Research Data Management Guideline
1 Introduction
The Institute strives to improve the quality, accessibility, and reusability of research data by establishing a consistent approach to managing research data in accordance with the FAIR principles. Such a consistent approach will increase research efficiency considerably because researchers (including their futures selves) will be able to intuitively access, understand, and work with the data even years after leaving them. This prevents a multiplication of effort and saves a great deal of time and resources in the long run. In addition, many journals nowadays require the publication of data if they are the basis of a (text) publication. Having an effective approach to data management from the outset will reduce the effort of making high-quality data available upon submission and publication of a manuscript.
Managing data effectively will not only help research to be robust, replicable, and reproducible, but can help to anticipate potential problems that can occur during the research process upfront (e.g., having a legal basis for publishing data). Given that handling research data can be challenging, these guidelines are meant to provide orientation for (new) employees, foster transparency both internally and externally, and thereby facilitate cooperation.
2 Context
This guideline operates in conjunction with the Institute’s Open Science Guideline and the associated implementation plan — specifically the sections on Open Data — which have been developed and come into effect jointly.
The requirements and recommendations for RDM at the MPIB laid out in this guideline and the associated RDM Implementation Plan are to be seen in the broader context of pertinent MPG guidelines as well as national and international guidelines, legal obligations, and requirements of major funding bodies on handling (research) data, for example:
- MPG’s Rules of Conduct;
- Data Protection Guideline of the MPG;
- EU General Data Protection Regulation;
- Guidelines on FAIR Data Management of the European Commission in Horizon 2020;
- Deutsche Forschungsgemeinschaft: Guidelines on the Handling of Research Data and Guidelines for Safeguarding Good Research Practice. Code of Conduct;
- Federal Data Protection Act;
- Berlin Data Protection Act.
3 Guiding Principles
Against the backdrop of an increasing volume of (digital) research data and the promotion of openness and transparency in research, effective RDM has emerged as a cornerstone of good scientific practice. This includes the data’s coherent organization, comprehensible documentation, and quality-assured publication and/or archiving. The FAIR principles have evolved into a set of guiding data management principles that researchers are recommended to adopt to improve the usefulness and impact of their data. FAIR is an acronym for making data:
- Findable: Uploaded to public repository with metadata, identifiable and locatable by a persistent identifier.
- Accessible: Available and obtainable for download; even if the data are restricted, metadata are open.
- Interoperable: Allowing others to intuitively operate on your data by applying community standard folder and file naming conventions, as well as standardized metadata schemata that are human and machine-readable.
- Reusable: Allowing widest possible reuse through good documentation and clear usage license and provenance information.
The overarching goal of FAIR is to optimize the reuse of data as well as enabling the traceability and reproducibility of research processes and results.
4 Responsibility of the MPIB
The Institute recognizes the publication of research data for reuse as scientific achievement (see also Research Assessment & Evaluation). Likewise, contributing to and/or developing good practices for handling research data in terms of guidelines are recognized as scientific achievements. Quality-assured data and software publications by its members and affiliates are part of the scientific output of the MPIB. The Institute’s RDM team supports researchers in fulfilling the best-practice recommendations of the present guidelines by:
- suggesting, selecting, configurating, and maintaining of appropriate technical tools;
- conceptualizing and offering RDM information and training events;
- maintaining an appropriate storage infrastructure that supports the adequate storage and availability of digital research data within the scope of technical, organizational, and financial opportunities;
- maintaining a centralized GDPR-compliant participant management software;
- deleting research data if legally required.
5 Researchers’ Responsibilities
Researchers are responsible for the complete documentation, backup, and storage of their research data. In particular, researchers are recommended to:
- Register their study in the Institute’s internal study registration tool.
- Provide a data management plan.
- Ideally store research data and materials relevant to the study and to reproducing its results on servers hosted by MPG or GWDG. Highly sensitive data like raw brain data, genetic data, and patience data must be stored on servers hosted by MPG or GWDG.
- If contact data are available (i.e., participant name, address, email, phone number), disclose the individuals that participated in studies via the MPIB’s central participant management tool, Castellum, so that requests for access or erasure can be handled efficiently.
- Prepare research data for publication (e.g., conversion into standard file formats, anonymization, data organization, upload).
- Evaluate and select suitable infrastructures for data archiving and publication.
- Delete research data if legally required (at least provide the necessary information).
- Comply with the relevant funding and grant requirements and contractual regulations.
- Transfer responsibilities when leaving a study.
6 Legal and ethical aspects
When managing research data, all researchers at MPIB comply with the applicable laws on the protection of personal data (GDPR) and intellectual property (copyright). The General Data Protection Regulation (GDPR) is the main piece of EU legislation governing the protection of personal data. It applies to all organizations that process data of EU citizens, regardless of where the organization is based. Note that other data protection laws exist, e.g., the California Consumer Privacy Act (CCPA) in the USA, the Data Protection Act in Japan, or the Privacy Act in Australia. When reusing data those may become relevant.
Copyright law protects the rights of authors of works such as scientific publications, software, and databases. It is important to ensure that the use and distribution of data and/or related materials complies with copyright laws. Further legal and ethical aspects that researchers take into account include ethical principles (Helsinki Declaration), special regulations by third-party funders (contract law) or export control), as well as obligations arising from employment contracts and/or from agreements with cooperation partners.
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Citation
@misc{max_planck_institute_for_human_development2025,
author = {{Max Planck Institute for Human Development} and Bengfort,
Tobias and Blunk, Josefine and Engelmann, Neele and Feg, Thomas and
Herzog, Stefan and Kleemeyer, Maike and Schwarze, Sina and Nix,
Sebastian and Peikert, Aaron and Pit, Ilse and Tilsley, Penelope},
title = {Research {Data} {Management} {Guideline}},
version = {1},
date = {2025-09-26},
url = {https://os-rdm.mpib.berlin/guidelines/},
doi = {10.17617/2.3682163},
langid = {en}
}