Research Data Management

1. Familiarise yourself with relevant policies, laws and regulations.
2. Determine the roles and responsibilities regarding data ownership, access, and long-term preservation, particularly in collaborative research involving multiple institutions, externally funded projects, or multiple researchers.
3. If applicable, write a data management section. Most funders will require this as part of the funding application. (funder requirements)
4. Write a data management plan (DMP) (DMPtool). For PhDs, this is part of the TGS course. If necessary, determine the costs of your data management. (guide)
5. For personal data:
   1. If you are processing personal data during your research, register your processing in the GDPR registration tool. (accessible via DMPtool)
   2. For full guidance regarding GDPR compliance, check guide on the use of personal data in research.
   3. If you process personal data in a way that may pose high risks (e.g. profiling, large-scale sensitive data, or public monitoring), check whether you need to carry out a Data Protection Impact Assessment (DPIA). (UT DPIA website)
6. For research software:
   1. If you are developing research software as part of your project, a software management plan (SMP) might be necessary. Please check the Practical Guide to Software Management Plans for guidance.
   2. For full guidance on research software management, check the University of Twente Research Software Guideline.

1. Ensure all research data is stored, archived and published with metadata (example) and additional documentation, including a README file (example).
2. Add discipline-specific metadata or keywords (link to discipline-specific metadata schemes)
3. For research software:
   1. Research software should include embedded comments for usability.
   2. Documentation for research software should include user documentation and deployment documentation, and in some cases, also developer documentation.
   3. Check the UT Research Software Guideline on how to choose a license for your software.

1. Store all collected data on the UT network storage provided by LISA. Recommended storage is Unishare. Other options can be found here: storage decision tree. The stored data should be accessible by at least one other research group member.
2. If it is difficult or even impossible to use the UT network storage, a suitable alternative data storage option must be decided in coordination with the faculty ICT account manager.
3. A copy of the research data can be stored in personal cloud services, preferably SurfDrive (OneDrive is not recommended).
4. Use of portable devices (external hard drive, USB stick or personal laptop) must be avoided as much as possible. Always use encryption (More info on encryption).
5. Research software should be developed using a version control system. Open software development is recommended, where possible.(guideline)
6. Non-digital research data and related materials, such as physical samples, lab notebooks, and informed consent forms, must be stored in accordance with clearly described procedures and standards within the research group and/or project and digitised where possible.
7. For personal data:
   1. The loss of personal or confidential data must be considered a data breach.
   2. Consent forms should be stored separately from the data.
   3. In the case of encrypted data files, the key should be stored in a separate location and shared with at least one other employee in the research group. Generally, this person will be the principal investigator or the chair of the research group.
   4. Personal data should be pseudonymised or anonymised as early as possible in the research workflow where feasible. (Practical Guide to pseudonymisation and anonymisation)

1. UniShare is the most recommended channel to be used to share data with colleagues within the University of Twente (UT) as well as with external people. SurfDrive service can also be used, if necessary.
2. For transferring large files Surf file sender is recommended.
3. In case of a Non-Disclosure Agreement with third parties, make sure all people with access to the data fall under the agreement. Bachelor's and Master's students with access will have to sign separate agreements with the UT. 
4. Before sharing personal data with external parties or receiving data from external parties, a Data Agreement should be drawn up, establishing the conditions under which data are shared, with the assistance of the faculty Privacy Contact Person.

1. Not later than one month after publishing a scientific work (paper, thesis or report), all data which are the basis of published results should be preserved for at least ten years and in accordance with FAIR principles.  
2. The recommended repository for archiving and publishing data underlying a publication is 4TU.ResearchData.
3. Raw data sets that need to be archived for further use within the group can be stored in AREDA (a static, low-cost archiving solution).
4. To promote the visibility and the sharing of your datasets, we recommend referring to the DOIs of your datasets in your articles or PhD thesis. You can reserve a DOI in advance.
5. For personal data:
   1. Pseudonymised personal data that does not fall under the special categories as stated in the GDPR can be archived in 4tu.Researchdata with restricted access or encrypted in AREDA.
   2. Consent forms and (encryption) keys must be archived separately from the pseudonymized data.
   3. For personal data, there is a maximum retention period (usually 10 years) after which the data needs to be deleted or completely anonymised.
6. 4TU.ResearchData allows you to easily archive and publish your research software directly from your Git repo.

1. All digital and/or non-digital research data and software must be registered and described by metadata in UT Research Information (Pure).
2. For each publication in UT Research Information, the underlying data should be linked, either by adding the link to the published dataset under Electronic version(s), and related files and links, or under Relations.
3. Research software should be registered in Pure under Data sets.

International

• Horizon EU Requirements on Open Science (link)
• European Code of Conduct for Research Integrity (link)
• EU General Data Protection Regulation (GDPR) (link)

National

• NWO Data management protocol (link)  
• National Open Science Programme (link)
• The Netherlands Code of Conduct for research integrity (link)
• Algemene verordening gegevensbescherming (AVG) (link)

LOCAL (University of Twente)

• Digital code of conduct (link)
• Information security policy (link)
• Privacy policy UT (link)
• Appropriate use of personal data in scientific research (link)
• UT Research Ethics Policy (link)

Data preservation (or archiving)

Specific way of storing research data, mostly static, and aimed at long-term preservation (in general at least 10 years) for verification and reuse. Data archiving should always be accompanied by proper metadata and documentation. It is planned (such as where, how, rights and responsibilities of having and/or giving access) at the start of the project, although in general implemented by the end. Data archiving is bound to legal constraints, such as privacy law and contract law.

Data publishing

Specific way of sharing research mostly static data, accompanied by assigning at least bibliographic metadata and a DOI and aimed at visibility, recognition, etc. Data publishing is bound to legal constraints, such as privacy law and contract law.

Data sharing

Sharing research data is the most general term for giving one or more individuals or even public access to the data by the creator. Data can be dynamic or static, and the purpose of sharing can be multiple: reuse, collaboration, verification, etc.

Data storing

Storing research data is a general term for securely keeping research on a device, or in a physical location, by the creator. The purpose is to keep the data readily available for processing, analysis, etc. during the project. The research data is still dynamic, but as soon as it becomes stable and static, it should be archived for long-term preservation, see below: Data preservation (or archiving).

Personal Data

Personal data means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

More information on GDPR and personal data can be found here.

Source: GDPR, Art. 4(1)

Research Data

Research data is evidence that underpins answers to research questions and is necessary to validate research findings. In the context of research data management, it also includes elements that make the data reusable or re-workable, e.g. documentation of the research process (e.g. in lab- or notebooks), or algorithms and scripts needed to access and interpret the data.

More information on research data can be found here NWO Research data management | Scope.

Research Software

Research software is defined as source code files, algorithms, scripts, computational workflows and executables that were created during the research process or for a research purpose.

More information on research software can be found here UT Research Software Policy.

Trusted digital repository

Also: trusted repository, trustworthy repository

A trusted digital repository is a Coretrustseal certified repository and recognized in the international community as a reliable and trustworthy source of data