Research Data Management (RDM)
Learn to create your own Data Management Plan
Within the framework of the Digital Skills Project, two courses on DMP will be offered at UNIFR in September 2020:
one for researchers in the humanities and social sciences conducted by FORS, and
Why do we need Research Data Management?
- Data documentation – Your research data needs to be recorded and filed.
- Better organization of the research data for the purpose of analysis, reporting, publishing and reuse.
- Keeping track of evolving versions – knowing the history of your data.
- Think about data security and integrity
- Is required through open data initiatives of SNSF & European Commission!
Research Data Management: Good Practice
Develop strategy of data acquisition and storage
- How is data acquired? e.g. paper notebook, excel spreadsheet, word file, collection of spectra from instruments, microscopy images….
- How is the data stored and organized? e.g. analogue in paper form, electronically on a computer and in which format…
- How is the data associated with a project? e.g. how do you identify to which project each dataset belongs?
Infrastructure allowing safe and secure data storage
- Where is your data stored? e.g. local computer, server, cloud, lab books. How is the security of these resources managed?
- Is the data sensitive and how are data security and data access rights managed?
Backup and versioning strategies
- Is there a backup solution in place?
- Do you have off-site backups?
- Do you preserve different versions of files?
- How is important data archived?
- It is recommended to archive your data on the UNIFR servers. The server backups allow recovering accidentally erased data within 30 days. Further information
Tools supporting your RDM
Collaborative project management tools
Collaborative writing tools (cloud based)
Strategies to share and publish your data
- There is a difference between storing data for own needs and making it accessible for others.
- "Open data" will require you to share the data underlying every publication.
- Is there a strategy in place to decide which data will be shared and how?
- What file format will you use to publish your data?
- What metadata will be required in order for others to understand your data and how will you publish it. If possible and existing, use the matadata standard of your discipline.
Intellectual Property (IP) consideration: who owns the data?
- Who owns the open data that you publish? Did you create it or are you reusing existing data? If you reuse data, please make sure you understand the licence terms attached to the data.
- Are there IP agreements in place that you need to consider?
- Under which licence will you publish your data? e.g. what rights will others that want to use your data, have? e.g. diverse Creative Commons (CC) licences. (Licence Selector tool - example)
- Please consider that some data you create might have a "commercial" value and you might only dicover this during the project. it would therefor be adviseable not to commit too strictly to a specific licence type at the beginning of a project.
- In case of doubt regarding license issues or intellectual property please contact Knowledge Technology Transfer/Industrial Relation Service of the university of Fribourg.
Data Management Plan (DMP)
What is a DMP?
DMP – Documentation of the Data Management strategy that you have in place.
An increasing number of funding agencies want to make sure that the results of publically financed research is also made available for the public. As of 1st September 2017 the SNSF requires the submission of DMP for each project. For H2020 projects this requirement has already been in place for a while.
Once you have Data Management in place, writing a DMP should be simple.
Creating a DMP
DMP Rules of the SNSF
The SNSF DMP is submitted via mySNF portal as part of the application. The DMP is a 'living' document evolving with the project and has to be updated regularly. DMP is mandatory for all disciplines except if no data will be collected, studied, generated or re-used.
The SNSF expects its funded researchers to share at least all data underlying a publication, meaning that these data have to be deposited on a FAIR data repository as soon as the publication is available. Some data may be subject to legal, ethical, copyright, confidentiality or other constraints. Such restrictions must be clearly described and justified in the DMP. They will be reviewed by the SNSF Administrative Offices. Embargo periods are possible.
Further information can be found:
- DMP Rules of the European Commission
- DMP Rules of the SNSF
Online Resources and Examples
- DMP guidelines by the SNSF
- DMP checklist by the Digital Curation Center (DCC)
- Examples for different disciplines provided by DCC
- More examples can be found on the webpages of:
- DCC guide on licensing questions
- EUDAT licencing tool
- Online tools for DMP creation: DMP Online and DMPTool
- Support with repository selection: re3data.org
- Webinar on DMP creation
- Recommended DMP guidance from other Swiss higher education institutions :
A set of legal rights extended to copyright owners (the author or creator, or other party to whom the rights have been assigned) that govern such activities as reproducing, distributing, adapting, or exhibiting original works fixed in tangible form.
The recorded factual material commonly accepted in the scientific community as necessary to validate research findings. Data include e.g. a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a person speaking, or a moon rock specimen (source: Reference Model for an Open Archival Information System, 2002)
A system that provides online access to research data; UNIFR does, so far, not offer an institutional data repository. Researchers wishing to provide permanent access to their data are asked to either use an appropriate disciplinary repository (see global registry of research data repositories www.re3data.org ) or a general repository like figshare or ZENODO. The SNSF gives examples for general data repositories fulfilling their requirements.
FAIR Data Principles
The FAIR Data Principles for scientific data management provide a guideline for data producers and publishers to enhance the reusability of scientific data. The principles requests data to be findable, accessible, interoperable and reusable
A service storing and providing online access to digital content. Content is typically produced by the institution that hosts the service. The pan-institutional publication repository of UNIFR is ReroDoc.
Documentation or information about a data set. It may be embedded in the data itself, or exist separately from the data. Metadata may describe the ownership, purpose, methods, organization, and conditions for use of data, technical information about the data, and other information. Many metadata standards exist across a broad range of disciplines and applications.
Typically used to describe publications, open access refers to online, freely available material that has few or no copyright or licensing restrictions.
- Open Data
Preservation (of data)
Ensuring that data remain intact, accessible and understandable over time. This requires preserving the integrity of digital files themselves, and can be considerably more complicated. Preservation operations may include preserving the software required to interact with the data or emulating older systems, migrating data to new formats and new media, and ensuring there is sufficient metadata to understand, interpret, manage and preserve the data.
The protection of personal information from unauthorized access by others.
Research data management means the organization of data, from its entry into the research cycle until dissemination and archiving.
Methods of protecting data from unauthorized access, modification, or destruction.