Definition

A data management plan (DMP) describes and documents how research data'Data that are a) created through scientific processes/research (e.g. through measurements, surveys, source work),  b) the basis for scientific research  (e.g. digital artefacts), or c) documenting the results of research, can be called research data.This means that research data vary according to projects and academic disciplines and therefore, require different methods of processing and management, subsumed under the term research data management” (Forschungsdaten.info, 2023). Read More and materials are handled during and after the project duration. It specifies how data and materials are generated, processed, stored, organized, published, and archived. Additionally, a DMP records responsibilities and addresses legal and ethical aspects. As a "living document," the DMP is regularly reviewed and updated as necessary throughout the project.

Introduction

Data management plans (DMPs) are regarded as essential tools in research data managementResearch data management is aimed at handling research data in a responsible and well-considered manner. The idea is to carefully organize, maintain and process research data using specific measures and strategies. The goal is to store data long-term and make it accessible and reusable by others, in line with good scientific practice. This enables easier verification of scientific findings, secures evidence, and allows for further evaluations and analyses of the data. Read More. They provide information on the data generated during a project, how it is used, and what happens to it during the research and after the project's conclusion. DMPs outline the planned handling of research data before, during, and after the project's end, while also defining roles and responsibilities (Jensen, 2011, p. 71).

A data management plan:

• Is usually presented as a guide or questionnaire aimed at structuring the description of data handling.
• Typically focuses on the research data of a (scientific) project or study.
• Includes general guidelines and questions tailored to project-specific needs.
• Can range from a few paragraphs to several pages.
• Should be created as early as possible, ideally during project planning and proposal submission.
• Functions as a living document, updated and refined throughout the project.
• Establishes a framework for managing research data during the project and beyond.

Video (German only):

Motivation

As with other aspects of research data management, there is a discrepancy between general science policy requirements and the actual implementation of data management plans (DMPs) within ethnographic disciplines. In these fields, DMPs have not yet become a routine part of research practice. In a survey conducted by the Specialized Information Service for Social and Cultural Anthropology (FID SKA)The Specialized Information Service for Social and Cultural Anthropology (FID SKA) provides members of the German Society for Empirical Cultural Studies and the German Society for Social and Cultural Anthropology, as well as other interested researchers to a limited extent, with selected online resources for research. Read More on the handling of research data in social and cultural anthropology, only 10 percent of respondents indicated that they were using a data management plan in their current project. Around 30 percent did not know whether such a plan existed within their research project or what a data management plan even was (Imeri, 2017).

Nevertheless, a DMP can support research project work. It provides researchers with clarity and an opportunity for reflection during the generation of research data.

Benefits of Data Management Plans: A DMP...

• ensures adherence to good scientific practiceGood scientific practice (GSP) represents a standardized code of conduct established in the guidelines of the German Research Foundation (DFG). These guidelines emphasize the ethical obligation of every researcher to act responsibly, honestly, and respectfully, also in order to strengthen public trust in research and science. They serve as a framework for guiding scientific work processes. Read More (e.g., transparency and traceability).
• facilitates project planning, particularly in budgeting for research data management.
• helps make data and materials interpretable and reusable at a later stage, potentially for other researchers as well.
• ensures project continuity, even in cases of personnel changes.
• requires detailed reflection on one’s own handling of data and materials.
• serves to meet the expectations and requirements of funding institutionsFunding institutions are organizations that provide financial support for scientific research, such as foundations, associations, or other entities. Internationally, most of these institutions have established guidelines for research data management (RDM) in research projects, meaning that potential funding is tied to specific requirements and expectations for handling research data. Some of the most well-known funding institutions in German-speaking countries include the Federal Ministry of Education and Research (BMBF), the education and science ministries of the federal states, the German Research Foundation (DFG), the Volkswagen Foundation, the Austrian Science Fund (FWF), and the Swiss National Science Foundation (SNF). Read More.

Even at the level of a bachelor's or master's thesis, it is useful to go through the key questions of a DMP, reflect on one's handling of data, and document where and how data is stored, among other things. This ensures that, at a later stage – such as when writing a dissertation on a similar topic – one can refer back to material from the master's thesis. In this sense, a DMP serves the internal usability of research data within the project in which they were created. The primary goal is to ensure that research data remain accessible during and after the project's completion, do not get lost, and are not damaged.

