Research data is the name given to data that is generated during a research process or is part of the research results. Handling research data in a sustainable and open way promotes the transparency, reproducibility and reuse of results and plays a role in safeguarding good academic practice. Responsible research data management is therefore beneficial to both researchers and potential subsequent users, making a significant contribution to the acquisition and dissemination of research insights.

What is Research Data?

As a researcher, you use data for your work or produce your own data, from which you derive your research findings. Measurement results, text editions, results from surveys, databases, field notes, software – research data is just as varied as the academic disciplines and methods in which it is generated. You should use secure technology to store your research data in the long term, making sure that you comply with data protection regulations. It is up to you as the expert to decide which of your digital materials and data are suitable for long-term storage – regardless of whether you intend to publish them.

Why Publish Research Data?

  • Publishing research data improves your eligibility for funding. Unless there are important reasons against doing so, many external funding bodies now expect their scholars to publish research data.
  • You boost your international visibility and reputation. Data publications can play an important part in your individual performance as a researcher. Provided with a permanent identifier such as DOI (digital object identifier), they appear on publication lists and can be cited.
  • Some scientific journals accept articles only if you also publish the corresponding data.
  • It makes your research transparent and reproducible: it allows other researchers to understand the research process and verify your results. Other researchers can make use of your data. They cite your research results and can arrive at interesting new conclusions.

Why Research Data Management?

Appropriate handling of research data is an important mark of quality in your academic work. The guidelines issued by funding organisations and rules governing good academic practice enforce a systematic and sustainable handling of research data.

The four FAIR Principles can serve as a guideline:

  • Findable
  • Accessible
  • Interoperable
  • Reusable

Only by storing research data in a structured way and with metadata can it be retrieved and used by others.

National Research Data Infrastructure

Germany is currently establishing a coordinated nationwide infrastructure for research data. The National Research Data Infrastructure (NFDI) aims to systematically manage, standardise and secure research data stocks in the long term and make them available for subsequent use. NDFI consortia bring together experts from academia, infrastructure facilities and specialist associations. An NFDI directorate will be responsible for pooling and coordinating the new network of NFDI consortia. The German Research Foundation is creating the NFDI in stages in three rounds of calls for proposals. Driven by the scientific community and users, consortia have already formed for a wide range of disciplines and published letters of intent for proposal submissions. On 26th June 2020 the Joint Science Conference (GWK) made its final funding decisions in the first round of the NFDI.

Research Data Management Service

We offer you both individual advice and subject-specific information events on the subject of research data – please contact us for more details.

Due to the coronavirus situation, we are currently offering consultations by email, phone and using virtual conferencing tools. We have also compiled external pages with information on various aspects of research data management.

Our Service:

  • Initial information: what is meant by research data?
  • Why should I secure and publish my research data?
  • What are the FAIR Principles and why should I adhere to them?
  • Guidelines from funding bodies on how to handle research data
  • Preparation of data management plans
  • Training and information events

 

 

  • Organisation of data workflows, data integration, data quality
  • Storage, backup, archiving
  • Data protection and data security
  • Handling of extensive, scattered, heterogeneous data
  • Specific analysis options for graph-based data, time series data, textual data

 

 

  • Repositories and identifiers: where can I publish my data so that colleagues can find it and it is always available for citation?
  • FAIR Principles: how do I structure data and metadata in such a way that it is findable, accessible, interoperable and reusable?
  • Copyright and data protection: which data am I allowed to publish and under which conditions?
  • Licences: which rights of use do I want to grant?
  • Subsequent use: where do I find research data in my subject and how do I cite it?

 

 

Workshops

Please note that all of the workshops presented below are held in German.

  • Dates:
    2 and 16 November 2021; 11 January 2022 and 25 January 2022 and 2 March 2022 and 16 March 2022.
    The workshop consists of two consecutive parts and takes place from 9:30am until 12am. Please register through the Competence School at the Research Academy Leipzig.
  • Location:
    Online; access data to the virtual classroom will be provided after registration.

What is a data management plan? Which requirements should I observe? How do I create one for my research project and what tools are useful for this?

Research funding bodies have increasingly high demands when it comes to data management. Many require a data management plan. The DFG, BMBF and the EU expect different information about the collection, storage and publication of project-related research data. In this course, what at first seems confusing and overwhelming is made manageable by means of a basic theoretical introduction and practical examples in the first part of the workshop. You will learn more about the requirements of different research funding bodies, what elements a data management plan should contain, and how to create one yourself using interactive tools.

In the practical second part of the workshop, we will evaluate your data management plans, provide further tips and suggestions, and have plenty of time for discussion.

Guide to data management plans

We will offer this workshop soon as a face-to-face format. Due to Corona virus pandemic this is under reserve. Meanwhile please use our online materials on zenodo and contact us if you have any questions.

Send email

New dates for 2021/22:

11 November 2021 and 31 January 2022, each from 9am until 4pm

Please register through the Competence School at the Research Academy Leipzig.

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