Data Sharing & Archiving | Data Sharing/Archiving

Sharing data means you make your research data available to others, which can be done during and/or after your research. Journals or funders may require you to provide (open) access to your research data or at least share your data with other researchers upon request. In this way, they are stimulating the public availability of data and scripts. You can share your data by depositing it in a trusted online repository openly available or available upon request.

If you share your data openly and especially if you publish Open Access, there is a large likelihood that you will reach a broader audience (e.g. not only your fellow scientist but also practitioners, policymakers, journalists and the general public) and more people will cite your work. They are better able to validate your findings and more people can read your work. As such, you will have a larger audience and an audience with more confidence in your study. The subset of your audience that will eventually cite your work is also likely to be bigger.

Data archiving concerns data storage after a research project ends. Data archiving aims in the first place at preventing physical data loss or destruction and securing the authenticity of data. Besides, it contributes to the quality and impact of your scientific work by enabling verification and possible reuse. For instance by allowing further analysis or follow-up research, or as a contribution to a data resource for the scientific community.

For sharing your research data during research, several options are recommended:

See also UT Research Support on sharing and sending data (as well as storage). Furthermore, use this tool to find the best solution for storing, sharing, transferring or collaborating on research data, during the research.

At the UT:
- Group/share UT Network storage (P-drive)
- custom filesystem (network-share) on the UT central hard disks
- Lightweight database (no costs for < 5 GB data storage)

External (in the cloud):
- SURFdrive (secure file storage and/or share these with colleagues/students)
- Dataverse (also Archiving possible)
- OneDrive (Microsoft) (offers a GDPR compliant solution for having multiple access to your data and sharing with others)
- SURFfilesender (safe sharing/sending (also encrypted possible) of data between student-supervisor or UT employee-external partner)
- Tech4people server at BMS Lab

More info on storing & sharing your research data

For archiving your research data we recommend using the UT DATA ARchive Areda:

*Important: Areda is currently only accessible with support of a data steward as part of an evaluation of the user experience. Based on input received from users, we are currently working to improve the system in a few key aspects. If you have any questions about this notice or Areda, please contact your faculty’s data steward

Areda is the University of Twente’s institutional data archive, supporting the secure archiving of research data at the end of a PhD or research project. Areda ensures safe, secure, and certified long-term preservation (at least 10 years) in line with the UT RDM policy. It supports all types of static data collected, generated, or used in UT research projects.

Archiving is more than storing files. It requires metadata for findability and documentation for interpretation, verification, interoperability, and reuse. Areda is integrated with the UT Research Information System (Pure), where you can register your dataset, add metadata (e.g., title, creator), and upload a README file.

Research data (zip or tar) that you want to archive can be uploaded to the research group’s “bucket,” accessible to all group members. This ensures the group always retains access to its research data. Access can only be restricted through encryption, which is mandatory for personal or confidential data. Digital informed consent forms and pseudonymization keys must never be stored in Areda. These must be encrypted and stored separately (e.g., in JOIN or on the p-drive). While folder structures are possible, it is recommended to store project data as a single archive of zip-files containing a well-structured dataset. All files are stored on ISO 27001 and NEN 7510 certified servers at the University of Twente, with backups hosted by SURF in Utrecht and Amsterdam.

For further guidance, please read the Archiving datasets in Areda: a guide and the Guidelines for the archiving of academic research for faculties of behavioural and social sciences in the Netherlands. For support on archiving your research data, contact the BMS data stewards or visit the Areda service portal.

For publishing your research data we recommend using a trusted repository:

To make your data and research more visible to the scientific community, in addition to archiving your data in Areda, you can also use trusted repositories to publish your data. By publishing/depositing your data set to a trusted repository, your data set gets a persistent digital identifier (e.g. DOI) which allows your data to be widely findable, accessible, and easily cited by others.

DANS, for social sciences and humanities data. DANS prefers open data, but also offers restricted access (access is limited and can only be granted on request) and the possibility to place an embargo on your data (your data will become available after a set period of time, with a maximum of two years). DANS has the Data Seal of Approval. A demo recording on how to upload a dataset to DANS is available on the UT DCC website.