Many funding agencies (DFG, FWF, SNF, Horizon Europe, Volkswagen Foundation) now require applicants to include details about research data management in funding proposals. In this context, the development and implementation of data management plans are increasingly being mandated.

A few examples are listed below, with further information available under the guidelines of funding institutions regarding research data management (Forschungsdaten.info, 2023i).

Methods

In principle, every researcher can create an individually formulated data management plan. A "good" DMP should address technical, organizational, structural, legal, and ethical aspects, as well as sustainability. Depending on the research project, the scope of a DMP may vary.

A data management plan ideally includes the following elements (Helbig et al., 2020), of which the first five should always be considered – this applies even to empirical final theses. Points six to nine are primarily intended for larger projects where data reuse is planned from the outset.

1. Project Information: Description of the project's content, administrative project details, funding institutions, project participants, and responsibilities.
2. Relevant Guidelines, Recommendations, and Third-Party Requirements: e.g., from professional associations, universities regarding data handling (if available).
3. Description of Planned Data Collection Methods and Resulting Data TypesThe terms 'file type' and 'file format' are often used interchangeably. A distinction is made between proprietary and open file formats. Proprietary formats usually require fee-based software to access, as they may not be compatible with other programs (e.g., PowerPoint for .ppt files or Photoshop for .psd files). In contrast, open formats such as .rtf or .png are based on standards and can be opened by many programs. Read More and FormatsThe terms 'file type' and 'file format' are often used interchangeably. A distinction is made between proprietary and open file formats. Proprietary formats usually require fee-based software to access, as they may not be compatible with other programs (e.g., PowerPoint for .ppt files or Photoshop for .psd files). In contrast, open formats such as .rtf or .png are based on standards and can be opened by many programs. Read More: (e.g., field notes, observation protocols, recorded interviews, photos, films, etc.).
4. Data StorageData storage generally refers to the process of saving data on a storage medium or device (digitalized data). Research data are unique and valuable, and should be stored securely to protect them from loss and unauthorized access. Various measures, such as regular backup routines, can help minimize potential data loss. Read More, SecurityData security encompasses all preventive physical and technical measures aimed at protecting both digital and analog data. Data security ensures data availability and safeguards the confidentiality and integrity of the data. Examples of security measures include password protection for devices and online platforms, encryption for software (e.g., emails) and hardware, firewalls, regular software updates, and secure deletion of files. Read More, and Organization: Type and location of storage, backupThe term backup means data protection or data recovery and refers to the copying of data as a precaution in the event that data is lost, e.g. due to hard drive damage or accidental deletion. The data can be restored with a backup. For this purpose, the data record is additionally saved on another data carrier (backup copy) and stored offline or online. Read More routines, data exchange, and measures to prevent data loss.
5. Ethical and Legal Aspects: Handling of research ethics questions, implementation of data protectionData protection includes measures against the unlawful collection, storage, sharing, and reuse of personal data. It is based on the right of individuals to self-determination regarding the handling of their data and is anchored in the General Data Protection Regulation (GDPR), the Federal Data Protection Act (Bundesdatenschutzgesetz), and the corresponding laws of the federal states. A violation of data protection regulations can lead to criminal consequences. Read More regulations (e.g., use of informed consentInformed consent refers to the agreement of research participants to take part in a study based on the basis of comprehensive and understandable information. The design of an informed consent must address both ethical principles and data protection requirements. Read More, anonymizationAccording to the German Federal Data Protection Act (BDSG § 3, para. 6 in the version valid until May 24, 2018), anonymization is understood to mean all measures for modifying personal data in such a way 'that the individual details about personal or factual circumstances can no longer be assigned to an identified or identifiable natural person, or can only be assigned to an identified or identifiable natural person with a disproportionate investment of time, cost and labor.” Anonymized data is therefore data that does not (or no longer) provide any information about the person concerned. As such, it is not subject to data protection or the General Data Protection Regulation (GDPR). Read More, pseudonymizationPseudonymization is 'the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organizational measures to ensure that the personal data cannot be attributed to an identified or identifiable natural person' (BlnDSG §31, 2020; EU GDPR Article 4 No. 5, 2016). Read More measures).
6. DocumentationResearch data not only form the basis of scientific publications by researchers but are also often made accessible to others. This requires that research data be documented in a clear and understandable way. This becomes essential if data publication is intended. Metadata - structured information about other data -plays a central role in finding, searching, and using research data. Various scientific communities have established metadata standards, which are conventions for describing and documenting research data through metadata. Read More: Planned types of data and accompanying materials (measures to ensure data traceability over time and by third parties).
7. ArchivingArchiving refers to the storage and accessibility of research data and materials. The aim of archiving is to enable long-term access to research data. On one hand, archived research data can be reused by third parties as secondary data for their own research questions. On the other hand, archiving ensures that research processes remain verifiable and transparent. There is also long-term archiving (LTA), which aims to ensure the usability of data over an indefinite period of time. LTA focuses on preserving the authenticity, integrity, accessibility, and comprehensibility of data. Read More and Data Retention Beyond Project Completion: Selection of suitable data for archiving, definition of conditions for archiving and reuse, choice of an appropriate archiving environment.
8. Responsibilities and Roles: Assignment of tasks for backups, creation, and maintenance of the DMP.
9. Costs and Effort: Planned expenses and resources for research data management (e.g., cost of pseudonymization efforts).

