Support Offers for the Research Process


Offers for the Different Phases of the Research Process

Creating a Data Management Plan

A data management plan, DMP for short, is a systematic documentation of your research data. Such a plan accounts for the processing, storage, archiving, access and use of your data and metadata, among other things.

Creating a DMP requires reseachers to think about issues such as data quality, used resources, and intellectual property.

There are several online tools which facilitate the generation of a DMP:

  • RWTH Aachen University's DMP Template
  • DMP Online
    DMP Online was developed by the UK-based Digital Curation Centre and is hosted at the University of Edinburgh. It provides templates for a broad range of funding bodies as well as a generic template that can be used for any research project, regardless of funding institution. The tool assists researchers with creating a data management plan in accordance with EU specifications.
  • DMPTool
    DMPTool is offered by the California Digital Library and provides instructions to meet the requirements of funding institutions that prescribe the submission of a DMP. The tool offers a generic DMP template and can be used free of charge. Furthermore, the DMPTool website presents a few examples of data management plans.

Documentation and Metadata

Metadata provide information on your research data, describing them in more detail and thus facilitating their interpretation. They are especially important for the documentation, administration, and classification of digital research data. With their help, the following questions can be answered, for instance:

  • Where do the data come from?
  • Who has generated these data? When and how were the data created?

In order to ensure the exchange and re-usability of metadata via digital information systems, uniformly standardized metadata should be used, if possible.

JISC Infokit provides an introduction to the topic of documentation and metadata. This guide informs you about the most important concepts and objectives surrounding the topic of metadata; prior knowledge on the topic is not required.

The presentation Explain It is a very short introduction to the topics of documentation and metadata.

The interactive Mantra course is a training program on documentation and metadata. It makes clear why it is so important to document research for oneself and others. Users also learn when and why to use metadata.

Creating a Metadata Schema

A metadata schema is a compilation of specifically defined data elements for the standardized description of a resource. An appropriat schema should be selected depending on criteria such as data type, origin of data, and  contexts of use.

There is a broad range of meta data schemata for data from different disciplines. Before creating a schema for the description of your research data, please check whether there is a standardized schema for your research field. Well-known standardized metadata schemata include Dublin Core and RADAR, for example.

Metadata Tool

The Metadata tool allows you to fill out the metadata according to a predefined schema for your institution. The schema does not tell you which metadata fields (author, discipline,...) must be or can be filled out, but rather makes it possible to use controllable vocabulary. Selecting or creating a suitable metadata schema is not at all trivial. The  is happy to assist you.

The Digital Curation Center, DCC for short, provides further information on discipline-specific metadata standards.

Examples for discipline-specific metadata schemata:

  • Archaeology and Cultural Heritage objects
    Archaeology Data Export Standard (ADeX, PDF)
    Visual Ressource Asociation Data (VRA)
    Categories for the Description for the Works of Art (CDWA)
  • Geodata
    Content Standard for Digital Geospatial Metadata (CSDGM, PDF)
    Data Documentation Initiative (DDI)
    ISO 19115 "Geographic Information – Metadata" (PDF)

If you have decided on what schema to use, the content of the data fields must be specified. In order to ensure re-usability, as far as possible, and to optimize search and research, the use of a controlled vocabulary, thesauri, and classification schemes is recommended. Again, a variety of discipline-specific and more general solutions is already available.

Personal Data Management

In order to implement data management as planned, the everyday research activities must be performed in a structured manner. Aside from documentation matters, the organization of data structures and, for example, the naming of samples, should be taken into account. For this reason, the following points are to be specified early on in the project:

  • Data organization, filing structures, data versioning
  • Documentation, metadata
  • Data storage and backup mechanisms during the project period
  • Responsibilities, access rights, rules for collaboration
  • Archiving or publication after project completion

The data management plan is a useful tool to support your personal data management activities. Furthermore, please refer to the Institutional Policy (de) on data management. The Research Data Management Team offers individual and group advising sessions in which appropriate data management solutions are developed. These jointly developed solutions make full use of the University's technical offerings and take the specific circumstances of your institution as well as disciplinary conventions into account.