Open Science Framework (OSF) is becoming more familiar in the social sciences. OSF is a free, open-source Web tool designed to help researchers collaboratively manage, store, and share their research process and files related to their research. Unlike the other repositories (such as DANS or Dataverse), which were built to simply house and share files once a research project is finished, OSF also allows researchers to store and interact with files during the research process and to preregister their work and upload preprints if they so desire. They have Guides and FAQ available. NOTE: by default OSF stores your data in the United States, choose Germany - Frankfurt as storage location instead, as US is not GDPR compliant.

We advise you to think about what data to share, with whom, how, when and for how long at the start of your research project and to capture these preferences in a Data Management Plan (DMP).
To write your DMP, please use the UT DMP-tool. The template in this tool is also accepted by NWO, ZonMw and EU.
As a guidance when writing a DMP you can follow the research data management course, for PhD candidates, registration for the course (online course + interactive session) is needed; for other UT staff, the online part of this course is available without registration.

Funder requirements: more and more (federal) funding bodies oblige their researchers to share their data to cut costs, save time and avoid double effort. This is in line with the OECD principles and European Commission’s Open Data pilot.
Journal requirements: some journals require you to make all data and related metadata underlying the results described in your paper freely available.
Promotes scientific integrity: by providing open access to your research data you allow other researchers to validate, replicate, reanalyze, reinterpret or correct your results. This will underpin and strengthen your own research.
Increase recognition/impact: sharing your data can lead to more citations (Piowar & Vision, 2013), which will increase the impact of your research in both your own and new disciplines or countries.
New opportunities: sharing your data can lead to new research opportunities, co-authorships and collaborations.
Reuse for educational purposes: your dataset can be used as a practical example for students to learn how to process, analyse and store research data.
Prevent loss: preparing and describing a dataset and its related metadata and methods to share it with others will enable you to identify and understand the dataset yourself after several years. Additionally, archiving your dataset in an online depository and spreading it amongst other researchers will help you retrieve the dataset when you’ve lost it.
Sensitive information: when your dataset contains sensitive, personal information about human subjects, sharing your dataset with others may violate (local) regulations, legislation, or ethical frameworks. In these cases, you can only share your data with others if informed consent is given by the subjects or if you have anonymised the data. The latter means that any personally identifying information is removed and you have made sure that your data can not be traced back to individual persons. This way you can create a more general, public dataset to share with others. Find more information on consent and ethics here.
Data obtained from third parties: if (parts of) your original data are owned by other institutions of researchers you don’t have the rights to share this dataset.
Confidentiality: your research data may be confidential because of agreements or contracts your research group has with (commercial) partners or sponsors, or for other reasons like financial value or the intention to apply for a patent.
What data you share with others depends on several things. It can depend on funder requirements, journal and depository policies, your own or your institution’s future plans, confidentiality and information the dataset contains. Journals can require you to share at least the dataset used to reach the conclusions drawn in your manuscript, including related methods, syntaxes and metadata. Funders can require you to make available all data produced for the research they funded, and colleagues can request only a small subset of your complete dataset. More information about different parts of a dataset and preferred file formats can be found here.
It is advisable to cite the dataset underlying your paper in the same way you cite the literature you used, even if you are the producer of the dataset yourself. This way, others can easily find and retrieve or request access to the dataset for re-use or verification purposes. In addition, it gives others the opportunity to refer to the dataset in their own papers, recognizing and rewarding the producer of the dataset.
A data citation should include the following elements:
- Creator
- Publication year
- Title of the dataset
- Publisher
- Identifier
The identifier (persistent identifier or DOI) is a unique code that is linked to the location where the dataset is stored. This way access to the dataset is permanent, even if the dataset is moved to a new location. Please refer to the DataCite website for examples of data citations. When you store your dataset in a data repository (like DVN) you will receive an identifier.
You can make your dataset openly available without any restrictions or make your dataset available upon request. It is important to think about this before you store or publish your dataset, so you can clearly state any rights or restrictions in your metadata file or in the depository. Please take into account that rights and restrictions also depend on the depository you use, the journal that publishes your paper or the organization that funds your research.