The working group 'Greening DH' of the association 'Digital Humanities in German-Speaking Countries e.V.' has compiled recommendations and suggestions for filling out data management plans (only in German) under the following link: https://dhd-greening.github.io/rdm/empfehlungen_dmp. These recommendations aim to promote resource-efficient and sustainable practices wherever possible.

Practical Examples

Completed DMPs as Examples and Templates (in German only)

• Data Management Plan in a simplified/reduced format for study projects (target group: students) in the form of a data overview table in Imeri et al., 2023, p. 231. https://doi.org/10.21248/ka-notizen.85.22
• Example DMP from Humboldt University with the German Research Foundation (DFG) as a funding institution. https://www.cms.hu-berlin.de/de/dl/dataman/muster-dmp-dfg
• Various example DMPs from different disciplines using various research methods (available in English). https://dmponline.dcc.ac.uk/public_plans 
• Data Management Plan of a project funded by the Berlin University Alliance, describing the handling of research data generated within the project. https://zenodo.org/record/7399810

Templates

• Example DMPs for different funding organizations such as Horizon 2020, BMBF, and the Volkswagen Foundation: https://www.cms.hu-berlin.de/de/dl/dataman/arbeiten/dmp_erstellen
• Template for Data Management Plans from Science Europe (Organisation representing major public organisations that fund or perform excellent, ground-breaking research in Europe): https://www.scienceeurope.org/our-priorities/research-data/research-data-management/
• Templates and guidelines for the DMP from the Austrian Science Fund FWF (Fonds zur Förderung der wissenschaftlichen Forschung): https://www.fwf.ac.at/ueber-uns/aufgaben-und-aktivitaeten/open-science/forschungsdatenmanagement

Discussion

Planning vs. Openness

Ideally, a Data Management Plan (DMP) should be created before or at the beginning of a project and document and define as precisely as possible how research data will be handled within the project. However, the well-intended rule of thumb, "always remember to plan ahead," is difficult to implement in the context of ethnographic researchEthnographic field research refers to the collection of empirical data on-site, meaning within concrete social settings, as opposed to laboratory research, archival research or standardized survey studies. The typically long-term participation of ethnographers in the daily life of the group under study allows for direct observation of social practices and processes, allowing for insights into actual behavior. It is important to note that researchers are always part of the situations in the field, and their assigned and assumed social positions significantly influence their data – that is, what they are able to observe and understand. Read More. The inherently exploratory nature of ethnographic research – at least in its initial field phase – requires a fundamental openness to unforeseen developments and topics that were not anticipated during the research planning stage. Frequently, original research questions prove irrelevant in the field, or planned methods turn out to be unfeasible. Ethnographers are therefore constantly required to adapt their approaches and research topics to the social realities encountered in the field. Thus, the characteristic openness and flexibility of the ethnographic research processAn attitude of methodological openness is essential in ethnographic research to adapt to the dynamics of social processes and respond to unforeseen events in the field. A fixed, unchangeable set of research methods does not meet these requirements. Furthermore, ethnographic research is also characterized by openness toward research materials after data collection; this approach encourages the continuous establishment of new theoretical perspectives on the material in order to allow for constructive and multi-layered interpretations. Read More, which is desirable from a methodological standpoint, stands in contrast to the scientific policy demand for forward planning in research data management and the systematic application of data management plans as a guiding framework throughout the research process.