Collaborative Work Activities

Communication and collaboration are a prerequisite for outstanding research. Research data management contributes to meeting the challenges of collaborative research. These challenges can be delineated as follows:

  • Collaborative research requires to explicitly address and communicate aspects and premises which otherwise might remain unformulated or even unconscious.
  • For this reason, additional expenditures for communication must be considered in the project plan.
  • Agreements and arrangements should be made in writing and made available to all project participants, via the data mangement plan, for example. 
  • Agreements can made concerning:
  • Storage locations
  • Communication channels
  • Templates for reports, notes, and documents
  • File names
  • Folder structures and names

There are several tools that support collaborative work activities:

E-Mail: The use of e-mail is omnipresent and thus is rarely reflected upon.

File servers: File servers are widely used, for instance as network drives.

Sciebo: Sciebo is an "ownCloud"-based non-commercial storage service for researchers and students at NRW universities
Documentation and Support (de)

GigaMove: GigaMove is a free web-based service for the exchange of large data files
Documentation and Support (de)

BSCW: BCSW is a tool for collaboration and file storage
Documentation and Support

SharePoint: SharePoint is a fee-based file storage and sharing solution for collaborative work activities offered by Microsoft.
Documentation and Support (de)

GitLab: GitLab is a repository manager to support software development projects
Documentation and Support (de)

Archiving Your Data

Good academic practice (de) requires the storage of research data for a period of at least ten years. However, to simply save the data on an external data carrier is not a viable option!

We recommend using simpleArchive for smaller datasets that do not include personal data. Good solutions include using the archiving service offered by RWTH’s IT Center.

There are also discipline-specific or institutional repositories. The Registry of Research Data Repository, re3data, a service offered by DataCite and supported by the German Research Foundation, provides a good overview of research data repositories. It is also possible to use the institutional repository RWTH Publications.

For archiving purposes, documentation of your data is of utmost importance. The documentation should include all information required to fully comprehend the data set, including all metadata.

Many research data repositories, such as RWTH Publications, assign a Digital Object Identifier, or DOI, to persistently index your data. The University Library of RWTH Aachen is registered as a data center with TIB, the German National Library of Science and Technology, and is authorized to assign DOIs.

The archiving service of the IT Center offers to assign a persistent identifier for archiving purposes.

Please refer to our instructions titled Archiving of Data in the Context of a Publication (de).

Long-Term Archiving

In general, long-term archiving means that the data are safely stored and made accessible for a period of more than 10 years. Aside from the physical storage of data, requirements for the future interpretability of the data are to be taken into account.

  • Is the data format suitable for long-term archiving?
  • Is a special software required for the interpretation of the data?
  • Are the metadata accurate and complete?

In order to ensure the usability of the data in an unknown future technological infrastructure, technical and descriptive metadata play a decisive role.

For detailed information on the topic of long-term archiving of research data, please refer to the NESTOR manuals Langzeitarchivierung von Forschungsdaten (de), Digital Curation of Research Data, available in German and English, or the Nestor Wiki.

The Rosetta software by ExLibris, which is currently being tested by the FDM project team, offers long-term archiving functionalities.

Persistent Identifiers

An identifier is a unique identification code that is permanently assigned to a (digital) resource. A classic example for an identifier is the International Standard Book Number, ISBN for short, used in the printing sector. For digital objects, frequently, the Uniform Resource Locator, or URL, is used, which has an average livespan of about 100 days. Due to this short life span of the URL, it is not guaranteed that the research data can be referenced in the long term. For this reason, persistent identifiers, or PID, are being used. Persistent identifiers can be seen as intermediaries between the reference and the object, making it possible to decouple the object from its “electronic” location. This increases the stability of the reference, as the possibility of so-called “broken links” (Error 404- Page not found) is reduced, even if the data are moved to another storage location.

The PID assigns a permanent, stable signifier, called URI, to the research data, and this assignment is valid for the data’s entire lifecycle, possibly even beyond.

The most well-known example of a PID is the Digital Object Identifier, DOI for short. It is the most widely used system to reference publications and research data. The DataCite association and its members are authorized to assign DOIs.

Using the IT Center’s archiving service, it is possible to assign an EPIC PID to the self-created archive nodes.

In the near future, the IT Center will offer the EPIC PID service for the research data of RWTH scholars. It is favorable that EPIC PIDs can be transformed into DOIs, as both are based on the Handle system. Thus, you are able to make the research data for a publication citable without any extra effort on your part.


For information on the topic of copyright, please refer to the Info Sheet on Copyright Protection (de).


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