Research Data Management

Knowing how to organize and manage research data is one of the most important prerequisites for ensuring the quality, security, durability, and reproducibility of research data, both during a research project and in long-term. We advise, support individually and in groups through workshops and events around open research data and open science. If you have any questions about research data management and data management plans, please contact us via E-Mail: researchdata@unibe.ch

Support

New Walk-in consultation for all researchers


You have questions on research data management but don’t want to write e-mails? Then come and visit us at our office! Starting on February 19, we will be offering a new walk-in consultation for all researchers from the University of Bern and the Inselspital every Monday from 2 pm to 4 pm. Whether you look for a convenient way to organize data in your project folder, whether you need help creating a data management plan (DMP) for your project or whether you don’t know how to publish your data – just drop in at our office in Hochschulstrasse 6, room 354 and we’ll do what we can to help you!

Your personal contact for research data management

Our research data management support is now even more focused on the specific issues of academic domains (groups of disciplines). You will also receive additional support for overarching issues such as handling sensitive data and data science. Data stewards are now available to you as contact persons for each domain and the overarching issues. They will support you in close cooperation with other support and infrastructure facilities of the University of Bern in the fulfillment of data-related requirements of the funding institutions (e.g. SNSF, EU, NIH) and generally in the implementation of good practices in research data management.

Do you have any questions? Do you need support with research data management? Write to researchdata@unibe.ch – your data steward will get back to you promptly!

Data Management Plan (DMP)

For all SNSF calls starting from 17 April 2023, including the current Spark call (Deadline 2 May 2023), a data management plan (DMP) is requested for approved grants only (SNSF). Researchers still have to consider data management when planning their projects and preparing applications, including budgeting. Read more

We review your data management plan (DMP) for the Swiss National Science Foundation SNSF or other funding organizations within 2-3 working days. If possible, please also send us your project plan so that we can give you customized feedback. You can find an introduction to writing a DMP for the SNSF in five short videos on YouTube. You can also take a look at our collection of DMP examples and submit your DMP for review here. This service is free of charge.

Research Data Repository of the University of Bern

On BORIS Portal you can publish your research data in open or restricted access according to the FAIR Data principles and link it to information about your research project. A factsheet with the most important information and a checklist for publishing datasets can be found here.

You can still submit publications (articles, books) to the publication repository BORIS: https://boris.unibe.ch.

Research Data Management Guideline

With this guideline on Research Data Management (RDM), the data stewards at the Open Science Team in the University Library of Bern aim to support researchers from all faculties at the University of Bern and Insel Hospital in research data management throughout the project life cycle. This includes dealing with ethical data collection, data reuse, data generation and data processing, data documentation, and preparing data and metadata, as well as code, and software for publication.

Research Data Management Guideline (PDF, 157KB)

Research data is "data collected or produced (e.g. measurements, questionnaires or source materials) in the course of scholarly activity which is used for the purposes of academic research (e.g. digital copies) or which document research findings [...]". forschungsdaten.info 

Some examples of research data

  • Sources: Texts, images, sound recordings, films/videos
  • Observations: Real-time data, examination data
  • Experiments: Laboratory values, spectrograms
  • Simulations: Simulation measurements, model measurements
  • References: Collection of already published datasets
  • Methodological methods such as questionnaires, software or simulations

A data management plan (DMP) forms the basis of good research data management. The DMP describes the life cycle of research data and is intended for long-term use. It describes how the data is to be produced, collected, documented, published and archived during a project.
An integral part of a DMP is the description of the research data in accordance with the FAIR principles. Among other things, a DMP should include information about the following:
        
     Data collection and documentation
     Ethical, legal and security issues
     Data storage and preservation
     Data exchange and reuse

The DMP is submitted along with the project proposal (SNF) or shortly after the project has commenced (H2020), and it should be updated and extended at regular intervals. As it describes discipline-specific practices and standards, the content may differ from project to project.

The Open Science Research Data Management Team will be happy to support you by advising how to comply with funding agencies' requirements in Open Research Data for the European Commission (e.g., Horizon Europe) and other national (SNSF) and international projects. We offer individual support and training sessions as well as workshops based on your needs for you and your team on request. Please contact us via openscience@unibe.ch.

