The Max Planck Institute for Legal History and Legal Theory (mpilhlt) has recently approved its Research Data Policy. In accordance with this document, our institute commits to systematic management of research data in line with established standards and best practices, thus assuring the quality of research, satisfying legal and ethical requirements and contributing to the responsible handling of resources. This blog series will bring you up-to-date with the main concepts of research data management and explain how you can benefit from implementing them into your research project.

Research Data Management: the Big Picture

Research data management (often abbreviated as RDM) refers to all aspects related to the handling of research data before, during and after a research activity. These aspects include (but are not limited to): the planning, collection, organisation, analysis, storage and publication of research data. In your everyday research practice, even if you do not actively engage with any digital humanities methods and tools, you nevertheless have to deal with lots of digital research data. For example, by going to an archive and taking pictures of the sources, you are already collecting research data. Setting off on a research trip and conducting interviews for your project is another classic example of research data production. Even such a mundane activity as systematising your knowledge in a spreadsheet may count as an example of research data generation. Naturally, not all of this data will end up in the final book or article and not all of it will turn out useful for you or others, but its correct handling is essential for the research process and, later on, it will allow for the subsequent evaluation of the data’s usefulness and maybe even for its potential publication and reuse.

Managing data deserves protection too. RDM is considered to be an integral part of good scientific practice. The German Research Foundation (DFG) and the Max Planck Society (MPG) commit to research data management and consider it a required standard for any quality research. Moreover, a lot of funding agencies require the submission of research data management plans as a part of their grant application process. DFG has published ‘Guidelines for Safeguarding Good Research Practice’, stating RDM as an essential part of these guidelines and of good research practice respectively. It is a legally binding document in the sense that any institution receiving funding from DFG has to ensure its fulfilment of the stipulations stated in the guidelines (so it applies to most institutions in Germany).

Following the publication of DFG’s Guidelines and as part of its responsibility to fulfil the requirements expressed there, MPG published its own guidelines called ‘Responsible Acting in Science: Rules of Conduct for Good Scientific Practice – How to handle Scientific Misconduct’. These rules of conduct are legally binding for all MPG’s researchers.

If you work outside MPG and Germany, it may still very well be the case that your organisation also adheres to the requirements of research data management and has its own policies about it, as the importance of RDM is increasingly recognised worldwide. It may be a good idea to check if your institution follows (or has its own) guidelines on the topic.

Research Data Management: Beyond the Formalities

Although requirements on the handling of research data are formally imposed by the official authorities, RDM is not just a formality, demanded from us by bigger players. It is indeed an important part of good scientific practice that can benefit your project and team and make the presentation of your research results stronger and more visible. I would go even further and argue that it would be helpful for any legal historian using computers for their research to be familiar with the key concepts of RDM. Let me give you just a few examples on how it can improve your work.

Imagine not a very nice situation: you somehow lost all your data. Maybe it was a virus on your PC, maybe you left your laptop on the train, or the cloud service you are using somehow did not save last month’s updates due to a technical error. In the end, all your notes, meeting reports, excel spreadsheets, facsimiles, literature lists, etc. are gone. RDM can help you prevent such a situation by making you aware of the importance of regular backups (check, for example, 3-2-1 backup rule).

Another common issue for many of us is document chaos and the ensuing difficulties with locating that file on your computer – a file that you definitely saved somewhere but cannot find now. Does the picture below look familiar?

Where is the most-updated document copy? And where is the version with your updates? With your colleague’s updates? The one reviewed by your supervisor? And this is only two documents’ iterations! RDM can offer strategies to avoid such problems: from tips on file naming and folder structure to more sophisticated techniques like using version control tools to save multiple versions of a document and facilitate teamwork.

Of course, the examples given here are very simple instances of what RDM can be used for, specifically mentioned here in order to show that everyone would benefit from learning about it. In reality, RDM is more complex, offering solutions at all stages of the research process. The implementation degree and scope in each case will vary depending on the complexity of your project data and your research goals.

If you want to learn more about what can go wrong in a research project when RDM is not taken into account, I highly recommend taking a look at Data Horror Stories.

One great thing about RDM is that it allows your research project to be more transparent and sustainable. The valuable research data behind your analysis can be used to support your findings. Moreover, it can also be published and cited and you can take full credit for all the hard (and often invisible) work invested into data collection and preparation. According to some evidence, papers providing access to their research data receive up to 25.36% more citations.

Publishing and sharing your data under open licenses can enable other scholars to reuse your data for their own research (giving credit to you, of course), thus contributing to the broader scientific community’s and public’s benefit (just imagine if the data you needed were already out there! How much time, money and effort that would save you?)

Research data management will also make sure that your data is properly stored and archived, has persistent identifiers such as DOIs (in case it gets published) and licenses (if appropriate), so that it can be cited and available 10 years after the publication.

Publishing the data you produce is definitely worth your time and effort. If you need to have this data anyway for your research purposes, why not make use of its full potential by investing a little into RDM, and turn your data into a publication? Don’t be surprised! Publishing data for humanities projects is commonplace and it counts as a fully-fledged publication. If you have never seen it done before, take a look at some humanities data journals out there: for example, at Journal of Open Humanities Data or Research Data Journal for the Humanities and Social Sciences.

