Research Data Management

To avoid errors, mix-ups and long search times in future, it is worth investing some time in creating a systematically organized file and folder structure already at the start of a project. This is especially important if you are collaborating with other research groups. Everyone involved in a project should agree to a scheme and stick to it. It is advisable to record the organizational and naming scheme in a document which you subsequently deposit with the published data as an accompanying document.

• Group related files in folders (e.g. for measurements, methods or project phases)
• Use clear, unique folder names
• Use a hierarchical folder structure (N.B.: too many nested levels results in long and complicated filepaths)
• Keep active and completed work in separate folders and delete any temporary files that are no longer required.

Make sure you use file names that are unique and are also meaningful for people who are not involved in the project. General elements that can form part of a name:

• Creation date (YYYY-MM-DD)
• Project reference/name
• Description of the content
• Name of creator (initials or whole name)
• Name of research team/department
• Version number

To avoid operating system constraints, use the following character/naming conventions:

• Short names
• No special characters (: & * % $ £ ] { ! @)
• Use underscores _ rather than blank spaces or dots
• Include a file suffix wherever possible (.txt, .xls, etc.)
• Do not rely on uppercase/lowercase distinctions

The careful choice of a file format can ensure that files can still be used after many years and consequently greatly facilitate reuse of the research data. When choosing a suitable format, various factors should be taken into consideration:

• Future-proofing: how many software products can read the data format?
• Open access to documentation
• No legal constraints (patents)
• No technical constraints (encryption, DRM)
• Established in community

The file formats for research data can vary widely depending on the discipline in question. The following file formats are recommended:

• Images: TIFF, TIF
• Documents: TXT, ASC, PDF/A
• Tabular data: CSV
• Audio files: WAV
• Databases: SQL, XML
• Structured data: XML, JSON, YAML

Further information about which file formats are recommended for long-term preservation can be found at here.

It is essential to use version control, especially for datasets that change over the course of a project. Individual datasets should be named sequentially and the names should include the save date (YYYY-MM-DD) along with the version number. The final version should be indicated as such. Maintaining a version table in which all changes and new names are recorded can help keep track of the datasets.

We recommend you back up your data using the university's IT system as it collects the data campus-wide and redundantly backs it up to two state-of-the-art tape libraries.

Click here for more information: Campus Backup/Archive (access only via campus network)

You should always adopt the 3-2-1 backup strategy:

• 3 copies of the data (1 original + 2 backups)
• Stored on 2 different types of media (external hard drives, USB sticks, SD cards, CDs, DVDs, Cloud)
• 1 copy off-site

Backup should be automated to run at regular intervals. Check that the backup was successful and that the data can be retrieved again if necessary.

Comprehensive documentation is essential to enable correct interpretation and reuse of the data at a later date. Among other things, the documentation should include details about the time and place the data was collected, the methods, tools, software and statistics models used, as well as information about the parameters chosen and any missing values, along with nomenclature and acronyms.

Click here for further information.

The decision about what data for a project should be archived and for how long depends on the academic value of the data as well as on legal, regulatory and financial factors.

As a minimum, however, all the data on which a publication is based must be stored and the corresponding metadata must be published online.

The Digital Curation Centre (DCC) and forschungsdaten.info list five steps for deciding what data to keep.

• Guide (English)
• Guide (German)

Wann immer möglich sollten Daten in fachspezifischen Repositorien hinterlegt werden. Diese sind auf die Bedürfnisse des Fachgebietes eingestellt, kennen sich mit spezifischen Datenformaten aus und bieten oft auch fachspezifische Metadaten an.

Welche Datenarchive können genutzt werden? Eine umfassende Liste an Datenrepositorien bietet SNF (Die Liste ist nicht abschliessend) und Scientific Data.

Der beste Ausgangspunkt für Ihre Suche nach einem geeigneten Repositorium ist das Research Data Repositories (re3data.org)

BORIS erfüllt die vom SNF gestellten Ansprüche an ein Repositorium. Für publikationsbegleitende Daten für die noch kein fachspezifisches Repositorium existiert, besteht zurzeit also die Möglichkeit der Ablage in BORIS. Für grosse Rohdatensätze ist BORIS jedoch nicht geeignet und wir empfehlen die Ablage in einem allgemeinen Repositorium.

Ein institutionelles Datenrepositorium (BORIS Portal Research Data, Research Project, Research Funding) befindet sich derzeit im Pilottest.

Sharing figure

Figshare  - store, share and discover research.

Sharing methods

protocols.io A secure platform for the development and exchange of reproducible methods.

Before being published, data should be provided with a license. You could use Creative Commons licenses version 4.0 to do so. You find more information about Creative Commons licenses here.

As part of the FAIR principles, funding bodies require a unique identifier to be assigned to the published data. When depositing your data in BORIS, a Digital Object Identifier (DOI) is assigned to each dataset. Click here for further information.

Research data generated and collected during a project can often be useful beyond its original purpose. It is therefore worthwhile making the data obtained publicly accessible. For this purpose it is important to ensure that your data is assigned persistent identifiers, good metadata is generated and sufficient documentation is provided to enable the data to be reused.
There are currently three ways of publishing research data.

Publication in a repository

Research data can be published in a disciplinary or a general repository. If possible, it is preferable to publish data in a disciplinary repository rather than in a generic one. Further information about selecting a suitable repository can be found in Finding a repository.

Publication in a data journal

Data papers published in data journals are documents that facilitate the dissemination and reuse of published data. These publications contain all information about data collection, methods, licenses and access rights along with information about potential reuse opportunities. The data itself is usually deposited in a repository.

The website of the Humboldt University of Berlin has a list of data journals.

Publication as a supplement to an article

Data can also be published as additional information for an article in a periodical. This is usually the data on which the publication is based which enables the findings to be understood. The data may either be deposited directly on the periodical's platform or in an external data repository.  

When citing data it is advisable to use either the standards applicable to the research field in question or the form suggested by the repository in which the dataset was deposited. If there are no particular standards or recommendations, Datacite recommends providing the following details as a minimum:

• Author
• Year of publication (of the dataset)
• Title
• Edition or version (optional)
• Publisher (for data this is usually the archive in which the data is stored)
• Resource type (optional)
• Persistent identifier (as a permanent linkable URL)

Information and action guide for publishing open source software.

Do you plan to submit a research project to the SNSF? Do you need any support with writing the Data Management Plan? Then please register to the  DMP Writing Lab.

Complying with SNF requirements

The research projects funded by the Swiss National Science Foundation are oblidge to publish research articles under Open Access and make reseach data publicly accessible in digital databases, if there are no legal, ethical, copyright or other issues applied. We offer trainings to support you in complying with this obligation. We will inform you about options, obligations and possible support.

Complying with Horizon Europe 2021-2027 requirements

Researchers holding an EU grant are required to make their research publications and research data openly available. We offer you and your research team dedicated workshops where we explain the requirements under EU H2020 in detail and offer advice addressing your specific questions.

We will offer similar workshops for EU Horizon Europe as well, as soon as the first projects are approved. Anyone from your research team who is interested in these workshops is also welcome!

These workshops will take place via Zoom.  Please note that the University of Bern requires you to confirm that you know your obligations vis-à-vis the EU. To do this, you are asked to sign a “Directive on the implementation of EU projects” the University has prepared. The Open Science Team will advise you on how to do this.

02.11.2021 and  05. 11.2021, 8:30-12:00 h, Room 304, Hochschulstrasse 4, 3012 Bern. Registration to the course is available here.