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. 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.

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)

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 ethics, protection and security challenges as well as research integrity issues are closely related to the Open Science strategy, which aims to promote transparency and reusability of research data and knowledge transfer.  

The Data Stewards in the Open Science Team support researchers at the University of Bern and Bern University Hospital by offering regular training courses  and upon request. We support you by advising on how to collect, reuse, process, store, share and publish research data according to the best research practice, legal regulations, funding agencies requirements and ethical standards in Open Science.  

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.

For information on research that falls under the Human Research Act, please refer to the "Ethics – Research with humans" tab. Information on research involving animals can be found under the "Ethics – Research with animals" tab. Information on working with genetic resources or their associated traditional knowledge can be found under the tab "Ethics – Research with genetic resources". More information can be found under Research Compliance and Good Research Practice.

Read more about ethics via forschungsdaten.info 

A data management plan (DMP) is a structured and document in which the handling of research data in a project is systematically described. It should contain information on how and with which tools (e.g. hardware and software) data is collected, processed, documented, stored, backed up, maintained, archived and, if necessary, published. The DMP also documents the required resources and responsibilities. Ideally, a DMP is drafted during the planning phase of a research project but should be regularly updated and supplemented as the project progresses. The DMP is therefore an instrument for work organization and project planning, but can also help other researchers to interpret and reuse the relevant research data. 

Based on: Glossar, Leibniz Universität Hannover (uni-hannover.de) 

Data provenance documents the provenance or origin of research data, and the processes, methods, tools and algorithms used to produce it. Information on the provenance of research data is crucial to ensure transparency/reproducibility of research and thus strengthen its credibility and trust in it. The relevant information can be recorded in readme files or metadata. Provenance information is central to the implementation of the FAIR Data principles. 

Based on: eResearch Alliance: Daten-Provenienz 

Data stewards are experts in research data management. They support researchers in the sustainable handling of research data. In addition, data stewards act as a link between researchers and (research) software engineers, IT and other infrastructure units. Other tasks include advising, training and raising awareness of good research data management practices. 

Based on: https://forschungsdaten.info/praxis-kompakt/glossar/ 

Data Transfer and Use Agreements (DTUA, DUA, or DTA) are contracts that govern the exchange of specific data between two parties. A data agreement establishes the permitted disclosure of a unique data set, allowable uses, and any privacy or security requirements necessary for receiving and handling the data.  A data agreement also assigns appropriate responsibilities to the researcher(s) and recipient(s) using the data. 

Based on: https://www.purdue.edu/business/sps/contractmgmt/DataTransferUseAgreement.html 

Electronic lab notebooks (ELN) and laboratory inventory management systems (LIMS) are digital tools that facilitate laboratory work, among other things. ELNs are used to store and record unstructured data, e.g. to organize protocols, notes and data from experiments. LIMS, on the other hand, are intended for structured and repetitive data that follow specific patterns, e.g. when tracking samples from well-defined, repeated and routine tests. 

Based on: https://www.scinote.net/blog/eln-vs-lims-how-to-choose/  

The term FAIR (Findable, Accessible, Interoperable and Reusable) Data was first coined in 2016 by the FORCE11 community for practices of sustainable Research Data Management. The main objective of the FAIR principles is the optimal preparation of research data, which should therefore be findable, accessible, interoperable and reusable for both humans and machines. 

Based on: FDM-Glossar, Freie Universität Berlin (fu-berlin.de); Wilkinson, Mark, et al. 2016. „The FAIR Guiding Principles for Scientific Data Management and Stewardship“. Scientific Data 3 (1): 160018. https://doi.org/10.1038/sdata.2016.18. 

Major research funders such as the Swiss National Science Foundation (SNSF), other national research funders or the European Union generally make the release of project funding conditional on the submission of a data management plan and on making research data publicly accessible (Open (Research) Data), provided there are no legal or ethical obstacles. 

Based on: https://www.snf.ch/en/dMILj9t4LNk8NwyR/topic/open-research-data 

A declaration of consent (or informed consent) includes informing the participants about what is planned with their data within the scope of a research project, for what purposes the data is to be collected and published, as well as the consent of the participants. Informed consent forms the basis for participation in scientific studies and any subsequent use of the data. It is therefore the basis of research that implements legal provisions and ethical principles. 

A license is a contractually agreed right of use. It allows the rights holder to allow their contractual partner to use a work in various ways (e.g. to copy, save or make it digitally accessible). Standardized Creative Commons licenses or instruments, in particular CC BY and CC0, are generally recommended where copyright claims to research data exist. 

