Available courses

The learning objectives of the course are:

  • Students will understand the basic concept and principles on how to conduct a good scientific doctoral thesis

  • Students will gain experience in the formal requirements of a doctoral thesis

  • Students will gain research competencies (from the idea -> research question -> study design -> analysis -> results)

  • Students will built knowledge in how to conduct a high quality scientific (pre)clinical research project

  • The course will promote a research network in an early phase of scientific work

  • The course will foster building an individual scientific toolbox in preparation of the doctoral thesis​

  • The course will offer prospects for different career paths

You will learn the major steps required to undertake a systematic review and meta-analysis of preclinical animal studies.

You will:

  • Understand what a systematic review is and why to perform one.
  • Understand the components of a good research question.
  • Understand the components of a protocol.
  • Understand the importance of preregistering your protocol and where to register.
  • Learn how to develop an accurate systematic search strategy.
  • Learn how to screen records for inclusion against preset criteria.
  • Understand the steps required for data extraction including study design information and quantitative data.
  • Learn how to assess studies for risk of bias and reporting.
  • Learn how to conduct meta-analysis of effect size data using R.
  • Understand how to report your completed systematic review.

This extensive course provides insight into the benefits and challenges associated with clinical research data sharing and provides you with practical strategies to address them effectively.

Throughout the course you will cover the following key topics:

  • FAIR and Open Data: Explore the principles of Findable, Accessible, Interoperable and Reusable (FAIR) data and the importance of open data practices.
  • Good data management practices: Learn essential techniques for efficient and organised data management to ensure the reliability and integrity of your research.
  • Legal aspects of data sharing: Understand the legal considerations involved in sharing research data to ensure compliance with regulations and ethical standards.
  • Interoperability and clean data: Gain knowledge on how to achieve interoperability between different systems and ensure clean data for seamless sharing and analysis.
  • Anonymisation of datasets: Explore techniques for anonymising datasets to protect patient privacy while enabling data sharing for research purposes.
  • Opportunities for actual data sharing: Discover different ways to share research data, foster collaboration and advance scientific progress.
  • Charité Berlin Services for Data Management and Data Sharing: Explore the services and resources provided by Charité Berlin to support effective data management and sharing in the context of clinical research.

While this course is primarily designed for clinical researchers at Charité, it may also be useful for researchers from other institutions or those involved in preclinical research.