Data Management and Storage

A Data Management Plan generally contains information on the following topics:

• Types of data that will be generated
• Data and metadata standards
• Policies for data access and sharing
• Data storage and preservation of access

There are many different Data Management Plan guidelines and templates. Exact data management requirements and conditions differ per funding agency, journal or institution. An overview of the most important guidelines from the BMS faculty is given here and an equivalent page from the UT can be found here. Moreover, a webform is available that provides the user with a template.

When working with personal data, various considerations with regard to data protection, privacy regulations and ethical and scientifically responsible behaviour should play a role in the data management phase. 

This page provides an overview of the conditions researchers should be aware of for various tasks, like levels of sensitivity and general regulations for gathering, processing and storing data. Links to the full source material and extra reading can be found here.

Raw data file: the raw data file contains the originally collected, unprocessed data.

Derived dataset: the derived dataset is the dataset underlying certain results or publications. You can derive different datasets from your raw data for different purposes.

Syntaxes: a syntax file containing the code, algorithms or commands used to create your derived dataset from your original, raw dataset. It also contains (stepwise) information about the transformations and analyses performed on the raw dataset.

Metadata file: a metadata file is a separate file attached to your dataset, which contains information about your dataset for future use (by yourself or others). For example, a metadata file should contain information on the following subjects: creator, access conditions, context, collection methods, time references, structure and organization of data files, variable names, labels and descriptions of variables and values, codes for missing values, file formats, and hard- and software used to process and analyse the data.

As common sense dictates, storing and sharing (sensitive) data should be handled with care (see Guidelines Personal Information). The level of precaution that should be taken depends on the sensitivity of the data and can range from ‘simple’ precaution to storage on a secured, isolated and off-line computer or encrypted USB sticks in the IGS data vault.

The directive on raw research data storage is minimally 10 years, to the extent that this is compatible with the GDPR stating to store personal data no longer than necessary.

Data should never be stored solely on personal and/or local drives: data storage on the m-/p-drive of UT is certified according to the ISO/IEC 27001 and NEN 7510 standards. This is the highest level of protection for your personal and also sensitive data.

(Raw)data will be stored on the central and secured BMS server, privacy the sensitive data of a project can be protected by encryption. Indicate this with a project sign-up at the BMS Lab.

The datafiles will be stored together with the EC approval in the same folder

Back-up on external, secure SSD drive: These drives are specially designed for the safe transportation of research data and of documents containing confidential, privacy-sensitive data.

BMS Datavault: BMS Lab offers a safe vault for your sensitive research data.

After the research, data will be stored in a trusted repository (e.g. DANS) or permanently stored on one of the secured servers of the faculty. This concerns at least the raw data.

The BMS Lab facilitates data storage for studies conducted within its lab and under the BMS faculty during and after the study. Researchers may however need to store data for up to 10 years and may wish to make it available for far longer periods or may wish to make their data (partially) accessible to colleagues and scholars. This can be a prerequisite for research grant applications, that require a data management plan.  For those interested in these more specialised facilities, the University of Twente’s BMS faculty cooperates with the LISA department. At LISA they have the tools and capacity for long-term storage and authorised (limited) sharing of data.

For more information or help with your data management, you can contact the BMS Lab.

To ensure long-term preservation that is independent of certain specific software, you are encouraged to save your files in commonly used and easily re-usable file formats with open documentation. Please find a list of different preferred and acceptable file formats for different types on the datalab website.

In general, any scientific work should be reproducible. This applies to the social sciences as much as it does to the natural sciences. In practice, this means that the whole process of how you handle data should be documented. Gathering, cleaning, coding, transforming and scaling as well as analyses performed should all be documented. It is good practice to perform the above tasks using syntax and to store the syntax along with the data.

Note that, even though it may be tempting to perform a ‘quick fix’ in the SPSS data view, such a change may become lost or be overlooked, rendering reproduction of the research more difficult.