Based on https://forschungsdaten.info/praxis-kompakt/glossar, Creative Commons   

Long-term archiving means securing data and its usability across several generations of hardware, software and file formats. 

Based on Universitätsbibliothek Bern, BerDA (German only) 

Metadata are a highly structured, standardized description of objects (including data). They provide information on content, structure, technical properties, usage rights and other properties in a concise form. Standardized metadata make information findable and usable for machines (e.g. algorithms, search engines). They are therefore crucial to the implementation of the à FAIR Data principles. 

Based on: https://en.wikipedia.org/wiki/Metadata 

“Open data” in the broad sense refers to all openly accessible and reusable data sets. In a narrower sense, the term is often used synonymously with “open government data” (open administrative data) and in contrast to “open research data” (open research data). Data is open if it is made accessible with as few legal and technical restrictions as possible. If data are released with a highly restrictive license or access barriers (e.g. payment or registration barriers), this can lead to research results not being reproducible, and the subsequent use of the data may be difficult or impossible. 

Based on: https://opendatahandbook.org/guide/de/what-is-open-data/  

A Persistent identifier (PID) is a long-lasting digital reference to a digital objects. In contrast to other identifiers such as URLs, PIDs always refer to the object itself. In this way, the identifier does not change, even if the location of the object (usually a website) changes. This ensures permanent traceability. Examples of PIDs are Digital Object Identifiers (DOI), Archival Resource Keys (ARKs), and Handle. 

Based on: https://forschungsdaten.info/, CODATA Research Data Management Terminology  

According to the Swiss Data Protection Act (DSG), personal data is any information relating to an identified or identifiable natural person. Personal data can be directly identifying information (e.g. name, address, IP address), but also information that can only identify a person in combination with other information (e.g. profession, place of residence). 

Based on: Art. 5 DSG Datenschutzgesetz Schweiz | Fact Sheet Datenschutz (Universität Basel) 

According to the Federal Act on Data Protection (FADP), data relating to health, political or religious beliefs or genetic data, for example, are considered to be sensitive personal data. The protection requirements that apply to this type of data are even stricter compared to non-sensitive personal data. 

In Switzerland, different terms are used for this type of data from canton to canton. In the canton of Basel-Stadt, for example, the term “special personal data” is used (IDG §3, para. 4). 

Based on: Art. 5 DSG Datenschutzgesetz Schweiz | Kanton Basel-Stadt, Datenschutzgesetz 

Preregistration means publishing the plan for a research project before or at the start of a research project. In subject areas such as psychology, the procedure is used to strengthen the method-led approach and increase the quality and transparency of research. The aim is to avoid dubious scientific practice (such as the subsequent adjustment of the research question to the results obtained). 

Based on: https://help.osf.io/article/145-preregistration 

Research results are reproducible if identical analysis methods are applied to the same data and yield the same results. This can only be achieved if the methods and procedures used are documented correctly and precisely, and all steps performed are documented. Reproducible results enable transparency and traceability. In contrast, replicability means that the results of the replicated study can be confirmed with new data and the same or other methods. However, the definitions of the terms reproducibility and replicability can vary depending on the discipline. 

Based on: https://book.the-turing-way.org/reproducible-research/reproducible-research | Plesser HE (2018) Reproducibility vs. Replicability: A Brief History of a Confused Terminology. Front. Neuroinform. 11:76. doi: 10.3389/fninf.2017.00076  

esearch data are all data that were created or used in the course of research and are considered fundamental for current and potential future knowledge gains in scholarship. In the scholarly community research data are regarded as necessary for the documentation and validation of research results. 

A distinction can be made between primary data (data that were collected specifically to answer a research question) and secondary data (data that were collected in a different context and are used in research). 

Metadata and documentation of data collection and processing during a research project are essential for the (re)usability of research data (FAIR data). If research data are published under open licenses, it is referred to as open research data. 

A research data repository is an online platform for the publication of research data. In line with the principles of FAIR Data and Open Research Data, metadata and documentation (e.g. ReadMe file, codebook, protocol) should be entered alongside the research data to make the data easier to find, understand and reuse. To regulate the subsequent use of the data, licenses can be attached to datasets. Access to sensitive data (e.g. personal data) can be restricted and regulated via a data transfer and use agreement (DTUA). 

Supplementary material (or supplementary data) is material (including data) that cannot be integrated into the main text of a scientific article due to space limitations. This material is not directly necessary to understand the results and conclusions of the article but may still be relevant to the reader for contextualization or further research. It is recommended to publish supplementary material on a publication or research data repository where a DOI or other PID can be assigned to it. 

Based on: International Journal of Epidemiology