Moreover, there are already many projects in (legal) history (and in our department) publishing their data.

Publishing articles open-access is great and many of us are already doing it, but if we want to fully embrace the Open Science approach, we need to start sharing our data too.

Sharing is Caring, or Who Owns Research Data?

If, after reading all this, you are still not convinced of why you should share your data – data which you have produced – then we really need to delve deeper into the matter and talk about who research data actually belongs to. First things first: there is no such thing as data ownership, at least not in the German legal context. The more accurate question in this regard would be, ‘Who has what rights to research data?’ And there are many factors coming into play when defining a possible answer:

• is the research data subject to copyright (raw data, for example, is not)?
• are there contractual agreements between a researcher and their employing institution, which define legal rights about the data?
• is there any data protection in place?
• who collected the data and under which circumstances (as a part of their employment, for example)? and so on.

In the end, there is no universal rule that could answer the question, ‘Who has what rights over research data?’ as each case should be reviewed individually. But even if you are not willing to share the data with others, keep in mind that you still need to archive it for a minimum of 10 years, for example, by placing it into your institution’s repository in accordance with the rules of good scientific practice. For that you do need research data management. And let’s really try to look at the data differently: it is not something that you do in addition and on top of your main responsibilities before the ‘real’ research begins. It is our primary sources, the foundation and ground of any research endeavour, and it deserves as much attention and work as any other part of the research process. In the end, it is also what remains after the research project is done. It is something that you produce and that will have your name on it. Isn’t it worth making it excellent?

As Open as Possible, as Closed as Necessary

One thing that is important to mention here: it is great to share your data, as it offers lots of benefits to data authors as well as their communities. However, it is not always possible to publish your sources as open data. There may be different reasons for this. If you have already been to the archives, you probably know that often they have very restrictive rules about possible reuse scenarios for digitized versions of sources obtained there. Your data may also contain personal information of other people (if you are doing an oral history project, for example). Your interviewees may not agree to give you permission to publish their personal details. Or their possible re-identification may cause negative consequences for them. Copyright and licence conditions may also prevent you from providing your data for a secondary use. That’s why in research data management we stick to the principle ‘as open as possible, as closed as necessary’ and also why RDM is not about making everyone share everything, but rather about making you aware of legal and ethical aspects that should be accounted for when working with data.

Research Data Management: Where to Start at the MPILHLT (and in Other Places)

If you are wondering how to start going about RDM, there is no need to worry as there is plenty of help out there. For example, our institute is taking a serious stance on improving our RDM. We have established a team of experts who are working on the RDM implementation. This team will come up with the workflow, tips and manuals as well as some educational events on the topic. Moreover, each department has appointed one of their researchers as their data steward. They will be your first point of contact in all RDM-related questions. I am serving as a data steward for Department II.

That is not to say, of course, that data stewards will do all the work for you. No; each project is still responsible for their own research data management. However, a data steward will guide you through the process. They will consult you on different aspects of RDM and help you choose best RDM strategies for your project. They will also help you to write (and regularly update) a research data management plan, a crucial document for any project producing research data.

If you work elsewhere, it is worth checking how the situation with RDM looks like at your workplace. Maybe you also have some colleagues responsible for data management. Or perhaps a library at your institution offers some support in this area, as often libraries take over some RDM tasks. Another good point of contact may be NFDI (Nationale Forschungsdateninfrastruktur) association and especially its consortia NFDI4Memory and Text+. It is a non-profit network association with subject-specific consortia building infrastructures for research data management. They provide not only resources and educational events in the field of RDM, but also offer consultations via their Helpdesks. You can ask their advice on any topic around RDM.

One of the main tools for RDM is a research data management plan. This document looks like a set of questions, which describe how you are planning to handle your data (what data you have, in what format, where you store it, etc.). It helps to both plan and document your data handling. Usually this document is required when you submit a grant application as proof that you thought about this aspect of your research. There are many online tools that may help you to create such a plan (for example, you can check the list here). But maybe your organisation has already got its own template, adapted to its needs. It is worth checking out. For instance, at our institute we already have such a template and, very soon, we will start the process of evaluation to understand which projects need a research data management plan.

You may think that you do not produce any data, but I hope that after reading this blog post you can take a look at your project and better reflect if that is actually the case.

Coming up next in Part 2: What is research data? Research data life cycle, metadata and FAIR principles.

References:

Deutsche Forschungsgemeinschaft. (2025). Guidelines for safeguarding good research practice. Code of conduct.

Kuschel, L. (2018). Wem “gehören“ Forschungsdaten? Forschung & Lehre.

Max Planck Digital Library. Data Management Plans. Research Data Management, Information Platform for Max Planck Researchers.

Max Planck Gesellschaft. (2021). Responsible acting in science: Rules of conduct for good scientific practice – How to handle scientific misconduct.

Smith, G. (2021). How sharing your data could increase your citation. Research Communities by Spring Nature.

Feature image: Server at mpilhlt © Christiane Birr.