end of the project, I focus on archiving and sharing the data: deciding what can be made available and under what conditions, and ensuring that the data remain understandable and usable for others.
I think the dual role of data steward and researcher is a major advantage. I know first-hand how research works in practice, that things change as a project progresses, and that researchers have many other responsibilities to deal with. I therefore understand why data management can sometimes feel like yet another administrative burden. At the same time, I believe that technological development and digitalisation now make it possible to do many things much more efficiently. By setting up appropriate data management processes, we can not only make researchers’ work easier but also save them time. I see this as one of the important roles of a data steward: helping to find solutions that make sense not only from an Open Science perspective, but also in everyday research practice.
Good data management is not just about what happens to the data at the end of a project, as it might initially seem. When data are well organised and documented from the outset, and we know how they were created and what happened to them throughout the research process, this makes the research itself easier and facilitates its subsequent verification and reuse. My own experience as a researcher helps me identify approaches that are, I hope, genuinely useful to my colleagues. The aim is not to add more obligations, but to help ensure that their research is transparent and trustworthy.
The diversity of research approaches means, above all, that there is no single universal approach to research data management that works equally well for every team. Experimental data are handled differently from interviews or text corpora. Each type of data has different requirements in terms of collection, documentation, storage, protection and subsequent sharing. Each discipline also has its own established practices. And when collaboration between institutions is added to the mix, it is important to ensure that the individual teams have a shared understanding of how different types of data are handled, while also complying with the internal rules of the institutions involved.
That is why, particularly at the beginning, it was important for us to establish common practices while still respecting the specific requirements of individual disciplines and methods. The aim was not for everyone to follow exactly the same steps or produce identical data outputs, but rather to ensure that the data were easy to find, accessible under appropriate conditions, interoperable and reusable by other members of the team and potentially by third parties. In practical terms, this meant agreeing on shared principles for file naming and documentation. These may seem like minor details, but in practice they make a considerable difference, especially when several people are working on a project at the same time or when someone who was not directly involved in creating the data needs to return to them later.
This diversity is also reflected in data sharing. Different types of data have different audiences, so for each dataset we try to identify a repository that reflects both the nature of the data and the community for which they are intended. These range from more general platforms such as OSF to disciplinary repositories and platforms such as GESIS or LINDAT/CLARIAH. This, too, cannot be resolved through a single decision at the beginning of a project; the most appropriate solution needs to be considered separately for each dataset.
“I believe that technological development and digitalisation now make it possible to do many things much more efficiently. By setting up appropriate data management processes, we can not only make researchers’ work easier but also save them time.”
In INTERFER, we work with a fairly broad range of data. At one end of the spectrum are purely quantitative data, while at the other are various types of qualitative data: recordings or transcripts of interviews, field notes from participant observation, and text corpora compiled from publicly available documents. In practice, however, it is becoming increasingly difficult to maintain a clear distinction between quantitative and qualitative approaches to data. Textual data, once associated primarily with qualitative research, are increasingly taking on a quantitative dimension. The same text corpus can be used for in-depth discourse analysis, automated text classification or the training of a language model. This creates quite specific data management requirements, because documentation and metadata that are sufficient for one type of use may not be useful for another.
From a data stewardship perspective, however, the most challenging data for me are clearly those collected through interviews, primarily because of the need to protect personal data and the safety of the respondents themselves. INTERFER focuses thematically on foreign interference, so some interviews are conducted with people in environments that are sensitive or even dangerous for various reasons. In such situations, it is not always possible to obtain written informed consent, because signing a document may itself pose a risk to the respondent. In these cases, we use oral consent, which is recognised as standard practice in research methodologies for sensitive contexts and is approved in advance by the ethics committee. Protecting these individuals is our absolute priority and takes precedence over any considerations of data openness. Such data are not shared, even in anonymised form. In these cases, anonymisation may simply not be sufficient. Even seemingly neutral details, when combined, can lead to the identification of an individual, which is absolutely unacceptable. From my perspective, this type of data is therefore the most difficult to manage, not so much because it is technically complex, but because every decision carries significant ethical weight.