Moreover, at the beginning of an ethnographic project, it is often difficult to determine what specific types of dataThe terms 'file type' and 'file format' are often used interchangeably. A distinction is made between proprietary and open file formats. Proprietary formats usually require fee-based software to access, as they may not be compatible with other programs (e.g., PowerPoint for .ppt files or Photoshop for .psd files). In contrast, open formats such as .rtf or .png are based on standards and can be opened by many programs. Read More will be generated, what potential reuse scenarios might emerge, and what specific data protectionData protection includes measures against the unlawful collection, storage, sharing, and reuse of personal data. It is based on the right of individuals to self-determination regarding the handling of their data and is anchored in the General Data Protection Regulation (GDPR), the Federal Data Protection Act (Bundesdatenschutzgesetz), and the corresponding laws of the federal states. A violation of data protection regulations can lead to criminal consequences. Read More and ethical researchResearch ethics addresses the relationship between researchers, the research field, and the subjects/participants of the research. This relationship is critically examined against the backdrop of vulnerabilities and power asymmetries created by the research process (Unger, Narimani & M’Bayo, 2014, p.1-2). Due to the processual and open-ended nature of ethnographic research, ethical questions arise throughout the research process in various ways, depending on the research context and methods. However, research ethics does not end with leaving the field; it also encompasses issues related to data archiving, data protection, and sharing research data with participants (see, for example, ethics guidelines by the DGSKA or the position paper on archiving, provision, and reuse of research data by the dgv). Read More challenges will arise (Demmer et al., 2020, pp. 43).

Consequently, a DMP in the context of ethnographic research cannot be treated as a fixed form that is simply completed at the beginning of a project and then "filed away." For social and cultural anthropology, only broadly structured data management plans are practical – ones that function as "living documents" and can be adapted to changing methodologies and workflows within the project. A sufficiently flexible DMP facilitates research data management, encourages reflection on the processes of data collection, documentation, and curation, and can thus positively impact scientific practice.

Tools

There are already various online tools available to help researchers create a Data Management Plan (DMP). These tools have different focuses and are aligned with different requirements and standards:

• RDMO: https://rdmorganiser.github.io/
  The Research Data Management Organiser (RDMO) is a generic tool designed for creating DMPs and operating as a local instance, allowing full customization to meet the needs of the hosting institution and its users. Key features of RDMO include:
  • Freely configurable questionnaires based on pre-installed questions, with the ability to easily add new questions to the local system
  • Pre-configured views to ensure compatibility with the requirements of certain research funders (H2020, SNF) and other tools (DMPTool, DMPonline)
  • Output of DMPs in various formats (e.g., .docx, .tex, .pdf)
  • Storage of "snapshots" to track the temporal development of a DMP
  • Multilingual support (German, English, French; Italian is in progress)
  • Suitable for both small projects and large collaborative projects
  • RDMO is available as open-source software on GitHub¹GitHub is an online service that enables software developers to collaborate on software projects and make them available for download by users.

• DMPTool: https://dmptool.org/
  The University of California Curation Center at the California Digital Library (CDL) operates DMPTool, a tool for the collaborative creation of data management plans. It is particularly tailored to the U.S. funding landscape, where many different funding institutions have their own specific DMP requirements. The tool helps users find the "right" template for their DMP and displays the relevant sections of funding guidelines for each part of the plan. The entered data can then be exported in a suitable format and attached to the funding application. The source code of the application is published under the MIT License.

• DMPonline: https://www.dcc.ac.uk/dmponline
  Developed by the Digital Curation Center/UK, DMPonline is available in English. It provides various templates that users can choose from. By answering a series of questions, users are guided to the most suitable template for their needs.

Using these tools, DMP content can be saved, adjusted, shared, collaboratively edited, and exported²More information and additional tools can be found at: https://www.forschungsdaten.org/index.php/DMP-Tools.

However, these listed tools are currently only partially useful for ethnographic projects. The development of an appropriate template for ethnographic research is still pending.