Adding DOI to datasets:

DOIs (Digital Object Identifier) provide a persistent and unique identifier for datasets. DOIs remain stable, ensuring long-term access to the data.

By assigning a DOI to a dataset, researchers can easily cite it in their publications, just like they would cite a journal article or a book. DOIs increase the visibility and discoverability of datasets by making them easily searchable and citable.

Many funding agencies and publishers now require researchers to make their datasets publicly available and assign DOIs to facilitate data sharing and reuse.

There are various methods to acquire a globally unique persistent identifier:

1. Publishing into an existing public repository is usually the best option for most types of data. This is because the repository will assign a globally unique persistent identifier. Generic repositories (such as Zenodo, Figshare) use versioning DOI to update a public dataset or document.

2. Create your own DOI which involves registering your content with a DOI registration agency or a DOI service provider. Some well-known DOI registration agencies include CrossRef, DataCite, mEDRA.

For more information on persistent identifiers, visit PID Forum.

Metadata:

Metadata provides essential details about the content, structure, and characteristics of the data, facilitating its discovery, interpretation, and use.Generating metadata in a machine-readable format makes your data more FAIR (Findable, Accessible, Interoperable, Reusable).

OpenAire guide on  "How to make your data FAIR?"

Choose a standard metadata for your dataset and an appropriate repository to deposit your data.

• Dublin core metadata schema
• RDA Standards
• FAIRsharing
• Data Curation Centre metadata list (DCC)

ELIXIR is an intergovernmental organization that brings together life science resources from across Europe. These resources include databases, software tools, training materials, cloud storage and supercomputers.

ELIXIR aims to coordinate these resources across Europe so that researchers can more easily find, analyze and share data, exchange expertise, and implement best practices. This allows them to gain greater insights into how living organisms work.

Why ELIXIR is needed?

• To manage the huge increase in life science data.
• To deal with the increasing complexity of data.
• To make it easier to find the right tools and training.
• To build a more robust bioinformatics infrastructure.
• To drive innovation and industry usage.

ELIXIR CZ is dedicated to the organization, storage, sharing and facilitation of interoperability of life-science data for further processing and analysis. The teams of experts develop and operate a wide range of tools, maintain specialized databases of biological data from diverse research areas across life sciences, and provide e-infrastructure services.

Elixir Czech Republic is a National node of many.

Explore Elixir Nodes.

For more information, visit Elixir-Europe.

The Data Steward position at the Faculty of Science aims to assist in managing research data. The primary responsibilities involve data management, data governance, and data policy enforcement.

How can a Data Steward assist you?

• Managing metadata, providing thorough documentation, creation of Data Management Plan (DMP) to support effective data management which has been a mandatory part of Horizon, GAČR and TAČR projects.
• Implementing the FAIR principles, focusing on findability, accessibility, interoperability, and reusability.
• Assist in storing research data in a trusted repository such as Figshare, Dryad, Zenodo, or university's data repository. These repositories enable public access and long-term preservation.
• Making your research data available under an appropriate license, which defines the degree of publicity and rights to use your data.

For assistance with research data and management, please contact MSc. Manali Das at This email address is being protected from spambots. You need JavaScript enabled to view it.

Workplace: Building C (Faculty of Science, Room Number-00055)

Office Hours: Monday (10:00 AM- 6:00 PM), Wednesday (10:00 AM -6:00 PM), Thursday (10:00 AM - 6:00 PM)

Is low-quality data hindering your research?

The key issues are reliability, accuracy, completeness and relevance.

• Poor documentation.
• Inconsistent formats, non-standard abbreviations, units, labels, formats.
• Data that lacks metadata or explanation about how it was collected, processed, or interpreted.
• Data that does not adhere to established standards or protocols.
• Data collected without proper consent or in violation of privacy laws and regulations.

High quality research data avoids these pitfalls. Good quality data facilitate reproducibility and reuse by providing organized raw data along with comprehensive supplemental documentation.

• Provide access to the raw, granular data from observations, surveys, sensors, etc. 
• Use widely-compatible, non-proprietary, and machine-readable file formats that can be utilized by others.
• Data is formatted in a standardized, consistent, and structured manner.
• Contains detailed contextual documentation and descriptive variable labels/codebooks to allow others to interpret the data.

Useful Links: 

• How to be FAIR with your data : https://zenodo.org/records/3405141
• Open Data: https://www.lib.jcu.cz/en/open-science/open-data
• Open Acces: https://www.lib.jcu.cz/en/open-science/open-access
• Metadata: https://www.lib.jcu.cz/en/open-science/metadata