FAIR/Open Data

Data sharing/publishing – an essential tool for accelerating and improving the research process. It brings new opportunities for profiling and active involvement of MUNI in European and international activities (e.g. European Open Science Cloud, EOSC, etc.).

Under pressure from grant providers, the need to store, share and publish research data is becoming increasingly relevant also within MUNI. Since 2014, it is a condition for selected areas of research of the EU Horizon 2020 program, since 2017, the obligation has been extended to all supported projects from this program. Similar requirements are already in place or being prepared by other international programming schemes (e.g. Horizon Europe). They also occur within the framework of the National R&D&I Policy 2021+ and subsequent interventions of support providers.

Activities around the management of research data across research institutions in the Czech Republic are dealt with by the Czech working group for the management of research data WG-RDM.cz.

Obligations for MUNI employees and students on how to proceed in the acquisition, storage and use of research data are currently regulated by the MUNI Research Data Directive.

Life Situations – Open/FAIR Data

As part of the review process, I need to save the data for publication so that it can pass peer-review

  1. I will find out the exact conditions of the journal for supporting research data.
  2. I will check if I have sufficient rights to meet the conditions of the magazine.
  3. I will save the research data in a suitable repository and send a link to the data set to the journal, which is currently only accessible to people with the link.
  4. There will be a review procedure, if the article is published, I will also publish its underlying research data.
  5. If I do not know the advice, contact methodological support.

I need to publish data within the Open Research Data Pilot as part of my project

  1. I will write a Data Management Plan.
  2. Based on the Data Management Plan, I will determine what data will need to be published.
  3. I will select a suitable storage according to the field / type of data.
  4. I will save the research data in a suitable repository and set the permissions for sharing them correctly.
  5. I publish the research data that can be published (or their metadata).
  6. If I do not know the advice, contact methodological support.

As part of the project, I am obliged to submit the Data Management Plan within 6 months

  1. Get acquainted with industry standards in my field, because I am a domain expert.
  2. If I do not know the advice, contact methodological support.
  3. If I know the advice but want feedback, turn to the Open Science methodology.
  4. (As there is a university-wide tool, it will be possible to use it as well.)

FAIR vs. Open Data

Making data available according to FAIR principles Data does not mean automatically making data available to anyone. The aim is to follow the principle ‘as open as possible, as closed as necessary’.

The abbreviation FAIR is formed from the initial letters of the four main principles of FAIR Data:

  • Findable
    • The first step in the (re)usability of data is to allow people to trace it.
    • The data should therefore be provided with appropriate
      • metadata,
      • globally unique and
      • persistent identifiers, and
    • be indexed in appropriate search engines.
  • Accessible
    • If the user finds the data, he must know the conditions under which he can access it.
      • Access can be authorized/authenticated.
        • Restricted, for example, on the basis of the fulfillment of certain conditions (signing of a contract on the regulation of access to data, etc.).
    • Metadata should be available based on a persistent unique identifier using standard open protocols.
      • Metadata should remain accessible even if the data itself is no longer available.
  • Interoperable
    • In order for data to be (reusable), it needs to be made available so that it can be integrated and processed by other applications, systems and users.
    • (Meta)data should use standard representations and data formats used in the field.
    • (Meta) ata should use controlled dictionaries and ontologies that follow FAIR principles.
    • (Meta)data are linked to other (meta) data via formalized references.
  • Reusable
    • Reusability is the basic goal of FAIR principles – to enable once created data to be reused not only by their authors, but also by other interested parties.
    • A necessary condition is the availability of well-described data, provided with appropriate metadata.
    • (Meta)data is distributed under a clear license.
    • (Meta)data are linked to a clear description of the origin of (meta)data.
    • (Meta)data follow the principles of stat-of-the-art standards and practices in a given research area (community of researchers in the field).

It is clear from the above that FAIR data should be traceable to anyone, in standard formats with appropriate metadata and identifiers to be reusable and traceable, but access to the data itself may be subject of the fulfillment of certain conditions (e.g. signing a contract and compliance with restrictive terms and conditions) and their use is restricted by a license setting forth data management rules.

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