It is certainly not the case that all research data must automatically be made fully public. Open Science does not mean openness at any cost; rather, it is about taking a responsible approach to research. In practice, we make these decisions on a case-by-case basis together with the researchers involved. There are several reasons why data may not be shared at all, or may only be shared under certain restrictions. First and foremost, it is necessary to consider the protection of the people from whom the data originate, whether this concerns the protection of personal data in accordance with the GDPR or, in more sensitive cases, the safety of respondents. There are also legal and contractual considerations. For example, data obtained from social media are subject to the terms of use of individual platforms, which generally do not allow such data to be freely redistributed to third parties.
I think it is important to distinguish between openness and transparency in the research process. For a wide range of reasons, it may sometimes be impossible to make the data themselves publicly available. However, it may still be possible to share documentation, such as analytical or methodological procedures, coding manuals or replication scripts. This information can make it much easier to understand how the research was conducted and how its conclusions were reached. Personally, I therefore see openness in science not simply as a question of whether everything is made public, but of whether the research process is sufficiently transparent and verifiable.
“For a wide range of reasons, it may sometimes be impossible to make the data themselves publicly available. However, it may still be possible to share documentation, such as analytical or methodological procedures, coding manuals or replication scripts. This information can make it much easier to understand how the research was conducted and how its conclusions were reached. Personally, I therefore see openness in science not simply as a question of whether everything is made public, but of whether the research process is sufficiently transparent and verifiable.”
In the humanities and social sciences, research data management has only begun to be addressed systematically in recent years, and not everyone is yet accustomed to it. In the past, greater emphasis was often placed on the research output itself, while questions about the data on which the research was based and how those data were handled tended to receive less attention. For this reason, there have been several cases in our project where certain issues only began to be addressed once the research was already under way. A great deal can still be fixed retrospectively. Documentation can be added, metadata completed, or file structures standardised at a later stage. However, some things are difficult, or even impossible, to correct without prior preparation. For example, if informed consent was not designed from the outset to cover future data sharing, this can significantly limit the possibilities for making the data available. Similarly, if clear rules for file naming and versioning are not established from the beginning, reconstructing which version of the data is actually the “final” one can be very time-consuming.
When I think about what is truly important to address before data collection begins, it is the establishment of a systematic working workflow. How will files and folders be named? How will versioning be handled? How will decisions be documented? Although these steps may seem like minor details, consistency and systematic organisation are among the most valuable things when reconstructing the research process later on.
From a researcher’s perspective, I think the way we have set up data management in INTERFER has real practical value. It is not just about meeting the formal requirements of the funding provider. I have seen for myself that when we work according to a predefined structure and agree on a basic framework in advance, it also makes subsequent work with the data significantly easier.
For example, a colleague and I were combining two quite different datasets. He had a corpus of speeches from the European Parliament, while I had detailed information about the composition of the European Parliament, including individual Members of the European Parliament. By combining these sources, we were able to create a much more comprehensive and richer dataset. Because we had already established a certain structure and rules for working with the data, linking the two datasets was actually quite straightforward.
We also have the relevant documentation and scripts, which means we can return to the work ourselves in the future, or someone else can build on it and extend or update the dataset. To me, this illustrates very well that data management does not have to be something we do only because a funding provider requires it or because we need somewhere to deposit the data at the end of a project. When it is set up well from the outset, it can make our work easier across research teams as well.
is a researcher at the Institute of International Studies, Faculty of Social Sciences, Charles University in Prague. Her research combines political science with cognitive linguistics and focuses primarily on questions of democratic and political legitimacy. Alongside her theoretical research, she seeks to connect computational methods with the humanities and social sciences.
She is a co-founder of the Center for Digital Humanities Integration (C4DHI) at the Institute of International Studies and a member of the Peace Research Center Prague. She is currently involved in the TWIN4DEM project, which examines the causes of democratic decline using computational social science methods, and the INTERFER project focused on foreign interference. Within INTERFER, she also serves as Open Science Coordinator and Data Steward.
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