Data Sharing/Archiving | Data Sharing & Archiving

Sharing or archiving data means you make your research data available to others. Journals or funders may require you to give 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 storing/archiving it in an openly available online depository or by making your dataset 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.

For sharing your research data, several options are recommended:

See also UT Research Support on sharing and sending data (as well as storage). Furthermore, the Overview of storage table is handy to check GDPR compliance.

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 trusted repository:

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.

Read the UT guidance on archiving research data.

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 your Data Management Plan.

Why share your data

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.

Why not share your data?

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 to share

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.

Data citation

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.

Rights/restrictions

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.