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Research GELIFES Research Data Management

Research Data Management Plan

Download the RDMP template

A research data management plan (RDMP) describes how primary (raw) and secondary (processed) data are collected and used in a project, how the data are stored and archived, who owns the data and other legal issues, and who has the final responsibility for the project’s data collection, processing and archiving. The RDMP includes the following sections that are outlined in the paragraphs below:

Where applicable, the institute’s specific regulations and/or preferred options are included and listed in italics.

The RDMP covers the complete trajectory of a research project from planning and grant application to archiving of the data in a secure repository at the end of the project. The RDMP is a dynamic document and should be updated in line with the progress of the project. If major changes (collection of additional data sets, changes in policies or cooperation partners) in the project planning occur, a new version of the RDMP is required.

Administration

The administrative section of a RDMP describes the project and contact details of the author and/or PI responsible for the data, and should include:

  1. Name of the researcher/student (including s-number) and the research group responsible for the project
  2. Project name, brief (3-5 sentences) description of the project and its expected results
  3. Funding agency and relevant project codes
  4. In case of joint projects: which external institutes/organizations are involved and who are the contact persons
  5. Start date and expected end date of the project
  6. Version and date of the RDMP, dates of last update of the RDMP
  7. Person(s) responsible for the project’s data management:
    • For MSc research projects: MSc student, daily supervisor & PI
    • For PhD projects: PhD student & (first) promotor/PI
    • For all other research (sub)projects: researchers & PI
    • In case of joint projects: indicate whether (part of) the data will be collected externally, and who (PI) is responsible for data management at the external institute.
Data collection

This section includes brief descriptions of the data that will be collected, generated and used in the project. See also the guidelines for a data archive in section 2.8.

  1. Indicate whether data from previous projects will be re-used, e.g., will the project extend existing long-term databases?
  2. Describe the source of the data, e.g., collected at own institute/field station, external institute, obtained in collaboration with external parties, acquired from commercial sources.
  3. Include an estimate of the expected quantity of data: file size (GB or TB) & number of files.
  4. Briefly describe what type of primary (raw) data will be collected. Primary data include at least the following:
    • scanned or electronic field logs, lab journals, score forms,
    • pictures of gels, microscopic observations,
    • output from data loggers,
    • video and audio recordings,
    • webcam/photo identification files: only when the resulting IDs are NOT included in other primary data sets, e.g. in field journals or data files,
    • sequencing and genotyping data,
    • micro array and hi throughput data: only if NOT stored in a public database on publication.
  5. Briefly describe how the primary data will be processed, i.e., what type of secondary (processed) data will be generated. Secondary data may include any or all of the following:
    • spreadsheets, databases, graphics,
    • output from statistical packages,
    • output from geographic information systems,
    • simulated datasets: only if NOT possible to reproduce from program code
    • program code in C/C++, NetLogo, Matlab, Maple, Mathematica etc. of all programs developed in the project & all associated parameter files,
    • R scripts (statistical analysis, graphs, etc.), Python or other batch scripts used for data processing,
    • specific program code/scripts, e.g., Z-Tree or other software packages used to produce or process primary data.
Data storage

This section describes how data will be stored and backed-up during the project, and how & where the final data will be archived at the end of the project:

  1. Indicate how & where primary and secondary data are stored during the project, and how often these data are backed-up. Basic rule-of-thumb for backups: The more important the data and the more often they change, the more regularly they need to be backed up. Storage options include the following:
  2. Indicate what version of the data is to be stored for how long, and how version control is realized, e.g., GitHub ( https://github.com/ ), a web-based repository hosting service for the version control system for software development Git.
  3. Indicate how & where the data will be archived at the end of the project. If (part of) the data is stored externally, indicate how & where this data is accessible, and who is responsible for this (part of) the data. Data archiving in the institute’s data repository is required for the following studies:
    • All publications in a scientific journal or book where the institute or a research group within the institute is the work address of the first or equal author (also in case of multiple addresses of the first or equal author).
    • All MSc reports done within the institute, supervised by an institute staff member.
    • External MSc studies done at another institute, but with an institute staff member being responsible for the final grading and/or when the student is registered with the RUG.
    • All PhD theses done within the institute, with an institute professor as first promotor.
    • All external PhD theses (e.g., done at a KNAW or NWO institute) with an institute professor as first promotor.
Data ownership

This section includes relevant statements with regard to the ownership of, and access to the data.

  1. Data ownership
    • All research data collected, generated or otherwise acquired by staff and PhD students that hold a permanent or temporary appointment at the University of Groningen, or by bursary students, MSc students and BSc students that are registered with the University of Groningen are by legal rights co-owned by the University of Groningen.
    • Within the Faculty of Science and Engineering, the research institute acts as the legal representative of the University of Groningen and has control of all research data collected, generated or otherwise acquired within the institute unless different arrangements are clearly documented and approved by the institute director.
  2. Indicate whether specific requirements apply with regard to licenses and storage of privacy sensitive data.
    • Medical Ethics Committee regulations for experimental human research.
  3. Indicate whether specific arrangements have been made with regard to access to the project data.
    • By default, data access in the institute repository is restricted to scientific staff members ultimately responsible for the corresponding research projects.
  4. Indicate whether other with regard to data ownership rights and accessibility:
    • permits or licenses for use and/or storage of specific data
    • copyrights of photos/movies
    • contractual obligations to (commercial) external research partners
    • embargo requirements for future publications
    • specific requirements of funding agencies with regard to data sharing and open access
    • specific requirements of scientific journals with regard to data sharing and open access
Data documentation

This section describes how the data archive is organized (metadata), which data formats are used & how these are accessible (indicate which additional software is required with links to source sites if relevant).

  1. Metadata provides information about the data collected in a project, allowing the data to be found, identified and interpreted after a project is finished. For each data archive a description of its contents is required; for large archives consisting of multiple folders, such a descriptive metadata file is also required for each folder. A metadata file is a simple text file named read_me_first.txt including at least the following information:
    • The description/reference of the publication (for MSc students: include your s-number; for publications: include the doi).
    • An overview of the contents of the zip archive and how this relates to the publication (indicate how the data was processed, which data was used in which chapter & to produce what results/plots).
    • Which computer programs were used & what version of that program & links to source sites where relevant.
    • If relevant: a short description plus links for data that are archived externally.
    • Contact details of the author(s)/data owner(s).
  2. The preferred way of data storage is as tab- or comma-delimited text files with variable names in the first line, with an associated R script that reads the data file, as this makes data robust towards future changes in software and data file formats. For other data types, consider using the file formats suggested in the repository manual (based on the KNAW-DANS Preferred Formats overview, May 2013) for similar reasons of compatibility and future accessibility.
  3. To ensure general accessibility of the data, all file names, metadata and other descriptive files, and comment lines in code must be in English. File names should not contain special characters other than dashes (-) or underscores (_); use underscores instead of spaces. Use the naming standards defined in the repository manual for all data archives deposited in the repository.
Useful links

The outline of the RDMP in this document is largely based on the following sources:

Specific guidelines for EU/NL funding agencies:

Last modified:06 March 2019 1.36 p.m.