Swiss National Science Foundation (SNSF)

The DMP is an integral part of the research application. The application cannot be submitted until the DMP has been completed. The DMP must be written in the same language as the research plan. The SNSF contributes up to 10’000 CHF to the costs of making research data accessible, under the condition that the repositories used for data sharing meet certain requirements (AR 2.13). The application for this additional funding must be taken into account when submitting the application. For more information, see the SNSF guidelines for researchers.

Valid for projects of the funding period 2014-2020 only

 

Horizon 2020

You must submit a DMP no later than six months after the start of the project to the EU’s project management portal. A template with guidance can be downloaded here.

All projects are part of the "Open Data Pilot", i.e. researchers must make research data underlying publications openly accessible. Exceptions for ethical, legal, contractual, copyright, and similar reasons are possible ("opt-out"), but they must be justified to the EU.

The European Research Council (ERC) requirements differ from the general Horizon2020 requirements only in details. This page gives a good overview and links to further resources and the ERC DMP form.

Horizon Europe

Information as of June 2022

Regarding participation in the Horizon Europe framework, Switzerland is considered as non-associated third country until further notice (more information). Substitute funding measures for Swiss researchers are mainly provided by the Swiss National Science Foundation (SNSF) and the State Secretariat for Education, Research and Innovation (SERI). More information on the Grants Office website.

For these replacement funding measures, guidelines for open access publishing and research data management apply as follows:

For the replacement measures by the SNSF, the SNSF Open Science requirements apply (see above and here).

For the substitute measures by SERI, the specifications of the European Union apply (see here). They stipulate that:

  • Data must be managed according to the FAIR data principles. FAIR stands for Findable, Accessible, Interoperable, Reusable.
  • A Data Management Plan (DMP) must be written, typically submitted six months after the start of the project, and updated at least at the end of the project period. It is recommended to use the official template.
  • Research data must be shared openly on a repository if there are no legal, copyright, ethical, contractual or similar clauses. Unlike under Horizon2020, the EU requires a mandatory CC0 or CC BY license (or equivalents).

If you are unsure what these requirements mean for your project, please contact us at openscience@unibe.ch.

Valid for projects of the funding period 2014-2020 only

National Institutes of Health (NIH)

Effective January 25, 2023, the following guidelines apply to NIH-funded projects:

Researchers must develop and submit a data management plan together with the project application, and update it regularly. The NIH will review the DMP. They may also review implementation during the life of the project. Failure to comply may affect the success chances of future applications.

In addition, researchers must share research data as soon as possible, at the latest at the time of a related publication appears or at the end of the project (whichever comes first). Legal and ethical considerations must be taken into account.

Please be aware that the funding agency can change the template. Please contact the research data management team via openscience@unibe.ch for details and further information.

Templates

Beispiele

How to write a successful research data management plan (DMP)? The Open Science Team will be happy to guide you through video modules available via  YouTube.

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.

Especially when working with a number of different people, it may be advisable to regularly save a milestone version of the file which then must not be changed or deleted.

To summarize, forschungsdaten.info recommends:

  • Use sequential numbering
  • Include the date and version number in the name
  • Use a version control table
  • Specify who is responsible for providing the final files
  • Use version control software for large data volumes
  • Save milestone versions


Further information and best practices

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. This information can be added complementary to your dataset, e.g., in the form of supplementary documentation in a ReadMe file.

Further information on data documentation can be found here and on ReadMe files here.

Metadata is information about data which is created in a structured and machine-readable form. The metadata helps other researchers find and reuse data. Depending on the particular discipline, there are various commonly used metadata standards and tools that can be used to describe datasets in different domains.

The repository of the University of Bern (BORIS Publications) (BORIS) uses the Dublin Core metadata element set. This metadata is automatically generated by filling in a form when depositing a dataset in the repository.

Data quality and metadata standards. The presentation link is under the BORIS Publications.

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.

Whenever possible, data should be deposited in subject-specific repositories. These are geared to the needs of the subject area, are familiar with specific data formats and often also offer subject-specific metadata.

