Research Data Management (RDM) is the organization of collecting or creating, analyzing, storing, sharing, archiving and publishing research data. By research data we mean any information or materials collected, generated, processed or analyzed to support or describe research findings. This includes both raw data (the unedited form of source data) and the edited versions of data used for analysis. Examples of research data include interviews and transcripts, audio and video recordings, images, computer simulation codes and results, instrument measurements, and geographic coordinates.
The goal of RDM is to make the management of research data as easy, safe, and efficient as possible. Proper planning is the most important first step. It will also help you comply with existing legislation and the requirements of funding agencies, for example.
Comply with legislation and ethical guidelines:
• By designing your research in such a way that it meets ethical guidelines
• And by carefully protecting the privacy of your research subjects
Boost your impact:
• By sharing and reusing data for further research
• And by increasing the discoverability and visibility of your work by citing research data
Transparency:
• Through making your research verifiable and replicable
• And providing insight into how you arrived at research results
Effective collaboration with companies:
• By working confidently and securely with company-sensitive information
• And thinking ahead of time about how intellectual property will be handled in research
Saving time:
• By using metadata and version control that it is easier to collaborate
• And prevent data becoming unusable or lost through careful documentation and archiving
Meeting the requirements of funding agencies:
• By making your data FAIR as much as possible.
• And enabling reuse by other researchers and students
An increasing number of research institutions, funding agencies and governments declare to work according to the FAIR principles of data management, i.e. Findable, Accessible, Interoperable and Reusable. The FAIR principles apply to the data itself, the metadata (information about the data), and the infrastructure (repository) in which the data is made available.
Hanze University of Applied Sciences aims to make research data as FAIR as possible. The FAIR principles state that data should be documented, managed and preserved in such a way that they remain discoverable, accessible and authentic (original) over time. The ultimate goal is using the data for further research and expanding existing knowledge.
Findable: The data should be findable by others. Metadata and research data should be easily findable by humans and computers (systems). Computer- and system-readable metadata are essential for automated findability of data.
Accessible: Data should be stored persistently, be easily accessible (authentication and authorization) and downloadable, and have a clearly defined user license.
Interoperable: The data ought to be combinable with other data. The data should be made compatible with other platforms/software/applications or methods of analysis, storage and processing.
Reusable: The ultimate goal of FAIR is to optimize the reusability of data. To achieve this, metadata and data are properly described so that they can be reproduced and/or combined in different (research) contexts.
A common misconception is that FAIR data is synonymous with open data. However, FAIR data does not always have to be openly accessible to everyone. In fact, in some situations it is not recommended to make your data openly accessible. Examples are: privacy-sensitive data, when follow-up research is being conducted within the project, or when multiple rounds of data collection are to take place.
If you make data available in an online data repository, you often have the option to restrict access to the data. In this case, third parties cannot simply download the data, but can only request access to the data. This allows you to control who can use the data.
Therefore, the guiding principle for research data is:
"As Open as Possible, as Closed as Necessary"
This Library Guide provides insight into the various ethical, legal, but also practical challenges you may face when working with research data. In addition, it offers guidance on how to organize data management so that your research runs smoothly. The guide is organized by research phase.
Click on the links below or go to the drop-down menu at the top of the page:
| Research Data Management | Preparation & planning | During your research | After the research |
Research data. When conducting research, you often need data that you analyze in order to answer your research question. These are research data. Research data can be quantitative in nature, such as numbers and measurements, or qualitative, such as audio and video recordings.
Raw data is the unprocessed, original version of the data. Also called source data.
Examples of edited data are data sets that have been cleaned and prepared for analysis.
Data management is the organization of collecting or generating, storing, sharing, analyzing, archiving, and publishing research data.
A data management paragraph is a part of a research proposal or grant application that briefly describes how data will be stored and managed during the research project and how it will be made available after the research is completed.
A data management plan (DMP) is a document that describes in more detail how research data will be collected, processed and managed during and after the research. It provides insight into what should be considered when collecting and processing data, it describes various data storage options, and provides information on data archiving and data dissemination.
Metadata describe (basic) characteristics of the research data, such as who created the dataset and what data the dataset contains. Metadata make it easier for you and others to find and reuse data at a later time.
A data repository is an online database for finding and making datasets available for reuse.
FAIR data is data that is findable, accessible, interoperable and reusable.
Open Data is data that can be used, reused, and shared by anyone without restrictions. with the only (possible) conditions being to cite the original creator and share under the same terms of use.
More definitions of terms can be found under 'Data jargon' on the Research Data Netherlands (RDNL) website.