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Project execution

Utilizing RDM in ongoing research ensures that research data are handled in a well-planned, structured manner in day-to-day academic work. This includes organized data storage and management, having a storage and backup strategy and the documentation of the research data.

folders iconOrganizing research data


  • Folder structures

    Plan out an effective filing structure that is as simple as possible to adhere to and is also clear and unambiguous – for future reference as well. All files should be easy to find and uniquely named. Keeping a folder system to a maximum of four levels and not much more than ten elements per folder are tried and true guidelines observed by many.

  • Depending on the research project and the nature of your data, different approaches may be advisable, such as folder structures based on data collection methods, data types, processing steps, individuals, locations, time categories, etc. The folder structure should be primarily designed around the workflows and routines characteristic of your project. If navigating through your folders is not intuitive or takes up significant time out of your day, you should rethink the system you have in place. In doing so, be sure to coordinate closely with your staff.

  • File names

    file naming

    The same applies to file naming. File names should be unique, indicate the content and status of the file and facilitate sorting. Make sure that file names are not excessively long and use hyphens and underscores or upper and lower case type for separation. Do not use spaces, periods or special characters in file names. For example, the file name MS_Sample17_Clean_19-11-06 could indicate that the file is sample no. 17 from Manfred Schmidt, cleaned on November 6, 2019. Naming conventions should be strictly observed, and only changed after prior consulting your collaborators.

  • For further practical information on this subject see:

    • Version control

      When you change files, it is often a good idea to keep earlier versions at hand and to adhere to versioning scheme. For example, you can give the file a sequential version number in the file name (see above), or save version information within the file (in the header, for example). Document version jumps and the associated work steps in your data documentation (see below). You should define what a version jump means and how they are designated and documented, especially in projects involving several individuals.

    • For further information on version control see:

Contact us at any time for advice on any data organization questions you may have at: [Email protection active, please enable JavaScript.]

backup iconStorage and security


  • Storage and backup strategies

    Losing unsaved research data can be frustrating, requiring work to be redone and jeopardizing publications, or even torpedo an entire research project in a worst-case scenario. Accidents can never be ruled out, like spilling a hot drink on your laptop, leaving your bag on the subway with your USB stick in it or overwriting the latest version of a file. However, a well thought-out storage and backup strategy will in most cases minimize the damage done.

    Where and in what format you should optimally store and edit your data depends chiefly on your research data and work routines. In general, you should avoid relying on individual devices and external data carriers. Cloud storage services are often recommended for automated synchronization of devices and users. Before using such services however you should carefully review the terms of use, encryption technologies employed, server locations and other factors. In many projects, additional considerations are important such as controlling access to highly sensitive data, handling large data volumes or the rights and role management for staff.

    Backup strategies can also vary from project to project. What data do you want to back up, and at what intervals? What data volume is concerned? How many restore points should be created, and retained for how long? As a rule of thumb, you should keep at least three copies of your data on at least two different storage media, with at least one copy saved at a separate location (i.e. a different fire compartment). Doing so keeps you well-prepared to overcome most accidents and incidents.

  • Data storage solutions at the University of Bonn

    Faculty and graduate students of the University of Bonn have access to a range of services offered by University IT:

See also the following for further information:

documentation iconDocumenting research data

Data Documentation

Documenting work steps is a cornerstone of good scientific practice, for it is essential to ensure that research results are transparent and reproducible and makes it easier for interested third parties (and the researchers themselves, after the fact) to understand the methodology employed and recreate it if necessary. Structured data documentation is recommended if digital data are a core element in your research work. Undocumented data can become worthless in the worst case, as its informational value can no longer be determined.

Documentation of your data may include the following:

Data collection

  • For what research project and what research questions were the data generated?
  • When, where and by whom was the data gathered?
  • What methods and procedures were employed and what measurement instruments were used, if any?

Data structure

  • What is the data content (interviews, temperature measurements, stock prices, text codes, lab samples, etc.)?
  • What is the data basis and range (e.g. ratio relative to the population, type of sampling)?
  • Scope of data (number of “cases” or “events”, description of characteristics and variables collected)
  • Explanations of codes, classifications, variable names, numbering, etc.
  • Description of the software environment (operating system, software used, versions)
  • Information on folder structures, file names, version control and formats (see above)

Data processing

  • Quality assurance and data cleansing measures
  • Anonymization and pseudonymization methods, as required
  • Conversion, formatting, normalization, other processing
  • Evaluation (analysis steps and methods)
  • Preparation and visualization techniques

There are a number of practical approaches to implementing documentation. Freely formulated documentation is always an option using in editors or word processing programs, but many prominent software packages offer internal documentation solutions, via description fields for individual data records, for example. Depending on the scope and character of your project, it may be better to work with project-wide documentation or differing documentation approaches for individual files or file groups. The documentation technology best suited for your purposes depends heavily as well on the academic field concerned. In laboratory sciences, for example, Electronic Lab Notebooks are increasingly in use which have been specially developed for the documentation of laboratory activities.

Plan in advance the scope and implementation of your data documentation regime. To ensure data reusability it is often advisable to focus on the requirements under relevant metadata standards (see section on Metadata Tagging under Publishing Research Data) and the criteria for selecting suitable repositories.

For basic data documentation, we offer a readme template that you are welcome to use for your datasets.

Contact us for any questions you may have concerning data documentation at: [Email protection active, please enable JavaScript.]

See the following for further information:

Image sources:

Attic: Bill Kasman 2014 & Scott Arneman 2009
File Names: Randall Munroe
File Formats: Bezjak et al. (2018): Open Science Training Handbook
Backup: verändert nach Foto von Kaboompics from
Dead Chef: Auke Herrema
Folders Icon: Bharat from the Noun Project
Backup Icon: ProSymbols from the Noun Project
Documentatin Icon: Juicy Fish from the Noun Project


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