Which data repositories can be used? A comprehensive list of data repositories is provided by the SNSF (the SNSF list is not exhaustive) and Scientific Data.

The best starting point for your search for a suitable repository is Research Data Repositories (re3data.org).

An institutional data repository (BORIS Portal Research Data, Research Project, Research Funding) has been officially launched. BORIS Portal allows you to archive and manage research data, to determine access options and manage rights, as well as to link project and researchers’ profiles, to make it accessible and clearly identifiable. Login to BORIS Portal research data, projects and fundings via your campus account.

Sharing figure

Figshare  - store, share and discover research.

Sharing methods

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

The Open Science Team at the University Library of Bern recommends licensing research data under the Creative Commons Public Domain Dedication (CC 0) or the newest version of the Creative Commons Attribution International Public License CC BY licenses to allow maximal reusability. If data cannot be openly published due to ethical or legal reasons, metadata and supplementary material can be published under CC0 to fulfil funders' agencies' requirements.

The Swiss National Science Foundation (SNSF) allows researchers to choose the best suitable license for the data based on the principle of reusability (e.g., SNSF Policy on Open Research Data).

The European Commission (EC) requires the latest available version of the CC BY or CC 0 or a licence with equivalent rights, following the principle “as open as possible as closed as necessary”. For details, please visit Horizon Europe Model Grant Agreement from 15. December 2021, v.1.1, p. 96 and the EU's open science policy (link).

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.

Data security IT-Department, University of Bern (German version only, Pdf)

  • Legal service office of the University of Bern (data protection, legal questions) E-Mail
  • Unitectra supports researchers in the commercialization of research results into new products and services (patents, licenses), in the negotiation of research agreements as well as in the creation of a spin-off company E-Mail

Data protection and research in general: EDÖB (addresses federal bodies and private persons, here for general information in German, French and Italian only). Researchers at the University of Bern must normally comply with the Cantonal Data Protection Act).

European General Data Protection Regulation (GDPR). Introductory Ordinance to the EU Data Protection Directive 2016/680 on the protection of personal data (Introductory Ordinance to the EU Data Protection Directive) of the Canton of Bern.

The data protection laws of some countries are recognized as equivalent to those of Switzerland (Transborder data flows).

Personal data is information about specific or identifiable natural or legal persons. The Data Protection Act of the Canton of Bern (KDSG) serves to protect individuals from improper data processing by authorities.

If you collect, analyze or otherwise process personal data in your research project with the help of IT applications (e.g. self-programmed apps, but also applications such as Qualtrics or RedCap), it may be necessary to draw up a concept for handling information security and data protection (ISDP).

Such a concept serves to clarify the requirements and existing or to be developed technical solutions in this area, as well as, if necessary, as the basis for control by the data protection authorities of the canton. The legal basis for this is the data protection legislation of the Canton of Bern, in particular, the Data Protection Act (KDSG), Art. 17a. The "Directives on Data Protection in the IT Sector of the University of Bern" must be followed (Link).

For information and advice on legal matters, please contact the Legal Services of the University of Bern, and for IT security matters and for filling out the relevant forms, please contact the IT Services. 

Research projects involving humans that fall under the Human Research Act (HRA) must be reviewed by ethics committees (e.g. animal experiment ethics SAMS or Cantonal Ethics Committee (CEC)). Please note that such a review may take approximately 60-90 days. For more information on the procedure, process and management of such projects, please visit the portal on human research in Switzerland Kofam.

Research projects that do not fall under the Human Research Act can be approved by the Ethics Committees of the University of Bern Phil.-hum., Phil.-hist.WISO und Animal Welfare Office UniBE.

Training and workshops in research data management aim to support researchers at the University of Bern to manage research data through the whole research data lifecycle from initiation, planning, and the start of the project until the end of the project. Moreover, many funding agencies, such as the Swiss National Science Foundation (SNSF) and the European Commission (e.g., H2020/Horizon Europe), require grant applicants to develop a data management plan (DMP) and demonstrate experience in data sharing and open science in order to receive a grant. To learn more please register to our training courses and workshops free of charge under the webpage here